Reviewerly/exHarmony huggingface dataset:
Cryptographic Systems Based on an Algebraic Structure

In this paper cryptographic systems based on the Dedekind and Galois structures are considered.We supplement the created cryptosystems based on the Dedekind structure with programs written in C ++ and discuss the inner structure of Galois in cryptography.It is well-known that such a structure is based on finite fields only.Our results reveals something more internal.The final section contains additional information about square-free and radical factorizations in monoids consisting in searching for a minimal list of counterexamples.As an open problem, we leave creating a program that would generate such a list and how to use such a list to create a cryptosystem.
Investigating Metals and Metalloids in Soil at Micrometric Scale Using µ-XRF Spectroscopy—A Case Study

Micrometric 2D mapping of distinct elements was performed in distinct soil grain-size fractions of a sample using the micro-X-ray Fluorescence (µ-XRF) technique. The sample was collected in the vicinity of São Domingos, an old mine of massive sulphide minerals located in the Portuguese Iberian Pyrite Belt. As expected, elemental high-grade concentrations of distinct metals and metalloids in the dependence of the existent natural geochemical anomaly were detected. Clustering and k-means statistical analysis were developed considering Red–Green–Blue (RGB) pixel proportions in the produced 2D micrometric image maps, allowing for the identification of elemental spatial distributions at 2D. The results evidence how elemental composition varies significantly at the micrometric scale per grain-size class, and how chemical elements present irregular spatial distributions in the direct dependence of distinct mineral spatial distributions. Due to this fact, elemental composition is more differentiated in coarser grain-size classes, whereas griding-milled fraction does not always represent the average of all partial grain-size fractions. Despite the complexity of the performed analysis, the achieved results evidence the suitability of µ-XRF to characterize natural, heterogeneous, granular soils samples at the micrometric scale, being a very promising investigation technique of high resolution.
Kernel Risk Sensitive Loss-based Echo State Networks for Predicting Therapeutic Peptides with Sparse Learning

The detection of therapeutic peptides is usually a biochemical experimental method, which is time-consuming and labor-intensive. Lots of computational biology methods had been proposed to solve the problem of therapeutic peptide prediction. However, the existing methods did not consider the processing of noisy samples. We propose a kernel risk-sensitive mean p-power error-based echo state network with sparse learning (KRP-ESN-SL). An efficient iterative optimization algorithm is used to train the model. The KRP-ESN-SL has better performance than other methods.
Lossless data hiding for JPEG image with optimized Huffman table

Lossless data hiding is a data hiding method that embeds secret data without distortion. In JPEG images, it generally refers to modifying the bitstream of the Huffman table and DCT coefficient, also known as the method of Huffman table modification. Currently, most JPEG lossless data hiding methods use coding redundancy in the default AC Huffman table. However, this preference is deficient in two ways. On the one hand, the Huffman tables of most JPEG images are optimized to save storage and increase transmission speed, causing the approach to be unavailable. In this paper, the embedding efficiency of each Run-Size (RS) has been analyzed, and an efficient mathematical model for the optimized Huffman table is established. On the other hand, current schemes tended to modify the AC Huffman table while ignoring the DC one. This is because the redundancy presented in the DC Huffman table is hard to exploit. In contrast, the modification on both optimized AC and DC Huffman tables (OAC, ODC) has been implemented in this paper. The experimental results show that the proposed scheme is superior in embedding capacity, file size preservation, and complexity without distortion compared to the state-of-the-art methods.
IPPT4KRL: Iterative Post-Processing Transfer for Knowledge Representation Learning

