Reviewerly/exHarmony huggingface dataset:
An Isabelle/HOL Formalisation of the SPARC Instruction Set Architecture and the TSO Memory Model

The SPARC instruction set architecture (ISA) has been used in various processors in workstations, embedded systems, and in mission-critical industries such as aviation and space engineering. Hence, it is important to provide formal frameworks that facilitate the verification of hardware and software that run on or interface with these processors. In this work, we give the first formal model for multi-core SPARC ISA and Total Store Ordering (TSO) memory model in Isabelle/HOL. We present two levels of modelling for the ISA: The low-level ISA model, which is executable, covers many features specific to SPARC processors, such as delayed-write for control registers, windowed general registers, and more complex memory access. We have tested our model extensively against a LEON3 simulation board, the test covers both single-step executions and sequential execution of programs. We also prove some important properties for our formal model, including a non-interference property for the LEON3 processor. The high-level ISA model is an abstraction of the low-level model and it provides an interface for memory operations in multi-core processors. On top of the high-level ISA model, we formalise two TSO memory models: one is an adaptation of the axiomatic SPARC TSO model (Sindhu et al. in Formal specification of memory models, Springer, Boston, 1992; SPARC in The SPARC architecture manual version 8, 1992. http://gaisler.com/doc/sparcv8.pdf ), the other is a new operational TSO model which is suitable for verifying execution results. We prove that the operational model is sound and complete with respect to the axiomatic model. Finally, we give verification examples with two case studies drawn from the SPARCv9 manual.
LA EDUCACIÓN SUPERIOR EN TIEMPOS DE COVID-19: ANÁLISIS COMPARATIVO MÉXICO - ARGENTINA

This paper shows an analytical comparison about the health measures due to coronavirus SARS_COV2 and its impact on teaching and learning processes of Argentina and Mexico Universities. With this purpose, bibliographical research and revision was made in order to identify the measures that have been adopted in both countries. Also data was obtained from a questionnaire of 20 items applied to 206 students and teachers from both countries.The most relevant results have shown that in Mexico it has been more difficult the process of adapting to the virtual teaching methodology and it is considered that the learning process could not be the adequate one. In Argentina, this scenario is almost the opposite.In consideration about the study material and evaluation instances, the opinions were more auspicious. But the “student/teacher” relationship was negatively affected because of the impossibility of direct contact in the classroom.Finally, it is considered that the initial conditions related to deploy the learning/teaching process guided by virtual environments were supposed to be well planned and managed for both countries, Argentina and Mexico. However, several improvement areas arise when we perform a deeper analysis.
Unsupervised Classification of Cell-Imaging Data Using the Quantization Error in a Self-Organizing Map

This study exploits previously demonstrated properties such as sensitivity to the spatial extent and the intensity of local image contrast of the quantization error in the output of a Self Organizing Map (SOM QE). Here, the SOM QE is applied to double color staining based cell viability data in 96 image simulations. The results show that the SOM QE consistently and in only a few seconds detects fine regular spatial increases in relative amounts of RED or GREEN pixel staining across the test images, reflecting small, systematic increases or decreases in the percentage of theoretical cell viability below the critical threshold. Such small changes may carry clinical significance, but are almost impossible to detect by human vision. Moreover, we demonstrate a clear sensitivity of the SOM QE to differences in the relative physical luminance (Y) of the colors, which here translates into a RED GREEN color selectivity. Across differences in relative luminance, the SOM QE exhibits consistently greater sensitivity to the smallest spatial increases in RED image pixels compared with smallest increases of identical spatial extents in GREEN image pixels. Further selective color contrast studies on simulations of biological imaging data will allow generating increasingly larger benchmark datasets and, ultimately, unravel the full potential of fast, economic, and unprecedentedly precise biological data analysis using the SOM QE.
Functional and role-playing positions in modern management teams: an educational institution case study

The analysis of scientific approaches to understanding the psychological characteristics of the role of the management team is important in the context of ensuring the organizational development of educational institutions. The purpose of the study is to explore the basic approaches and views of modern researchers on defining team roles, to identify the main functional and role positions in the management team.Determining the types of leadership roles of educational institutions based on empirical research data. Also based on the method of case studies problem-situational analysis − solving leadership problems by educational managers − were analyzed the effectiveness of role-playing positions in management teams. Conclusions were made about the need for some correction of understanding and assessment of the importance of all roles in the staff of heads of educational institutions. Each of these command roles related to specific personality traits identified by the tests.An analysis of the orientation of education leaders towards team roles shows that they tend to focus on all team roles. However, one can say that some roles are more attractive to researchers, while others are less attractive. The government can use the research findings to create and implement training programs for educational leaders.
Learning Latent Spiculated Features for Lung Nodule Characterization

