Reviewerly/exHarmony huggingface dataset:
A Robust Discrete Wavelet Transform Based Adaptive Watermarking Scheme in YCbCr Color Space against Camcorder Recording in Cinema/Movie Theates

Camcorder-based piracy is increasing nowadays because of the high quality of camcorder devices.The most affected industries are theatre and cinema.To defeat theatre piracy which causes huge loss to the motion picture industry of the world, here in this paper, a robust discrete wavelet transform (DWT) based video watermarking approach has been presented in which a payload formed with date-time of the show, registration number of theatres, and six-digit zonal Improvement code (ZIP) of the theatre location embedded in the movie before it plays back in the theatre.To assure that the video and the algorithm are of good quality and robustness, the adaptive watermarking pattern has been used to minimize the watermarking area in the host video.In this study, YCbCr color space has been adopted to use luminance factor of the image to watermark because the luminance part of the image is the least sensitive to the human visual system.This approach shows the convincing robustness against the geometrical attack and camcording attack, which is the most affecting factor in camcorder piracy.As the proposed approach is good enough to disclose the place and time of piracy, it may deter the pirate to do piracy.
Optimality-based clustering: An inverse optimization approach

We propose a new clustering approach, called optimality-based clustering, that clusters data points based on their latent decision-making preferences. We assume that each data point is a decision generated by a decision-maker who (approximately) solves an optimization problem and cluster the data points by identifying a common objective function of the optimization problems for each cluster such that the worst-case optimality error is minimized. We propose three different clustering models and test them in the diet recommendation application.
Efficient computation of backprojection arrays for 3D light field deconvolution

Light field deconvolution allows three-dimensional investigations from a single snapshot recording of a plenoptic camera. It is based on a linear image formation model, and iterative volume reconstruction requires to define the backprojection of individual image pixels into object space. This is effectively a reversal of the point spread function (PSF), and backprojection arrays H' can be derived from the shift-variant PSFs H of the optical system, which is a very time consuming step for high resolution cameras. This paper illustrates the common structure of backprojection arrays and the significance of their efficient computation. A new algorithm is presented to determine H' from H, which is based on the distinct relation of the elements' positions within the two multi-dimensional arrays. It permits a pure array re-arrangement, and while results are identical to those from published codes, computation times are drastically reduced. This is shown by benchmarking the new method using various sample PSF arrays against existing algorithms. The paper is complemented by practical hints for the experimental acquisition of light field PSFs in a photographic setup.
Handwritten Digit Recognition Using Deep Learning

Traditional systems of handwritten Digit Recognition have depended on handcrafted functions and a massive amount of previous knowledge. Training an Optical character recognition (OCR) system primarily based totally on those stipulations is a hard task. Research in the handwriting recognition subject is centered on deep learning strategies and has accomplished breakthrough overall performance in the previous couple of years. Convolutional neural networks (CNNs) are very powerful in perceiving the structure of handwritten digits in ways that assist in automated extraction of features and make CNN the most appropriate technique for solving handwriting recognition problems. Here, our goal is to attain similar accuracy through the use of a pure CNN structure.CNN structure is proposed to be able to attain accuracy even higher than that of ensemble architectures, alongside decreased operational complexity and price. The proposed method gives 99.87 accuracy for real-world handwritten digit prediction with less than 0.1 % loss on training with 60000 digits while 10000 under validation.
A secure key dependent dynamic substitution method for symmetric cryptosystems

The biggest challenge for symmetric cryptosystems is to replace their static substitution with dynamic substitution, because static substitution S-boxes make the symmetric block ciphers more vulnerable to attacks. Previous well-known dynamic key-dependent S-boxes are lacking in dynamicity and do not provide optimal security for symmetric block ciphers. Therefore, this research aims to contribute an effective and secure method for designing key-dependent dynamic S-box with dynamic permutations to make the symmetric block ciphers optimally secure. The proposed S-box method has been experimentally evaluated through several measures such as bit independence criteria, non-linearity, hamming distance, balanced output, strict avalanche criteria including differential and linear approximation probabilities. Moreover, the randomness properties of proposed method have also been evaluated through several standard statistical tests as recommended by the National Institute of Standards and Technology (NIST). Thus, the results show that the proposed method, not only retains effective randomness properties but it also contains, good avalanche effect (up to 62.32%) which is significantly improved than others. Therefore, the proposed substitution method is highly sensitive to the secret key because, only a single bit change in key generates an entirely new S-box with all 256 values at different positions. Thus, the overall evaluation shows that the proposed substitution method is optimally secure and outperforming as compared to the existing S-box techniques. In future, the proposed method can be extended for different key sizes (192–256 bits) or even more.
Individual Educational Trajectories in Higher Education Institution - Global Challenges for Regions

