Reviewerly/exHarmony huggingface dataset:
Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables

Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions. However, it remains challenging for existing methods to address advanced conditional generative problems without annotations, which can enable multiple applications like image-to-image translation and image editing. We present a unified Bayesian framework for such problems, which introduces an inference stage on latent variables within the learning process. In particular, we propose a variational Bayesian image translation network (VBITN) that enables multiple image translation and editing tasks. Comprehensive experiments show the effectiveness of our method on unsupervised image-to-image translation, and demonstrate the novel advanced capabilities for semantic editing and mixed domain translation.
Interpretation of pharmacokinetic data in preclinical studies of new drugs. Single intravenous administration

Review. Further development of the methodological level of the decision support system proposed for the organization of primary selection and experimental preclinical study of new drugs. Methods for describing and interpreting the main pharmacokinetic experiment (single intravenous administration of drugs) data are systematized. A scheme for choosing pharmacokinetic models based on visual pattern recognition is proposed. Visual data patterns allow prediction of pharmacokinetic behavior and properties of new drugs.
Security transmission technology for satellite communication based on integer domain chaotic system

Ensuring information security is of great significance to a satellite communication system. In this paper, a chaotic encryption scheme is proposed for a satellite communication system. Firstly, five chaotic sequences are generated by a five-dimensional chaotic system. And then, XOR, scrambling and interpolation are performed on the data in turn. The simulation results show that the bit error rate of this method approaches 0 when the SNR is greater than 18, and the bit error rate of the illegal receiver is as high as 35.3% when the correct key is unknown. Therefore, secure communication can be achieved.
Cryptographic core design security verification and vulnerability detection based on information flow analysis

Cryptographic cores are the key components to enforce information security properties related to confidentiality and integrity. Since the security of a cryptographic core implementation and the security of the cryptographic algorithm itself from the mathematical perspective are two different problems, cryptographic cores may contain hidden security vulnerabilities such as design flaws and side channels. Security analysis methods based on functional verification rely heavily on the quality of test vectors and usually have low test coverage, which is difficult to meet the security verification requirements of security-critical components like cryptographic cores. This work proposes a cryptographic core security verification and vulnerability detection method from the perspective of information flow security. The proposed method can accurately measure all logical information flows in cryptographic core designs to formally verify security properties such as confidentiality and integrity. It can detect potential security vulnerabilities in cryptographic implementations by capturing harmful information flows. Experimental results show that our information flow security verification method is effective in detecting sensitive information leakage caused by the design vulnerabilities and side channels in cryptographic cores.
Integrating GeoGebra Math Activities into Learning Management Systems (LMS)

The article discusses the technological aspects of integrating the activities of GeoGebra Math, a dynamic online mathematics teaching and learning platform, into Learning Management Systems (LMS-Learning Management System) based on real models of teacher-student interaction.This article covers the following three modules: basic functionalities, process preparation and process implementation.The first module details how GeoGebra activities are integrated with various technologies and tools. The applications in question are GeoGebra Group, GeoGebra TimeLine, MicroSoft OneNote, and Google Classroom.The second module explains how to complete the following procedures: Online registration on the GeoGebra platform; Installation of the GeoGebra Classic application on a PC; Create and Join a GeoGebra Group - GeoGebra; Sign up for Microsoft OneNote and open a new notebook (a new digital book); Create and enroll in a Google Classroom course.The third module uses homothetics as an example to demonstrate how to create an interactive resource in the GeoGebra Classic Online application and how to transfer this resource as an activity from the local GeoGebra Classic application to the GeoGebra Online platform. The technological features of GeoGebra Sharing in GeoGebra Group, GeoGebra TimeLine, MicroSoft OneNote and Google Classroom are described on a practical example of the joint involvement of a teacher and a virtual group of participants, supported by appropriate illustrations.The article examines and justifies the potential for integrating the activities of the GeoGebra Math online mathematical platform into Learning Management Systems (LMS), which can be successfully used in mathematics education both in schools and universities.
Research on image recognition mechanism based on deep learning

This paper discusses the important role of deep learning technology in optimizing the image recognition function of digital libraries. On this basis, it analyzes the necessity and feasibility of building an image recognition mechanism based on deep learning technology, and expounds the application of deep neural network to construct images. The main contents of the recognition mechanism, including the mobile vision big data input layer, the mobile visual resource organization layer, the deep neural network analysis layer and the mobile vision service interaction layer, etc. Realization of personalized smart service in library.
Computational causal discovery: Advantages and assumptions

