Reviewerly/exHarmony huggingface dataset:
On the use of Egyptian fractions for stream ciphers

Within the scope of the mutual interference between modern number theory and chaos-based cryptography, and as it is well-known and already explored for the decimal or the continued fraction expansion of irrational numbers, interesting random-like behaviors seem to be hidden in Egyptian fraction expansions, thus suggesting new chaos-based encryption systems. In fact, at a practical level, and as will be shown through the present study, concatenation of involved denominators and binary expansion generally lead to pseudo-random streams which satisfactorily pass the NIST statistical test suite for randomness. Then, to a certain extent, and for cryptographic purposes, this can be considered as a new tool to complete the conventional class of already-in-use pseudo-random number generators. Some mathematical issues, however, have to be clarified, as for example the chaoticity of the process obtained after converting these denominators to fractional parts of a wandering sequence of real numbers in the unit interval. To the best of our knowledge, and from a cryptographic point of view, previous works on the subject are limited to statistical tests (and subsequent cryptographic analysis) of the resulting one-time pads or stream ciphers, but no rigorous study has been conducted to justify these methods within chaos theory. Considering Egyptian expansions as a working example and using the term “chaotic” in the sense of Devaney, this is what our proposal is aimed at in the present work.
FPGA-based implementation of 2D Normalized Cross-Correlation for Large Scale Signals

About every three years, the high-end image resolution quadruples: what we called high-resolution in 2018 is turning standard now. Even simpler embedded devices can shoot videos at 4K resolution. The combination of this rush to wider frames with the blooming era of Computer Vision (CV) constantly pushes the performance requirements of the underlying processing. Template matching is one of the CV's foundations as it enables the localization of objects inside images. It exploits similarity functions such as the 2D Cross-Correlation and its variants Normalized Cross-Correlation (NCC) and Zero-mean NCC (ZNCC). However, these computations do not scale gracefully with resolution. We propose two novel FPGA-based implementations with low hardware resource consumption for the 2D NCC and ZNCC for large-scale images. We succeeded in fitting our accelerator on the 3CG class of Xilinx Zynq UltraScale+ ARM-based MPSoCs, among the smallest embedded-grade classes that do not even include dedicated CV hardware, which adds a 14% cost overhead, thus enabling accelerated template matching for local preprocessing in IoT applications. We achieve this while attaining a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{3.52}\times$</tex> speedup over non-embedded systems and remaining <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{43.2}\times$</tex> more power efficient for NCC. Finally, to fully exploit the heterogeneous nature of our target hardware, we provide a runtime hardware selection algorithm to automatically target the proper hardware/software implementation for best performance.
A Copula Based-Approach for Image Splicing Detection

Measuring properties in pixels to reveal an image description is of great essential for image processing. One of obstacles in image splicing detection is an unavailability of complete-reference image. In this study, a statistical method is proposed using a cumulative joint distribution of colours pixel. Consider an image consisting of particular pixels with RGB (Red, Green, Blue) components, each colour provides unique behaviour of probability function that implicitly sticking in marginal distributions. Copulas provide a cumulative joint distribution that consist of the univariate marginals and load their dependency structure, since the deficiency of linear dependency under non-linear strictly transformation. Copulas can determine the distinguish the cumulative joint distribution in the edge boundaries of image splicing. According to experimental results, Gaussian and Archimedean copulas provide deeper description to detect the contrast copula parameters of splicing image distribution.
Security at Virtualization Level in Cloud Computing

Cloud computing is perhaps the most energizing advancements today because of its cost decrease, adaptability, and versatility. With the quick development of cloud computing innovation, information security is getting progressively significant. To survey whether to move to cloud computing, it is imperative to think about the advantages and related dangers. Accordingly, security and other existing issues in the cloud imply that cloud clients need more opportunity to consider moving to cloud conditions. Be that as it may, security-related issues are perhaps the most begging to be proven wrong themes in cloud computing, driving a few organizations to take a gander at this innovation uncertainly and to act with alert. Virtualization is the vital segment of cloud computing. The cloud offers a more solid assistance at a lower cost and in this manner lessens equipment framework. With the advancement of PC security in the cloud at the virtual level they become the fundamental issues. For this security issue, an answer dependent on division by success is proposed. The security danger of virtualization is characterized and the arrangement is given dependent on this methodology by giving a faker virtual worker to the whole virtual worker independently, and consequently the security danger can be diminished. In this paper give an overview of the cloud computing, Virtualization, Types of Virtualization and Virtualization security issues.
A product recommendation system for solving the cold start problem

