Reviewerly/exHarmony huggingface dataset:
PEDAGOGICAL CONDITIONS FOR THE FORMATION OF LEADERSHIP QUALITIES OF FUTURE HEADS OF PRESCHOOL EDUCATION INSTITUTIONS

The urgency of the studied problem is because in conditions of significant socio-economic changes, the system of human resource management technologies becomes significant, especially relevant is the use of the potential of leadership influence. In this regard, this article is aimed at revealing the problem of developing leadership qualities of heads of preschool education institutions. The article highlights current issues of leadership development of future heads of preschool education. The leading method of the research of this problem is the analysis of the researched problem in pedagogical theory and practice and experimental check of pedagogical conditions of formation of leadership qualities of future heads of preschool educational institutions that allows to reveal and characterize features of formation of leading qualities of future heads of preschool educational institutions. The article identifies the components, criteria, indicators and describes the levels of leadership development of future leaders of preschool education. The basic concepts of the problem are defined: leader, leadership, professional qualities of the leader, leadership qualities of future heads of preschool educational institutions, pedagogical conditions of formation of leadership qualities of future heads of preschool educational institutions. Relevant requirements to the personal and professional qualities of a leader are revealed. The structural components of leadership qualities of future heads of preschool education institutions are characterized, namely: motivational-value, integrative-activity, directional-activity, emotional-reflexive. The pedagogical conditions of the formation of leadership qualities of future heads of preschool education institutions are substantiated. The offered materials of the article represent practical value for heads of preschool education institutions, educators-methodologists, and future teachers of preschool education.
Virality Prediction for News Tweets Using RoBERTa

The virality of a tweet is essential to convey its message to a broader audience and, eventually, to generate influence. This is especially important for news outlets as they struggle to transition from traditional media to online formats. As their usual readers will not migrate directly to digital news outlets need to gather new audiences from the spaces where real-time information and discussions are happening; this is Social Media and in particular Twitter. Since the news websites and Twitter languages differ greatly news outlets need to write their tweets properly to maximize their impact on Twitter. We propose a method to predict if a tweet will be influential or not influential based on its text using a variant of Google BERT named RoBERTa, and a corpus of 5000 high-quality and automatically labeled highly-influential and non-influential tweets to train and classify tweets in these categories. Our method reaches an F1 of 0.873, improving 4 and 9 over approaches using LSTMs and n-grams respectively.
Parallel Minimum Spanning Forest Computation using Sparse Matrix Kernels

Previous chapter Full AccessProceedings Proceedings of the 2022 SIAM Conference on Parallel Processing for Scientific Computing (PP)Parallel Minimum Spanning Forest Computation using Sparse Matrix KernelsTim Baer, Raghavendra Kanakagiri, and Edgar SolomonikTim Baer, Raghavendra Kanakagiri, and Edgar Solomonikpp.72 - 83Chapter DOI:https://doi.org/10.1137/1.9781611977141.7PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Formulations of graph algorithms using sparse linear algebra have yielded highly scalable distributed algorithms for problems such as connectivity and shortest path computation. We develop the first formulation of the Awerbuch-Shiloach parallel minimum spanning forest (MSF) algorithm using linear algebra primitives. We introduce a multilinear kernel that operates on an adjacency matrix and two vectors. This kernel updates graph vertices by simultaneously using information from both adjacent edges and vertices. In addition, we explore optimizations to accelerate the shortcutting step in the Awerbuch-Shiloach algorithm. We implement this MSF algorithm with Cyclops, a distributed-memory library for generalized sparse tensor algebra. We analyze the parallel scalability of our implementation on the Stampede2 supercomputer. Previous chapter RelatedDetails Published:2022eISBN:978-1-61197-714-1 https://doi.org/10.1137/1.9781611977141Book Series Name:ProceedingsBook Code:PP22Book Pages:iii + 83
Quantum pattern recognition in photonic circuits

Abstract This paper proposes a machine learning method to characterize photonic states via a simple optical circuit and data processing of photon number distributions, such as photonic patterns. The input states consist of two coherent states used as references and a two-mode unknown state to be studied. We successfully trained supervised learning algorithms that can predict the degree of entanglement in the two-mode state as well as perform the full tomography of one photonic mode, obtaining satisfactory values in the considered regression metrics.
Risk Assessment Driven Use of Advanced SIEM Technology for Cyber Protection of Critical e-Health Processes

