Reviewerly/exHarmony huggingface dataset:
Photorealistic True-Dimensional Visualization of Remote Panoramic Views for VR Headsets

Virtual Reality headsets have evolved to include unprecedented display quality. Meantime, they have become light-weight, wireless and low-cost, which has opened to new applications and a much wider audience. Photo-based omnidirectional imaging has also developed, becoming directly exploitable for VR, with their combination proven suitable for: remote visits and realistic scene reconstruction, operator’s training and control panels, surveillance and e-tourism. There is however a limited amount of scientific work assessing VR experience and user’s performance in photo-based environment representations. This paper focuses on assessing the effect of photographic realism in VR when observing real places through a VR headset, for two different pixel-densities of the display, environment types and familiarity levels. Our comparison relies on the observation of static three-dimensional and omnidirectional photorealistic views of environments. The aim is to gain an insight about how photographic texture can affect perceived realness, sense of presence and provoked emotions, as well as perception of image-lighting and actual space dimension (true-dimension). Two user studies are conducted based on subjective rating and measurements given by users to a number of display and human factors. The display pixel-density affected the perceived image-lighting and prevailed over better lighting specs. The environment illumination and distance to objects generally played a stronger role than display. The environment affected the perceived image-lighting, spatial presence, depth impression and specific emotions. Distances to a set of objects were generally accurately estimated. Place familiarity enhanced perceived realism and presence. They confirmed some previous studies, but also introduced new elements.
Interactive Guiding Sparse Auto-Encoder with Wasserstein Regularization for Efficient Classification

In the era of big data, feature engineering has proved its efficiency and importance in dimensionality reduction and useful information extraction from original features. Feature engineering can be expressed as dimensionality reduction and is divided into two types of methods, namely, feature selection and feature extraction. Each method has its pros and cons. There are a lot of studies that combine these methods. The sparse autoencoder (SAE) is a representative deep feature learning method that combines feature selection with feature extraction. However, existing SAEs do not consider feature importance during training. It causes extracting irrelevant information. In this paper, we propose an interactive guiding sparse autoencoder (IGSAE) to guide the information by two interactive guiding layers and sparsity constraints. The interactive guiding layers keep the main distribution using Wasserstein distance, which is a metric of distribution difference, and it suppresses the leverage of guiding features to prevent overfitting. We perform our experiments using four datasets that have different dimensionalities and numbers of samples. The proposed IGSAE method produces a better classification performance compared to other dimensionality reduction methods.
Learned pseudo-random number generator: WGAN-GP for generating statistically robust random numbers

Pseudo-random number generators (PRNGs) are software algorithms generating a sequence of numbers approximating the properties of random numbers. They are critical components in many information systems that require unpredictable and nonarbitrary behaviors, such as parameter configuration in machine learning, gaming, cryptography, and simulation. A PRNG is commonly validated through a statistical test suite, such as NIST SP 800-22rev1a (NIST test suite), to evaluate its robustness and the randomness of the numbers. In this paper, we propose a Wasserstein distance-based generative adversarial network (WGAN) approach to generating PRNGs that fully satisfy the NIST test suite. In this approach, the existing Mersenne Twister (MT) PRNG is learned without implementing any mathematical programming code. We remove the dropout layers from the conventional WGAN network to learn random numbers distributed in the entire feature space because the nearly infinite amount of data can suppress the overfitting problems that occur without dropout layers. We conduct experimental studies to evaluate our learned pseudo-random number generator (LPRNG) by adopting cosine-function-based numbers with poor random number properties according to the NIST test suite as seed numbers. The experimental results show that our LPRNG successfully converted the sequence of seed numbers to random numbers that fully satisfy the NIST test suite. This study opens the way for the "democratization" of PRNGs through the end-to-end learning of conventional PRNGs, which means that PRNGs can be generated without deep mathematical know-how. Such tailor-made PRNGs will effectively enhance the unpredictability and nonarbitrariness of a wide range of information systems, even if the seed numbers can be revealed by reverse engineering. The experimental results also show that overfitting was observed after about 450,000 trials of learning, suggesting that there is an upper limit to the number of learning counts for a fixed-size neural network, even when learning with unlimited data.
The Zhegalkin Polynomial of Multiseat Sole Sufficient Operator