The recommendation of product or service based on application needs different product attributes and user requirements to be analyzed. But if both the kinds of information are unavailable then such kind of issue in the recommendation system is known as the clod start problem.[1] In this presented work, the effort is made in order to understand and resolve the issue of recommendation system. Thus, a four phase recommendation system is introduced in this work. In first phase, the web usage data is preprocessed which is used for further recommendations of products. In next phase, the frequent pattern based recommendation is performed. In third phase, the recommendation is made on the basis of user current search (user click streams) and similar user behaviors available in web access logs. Additionally, the final recommendation is made on the basis of filtering the results obtained in third phase and the cost, brand and social review of the product or service. The implementation of the system is performed on JAVA technology and the results in terms of accuracy, error rate is computed. The results show the prediction is accurate and required less computational resources. Thus, in recommendation system design the model is acceptable for use and future extension of work for solving the issue of cold start problem.
Auto-Tuning Deep Stereo on the Fly

Stereo depth estimation based on learning with CNNs has become the dominant paradigm due to their significant leap in performance. However, since the inconsistency of the distribution between the training data (synthetic image) and the actual scene, the generalization ability is insufficient for deployment. Recently, the work MADNet with modular adaptation was proposed to tackle this issue on the fly. However, it still runs slowly with not-so-ideal performance. This paper introduces a “harmless training or inference” (HToI) framework to improve their work in both convergence and prediction speed. Firstly, in the actual scenario, the images arrive in sequence without ground-truth. Online adaptation to new target domains is only through self-supervised loss with errors (e.g., occluded areas). This paper introduces a confidence-weighted map (CWM) to mask the incorrect loss. The confidence map will guide the model to use the correct loss for online adaptation to obtain better accuracy. Secondly, this paper proposes a supervisor, implemented by Mann-Kendall trend detection on loss (MKL), to monitor the performance changes during the adaptation. According to the MKL, the model can autonomously determine whether to keep harmless training. If not training (i.e., only inference), the model will only predict the disparity, significantly reducing the running time. Finally, experiments on real-world stereo datasets confirm the effectiveness of CWM and MKL, which can make the model adapt to the scene more stably and accurately.
Atom : Novel data capturing technique for robotic teleoperation

ATOM, Advanced Tele-Operation Method is a data capturing technique for teleoperation machines. This approach is cheaper, efficient and also reduces the requirement for wearable components compared to existing methods. It also provides feedback with a unique interface experience by exploiting the virtual reality technology. The VR eyewear gives us the 360 degree vision as seen through the eyes of the robot camera in a remote area. The proposed method uses image processing using the media pipe machine learning tool to detect and observe hand movement of the user, measures the spatial depth using object detection and converts it into a useful format for controlling the arms of the robot. This gives us the dynamic experience along with an improved situational awareness while dealing with complex and risky operations. This method focuses on the ability of the proposed method to to retrace the users arm movements and gestures with minimum error using an open source 6 DOF -degree of freedom robotic arm.
Software package for testing three-dimensional shape measurement methods using structured lighting

Abstract This work aims at creating a universal software package for the development and testing of triangulation methods using structured lighting for measuring the three-dimensional geometry of objects in difficult ambient lighting conditions. As a result, a software package meeting the stated requirements is created. Lighting is based on the Fong model. A method for preloading objects is implemented to optimize the operation of the software package. An accelerated method for creating shadow maps is proposed and implemented. The developed software package is shown to successfully perform all required functions.
A data-driven approach to neural architecture search initialization

Abstract Algorithmic design in neural architecture search (NAS) has received a lot of attention, aiming to improve performance and reduce computational cost. Despite the great advances made, few authors have proposed to tailor initialization techniques for NAS. However, the literature shows that a good initial set of solutions facilitates finding the optima. Therefore, in this study, we propose a data-driven technique to initialize a population-based NAS algorithm. First, we perform a calibrated clustering analysis of the search space, and second, we extract the centroids and use them to initialize a NAS algorithm. We benchmark our proposed approach against random and Latin hypercube sampling initialization using three population-based algorithms, namely a genetic algorithm, an evolutionary algorithm, and aging evolution, on CIFAR-10. More specifically, we use NAS-Bench-101 to leverage the availability of NAS benchmarks. The results show that compared to random and Latin hypercube sampling, the proposed initialization technique enables achieving significant long-term improvements for two of the search baselines, and sometimes in various search scenarios (various training budget). Besides, we also investigate how an initial population gathered on the tabular benchmark can be used for improving search on another dataset, the So2Sat LCZ-42. Our results show similar improvements on the target dataset, despite a limited training budget. Moreover, we analyse the distributions of solutions obtained and find that that the population provided by the data-driven initialization technique enables retrieving local optima (maxima) of high fitness and similar configurations.
Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation

The literature on “benign overfitting” in overparameterized models has been mostly restricted to regression or binary classification; however, modern machine learning operates in the multiclass setting. Motivated by this discrepancy, we study benign overfitting in multiclass linear classification. Specifically, we consider the following training algorithms on separable data: 1) empirical risk minimization (ERM) with cross-entropy loss, which converges to the multiclass support vector machine (SVM) solution; 2) ERM with least-squares loss, which converges to the min-norm interpolating (MNI) solution; and 3) the one-vs-all SVM classifier. First, we provide a simple sufficient deterministic condition under which all three algorithms lead to classifiers that interpolate the training data and have equal accuracy. When the data is generated from Gaussian mixtures or a multinomial logistic model, this condition holds under high enough effective overparameterization. We also show that this sufficient condition is satisfied under “neural collapse”, a phenomenon that is observed in training deep neural networks. Second, we derive novel bounds on the accuracy of the MNI classifier, thereby showing that all three training algorithms lead to benign overfitting under sufficient overparameterization. Ultimately, our analysis shows that good generalization is possible for SVM solutions beyond the realm in which typical margin-based bounds apply.
Fraud Committed Using Social Engineering: Problem Statement

In the theses of the speech, the issues of countering fraud using social engineering are revealed.
Analysis of Lightweight BC-Based AEAD

In this chapter we discuss some of the security aspect of (T)BC-based lightweight AEAD schemes.
Online Education as an Indicator of Effective Public Administration of Higher Education

The relevance of this article is determined by two social processes related to the institutionalization of online education in the higher education system of the Russian Federation. The first is that the total number of students studying in electronic format is steadily increasing. The second one is manifested in the state's desire to use online education as an indicator that evaluates the effectiveness of public administration in the field of educational policy. The author hypothesizes that online education cannot be an indicator of the effectiveness of public administration, since the ill-considered implementation of this form of educational process will inevitably lead to a decrease in the quality of student education and a systemic deterioration in the training of specialists. To substantiate this hypothesis, an empirical study was conducted using the group interview method in student groups. The analysis of the obtained data fully confirmed this hypothesis.
Double stage Siamese network object tracking algorithm based on template update

Aiming at the problem that the tracking accuracy of SiamRPN declines when the appearance of the object changes dramatically, this paper proposes a double stage Siamese network object tracking algorithm based on template update. The algorithm can be divided into two stages. In the first stage, the SiamRPN was used for preliminary object tracking to get the initial prediction, updating network was introduced to update the object template in the second stage. Pixel-wise correlation and corner prediction head were applied in this algorithm to improve the feature representation. A large number of experiments on OTB2015 and VOT2018 show that our approach has strong robustness for the appearance of the object dramatic change, and was able to improve tracking results of comparison algorithm, effectively improves the tracking precision and success rate.
Research on a Service-oriented Dynamic Adaptive Software Architecture

Aiming at how to implement dynamic adaptive reuse service components efficiently during the service-oriented modeling process, this paper proposes a new service-oriented dynamic adaptive software architecture based on a service component reuse method via gradient descent optimization, which can be effectively implemented as dynamic adaptive reuse service component for service modeling. Among them, based on the &#x201C;query-select-reuse&#x201D; strategy, this paper guides the process of the intermediate service component to achieve dynamic adaptive reuse, and with this approach, the gradient descent-based optimization parameter method defines the optimal service component query matching strategy, and it finally achieves the most excellent adaptive reuse performance of service component. Finally, the real data experiment and case analysis verified the efficiency and practicability of the proposed framework to achieve adaptive optimal reuse of service components.
Relations Between Privacy, Verifiability, Accountability and Coercion-Resistance in Voting Protocols

This paper studies quantitative relationships between privacy, verifiability, accountability, and coercion-resistance of voting protocols. We adapt existing definitions to make them better comparable with each other and determine which bounds a certain requirement on one property poses on some other property. It turns out that, in terms of proposed definitions, verifiability and accountability do not necessarily put constraints on privacy and coercion-resistance. However, the relations between these notions become more interesting in the context of particular attacks. Depending on the assumptions and the attacker’s goal, voter coercion may benefit from a too weak as well as too strong verifiability.
Relationship between the Social Emotional Competences of Secondary School Teachers and Students in a virtual teaching experience

This paper is part of a doctoral research that analyses the relationship between socioemotional competences (SEC) and pedagogical beliefs in secondary school teachers. Specifically, we sought to study the relationship between the SEC of 115 teachers and 228 secondary school students in two UGELs (Local Education Management Units) in Peru in the context of the "Learning at home" experience as a way of responding to the challenges arising from Covid-19. The problem was approached from the quantitative paradigm with a correlational design. Two questionnaires were applied to measure the study variables based on the CASEL proposal. The results indicate that the levels of SEC are acceptable and do not differ significantly in both groups, while there is a low and very low correlation between the variables and sub-variables of the study. Results are discussed and conclusions are drawn.
Rehabilitate Sustain from Earth to Sea: A Design Study on Interplaying Biosocial Sculpture In-Between the Intertidal Zone