Abstract The approach presented in this paper provides effective protection of critical business processes by applying advanced SIEM technology in a rigorous fashion, based on the results of accurate risk assessment. The proposed SIEM tool advances the State of The Art of the technology along two axes, specifically: privacy and integrity. The advancements are achieved via combined use of two of the most promising technologies for trusted computing, namely: Trusted Execution Environment (TTE) and Homomorphic Encryption (HE). The approach is validated with respect to a real use case of a Smart Hospital (i.e., one where IT is massively used), with challenging security requirements. The use case is contributed by one of the major public hospitals in Italy. Experiments demonstrate that, by relying on continuous monitoring of security relevant events and advanced correlation techniques, the SIEM solution proposed in this work effectively protects the critical workflows of the hospital business processes from cyber-attacks with high impact (specifically: serious harm to or even death of the patient).
Selection Heuristics on Semantic Genetic Programming for Classification Problems

Abstract Individual semantics have been used for guiding the learning process of Genetic Programming. Novel genetic operators and different ways of performing parent selection have been proposed with the use of semantics. The latter is the focus of this contribution by proposing three heuristics for parent selection that measure the similarity among individuals' semantics for choosing parents that enhance the addition, Naive Bayes, and Nearest Centroid. To the best of our knowledge, this is the first time that functions' properties are used for guiding the learning process. As the heuristics were created based on the properties of these functions, we apply them only when they are used to create offspring. The similarity functions considered are the cosine similarity, Pearson's correlation, and agreement. We analyze these heuristics' performance against random selection, state-of-the-art selection schemes, and 18 classifiers, including auto-machine-learning techniques, on 30 classification problems with a variable number of samples, variables, and classes. The result indicated that the combination of parent selection based on agreement and random selection to replace an individual in the population produces statistically better results than the classical selection and state-of-the-art schemes, and it is competitive with state-of-the-art classifiers. Finally, the code is released as open-source software.
Following the Trail of Citational Justice: Critically Examining Knowledge Production in HCI

Citations are nodes in the networks of knowledge we create. Portals to conversations with the past and bonding material with the scholarship of the present. Choosing who we cite is a practice signaling who we recognize and respect as a knowledge source. Therefore, we recognize citations as a relational practice. As this relational characteristic of citing is mediated by wealth we distribute across those who we cite, it is imperative to interrogate how just these practices are. We ought to engage with Citational Justice. Building on recent work discussing citational practices within HCI [9], we use the opportunity of this workshop to expand this conversation into deeper reflection on how we cite and the practices and infrastructures surrounding citations. Our goal with this workshop is two-fold. First, to create common language to collectively reflect, interrogate our own citational practices and reverberations, while fleshing out concrete steps to make these practices just in our work and communities we are part of. Second, to invite participants to re-imagine citational practices and the systems and infrastructures necessary to make such practices feasible. We invite a diverse group of participants from the CSCW community interested in examining their citational practices and the systems surrounding them.
A Detail Analysis and Implementation of Haar Cascade Classifier

Haar cascade is a one of the popular machine learning algorithm used for object detection. The Haar algorithm identifies objects in image as well as video. The Haar algorithm was initially used to identify the body parts; later, it was used to for identifying any kind of object. The Haar identify the objects based on the features provided. The Haar algorithm is divided into four-part/stages: Haar feature selection, creating integral images, AdaBoost training, and cascading classifiers; the detailed analysis and implementation of all the four stages along with statistical data have been carried out in this paper, and also the process/step to be followed to create the Haar cascade file has been discussed in this paper. We can conclude that the Haar algorithm is more effective to recognize the object based on the features.
The Code-Switching Phenomenon during Oral Presentations among the Business Programme Students

In Malaysia, English as a second language is used as a medium of instruction in most institutions of higher learning. Although it is compulsory to use English as a medium of instruction for all academic programmes, code-switching is still prevalent in the classroom for instance during an oral presentation. This phenomenon is also common among the students in the bachelor’s degree in International Business (BIB) at a private university in Selangor, Malaysia that became the context of this study. Hence, this research aimed to explore the BIB students’ perceptions on the utilisation of code-switching during oral presentations, and to identify the frequency of its usage in their speech. The participants of this study were 203 students from the Faculty of Business Management and Professional Studies (FBMP) of a private university in Selangor, Malaysia, who are currently doing their bachelor’s degree in International Business. The research instrument employed was a questionnaire that was accessible through a google form link by all the 203 students. Descriptive statistics were run to analyse the data collected. The findings of this study revealed that the students perceived positively the use of code-switching during oral presentations. Majority of them agreed that they code-switched to overcome their lack of proficiency in English such as limited vocabulary, and to ease their oral presentations. Most of the students admitted they frequently code-switched during oral presentations. From these findings, important implications and recommendations for future research were elucidated.
On Multi-User Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Massive MIMO Systems