Computer-aided Diagnosis (CAD) systems have long aimed to be used in clinical practice to help doctors make decisions by providing a second opinion. However, most machine learning based CAD systems make predictions without explicitly showing how their predictions were generated. Since the cognitive process of the diagnostic imaging interpretation involves various visual characteristics of the region of interest, the explainability of the results should leverage those characteristics. We encode visual characteristics of the region of interest based on pairs of similar images rather than the image content by itself. Using a Siamese convolutional neural network (SCNN), we first learn the similarity among nodules, then encode image content using the SCNN similarity-based feature representation, and lastly, we apply the K-nearest neighbor (KNN) approach to make diagnostic characterizations using the Siamese-based image features. We demonstrate the feasibility of our approach on spiculation, a visual characteristic that radiologists consider when interpreting the degree of cancer malignancy, and the NIH/NCI Lung Image Database Consortium (LIDC) dataset that contains both spiculation and malignancy characteristics for lung nodules.Clinical Relevance - This establishes that spiculation can be quantified to automate the diagnostic characterization of lung nodules in Computed Tomography images.
Effectiveness of GAN-based Synthetic Samples Generation of Minority Patterns in HEp-2 Cell Images

In this paper, we present a framework to address the augmentation of images for the rare and minor appearance of mitotic type staining patterns, for Human Epithelium Type2 (HEp-2) cell images. The identification of mitotic patterns among non-mitotic/interphase patterns is important in the process of diagnosis of various autoimmune disorders. This task leads to a pattern classification problem between mitotic v/s interphase. However, among the two classes, typically, the number of mitotic cells are relatively very less. Thus, in this work, we propose to generate synthetic mitotic samples, which can be used to augment the number of mitotic samples and balance the samples of mitotic and interphase patterns in classification paradigm. An effective feature representation is used, to validate the usefulness of the synthetic samples in classification task, along with a subjective validation done by a medical expert. The results demonstrate that the approach of generating and mingling synthetic samples with existing training data works well and yields good performance, with 0.98 balanced class accuracy (BcA) in one case, over a public dataset, i.e., UQ-SNP I3A Task-3 mitotic cell identification dataset.
rrSDS: Towards a Robot-ready Spoken Dialogue System

Spoken interaction with a physical robot requires a dialogue system that is modular, multimodal, distributive, incremental and temporally aligned. In this demo paper, we make significant contributions towards fulfilling these requirements by expanding upon the ReTiCo incremental framework. We outline the incremental and multimodal modules and how their computation can be distributed. We demonstrate the power and flexibility of our robot-ready spoken dialogue system to be integrated with almost any robot.
Combating Hard or Soft Disasters with Privacy-Preserving Federated Mobile Buses-and-Drones based Networks

It is foreseeable the popularity of the mobile edge computing enabled infrastructure for wireless networks in the incoming fifth generation (5G) and future sixth generation (6G) wireless networks.Especially after a 'hard' disaster such as earthquakes or a 'soft' disaster such as COVID-19 pandemic, the existing telecommunication infrastructure, including wired and wireless networks, is often seriously compromised or with infectious disease risks and should-not-close-contact, thus cannot guarantee regular coverage and reliable communications services.These temporarily-missing communications capabilities are crucial to rescuers, health-carers, or affected or infected citizens as the responders need to effectively coordinate and communicate to minimize the loss of lives and property, where the 5G/6G mobile edge network helps.On the other hand, the federated machine learning (FML) methods have been newly developed to address the privacy leakage problems of the traditional machine learning held normally by one centralized organization, associated with the high risks of a single point of hacking.After detailing current state-of-the-art both in privacypreserving, federated learning, and mobile edge communications networks for 'hard' and 'soft' disasters, we consider the main challenges that need to be faced.We envision a privacy-preserving federated learning enabled buses-and-drones based mobile edge infrastructure (ppFL-AidLife) for disaster or pandemic emergency communications.The ppFL-AidLife system aims at a rapidly deployable resilient network capable of supporting flexible, privacy-preserving and low-latency communications to serve large-scale disaster situations by utilizing the existing public transport networks, associated with drones to maximally extend their radio coverage to those hard-to-reach disasters or shouldnot-close-contact pandemic zones.
New self-dual codes of length 68 from a $ 2 \times 2 $ block matrix construction and group rings