Obey the traditional idea of “Tian-Fu Di-Zai” (The Heaven Overspreads and Earth Sustains All, 天覆地載) in Chinese culture, this monography focuses on “Rise above both Self and Matters” (物我相忘) of Taoist by “Rehabilitate Sustain from Earth to Sea” (地載海涵). It cumulated subtly into consciousness of release-and protect lives. The motive of teapot craft design originated from unconsciously kill-and-persecute to environ-ecology by human self-centeredness since the Anthropocene. Sustainability become one of the most thematic of recent artistic creation, comparing to black-faced spoonbill pottery as one of the themes in this thesis, it is found that ecology is mostly an expression of creative skills in earlier artists’ works than the social responsibility not shown, although. Thus, this paper precipitate-and-transform from “The Identity about the Matter and Me” (物我合一) firstly, then represent the (Bio)Social Sculpture among human and wildlife in the intertidal ecosystem by “Sociological Imagination”. Aquatic system intertwining mountains, creeks, lakes, rivers into sea, which continued those real stories like starved Buffalos of cold highland, Bear strayed into boar trap, Leopard cat road killed, Elephant died of pineapple bomb, Whale fall organism and wisdom of Octopus. That constituted a discourse subjects of ceramic art compounded by such endless tragedies under the high and low tides. In the “Di-Zai” (Earth Sustain, 地載) series, which succeeding created 10 items of works and named respectively as: (1) Break off the shell; (2) Survival of the fittest; (3) Mimicry and camouflage; (4) Gaze with longing eyes; (5) Habitat restoration; (6) Fight for the foods with human; (7) Community symbiosis; (8) At the mercy of nature; (9) Epimeletic incubation, and; (10) Flight of ideas, etc. Another “Hai-Han” (Sea Rehabilitate, 海涵) series of 10 items, respectively named: (1) Deep-sea submergence; (2) Benthic organisms; (3) Pandora’s box, origin of species; (4) Semi-biological mug; (5) Implements of waste antiquity; (6) Petrochemical disposal creation; (7) Co-synthesis tea pot; (8) Relationship in reflexivity; (9) Earth grow myriads of objects, and; (10) Mental image of representation, etc. Inclusion, after the social practice throughout crafts design and creation, this article suggests a new methodology of “Biosocial Sculpture” plays a comprehensive role of ‘interactive design’ to eco-artistic creation”, that is, approaching Gesamtkunstwerk of the Sociological Imagination between humans and marine creatures. It reinterpret-and-transform the delicate “Human-Matter” (人物) relationship, reappear the form of ideas originate from the ceramic art creators. Furthermore, these works not only initiate popular to pay attention to the human selfishness and disregard to biology, but also transform the social relationship to the environmental art education. It highlights a “social interaction design” engaging popular praxis based on daily tea wares with art education influence directly, keeping on recreation for a “surpass selfhood” (自我超越) endlessly in future.
Research on News Recommendation Based on Deep Knowledge Perception Network

With the explosion of information in the Internet age, news recommendation uses various technologies to explore user interests and provide users with personalized news matching in order to reduce the load of user information. ID-based algorithms such as content-based recommendation and collaborative filtering are not very suitable for news recommendation. Because of the iterative speed of news, a large number of new items will be generated in a short time, and it will also replace outdated news. It will face data sparseness and Problems such as cold start have certain limitations. This paper uses knowledge-based convolutional neural network and hierarchical attention mechanism to obtain the feature text representation of news text, and combines these features to learn that users are responsible for behavioral data to provide new recommendations. We have conducted experiments on real news data sets, and the results show that our model is significantly better than the benchmark method.
Research on Local Adaptive Enhancement of Aerial Images Based on Particle Swarm Optimization

In the field of aerospace, when the aerial images are used for object extraction, due to the existence of objective factors, the sharpness or readability of the object in the aerial image is greatly decreased. Therefore, after the aerial images are obtained, it is necessary to perform noise reduction or enhancement processing on the characteristic edges of the region of interest in the image. Based on the unsharp masking filter, a local adaptive enhancement method is proposed in order to improve the quality of aerial images. The method is improved on the basis of particle swarm and it can improve the readability of the feature information of the region of interest and the visual quality of the image, which can provide some references for the research of automatic segmentation algorithm of aerial image object and the extraction technology of aerial image object.