Among functionally complete sets of Boolean functions, sole sufficient operators are of particular interest. They have a wide range of applicability and are not limited to the two-seat case. In this paper, the conditions, imposed on the Zhegalkin polynomial coefficients, are formulated. The conditions are necessary and sufficient for the polynomial to correspond to a sole sufficient operator. The polynomial representation of constant-preserving Boolean functions is considered. It is shown that the properties of monotone and linearity do not require special consideration in describing a sole sufficient operator. The concept of a dual remainder polynomial is introduced. The value of it allows one to determine the self-duality of a Boolean function. It is proved that the preserving 0 and 1 or preserving neither 0 nor 1 Boolean function is self-dual if and only if the dual remainder of its corresponding Zhegalkin polynomial is equal to 0 for any sets of function variable values. Based on this fact, a system of leading coefficients is obtained. The solution of the system made it possible to formulate the criterion for the self-duality of the Boolean function represented by the Zhegalkin polynomial. It imposes necessary and sufficient conditions on the polynomial coefficients. Thus, it is shown that Zhegalkin polynomials are a rather convenient tool for studying precomplete classes of Boolean functions.
Long and Short-Term Interest Contrastive Learning Under Filter-Enhanced Sequential Recommendation

Existing self-supervised sequential recommendations face the problem of noisy interactions and sparse sequence data, and train models based only on item prediction losses, so they usually fail to learn an appropriate sequential representation. We propose long and short-term interest contrastive learning under filter-enhanced sequential recommendation (FLSCSR). Specifically, a filtering algorithm is used on the user’s interaction sequences to attenuate the noisy information in the sequence data. Two independent encoders are used to model the user’s long-term and short-term interests separately on the filter-based enhanced interaction sequences. Then user-specific gating mechanisms are constructed to capture the long-term and short-term interests tailored to the user’s personalized preferences, which are incorporated into the attention network to achieve better learning of interest representations in sequence recommendations. In addition, representation alignment learning goals are proposed to minimize the discrepancy between long-term and short-term interest representations in personalized global contexts and local sequence representations. Experiments were conducted on three public and industrial datasets, where the FLSCSR model could obtain superior performance compared to the benchmark model: AUC improves by 0.76%-2.02%, GAUC improves by 0.55%-1.01%, MRR improves by 1.19%-2.09%, and NDCG@2 improves by 1.07%-2.26%.
On Simplifying Expressions with Mixed Boolean-Arithmetic

Mixed Boolean-Arithmetic expressions (MBA-expressions) with $t$ integer $n$-bit variables are often used for program obfuscations. Obfuscation consists of replacing short expressions with longer equivalent expressions that seem to take the analyst more time to explore. The paper shows that to simplify linear MBA-expressions (reduce the number of terms), a technique similar to the technique of decoding linear codes by information sets can be applied. Based on this technique, algorithms for simplifying linear MBA-expressions are constructed: an algorithm for finding an expression of minimum length and an algorithm for reducing the length of an expression. Based on the length reduction algorithm, an algorithm is constructed that allows to estimate the resistance of an MBA-expression to simplification. We experimentally estimate the dependence of the average number of terms in a linear MBA-expression returned by simplification algorithms on $n$, the number of decoding iterations, and the power of the set of Boolean functions, by which a linear combination with a minimum number of nonzero coefficients is sought. The results of the experiments for all considered $t$ and $n$ show that if before obfuscation the linear MBA-expression contained $r=1,2,3$ terms, then the developed simplification algorithms with a probability close to one allow using the obfuscated version of this expression find an equivalent one with no more than $r$ terms. This is the main difference between the information set decoding technique and the well-known techniques for simplifying linear MBA-expressions, where the goal is to reduce the number of terms to no more than $2^t$. We also found that for randomly generated linear MBA-expressions with increasing $n$, the average number of terms in the returned expression tends to $2^t$ and does not differ from the average number of terms in the linear expression returned by known simplification algorithms. The results obtained, in particular, make it possible to determine $t$ and $n$ for which the number of terms in the simplified linear MBA-expression on average will not be less than the given one.
Mobile Banking during the COVID-19 Pandemic from Customers' Perspective: A Survey Study for Customers of Banks Operating in the City of Taif

The study aimed to identify the impact of electronic banking and mobile services dimensions during the Corona pandemic on customers who deal with banks operating in Taif. Know the effect of phone banking during Corona on customer behaviour. The results of the study showed that it found through the analysis that the use of mobile banking services does not require a lot of thinking effort, that it cannot be frustrating, and that mobile banking services are not a difficult way to accomplish banking transactions. It was also found that mobile banking services fit the way the surveyed groups wanted to control their banking movements. Most of them used mobile banking services because they were accustomed to completing all their tasks through their mobile banking during the pandemic
The Influence of Using Ambonese Malay Language on Learning of Indonesian Language of Students at PAUD Sinar Terang Anak Bangsa