&lt;p style='text-indent:20px;'&gt;Many generator matrices for constructing extremal binary self-dual codes of different lengths have the form &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ G = (I_n \ | \ A), $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; where &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ I_n $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is the &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ n \times n $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; identity matrix and &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ A $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is the &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ n \times n $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; matrix fully determined by the first row. In this work, we define a generator matrix in which &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ A $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is a block matrix, where the blocks come from group rings and also, &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ A $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is not fully determined by the elements appearing in the first row. By applying our construction over &lt;inline-formula&gt;&lt;tex-math id="M8"&gt;\begin{document}$ \mathbb{F}_2+u\mathbb{F}_2 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and by employing the extension method for codes, we were able to construct new extremal binary self-dual codes of length 68. Additionally, by employing a generalised neighbour method to the codes obtained, we were able to construct many new binary self-dual &lt;inline-formula&gt;&lt;tex-math id="M9"&gt;\begin{document}$ [68, 34, 12] $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-codes with the rare parameters &lt;inline-formula&gt;&lt;tex-math id="M10"&gt;\begin{document}$ \gamma = 7, 8 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and &lt;inline-formula&gt;&lt;tex-math id="M11"&gt;\begin{document}$ 9 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; in &lt;inline-formula&gt;&lt;tex-math id="M12"&gt;\begin{document}$ W_{68, 2}. $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; In particular, we find 92 new binary self-dual &lt;inline-formula&gt;&lt;tex-math id="M13"&gt;\begin{document}$ [68, 34, 12] $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-codes.&lt;/p&gt;
A SAT-Based Approach for Mining High Utility Itemsets from Transaction Databases

Mining high utility itemsets is a keystone in several data analysis tasks. High Utility Itemset Mining generalizes the frequent itemset mining problem by considering item quantities and weights. A high utility itemset is a set of items that appears in the transadatabase and having a high importance to the user, measured by a utility function. The utility of a pattern can be quantified in terms of various objective criteria, e.g., profit, frequency, and weight. Constraint Programming (CP) and Propositional Satisfiability (SAT) based frameworks for modeling and solving pattern mining tasks have gained a considerable attention in recent few years. This paper introduces the first declarative framework for mining high utility itemsets from transaction databases. First, we model the problem of mining high utility itemsets from transaction databases as a propositional satifiability problem. Moreover, to facilitate the mining task, we add an additional constraint to the efficiency of our method by using weighted clique cover problem. Then, we exploit the efficient SAT solving techniques to output all the high utility itemsets in the data that satisfy a user-specified minimum support and minimum utility values. Experimental evaluations on real and synthetic datasets show that the performance of our proposed approach is close to that of the optimal case of state-of-the-art HUIM algorithms.
F-DPC: Fuzzy Neighborhood-Based Density Peak Algorithm

Clustering is a concept in data mining, which divides a data set into different classes or clusters according to a specific standard, making the similarity of data objects in the same cluster as large as possible. Clustering by fast search and find of density peaks (DPC) is a novel clustering algorithm based on density. It is simple and novel, only requiring fewer parameters to achieve better clustering effect, without the requirement for iterative solution. And it has expandability and can detect the clustering of any shape. However, DPC algorithm still has some defects, such as it employs the clear neighborhood relations to calculate local density, so it cannot identify the neighborhood membership of different values of points from the distance of points and It is impossible to accurately cluster the data of the multi-density peak. The fuzzy neighborhood density peak clustering algorithm is proposed for this shortcoming (F-DPC): novel local density is defined by the fuzzy neighborhood relationship. The fuzzy set theory can be used to make the fuzzy neighborhood function of local density more sensitive, so that the clustering for data set of various shapes and densities is more robust. Experiments show that the algorithm has high accuracy and robustness.
“This is the best teacher in the whole world!”: The influence of the school teacher on professional self-determination of future teachers