Knowledge Graphs (KGs), a structural way to model human knowledge, have been a critical component of many artificial intelligence applications. Many KG-based tasks are built using knowledge representation learning, which embeds KG entities and relations into a low-dimensional semantic space. However, the quality of representation learning is often limited by the heterogeneity and sparsity of real-world KGs. Multi-KG representation learning, which utilizes KGs from different sources collaboratively, presents one promising solution. In this paper, we propose a simple, but effective iterative method that post-processes pre-trained knowledge graph embedding (IPPT4KRL) on individual KGs to maximize the knowledge transfer from another KG when a small portion of alignment information is introduced. Specifically, additional triples are iteratively included in the post-processing based on their adjacencies to the cross-KG alignments to refine the pre-trained embedding space of individual KGs. We also provide the benchmarking results of existing multi-KG representation learning methods on several generated and well-known datasets. The empirical results of the link prediction task on these datasets show that the proposed IPPT4KRL method achieved comparable and even superior results when compared against more complex methods in multi-KG representation learning.
Vision-Based Efficient Robotic Manipulation with a Dual-Streaming Compact Convolutional Transformer

Learning from visual observation for efficient robotic manipulation is a hitherto significant challenge in Reinforcement Learning (RL). Although the collocation of RL policies and convolution neural network (CNN) visual encoder achieves high efficiency and success rate, the method general performance for multi-tasks is still limited to the efficacy of the encoder. Meanwhile, the increasing cost of the encoder optimization for general performance could debilitate the efficiency advantage of the original policy. Building on the attention mechanism, we design a robotic manipulation method that significantly improves the policy general performance among multitasks with the lite Transformer based visual encoder, unsupervised learning, and data augmentation. The encoder of our method could achieve the performance of the original Transformer with much less data, ensuring efficiency in the training process and intensifying the general multi-task performances. Furthermore, we experimentally demonstrate that the master view outperforms the other alternative third-person views in the general robotic manipulation tasks when combining the third-person and egocentric views to assimilate global and local visual information. After extensively experimenting with the tasks from the OpenAI Gym Fetch environment, especially in the Push task, our method succeeds in 92% versus baselines that of 65%, 78% for the CNN encoder, 81% for the ViT encoder, and with fewer training steps.
Predicting melanoma survival and metastasis with interpretable histopathological features and machine learning models

Introduction Melanoma is the fifth most common cancer in US, and the incidence is increasing 1.4% annually. The overall survival rate for early-stage disease is 99.4%. However, melanoma can recur years later (in the same region of the body or as distant metastasis), and results in a dramatically lower survival rate. Currently there is no reliable method to predict tumor recurrence and metastasis on early primary tumor histological images. Methods To identify rapid, accurate, and cost-effective predictors of metastasis and survival, in this work, we applied various interpretable machine learning approaches to analyze melanoma histopathological H&amp;amp;E images. The result is a set of image features that can help clinicians identify high-risk-of-metastasis patients for increased clinical follow-up and precision treatment. We use simple models (i.e., logarithmic classification and KNN) and “human-interpretable” measures of cell morphology and tissue architecture (e.g., cell size, staining intensity, and cell density) to predict the melanoma survival on public and local Stage I–III cohorts as well as the metastasis risk on a local cohort. Results We use penalized survival regression to limit features available to downstream classifiers and investigate the utility of convolutional neural networks in isolating tumor regions to focus morphology extraction on only the tumor region. This approach allows us to predict survival and metastasis with a maximum F1 score of 0.72 and 0.73, respectively, and to visualize several high-risk cell morphologies. Discussion This lays the foundation for future work, which will focus on using our interpretable pipeline to predict metastasis in Stage I &amp;amp; II melanoma.
Superactivating Bound Entanglement in Quantum Networks via Quantum Zeno Dynamics and a Novel Algorithm for Optimized Zeno Evolution