I would like to congratulate James Woodward for another landmark accomplishment, after publishing his Making things happen: A theory of causal explanation (Woodward, 2003). Making things happen gives an elegant interventionist theory for understanding explanation and causation. The new contribution (Woodward, 2022) relies on that theory and further makes a big step towards empirical inference of causal relations from non-experimental data. In this paper, I will focus on some of the emerging computational methods for finding causal relations from non-experimental data and attempt to complement Woodward's contribution with discussions on 1) how these methods are connected to the interventionist theory of causality, 2) how informative the output of the methods is, including whether they output directed causal graphs and how they deal with confounders (unmeasured common causes of two measured variables), and 3) the assumptions underlying the asymptotic correctness of the output of the methods about causal relations. Different causal discovery methods may rely on different aspects of the joint distribution of the data, and this discussion aims to provide a technical account of the assumptions.
A Scalable Lightweight RDF Knowledge Retrieval System

Currently, there are numerous knowledge retrieval systems available to researchers, among which the RDF retrieval system is the most common. However, in practice, these systems are often plagued with problems, such as long index constructions and loading times and require large amounts of disk storage space, which are faults that make the system non-conducive to the dynamic incremental updates of data. This paper proposes a scalable lightweight RDF retrieval system, which has the following characteristics: 1) optimization of the index structure to reduce disk occupation and speed up the construction; 2) query optimizations based on query strategy selection; and 3) an interactive visual operation interface. The final evaluation results show that our system uses less disk space and has faster index construction times, and the query performance is competitive with the latest RDF retrieval systems.
Multi-task Metric Learning for Text-independent Speaker Verification

In this work, we introduce metric learning (ML) to enhance the deep embedding learning for text-independent speaker verification (SV). Specifically, the deep speaker embedding network is trained with conventional cross entropy loss and auxiliary pair-based ML loss function. For the auxiliary ML task, training samples of a mini-batch are first arranged into pairs, then positive and negative pairs are selected and weighted through their own and relative similarities, and finally the auxiliary ML loss is calculated by the similarity of the selected pairs. To evaluate the proposed method, we conduct experiments on the Speaker in the Wild (SITW) dataset. The results demonstrate the effectiveness of the proposed method.
Min-Based Conditioning of Possibilistic EL Ontology

The EL is a tractable family of lightweight description logics that underlay the OWL EL profile. It guarantees the tractability of the reasoning process, especially for concept classification. In particular, such a fragment is widely used for medical applications. This paper investigates the evolution of EL ontologies when a new piece of information that can be conflicting or attached with a confidence level reflecting its credibility or priority is available. To encode such knowledge, we propose an extension of EL description logic within the possibility theory, which provides a natural way to deal with ordinal scale reflecting ranking between pieces of knowledge. We then show how such a ranking between axioms is induced from the ontology with the presence of new information and study the evolution process at the semantic level. Finally, we propose a polynomial syntactic counterpart of the evolution process while preserving the consistency of the ontology.
Music-to-Dance Generation with Optimal Transport

Dance choreography for a piece of music is a challenging task, having to be creative in presenting distinctive stylistic dance elements while taking into account the musical theme and rhythm. It has been tackled by different approaches such as similarity retrieval, sequence-to-sequence modeling and generative adversarial networks, but their generated dance sequences are often short of motion realism, diversity and music consistency. In this paper, we propose a Music-to-Dance with Optimal Transport Network (MDOT-Net) for learning to generate 3D dance choreographies from music. We introduce an optimal transport distance for evaluating the authenticity of the generated dance distribution and a Gromov-Wasserstein distance to measure the correspondence between the dance distribution and the input music. This gives a well defined and non-divergent training objective that mitigates the limitation of standard GAN training which is frequently plagued with instability and divergent generator loss issues. Extensive experiments demonstrate that our MDOT-Net can synthesize realistic and diverse dances which achieve an organic unity with the input music, reflecting the shared intentionality and matching the rhythmic articulation. Sample results are found at https://www.youtube.com/watch?v=dErfBkrlUO8.
Deexaggeration

We introduce a new task in hyperbole processing, deexaggeration, which concerns the recovery of the meaning of what is being exaggerated in a hyperbolic sentence in the form of a structured representation. In this paper, we lay the groundwork for the computational study of understanding hyperbole by (1) defining a structured representation to encode what is being exaggerated in a hyperbole in a non-hyperbolic manner, (2) annotating the hyperbolic sentences in two existing datasets, HYPO and HYPO-cn, using this structured representation, (3) conducting an empirical analysis of our annotated corpora, and (4) presenting preliminary results on the deexaggeration task.
The Shift of Lexicon in Traditional Technology System in Tolaki Community at Konawe District of Southeast Sulawesi