The need to introduce individualization of education in the universities of Yakutia is dictated by the need to find optimum solutions for preserving human capital in the northern territories, attracting applicants to regional universities and the labor market’s need for competitive specialists from the local population. The purpose of the study is to develop innovative approaches to the educational process in regional universities in the context of global challenges. Research methods: analysis and processing of data from an array of theoretical sources and educational practice; modeling. Scientific novelty of the research: a personality-oriented model of education at the university has been built, taking into account the regional specifics and factors affecting the formation of individual educational trajectories of students.
Deriving Static Security Testing from Runtime Security Protection for Web Applications

Context: Static Application Security Testing (SAST) and Runtime Application Security Protection (RASP) are important and complementary techniques used for detecting and enforcing application-level security policies in web applications. Inquiry: The current state of the art, however, does not allow a safe and efficient combination of SAST and RASP based on a shared set of security policies, forcing developers to reimplement and maintain the same policies and their enforcement code in both tools. Approach: In this work, we present a novel technique for deriving SAST from an existing RASP mechanism by using a two-phase abstract interpretation approach in the SAST component that avoids duplicating the effort of specifying security policies and implementing their semantics. The RASP mechanism enforces security policies by instrumenting a base program to trap security-relevant operations and execute the required policy enforcement code. The static analysis of security policies is then obtained from the RASP mechanism by first statically analyzing the base program without any traps. The results of this first phase are used in a second phase to detect trapped operations and abstractly execute the associated and unaltered RASP policy enforcement code. Knowledge: Splitting the analysis into two phases enables running each phase with a specific analysis configuration, rendering the static analysis approach tractable while maintaining sufficient precision. Grounding: We validate the applicability of our two-phase analysis approach by using it to both dynamically enforce and statically detect a range of security policies found in related work. Our experiments suggest that our two-phase analysis can enable faster and more precise policy violation detection compared to analyzing the full instrumented application under a single analysis configuration. Importance: Deriving a SAST component from a RASP mechanism enables equivalent semantics for the security policies across the static and dynamic contexts in which policies are verified during the software development lifecycle. Moreover, our two-phase abstract interpretation approach does not require RASP developers to reimplement the enforcement code for static analysis.
Journal Finder for TRDIZIN: Baseline Study

One of the main steps in publication of a paper is finding a related journal for the work of the researchers. In the recent years, there have been an increase in scientific papers publications. This situation leads the introduction of journal recommender systems by leading academic publishers. Without using a journal recommender system, this step would be a very time consuming task. This study reviewed similar studies in the literature. Current study is the first version of journal recommender system for TRDIZIN index which has an increasing amount of articles. A dataset is created by collecting titles, keywords, and abstracts of papers from dergipark web page. Using the collected dataset, a target journal from TRDIZIN is suggested according to title, abstract and keyword of the given article. For the first version of the journal recommender system, cosine similarity is used. The results of the suggested algorithm are evaluated by using performance criteria as the nearest 5 and 10 journals' accuracy.
Artificial Intelligence Based on the Neurons Networks at the Service Predictive Bearing

In the industrial environment, production systems are increasingly complex and cannot be free from disturbances and failures. Indeed, the following study is considered as a point of change in the service domain to effectively track disturbances and failures, by allowing the transition from old maintenance to smart maintenance. However, the following document represents a sort of passage between the old and the new maintenance by treating the operation of the bearings in the rotating mechanical systems, the study consists in studying the modes of failures of the bearings. A prediction model is developed based on neural networks.
Higher education as a sustainable development tool

The article is devoted to the assessment of the role of higher education in ensuring socio-economic sustainability of society development. Based on the analysis of international and Russian legal acts, it identifies the main indicators that allow analyzing the impact of higher education system on the implementation of sustainable development goals. Particular attention is paid to assessment of accessibility of education. Based on the analysis of statistical data, it is concluded that there is a problem of differentiation of regions and territories in terms of access to higher education due to uneven location of educational institutions, different density of students, low inter-regional mobility of population and disproportions in incomes of the population. Regional differentiation in access to higher education in Russia leads to a number of socio-economic problems that reduce the sustainability of regional development, including the outflow of young people and uncompensated labour migration.
No Finite Model Property for Logics of Quantified Announcements