An arbitrary amount of entanglement shared among nodes of a quantum network might be nondistillable if the nodes lack the information on the entangled Bell pairs they share. Making such a system distillable, which is called the superactivation of bound entanglement (BE), was shown to be possible through systematic quantum teleportation between the nodes, requiring the implementation of controlled-gates scaling with the number of nodes. In this work, we show in two scenarios that the superactivation of BE is possible if nodes implement the proposed local quantum Zeno strategies based on only single qubit rotations and simple threshold measurements. In the first scenario we consider, we obtain a two-qubit distillable entanglement system as in the original superactivation proposal. In the second scenario, we show that superactivation can be achieved among the entire network of eight qubits in five nodes. In addition to obtaining all-particle distillable entanglement, the overall entanglement of the system in terms of the sum of bipartite cuts is increased. We also design a general algorithm with variable greediness for optimizing the QZD evolution tasks. Implementing our algorithm for the second scenario, we show that a significant improvement can be obtained by driving the initial BE system into a maximally entangled state. We believe our work contributes to quantum technologies from both practical and fundamental perspectives bridging nonlocality, bound entanglement and the quantum Zeno dynamics among a quantum network.
A New Approach for Evaluation True Stress–Strain Curve from Tensile Specimens for DC04 Steel with Vision Measurement in the Post-Necking Phases

The paper presents an experimental evaluation of deformation of flat samples during uniaxial tensile testing, including uniform deformation and post-necking phases. The authors recommend a specially designed vision extensometer and simplified image processing method for analytical correction of triaxial test results for extended stress–strain curve estimation. A modified correction model is proposed, based on the application of Gaussian functions, to determine the neck geometry of the tested sample. The vision extensometer can monitor a specimen’s elongation using two fibre-optic gauges inserted into the material. Measurements taken from the vision extensometer are compared with readings from analogue gauges within the range of uniform deformation. The analytical correction model’s ability to correctly assess the extended true stress–strain curve in the post-necking phase was investigated. Image processing forms the basis of an efficient method for identifying the contour of the specimen’s neck. Digital image correlation (DIC) was used to verify the proposed solutions and assess the results obtained for the uniform and post-neck deformation phases. The change in thickness of the sample was experimentally measured throughout the tensile test with a digital gauge sensor and compared with the results of the digital image correlation.
CONDITIONS OF THE IMPLEMENTATION OF THE SCIENTIFIC-METHODICAL SUPPORT OF THE PROFESSIONAL-METHODICAL DEVELOPMENT OF TEACHERS OF THE GENERAL SECONDARY EDUCATION INSTITUTION

International scientific conference "Modern scientific developments in pedagogy and psychology" : conference proceedings (November 3–4, 2022. Riga, the Republic of Latvia). Riga, Latvia : "Baltija Publishing", 2022. 204 pages.
Generalized Closed Set in Intuitionistic Fuzzy Topology

The goal of this paper is to introduce new sets called (݃ ȴ -closed set and ݃ ȴ -open set) defined on a intuitionistic topological space.A sufficient condition is presented to be a ݃ ூ -closed set and ݃ ȴ -open set.And we also discuss union and intersection around these groups.Finally, we introduce a new intuitionistic separation axiom called ॅ ȴ భ/మ -space and verify its basic properties.
KADAR pH, KESADAHAN DAN BESI (Fe) PADA AIR SUMUR BOR DI SAMARINDA

Clean water is a human need for daily needs such as bathing, washing and toileting. In addition, clean water as a source of drinking water provided that it has been disinfected for example by cooking. The clean water quality must meet the requirements of Health Ministry Regulation No. 416 years 1990 to avoid health problems. Purpose of this study is analysis water quality of Drill Well Water for parameters pH, hardness and Iron (Fe)that used by residents in Samarinda especially on Jl. Gerilya, Gg. Sepakat, RT. 101. Research method used is field observation, taking drill well water samples and conducting analysis in the laboratory. Result of the study obtained that the levels of pH, hardness and Iron (Fe) in Drill Well Water obtained results for pH levels, namely samples A: 6.81, sample B: 6.87, sample C: 6.80, sample D: 6.66, and sample E: 6.28. Hardness level is sample A: 136 mg/L sample B: 114 mg/L sample C: 104 mg/L, D sample: 112 mg/L, and sample E: 298 mg/L and Iron content (Fe) sample A: 0.1389 mg/L, sample B: 0.1439 mg/L, sample C: 0.1464 mg/L, sample D: 0.1190 mg/L, and sample E 0.5608 mg/L. Base in the results, the drill well water used still meets the requirements.0000000000
DYNAMICS OF THE THREE-QUBITS TAVIS — CUMMINGS MODEL