In view of reducing the complexity of signal detection in massive multiple-input multiple-output (MIMO) receivers, the use of non-coherent detection is favored over usual coherent techniques that require complex channel estimation. In this paper, a deep-learning approach to implement non-coherent multiple-symbol detection for differential phase-shift keying (DPSK) multi-user massive MIMO systems is proposed. The proposed deep-learning implementation reduces the high computational complexity required in conventional DPSK detection techniques. Two deep-learning-based multiple-symbol differential detection receiver designs are proposed and compared with decision-feedback differential detection (DFDD), and conventional multiple-symbol differential detection (MSDD) for the same system parameters. Multiple-symbol differential sphere detection (MSDSD) is used to implement conventional MSDD. The results show that the proposed deep-learning-based multiple-symbol differential detection implementation outperforms decision-feedback differential detection and achieves an optimal performance compared to conventional multiple-symbol differential detection implemented by multiple-symbol differential sphere detection for single-user and multi-user scenarios.
Garbage Collecting Ship Robot Using Arduino Uno Microcontroller Based on Android Smartphone

Garbage collection in the Siak tributary used an excavator where the excavator cannot reach the garbage in the middle of the Siak tributary. To make it easier to collect garbage in the middle of the Siak tributary, a robotic ship collecting robot system is make using: ardino uno microcontroller with joystick controller ps 2 based on an android smartphone. The whole tool was divided into several parts, which consist of an Android smartphone, PS 2 wireless receiver, Arduino Uno microcontroller, PS 2 joystick, motor relay driver, DC motor, servo motor, and camera. This tool works when the PS 2 Joystick connects the connection to the PS 2 wireless receiver, and the PS 2 wireless receiver then to the Arduino Uno microcontroller to process commands, from the Arduino Uno microcontroller then to the motor relay driver that works to regulate the voltage of the electric current to drive the DC motor, and Servo motors function to transport trash in the river, as well as smartphones as displays to display the results of the camera. The method used is the prototype method. The results showed that the PS 2 joystick could communicate with Arduino Uno with a distance of 25 meters if there is no barrier and 15 meters if there is a barrier and the robot ship is only in the form of a prototype that is only able to take out garbage such as drink bottles caps, caps galon, and drink glass 200 gram.
COVID-19 and death of older adults in the Northeast region of Brazil: a survival analysis

Objective: to analyze survival and factors associated with increased risk of death for older adults diagnosed with COVID-19, living in the Northeast region of Brazil. Method: retrospective observational study developed with secondary data provided by the Brazilian Ministry of Health, between June 14 and December 26, 2020. The Kaplan-Meyer method, the time-dependent cox regression model was used, including covariates (age, sex, skin color, comorbidities, admission to the ICU, ventilatory support). Results: out of 9,306 individuals analyzed, 55.9% died and 44.1% survived. The highest risk of death was observed for those aged 80-89 (HR=1.95), brown-skinned (HR=1.99), with immunodeficiency (HR=1.259) or kidney disease (HR=1.147), admitted to the ICU (HR=1,795) and in use of ventilatory support (HR=1606). Conclusion: among older adults residing in the Northeast region of Brazil, there was a higher risk of death from COVID-19 for octogenarians, brown-skinned, with comorbidities, hospitalization in the ICU, followed by the use of ventilatory support. The creation of health prevention strategies that identify older adults with these profiles is suggested to prevent deaths in future pandemic situations.
A Compact Digital Signature Scheme Based on the Module-LWR Problem

We propose a new lattice-based digital signature scheme $$\textsf {MLWRSign} $$ by modifying $$\textsf {Dilithium} $$ , which is one of the second-round candidates of NIST’s call for post-quantum cryptographic standards. To the best of our knowledge, our scheme MLWRSign is the first signature scheme whose security is based on the (module) learning with rounding (LWR) problem. Due to the simplicity of the LWR, the secret key size is reduced by approximately 30% in our scheme compared to Dilithium, while achieving the same level of security. Moreover, we implemented MLWRSign and observed that the running time of our scheme is comparable to that of Dilithium.
The restrictions of the freedom of information during the Covid-19 pandemic