Teachers and students use Ambonese Malay in learning Indonesian because learning a second language is inseparable from knowledge of the first language mastered by early childhood. This study aims to determine the effect of using Ambonese Malay on Indonesian language learning in early childhood education. This research is a sociolinguistic study to describe the phenomenon of language use. This study's results indicate three influences of the use of Ambonese Malay on students' learning of Indonesian. First, Ambonese Malay has a role as a 'language transfer tool' for children learning to understand a second language, namely Indonesian. Second, it is easy for the teacher to teach Indonesian to children because they can use Ambonese Malay vocabulary as the language of instruction to help students understand what the teacher means by learning Indonesian. Third, students gain knowledge of Indonesian vocabulary through the Ambonese Malay used by students and teachers in class.
The next layer: Towards open pedagogy in geospatial education

Abstract Open‐source software and open data are becoming increasingly popular in the teaching and learning of geographic information science. The cost savings that are derived from using free software over proprietary software are one driving factor, yet the move from “closed” to “open” represents much more than financial austerity—it signifies a broader shift in educational philosophy. This article documents the gradual transition of an introductory undergraduate course in geographic information systems from an entirely closed course to one that has become increasingly open. Having completely adopted the first three layers of open—software, data, and educational resources—the course is now turning toward the next layer: embracing the philosophy of open pedagogy.
Recursive-Parallel Algorithm for Solving the Maximum Common Subgraph Problem

The paper proposes an algorithm for solving the problem of finding the maximum common subgraph. Both the sequential and the parallel version of the algorithm, their software implementation are described, and an experimental study of their effectiveness is carried out. This problem is one of the most famous NP-complete problems. Its solution may be required when solving many practical problems related to the study of complex structures. We solve it in a statement when we need to find all possible isomorphisms of the found common subgraph. Due to the extremely high complexity of the problem, it is natural to want to speed up its solution by parallelizing the algorithm. To organize parallel computing, the author used the RPM_ParLib library. It allows to develop effective applications for parallel computing on a local network under the control of the runtime environment .NET Framework.The library supports a recursive-parallel programming style and provides effective work distribution and dynamic load balancing of computational modules during program execution. It can be used for applications written in any programming language supported by the .NET Framework. The purpose of the numerical experiment was to study the acceleration achieved due to the recursive-parallel organization of calculations. For the experiment, the author developed a special application in the C# language designed to generate various sets of initial data with specified parameters. The paper describes the features of the generated initial pairs of graphs, as well as the results obtained during the experiment.
The Intrinsic Electron with Its Properties Such as Inner Structure and Self-Mass Is in Conflict with Quantum Field Theory

The quantum field theory (QFT) is one of branches of the Standard Model.According to QFT, quantum fields are the primary entities and particles are the excitations of these fields, coming in discrete lumps with no inner structures and with properties assigned by declaration.Such view is in conflict with the observed vacuum energy density, 140 orders of magnitudes less than required by the QFT.In addition, such view is challenged by Aphysical Quantum Mechanics (AQM), a deeper quantum theory.According to AQM, the fundamental understanding of quantum reality is expanded by the addition of two fundamental categories, aphysical and elementary consciousness of elementary particles.Based on AQM and as an example, the total ontology of the intrinsic (fundamental) electron is presented with its inner structure of perfect geometry consisting of the physical charged c-ring and aphysical cylinder, and with its properties such as self-mass, spin, magneto-electrostatic field configuration and magnetic moment.The position parameter in the inner structure demonstrates that there are no two identical intrinsic electrons in the Universe thus placing a question mark over the QFT principle of indistinguishability.
An Efficient Batch Bayesian WIV Doppler and Bearing Estimator for 3D Target Tracking

The paper focuses on the 3D TMA problem of a tough target-observer geometry where the observer is motionless and the target is approaching at a constant speed. We first formulate the pseudolinear equations using bearings, elevations and Doppler frequency and derive the measurement noise covariance matrix, which is used as the weighted matrix of the estimator, much more complex than that in 2D. An efficient batch Bayesian filtered WIV estimator is then proposed, based on the principle of instrumental variables and mean filtering to the measurements. Simulation results illustrate that the estimating bias tends to be small as the number of instants increases and the noise fluctuation decreases, compared with the basic weighted pseudolinear estimator. And the RMSE fits the Cramér-Rao Bound for small levels of noise. It is verified that the proposed estimator is effective in reducing bias and improving estimating accuracy in 3D TMA.
Sales Forecasting Model Based on Ensemble Learning and Its Application in Anomaly Detection