In this article, we have studied the entanglement dynamics of three identical qubits (natural or artificial two-level atoms) resonantly interacting with the one mode of the thermal field of a microwave lossless resonator via one-photon transitions. An exact solution of the quantum time Schrodinger equation is found for the total wave function of the system for the initial separable and entangled states of qubits and the Fock initial state of the resonator. On the basis of this solution, an exact solution of the quantum Liouville equation for the total time-dependent density matrix of the system in the case of a thermal field of the resonator is constructed. The exact solution for the full density matrix is used to calculate the criterion of entanglement of pairs of qubits negativity. The resultsof numerical simulation of the time dependence of the negativity of pairs of qubits showed that with an increase in the intensity of the thermal resonator field, the degree of entanglement of pairs of qubits decreases. It is also shown that In the model under consideration, for any initial states of qubits and intensities of the thermal field of the resonator, the effect of sudden death of entanglement takes place. This behavior of the entanglement parameter in the model under consideration differs from that in the two-qubit model. For two-qubit model, the effect of the sudden death of entanglement takes place only for the initial entangled states of qubits and intense thermal fields of the resonator.
Does Environmental Sustainability Mediate the Effect of Institutional Quality on Inclusive Development in Sub-Sahara Africa

This study was set to investigate the effect of institutional quality and environmental sustainability on inclusive human development in 35 Sub-Sahara Africa over the period 2010 to 2017. The results of a structural equation modelling following Baron and Kenny (1986) showed that the effect institutional quality on inclusive human development is partially mediated by environmental sustainability.
Processing Real-Life Recordings of Facial Expressions of Polish Sign Language Using Action Units

Automatic translation between the national language and sign language is a complex process similar to translation between two different foreign languages. A very important aspect is the precision of not only manual gestures but also facial expressions, which are extremely important in the overall context of a sentence. In this article, we present the problem of including facial expressions in the automation of Polish-to-Polish Sign Language (PJM) translation-this is part of an ongoing project related to a comprehensive solution allowing for the animation of manual gestures, body movements and facial expressions. Our approach explores the possibility of using action unit (AU) recognition in the automatic annotation of recordings, which in the subsequent steps will be used to train machine learning models. This paper aims to evaluate entropy in real-life translation recordings and analyze the data associated with the detected action units. Our approach has been subjected to evaluation by experts related to Polish Sign Language, and the results obtained allow for the development of further work related to automatic translation into Polish Sign Language.
Multi-Scale Deep Information and Adaptive Attention Mechanism Based Coronary Reconstruction of Superior Mesenteric Artery

Vascular images contain a lot of key information, such as length, diameter and distribution. Thus reconstruction of vessels such as the Superior Mesenteric Artery is critical for the diagnosis of some abdominal diseases. However automatic segmentation of abdominal vessels is extremely challenging due to the multi-scale nature of vessels, boundary-blurring, low contrast, artifact disturbance and vascular cracks in Maximum Intensity Projection images. In this work, we propose a dual attention guided method where an adaptive adjustment field is applied to deal with multi-scale vessel information, and a channel feature fusion module is used to refine the extraction of thin vessels, reducing the interference and background noise. In particular, we propose a novel structure that accepts multiple sequential images as input, and successfully introduces spatial-temporal features by contextual information. A further IterUnet step is introduced to connect tiny cracks caused using CT scans. Comparing our proposed model with other state-of-the-art models, our model yields better segmentation and achieves an average F1 metric of 0.812.
VLSI Design of a High-Performance Multicontext MQ Arithmetic Coder