This research discusses the lexicon used for traditional technology systems in the Tolaki community. Lexicon is a language component containing information about the meaning and usage of words, the richness of words a language has. Lexicon runs into a shift due to certain factors such as changes in norms, culture, and environment and the development of science and technology. The level of shift and the change in the meaning of the lexicon for traditional technology systems in Tolaki community, Konawe Regency, Southeast Sulawesi are analyzed with the method of qualitative descriptive analysis. The data are taken from written sources, literature studies, by examining and recording some lexicons from the book "Tolaki Culture" by Abdurrauf Tarimana. This book discusses the lexicon used for traditional technology systems in the Tolaki community. The validation of the data is then substantiated by questionnaire distribution in which the informants fill in lexicon data for agricultural technology systems and imply them in Tolaki language. The lexical-semantic theory by Pateda is applied and the results of the data analysis show that the lexicon for agricultural technology system in Tolaki is extinct and undergoing a shift. 115 lexicons of traditional technology systems are analyzed and among them are 50 (44%) lexicons undergoing extinction, 29 (25%) undergoing a shift, and 36 (31%) undergoing no shift.
Non-fungible token: a promising digital tool for business

This paper presents a review of the current status and development trends of the non-fungible tokens’ technology (NFT), which are digital rights to unique objects recorded in the blockchain. The object of the study is the non-interchangeable token technology. The subject of the study is the fields of practical application of NFT. The method is an analytical review of scientific publications. The current state and socio-cultural sources of the NFT market related to works of art and media objects are analysed. The technological and legal problems preventing the wide spread of NFT in the business environment are identified. The prospects for the use of non-fungible tokens in the field of protection and commercialization of patent law objects are considered.
Conflict-Based Inconsistency-Tolerant Query Answering

In this paper, we place ourselves in the context of inconsistency-tolerant query answering over lightweight ontologies, which aims to query a set of conflicting facts using an ontology that represents generic knowledge about a particular domain. Existing inconsistency-tolerant semantics typically consist in selecting some of (maximal) consistent subsets of facts, called repairs. We explore a novel strategy to select the most relevant repairs based on the stratification of the assertional base into priority levels that we automatically induce from the ontology. We propose a method that exploits conflict statistical regularities between facts to induce an embedding, in which each fact is represented by a vector. Based on Euclidean distances between facts, we classify the assertions from the most reliable to the least important ones. We then use these distances to define relevant repairs. Interestingly enough, we show that the obtained repair is done in polynomial time.
Compressed optical image encryption in the diffractive-imaging-based scheme by input plane and output plane random sampling

The successful recovery of the plaintext in the simplified diffractive-imaging-based encryption (S-DIBE) scheme needs to record one intact axial intensity map as the ciphertext. By aid of compressive sensing, we propose here a new image encryption approach, referred to as compressed DIBE (C-DIBE), which allows further compression of the intensity map. The plaintext is sampled before being sent to DIBE. Afterwards, the intensity map recorded by the CCD camera is also processed by such sampling operation to generate the ciphertext. For decryption, we first obtain the sparse plaintext using the proposed phase retrieval algorithm, and then reobtain the primary plaintext from it via compressive sensing. Numerical results show that a proper proportion of the intensity map (e.g. 50%) is enough to totally recover a grayscale image. We achieve multiple-image encryption by space multiplexing without enlarging the size of the ciphertext. The robustness of C-DIBE against brute-force attack evidently outperforms S-DIBE due to the extended key space. Numerical simulation has been presented to confirm the proposal.
Self-development as a factor of professional growth of future teachers

The problem of improving the professionalism of teaching staff for higher education is certainly important in the Kazakhstan education system. In modern conditions of constant changes in the content of education, the graduate model, the teacher must be ready for constant professional growth, freely orient in his profession and related fields of activity. The article is devoted to an important problem, the study of personal and professional self-development of a future teacher. The purpose of the study is to theoretically study aspects of self-development in the context of the professional growth of future teachers. The sample of respondents was 104 people. Data was collected using a questionnaire adapted from renowned researchers. The article considers the factors of professional and personal self-development of teachers and reveals the possibilities of psychological support for the formation of a pedagogical position. Support is interpreted as assistance and support of a future teacher in the process of his professional and personal development.&#x0D; Keywords: Competencies; professional development; personal self-development; self-development; stimulation.
Evaluating the Impact of Telework on Improving Employee Performance - a study in a number of companies for Internet services in Iraq