Quantification over public announcements shifts the perspective from reasoning strictly about the results of a particular announcement to reasoning about the existence of an announcement that achieves some certain epistemic goal.Depending on the type of the quantification, we get different formalisms, the most known of which are arbitrary public announcement logic (APAL), group announcement logic (GAL), and coalition announcement logic (CAL).It has been an open question whether the logics have the finite model property, and in the paper we answer the question negatively.We also discuss how this result is connected to other open questions in the field.
Spinning Solitude: Coronavirus and the Philosopher

This fictionalized script (fictional dialogue between Coronavirus and the Philosopher) traces the contours of the conversation that seeks to fathom the crisis unleashed by the outbreak and global spread of the coronavirus (COVID-19 pandemic) and the ensuing anxieties created in our current social living. The scenario of deepened isolation of the self from the other (social distancing and 'stay-at-home' or various lockdowns) is considered, and it is proposed (by the philosopher, I presume) that isolation, while an unavoidable requirement, does not mean it is some mental lassitude but rather may be seen as an enthusiastic concern toward recovering physical and mental wellbeing of the larger communities concerned to control the possible avenues of transmission of the contagion. The conversation meanders around the issue of quarantine, its attraction or otherwise, and who benefits from this restriction, its effects on one's mental constitution, etc. Philosophers have been known to isolate themselves in other contexts and situations (Yājñavalkya and the Buddha withdrawing to the forest; the Jain mendicants crossing "the ford"; the Stoics withdrawing from society, Nietzsche's retreating regularly to the sanatorium; Heidegger to the Black Forest; Kant's unsocial sociability and Wittgenstein living lonesome lives, etc.) give us a taste of what is to come in the dialogue to resist the calamity of the coronavirus and its grim effects that engulf the entire humanity.
Double random phase encoding schemes with perfect forward secrecy for robust image cryptography

Digital cryptosystems can provide perfect forward secrecy (PFS) for key exchange protocols based on the Diffie–Hellman (DH) scheme. However, key exchange algorithms are optimally designed only to encode small datasets, such as text and voice sets, which makes rapidly processing large-scale datasets difficult. In this paper, we propose new schemes that can efficiently and securely provide PFS in double random phase encoding (DRPE) schemes for robust image cryptography. We demonstrate that the proposed complex sinusoidal waveform versions of the DH algorithm with fusion of a random phase mask (RPM) and ephemeral secret exponents can guarantee PFS. Different experimental results reveal that the proposed schemes can enhance the security of DRPE-based image cryptosystems using a one-time RPM and PFS. We also propose a ring-type PFS scheme in which an unlimited number of users can securely share a temporary session key, which is an extension of PFS for only two users. We provide formal proof for the schemes and prove feasibility through numerical simulations.
Developing soft skills in undergraduate students: A case at a Chilean private university

The role of non-cognitive skills in twenty-first century’s professionals is a critical success factor in current workforce. Undoubtedly, infusing these interpersonal attributes into the curriculum is a huge challenge for Chilean Higher Education Institutions (HEI). In fact, some HEIs are currently integrating these generic skills through direct instruction, while others from a cross-curricular approach, but without having considered proper teacher training. More importantly, developing soft skills in new generations has the potential to have profound and transformative effects on the professional field. This article provides an over-view of the current state of the art in soft-skills-based education. For this purpose, a group of 55 undergraduate students of a Chilean private university were surveyed. Findings indicate that these students are not acquiring soft skills, at a competitive level, that would help them to advance in their future career.
Information Needs and Behaviour for Sustainable Farming Among Millennial and Progressive farmers

Abstract Continuous innovation towards a more sustainable agriculture is an important trend in today’s modern era. Farmers are consistently required to apply essential technologies in enhancing crucial farming practices, regardless of age. The purpose of this qualitative research, therefore, was to carefully examine the information needs and search behaviours of two major farm groups, termed millennial and progressive, using a multi-case study approach. Data were collected by conducting semi-structured interviews with selected participants, comprised of 10 millennial and 5 progressive farmers. Consequently, various trends in information needs and search behaviours in both cases, were discovered, by employing explanation technique. The results showed the millennial generation farmers were more inclined to source for materials on technology access and marketing partnership from diverse communities. Meanwhile, the progressives were known to explore data regarding technology and efforts to improve on farm product quality from the Agriculture Department as well as market insights from close network.
IPAPRec: A Promising Tool for Learning High-Performance Mapless Navigation Skills With Deep Reinforcement Learning