The MQ arithmetic coding, which is an adaptive arithmetic coding developing from Q coding, has become a major throughput bottleneck of JPEG2000 compression due to its inherent serial operations. To overcome the bottleneck, this brief proposes a high-performance hardware architecture of the multicontext MQ coder. The proposed architecture is capable of concurrent coding for two adjacent more probable symbols (MPSs). Performance analysis results show that the proposed coder consumes 1.61 CXD pairs per cycle and achieves a throughput of 506.93 MSymbols/s under 0.5 bpp bit rate. The proposed architecture not only achieves high throughput, but also maintains both low hardware utilization and low power consumption. Compared with the state-of-the-art two-context coder, the figure of merit (FoM) is increased by 38%. Compared with the single-context coder, the power-delay product (PDP) is reduced by 49%.
Information Analytics in the Structure of Ukrainian National Libraries Activity (Based on Analysis of their Sites)

The objective of the study is to determine the optimal structure for organizing information and analytical activities of Ukrainian libraries of the national level. The method of analysis was used for identifying the diversification of the forms of organization of information and analytical activities of the indicated libraries. The results of the study indicate the existence of two forms of organizing national libraries regarding the implementation of information and analytical activities, one of which is based on realizing their functions in different departments of libraries, and another one is aimed at creating a separate unit in the organizational structures of libraries. The conclusions of the study proved the dependence of the form of organization of information and analytical activities in national libraries on their level in the library system. Creating separate departments carrying out information and analytical activities can be considered as a perspective form of organizing information and analytical activities of Ukrainian national libraries due to the increasing responsibility of employees for the timing of the work and integration of analysts of various specializations, as well as creating conditions for developing the cognitive component of library activity.
Chatbot-Based Movie Recommender System Using POS Tagging

The movie recommender system is a technology designed to make it easier for users to provide recommendations quickly and among the many pieces of information. Because the number of movies is huge, it causes a person to be confused in determining the choice of the movie to watch. Many movie recommending systems have been developed, but users cannot interact intensively. Based on these problems, we developed a chatbot-based conversational recommender system, which can interact intensively with the system. The developed chatbot uses normal language handling to permit the framework to comprehend what the user enters as natural language. POS Tagging is used to find tags in the form of movie titles with patterns in the POS Tagging model. However, the algorithm of those used on POS Tagging does not pay attention to the sentence entity, so the predicted title must correspond to the provisions of POS Tagging. Multinomial Naive Bayes looks for similarities of user input to datasets on intents. The dataset with the highest probability value or almost equal to the sentence entered by the user can be used as a response to user input. The test results of the chatbot application showed that the match rate between response and user input was 89.1%. Thus, the developed chatbot can be used well to provide practical and interactive movie recommendations.
Glimmers of Digital Publishing Innovation

As an exercise, take a moment to consider: What is the most innovative eBook or electronic publication that you have read in the past couple of years?Alternatively, what is the most innovative electronic publication, broadly speaking, that you have ever read?You might be asking, what does innovative have to do with it?Isn't an eBook essentially the same as print book, just one that you can read on a mobile device?How do you define innovative in terms of electronic publications?The mere fact that you are reading this, however, may predispose you to have some idea, or recollection, of something pioneering and inventive in the realm of digital texts.This exercise has a second part: If the innovative eBook you consideredif, in fact, you recalled any in particular-is one you read more than five years ago, is it still available?Can you access it, read it again?What about an innovative eBook you may have read more than a decade ago?Innovative, in terms of an eBook, can mean a lot of things.For someone with vision or reading disabilities, for example, it might mean an expertly formed, accessible text, one with images that are well described, with charts and tables that are not gibberish with a screen reader.Innovative can mean the seamless and effective use of text, audio, and video.It might mean interactivity, immersing the reader in the text, as a participant.Most of us, on average, however, don't ask a lot from the eBooks we may read on a regular basis.We expect them to function.That's what most publishers, vendors and platforms focus on.If we check out an eBook from a public library,