The study aimed to determine the level of correlation and influence between the dimensions of remote work and the performance of employees in the search community companies. Which includes companies (Earthlink), (Supercell), (Al-Jazeera), (Ornit) and (Hrins), which are specialized in providing Internet services to the beneficiaries in Baghdad, and the study sample was determined using The comprehensive inventory method in view of the limited number of targeted employees, who numbered (119) individuals. The study adopted the descriptive analytical approach, as data and information were collected through the questionnaire, which was a major tool in data collection, in addition to personal interviews with the concerned departments and workers in those companies. The data was processed statistically through the statistical analysis program (SPSS.25) and the extension (AMOS), as a number of statistical methods were used in the field of descriptive and analytical statistics. The study reached a number of results, the most prominent of which was the presence of significant correlation and impact relationships between the independent variables (remote work), the dependent (worker performance),One of the most prominent conclusions was that the respondents’ views were in general agreement on most of the paragraphs of the questionnaire, which indicates a high harmony among the employees of the companies surveyed by virtue of the great symmetry in the field of activity, and the tendency in this agreement was in favor of the main variable (remote work). Presented search several recommendations and proposals that would advance the reality of companies' work to provide a better service that meets the needs and aspirations of the beneficiaries.
Abstract: A Database and Neural Network for Highly Accurate Classification of Single Bone Marrow Cells

Fast and accurate morphological classification of cells in bone marrow samples is a key step in the diagnostic workup of many disorders of the hematopoietic system such as leukemias. In spite of its long-established key position, morphological examination of bone marrow samples has been difficult to automatise, and is still mainly performed manually by trained cytologists on light microscopes. In our contribution [1], we present a neural network for classification of light microscopy images of bone marrow samples.
Applying machine learning methods to prediction problems of lattice observables

We discuss the prediction of critical behavior of lattice observables in SU(2) and SU(3) gauge theories. We show that feed-forward neural network, trained on the lattice configurations of gauge fields as input data, finds correlations with the target observable, which is also true in the critical region where the neural network has not been trained. We have verified that the neural network constructs a gauge-invariant function and this property does not change over the entire range of the parameter space.
Finding Structural Knowledge in Multimodal-BERT

In this work, we investigate the knowledge learned in the embeddings of multimodal-BERT models. More specifically, we probe their capabilities of storing the grammatical structure of linguistic data and the structure learned over objects in visual data. To reach that goal, we first make the inherent structure of language and visuals explicit by a dependency parse of the sentences that describe the image and by the dependencies between the object regions in the image, respectively. We call this explicit visual structure the \textit{scene tree}, that is based on the dependency tree of the language description. Extensive probing experiments show that the multimodal-BERT models do not encode these scene trees.Code available at \url{https://github.com/VSJMilewski/multimodal-probes}.
Efficient Bayesian Learning of Sparse Deep Artificial Neural Networks

In supervised Machine Learning (ML), Artificial Neural Networks (ANN) are commonly utilized to analyze signals or images for a variety of applications. They are increasingly performing as a strong tool to establish the relationships among data and being successfully applied in science due to their generalization ability, noise and fault tolerance. One of the most difficult aspects of using the learning process is optimization of the network weights. A gradient-based technique with a back-propagation strategy is commonly used for this optimization stage. Regularization is commonly employed for the benefit of efficiency. This optimization gets difficult when non-smooth regularizers are applied, especially to promote sparse networks. Due to differentiability difficulties, traditional gradient-based optimizers cannot be employed. In this paper, we propose an MCMC-based optimization strategy within a Bayesian framework. An effective sampling strategy is designed using Hamiltonian dynamics. The suggested strategy appears to be effective in allowing ANNs with modest complexity levels to achieve high accuracy rates, as seen by promising findings.
Automatically adapting the number of state particles in SMC$^2$

Sequential Monte Carlo squared (SMC$^2$) methods can be used for parameter inference of intractable likelihood state-space models. These methods replace the likelihood with an unbiased particle filter estimator, similarly to particle Markov chain Monte Carlo (MCMC). As with particle MCMC, the efficiency of SMC$^2$ greatly depends on the variance of the likelihood estimator, and therefore on the number of state particles used within the particle filter. We introduce novel methods to adaptively select the number of state particles within SMC$^2$ using the expected squared jumping distance to trigger the adaptation, and modifying the exchange importance sampling method of \citet{Chopin2012a} to replace the current set of state particles with the new set of state particles. The resulting algorithm is fully automatic, and can significantly improve current methods. Code for our methods is available at https://github.com/imkebotha/adaptive-exact-approximate-smc.