Introduction. The article analyzes the role of a particular teacher in the formation of professional identity for future teachers - students of a pedagogical university. Recent scientific studies showed that the idea of a “perfect teacher” formed at school is often stable and has negative impact on the professional development and separation of experience from the prospects of professional growth. However, most studies analyze the image of the abstract "ideal teacher" without reference to a particular person, who influenced the choice of profession and general professional guidelines of the future teacher. Materials and Methods. In the study, narratives of students of Mini University were decomposed. The narratives were completed over a questionnaire as a part of a professional competition "Best Teacher". The characteristics of the best teacher from the narratives were classified according to the system proposed in the monograph “Portrait of a teacher”. Results. The data obtained showed that for the most students important characteristics are personality characteristics that influence communication with students: emotional (43%) and communicative (23%). In second place is a group of teacher’s abilities related to the depth of his knowledge, the need to expand his knowledge and practical application in solving pedagogical problems (cognitive (12%) and practical (14%) abilities), and in the third - axiological abilities of a teacher (8% ) related to values and civic position. Summarizing all the characteristics of the best teachers identified in narratives, we can conclude that for students participating in the survey, an important role is played by emotional informal contact with the teacher: responsiveness and openness, willingness to help and answer questions, creating motivation for learning, self-education and finding solutions as well as dedication to their work, love for the subject and for children, enthusiasm and creativity in work, achievement by students of high results in final exams and various competitions, work experience. Discussion and Conclusions. The data from the narratives about the best teacher reflect a certain image of the ideal teacher, intuitively formed during school education when observing the situation “from the inside”. It is curious that these ideas fully correspond to the characteristics of successful teachers defined by J. Hattie in his meta-study. In the scientific work of the learning situation “from the outside”, J. Hattie found that the maximum success is achieved by teachers who differ in communicative, emotional and active abilities in the structure of pedagogical intelligence, first of all: 1) using a variety of methods and technologies in the classroom; 2) making high demands on students; 3) building a positive relationship between teacher and student. This allows us to determine the role of the school teacher in identifying the pedagogical giftedness of future teachers and in choosing of the teaching profession.
Semi-supervised self-growing generative adversarial networks for image recognition

Image recognition is an important topic in computer vision and image processing, and has been mainly addressed by supervised deep learning methods, which need a large set of labeled images to achieve promising performance. However, in most cases, labeled data are expensive or even impossible to obtain, while unlabeled data are readily available from numerous free on-line resources and have been exploited to improve the performance of deep neural networks. To better exploit the power of unlabeled data for image recognition, in this paper, we propose a semi-supervised and self-generative approach, namely the semi-supervised self-growing generative adversarial network (SGGAN). Label inference is a key step for the success of semi-supervised learning approaches. There are two main problems in label inference: how to measure the confidence of the unlabeled data and how to generalize the classifier. We address these two problems via the generative framework and a novel convolution-block-transformation technique, respectively. To stabilize and speed up the training process of SGGAN, we employ the metric Maximum Mean Discrepancy as the feature matching objective function and achieve larger gain than the standard semi-supervised GANs (SSGANs), narrowing the gap to the supervised methods. Experiments on several benchmark datasets show the effectiveness of the proposed SGGAN on image recognition and facial attribute recognition tasks. By using the training data with only 4% labeled facial attributes, the SGGAN approach can achieve comparable accuracy with leading supervised deep learning methods with all labeled facial attributes.
Signatures with Tight Multi-user Security from Search Assumptions

We construct two tightly secure signature schemes based on the computational Diffie-Hellman (CDH) and factoring assumptions in the random oracle model. Our schemes are proven secure in the multi-user setting, and their security loss is constant and does not depend on the number of users or signing queries. They are the first schemes that achieve this based on standard search assumptions, as all existing schemes we are aware of are either based on stronger decisional assumptions, or proven tightly secure in the less realistic single-user setting. Under a concrete estimation, in a truly large scale, the cost of our CDH-based scheme is about half of Schnorr and DSA (in terms of signature size and running time for signing).
Changes in Irradiance and Illuminance on Earth Surface during 11-Year Solar Activity Cycle

The article formulates the analytic expression approximating the sequence of Gregorian 11-year solar cycles and the expression of solar activity within one cycle. The dependences of effective thermodynamic temperature of the Sun photosphere and the solar constant, and the solar illuminance constant at the upper border of Earth atmosphere on the year number within one solar cycle were obtained. The generalised analytic expression for integral transmittance of atmosphere (within its spectral window) on the Earth surface for the direct and diffuse components of solar radiation and their sums at different solar altitude angles is presented. The analytic expressions of dependences of irradiance and illuminance on the Earth surface within spectral window of atmosphere and within the visible region of solar radiation spectrum on the year number within a certain solar cycle at different solar altitude angles are obtained. The results of calculation of direct and diffuse components of irradiance and illuminance and their sums in the case of clear sky are presented for example. The proposed approach allows similar calculations to conduct for different types of sky cover.