This article studies how to improve the generalization performance and learning speed of the navigation agents trained with deep reinforcement learning (DRL). Although DRL exhibits huge potential in robot mapless navigation, DRL agents performing well in training scenarios are often found to perform poorly in unfamiliar scenarios. In this work, we propose that the representation of LiDAR readings is a key factor behind the degradation of agents’ performance and present a powerful input preprocessing (IP) approach to address this issue. As this approach uses adaptively parametric reciprocal functions to preprocess LiDAR readings, we refer to this approach as IPAPRec and its normalized version as IPAPRecN. IPAPRec/IPAPRecN can highlight important short-distance values and compress the range of less-important long-distance values in laser scans, which well address the issues induced by conventional representations of laser scans. Their high performance was validated by extensive simulation and real-world experiments. The results show that our methods can substantially improve navigation agents’ generalization performance and greatly reduce the training time compared to conventional methods.
Increasing the efficiency of post-selection in direct measurement of the quantum wave function

Direct weak or strong measurement of quantum wave function has been demonstrated based on the post-selection; however, the efficiency of the measurement is greatly limited by the success probability of the post-selection. Here we propose a modified scheme to directly measure photon's wave function by simply inserting a liquid crystal plate after the coupling between the pointer and wave function. Our modified method can significantly increase the efficiency of the post selection. Numerical simulations demonstrate that our proposal can have a greater efficiency in higher resolution and higher fidelity.
Algorithm of Pulmonary Vascular Segment and Centerline Extraction

Because pulmonary vascular lesions are harmful to the human body and difficult to detect, computer-assisted diagnosis of pulmonary blood vessels has become the focus and difficulty of the current research. An algorithm of pulmonary vascular segment and centerline extraction which is consistent with the physician's diagnosis process is proposed for the first time. We construct the projection of maximum density, restore the vascular space information, and correct random walk algorithm to satisfy automatic and accurate segmentation of blood vessels. Construct a local 3D model to restrain Hessian matrix when extracting centerline. In order to assist the physician to make a correct diagnosis and verify the effectiveness of the algorithm, we proposed a visual expansion model. According to the 420 high-resolution CT data of lung blood vessels labeled by physicians, the accuracy of segmentation algorithm AOM reached 93%, and the processing speed was 0.05 s/frame, which achieved the clinical application standards.
Complete traceability multimedia fingerprinting codes resistant to averaging attack and adversarial noise with optimal rate

Abstract In this paper we consider complete traceability multimedia fingerprinting codes resistant to averaging attacks and adversarial noise. Recently it was shown that there are no such codes for the case of an arbitrary linear attack. However, for the case of averaging attacks complete traceability multimedia fingerprinting codes of exponential cardinality resistant to constant adversarial noise were constructed in Egorova et al. (Probl Inf Transm 56(4):388–398, 2020). We continue this work and provide an improved lower bound on the rate of these codes.
Modelling time-varying first and second-order structure of time series via wavelets and differencing

Most time series observed in practice exhibit time-varying trend (first-order) and autocovariance (second-order) behaviour. Differencing is a commonly-used technique to remove the trend in such series, in order to estimate the time-varying second-order structure (of the differenced series). However, often we require inference on the second-order behaviour of the original series, for example, when performing trend estimation. In this article, we propose a method, using differencing, to jointly estimate the time-varying trend and second-order structure of a nonstationary time series, within the locally stationary wavelet modelling framework. We develop a wavelet-based estimator of the second-order structure of the original time series based on the differenced estimate, and show how this can be incorporated into the estimation of the trend of the time series. We perform a simulation study to investigate the performance of the methodology, and demonstrate the utility of the method by analysing data examples from environmental and biomedical science.
Effect of Learners' Learning Presence on Learning Flow and Learning Satisfaction in Non-Contact University Class

With the innovative development of ICT, a change in the social paradigm has an effect on education, so the current educational environment has been drastically transformed into online education. In particular, non-contact classes are conducted in all universities, as e-Learning which has been only conducted in cyber universities is also applied to the general education programs managed by general universities, in the midst of COVID-19. This study, therefore, aims to examine the effect of learners' learning presence on both learning flow and learning satisfaction in non-contact university class. The subjects included 253 undergraduates majoring in the department of aviation tourism & service in H University located in Chungnam, and a statistical analysis was conducted by using SPSS WIN 21.0. The findings are as follows: First, cognitive presence, emotional presence and social presence, among the learning presence has significantly positive effects on the learning flow. Second, cognitive presence, emotional presence and social presence, among the learning presence has significantly positive effects on the learning satisfaction. Third, the learning flow has a significantly positive effect on the learning satisfaction. The results suggest that it is necessary to make efforts to enhance the learning satisfaction by managing systematic learner-centered curriculum and using various contents in non-contact university class.