The article is devoted to the study of the issues of restrictions on the freedom of information that has arisen under the impact of the novel coronavirus outbreak. Another goal of the paper is identifying ways to protect such rights or to indicate which amendments to the law might be of use. The research methodology is based on general and special scientific methods, in particular: analytical, comparative-legal, systemic, and structural. The structure of the work includes: the review of international and Ukrainian legislation related to the freedom of information; the possibilities of its restriction; possible ways to enabling safe and secure management of the freedom of information during the coronacrisis. An analysis of international experience was carried out, as well as aspects of the protection of civil liberties such as freedom of speech, the right of peaceful assembly, etc. Several problematic issues were identified. Although, the general results of the study can be interpreted as alarming trends in the field of human rights and civil liberties. Particularly, it is multiple violations of the freedom of information all around the world under quarantine restrictions.
Anomaly Detection for the Centralised Elasticsearch Service at CERN

For several years CERN has been offering a centralised service for Elasticsearch, a popular distributed system for search and analytics of user provided data. The service offered by CERN IT is better described as a service of services, delivering centrally managed and maintained Elasticsearch instances to CERN users who have a justified need for it. This dynamic infrastructure currently consists of about 30 distinct and independent Elasticsearch installations, in the following referred to as Elasticsearch clusters, some of which are shared between different user communities. The service is used by several hundred users mainly for logs and service analytics. Due to its size and complexity, the installation produces a huge amount of internal monitoring data which can be difficult to process in real time with limited available person power. Early on, an idea was therefore born to process this data automatically, aiming to extract anomalies and possible issues building up in real time, allowing the experts to address them before they start to cause an issue for the users of the service. Both deep learning and traditional methods have been applied to analyse the data in order to achieve this goal. This resulted in the current deployment of an anomaly detection system based on a one layer multi dimensional LSTM neural network, coupled with applying a simple moving average to the data to validate the results. This paper will describe which methods were investigated and give an overview of the current system, including data retrieval, data pre-processing and analysis. In addition, reports on experiences gained when applying the system to actual data will be provided. Finally, weaknesses of the current system will be briefly discussed, and ideas for future system improvements will be sketched out.
Sensitivity-Based Optimization for Blockchain Selfish Mining

In this paper, we provide a novel dynamic decision method of blockchain selfish mining by applying the sensitivity-based optimization theory. Our aim is to find the optimal dynamic blockchain-pegged policy of the dishonest mining pool. To study the selfish mining attacks, two mining pools are designed by means of different competitive criterions, where the honest mining pool follows a two-block leading competitive criterion, while the dishonest mining pool follows a modification of the two-block leading competitive criterion through using a blockchain-pegged policy. To find the optimal blockchain-pegged policy, we set up a policy-based continuous-time Markov process and analyze some key factors. Based on this, we discuss monotonicity and optimality of the long-run average profit with respect to the blockchain-pegged policy and prove the structure of the optimal blockchain-pegged policy. We hope the methodology and results derived in this paper can shed light on the dynamic decision research on the selfish mining attacks of blockchain.
Low-overhead fault-tolerant error correction scheme based on quantum stabilizer codes

Fault-tolerant error-correction (FTEC) circuit is the foundation for achieving reliable quantum computation and remote communication. However, designing a fault-tolerant error correction scheme with a solid error-correction ability and low overhead remains a significant challenge. In this paper, a low-overhead fault-tolerant error correction scheme is proposed for quantum communication systems. Firstly, syndrome ancillas are prepared into Bell states to detect errors caused by channel noise. We propose a detection approach that reduces the propagation path of quantum gate fault and reduces the circuit depth by splitting the stabilizer generator into X -type and Z -type. Additionally, a syndrome extraction circuit is equipped with two flag qubits to detect quantum gate faults, which may also introduce errors into the code block during the error detection process. Finally, analytical results are provided to demonstrate the fault-tolerant performance of the proposed FTEC scheme with the lower overhead of the ancillary qubits and circuit depth.
Professional Representations of Students in Context of the Development of Digital Technologies

In this paper, the authors reveal the peculiarities of the ideas of students of pedagogical specializations about the profession in the context of the development of digital technologies. The purpose of the research is to study the peculiarities of the ideas of students of pedagogical specializations about the profession in the context of the development of digital technologies. The paper analyzes the main theoretical approaches to the study of the peculiarities of the formation of ideas about the profession of teaching personnel. The specific features of professional pedagogical activity are considered. The possibilities of vocational education in the context of the development of digital technologies are analyzed. The study involved 122 respondents—students of pedagogical specializations of the Southern Federal University. The research methods were psychodiagnostic techniques and methods of mathematical statistics. The data obtained as a result of the study will allow identifying areas in the context of the development of digital technologies, which will determine effective methods of personal self-realization in the context of the profession being acquired, optimize the motivational component, and allow adjusting the programs, content and forms of training for future professionals.