Efficient sales forecasting is essential for production companies, which is also helpful to regulate the business strategies. However, due to the complex characteristics of sales data and vulnerability to external interference, the existing prediction methods based on a single model cannot achieve satisfactory results. In this paper, in order to improve the prediction accuracy, we propose a forecasting model based on ensemble learning of both homogeneous and heterogeneous machine learning models to cope with different kinds of complexity in sales data. Results based on the Chinese cigarette sales data show that our model is competitive compared to single machine learning model and the classical time series-based predication model. Besides, the proposed forecasting model is applied to anomaly detection of sales data by evaluating the residual between the predicted value and the observed one.
Research on Siamese network algorithm based on parallel channel attention mechanism for target tracking

In order to effectively improve the tracking performance of the target in various complex environment in the tracking process, the reinforcement research of target features has become one of the important work. In this paper, a Siamese network target tracking algorithm based on parallel channel attention mechanism (PCAM) is proposed by combining feature cascade algorithm with visual attention. Firstly, the characteristics of SENet network and ECA network are fully analyzed. Secondly, the parallel channel attention mechanism is constructed based on ECA module, which integrates global average pooling and maximum pooling. Parallel channel attention mechanism not only solves the problem of channel correlation reduction of SENet module, but also solves the problem of target feature information enhancement. Thirdly, the output model of channel attention is used as the input of spatial attention model to realize the effective complement to channel attention mechanism. By calculating the weight value of different spatial locations, the structural relation between spatial location information is constructed, the feature expression ability of the model is enhanced. Finally, the algorithm is evaluated on standard data sets OTB100, OTB2013, OTB2015, VOT2016 and VOT2018. Experimental results show that the PCAM has stronger feature extraction performance for complex environment, higher target tracking accuracy and robustness, and has strong advantages compared with other comparative experiments.
Pedestrian Tracking Algorithm Based on Attention Mechanism and Data Association

In this paper, a pedestrian tracking algorithm based on anchor free CBAM (Convolutional Block Attention Module) convolutional attention mechanism network is proposed to solve the problems such as target identity switching and tracking target loss that often occur in the process of multi-target pedestrian tracking. First, attention mechanism is added in the stem stage of the backbone network to extract high-resolution features that are more expressive in space and channels, and enhance the performance of re-recognizing branches. At the same time, in order to better match multiple targets between frames, byte data association strategy is referenced. The test is conducted on MOT15, MOT17 and MOT20 datasets on Mot Challenge official website. Compared with the original FairMOT algorithm, the average tracking accuracy is increased by 1.4%, 2.2% and 0.8% respectively, and the number of identity switching is also significantly reduced. Compared with other mainstream algorithms, the algorithm in this paper can effectively improve the identity switching problem caused by pedestrian occlusion and further improve the accuracy of the algorithm.
Beyond Implementation: Enabling Sustainable Transformations of Digital Teaching and Learning in Higher Education

This chapter examines how sustainable transformation of digital practices in higher education can be conceptualised and enacted. The COVID pandemic sparked what is often referred to as the largest digitalisation experiment in the history of higher education. On the face of it, this was a highly successful operation, giving large number of students the opportunity to complete their studies. However, many approaches to digitalisation during the pandemic were characterised by quick fixes and “solutionism”. There is therefore a need to address digitally mediated teaching in a long-term and more strategic perspective. We explore this issue in two ways: First, we theoretically delineate the concept of sustainable transformation of digital practices and develop an analytical framework aimed to support empirical research and educational development. Second, we illustrate the value of this analytical approach with reference to the experiences of one faculty at a research-intensive university in Norway, with specific attention to how this faculty addressed the COVID crisis over a period of nearly two years. We conclude by discussing implications for further research and practice.
Hexatomic: An extensible, OS-independent platform for deep multi-layer linguistic annotation of corpora

Linguistic research aims to understand how languages work.This includes finding regularities, understanding language use and/or production in a specific context, and other linguistic research topics.The data for this research are language artifacts, e.g., recordings or transcriptions of spoken language and records of written language, that are often collected in corpora.In order to analyze languages through these corpora, linguists apply different methods.Some of these methods are computational and include automated annotation of corpora through natural language processing, or gathering statistical insights into corpora through machine learning.Other methods are computer-aided, and include manual annotation of corpora across multiple annotation layers.Linguistic phenomena often occur on different aspects of language, e.g.morphology, part-of-speech, lemmatization, constituent and dependency syntax, entities, coreference, discourse and information structure.For a deep analysis and understanding of language data it is necessary to include annotation layers for these different aspects in a single multi-layer corpus.These annotations often have to be made manually, as linguists' insights into specific language features often surpass the generalized models used in machine learning.Additionally, for languages with fewer speakers such as those investigated in linguistic typology and language documentation projects, machine learning models may not exist or there may just not be enough language data for training model.