Reviewerly/exHarmony huggingface dataset:
RUSSIAN PREDICATIVES AND FREQUENCY METRICS

This paper introduces five metrics for measuring the frequencies of dative predicatives in Russian.А dative predicative is a word or multiword expression licensing the dative-predicative-structure, where the semantic subject of the non-agreeing non-verbal predicate is marked by the dative case. I measure the frequencies of the predicatives in the contact position &lt;-1;1&gt; with the same-clause dative subject pronouns in 1Sg (m-metrics) and 3Sg (e-metrics). The m-metrics is applied for retrieving a list of dative predicatives from a corpus. I argue that for each large text collection there is a minimal m-value confirming that an item belongs to the core of the dative-predicative structure. The m/e score makes up the third metrics that shows whether an element is oriented towards the use in the 1 st person or not. Basing on the m-metrics, I retrieved 3 lists of predicatives in the subcorpus of 2000–2021 texts included in the Russian National Corpus. The A list includes 87 items with m  10, the B list includes 44 items with m  50, the C list includes 24 items with m  100. 72-79% of items in each list have an m/e value  1,25. A linguistic interpretation of this result is that for each list of dative predicatives it is true that the majority of its elements are autoreferential expressions oriented towards the use in the 1st person present indicative tense in the direct speech. The fourth metrics shows the total number of occurrences of a word or multiword expression in the corpus (N). I argue that the N score must be measured before POS tagging, and lemmatization. The fifth and the last metrics is the m/N score. The RNC data suggest an inverse correlation between the score of an item in the context specific for dative-predicative structures (m) and its overall frequency in the corpus (N). This effect is explained by the regular homonymy of high frequent predicatives with high frequent adverbials and parenthetical expressions.
Diachronicon: a new resource for the study of Russian constructions in a microdiachronic perspective

The article is devoted to the linguistic characteristics of the database "Diachronicon" and describes the features of the diachronic markup of Russian language constructions, as well as tags specially designed for searching through a diachronic database. A special comment field used in the database is separately justified. In addition, the computer interface of the “Diachronicon" is presented and described. The developed resource provides extensive opportunities for systematic study of not only specific constructions, but also general mechanisms of idiomatization and grammaticalization. The database allows the researcher to simultaneously compare several separate plots, search through a list of constructions and their characteristics in diachrony, track the history of syntactic and semantic changes and limitations of compatibility of different constructions.
An Investigation for Cursive Context-Specific Printed Script Recognition Techniques

Selecting suitable techniques and datasets for a machine learning problem is a great challenge. This research investigates the available machine learning architectures, datasets, and software tools, for cursive context-specific printed text. Despite the existence of millions of speakers around the globe and rich literary history of more than thousands of years, it is hard to find computational linguistic works related to the Punjabi Shahmukhi script, a member of Perso-Arabic context-specific script – a low-resource language family. An investigation of related work through collected statistics inspired by the popularity and success of artificial neural networks and associated techniques have presented. The historical development of the script follows it in a literary sense and the importance and motivation in the development of optical character recognition systems, algorithms, and techniques. The paper discussed the available datasets and the need for a custom dataset. Based on recent research trends in machine learning frameworks, the work presents the most popular deep learning techniques, convolutional neural networks, and recursive neural networks for cursive context-specific scripts. This paper incorporates actual knowledge to promote research in machine learning and natural language processing disciplines and the selection of algorithms, architectures, and resources.
A Deep Network for Explainable Prediction of Non-imaging Phenotypes Using Anatomical Multi-view Data

Large datasets often contain multiple distinct feature sets, or views, that offer complementary information that can be exploited by multi-view learning methods to improve results. We investigate anatomical-multi-view data, where each brain anatomical structure is described with multiple feature sets. In particular, we focus on sets of white matter microstructure and connectivity features from diffusion MRI, as well as sets of gray matter area and thickness features from structural MRI. We investigate machine learning methodology that applies multi-view approaches to improve the prediction of non-imaging phenotypes, including demographics (age), motor (strength), and cognition (picture vocabulary). We present an explainable multi-view network (EMV-Net) that can use different anatomical views to improve prediction performance. In this network, each individual anatomical view is processed by a view-specific feature extractor and the extracted information from each view is fused using a learnable weight. This is followed by a wavelet-transform-based module to obtain complementary information across views which is then applied to calibrate the view-specific information. Additionally, the calibrator produces an attention-based calibration score to indicate anatomical structures’ importance for interpretation. In the experiments, we demonstrate that the proposed EMV-Net significantly outperforms several state-of-the-art methods designed for non-imaging phenotype prediction based on the Human Connectome Project (HCP) Young Adult dataset with 1065 individuals. EMV-Net significantly outperforms compared methods for predicting age, strength, and picture vocabulary. Specifically, our approach specifically decreases the Mean Absolute Error (MAE) for age prediction by at least 0.24 years and improves the correlation coefficient for predicting the other two phenotypes by at least 0.13. Our interpretation results show that for different views, fractional anisotropy of white matter diffusion measures and the surface thickness of gray matter measures are generally more important.
Prospects and risks of using cryptocurrency in the modern economic space

The modern information society required the emergence of a new financial means of payment. This means of payment is more innovative than conventional money. This means is called cryptocurrency. The purpose of the article is to assess the prospects and risks of using cryptocurrencies in the current economic environment. The modern information society required a new financial means of payment. This innovative means of payment was a new digital currency – cryptocurrency. Cryptocurrency is digital money used instead of real money. At the same time, cryptocurrencies will not be able to completely replace national currencies in the near future. This is due to the prohibition of certain countries. A serious threat posed by cryptocurrencies is the possibility of illegal activities. Confidentiality and security of cryptocurrency transactions are high. Therefore, the government cannot track any user by their wallet address or monitor their data. Cryptocurrencies can be used to pay for smuggling, drug trafficking, human trafficking, arms sales, and terrorist financing. The risks of cryptocurrencies are also the bankruptcy of exchanges, which will lead to a drop in the cryptocurrency rate and the lack of guarantees. Traditional money is guaranteed by state assets, while cryptocurrencies are not. Cryptocurrencies can be considered a revolutionary tool in the field of monetary payments. Cryptocurrency is a serious competitor to traditional currencies. The high volatility of cryptocurrencies allows them to be used as one of the instruments of an investment portfolio. However, the trends traced in the article suggest that the role of electronic means of payment will continue to grow steadily over time. Today, cryptocurrencies are acquiring such properties as riskiness, decentralization, and alternative nature compared to fiat money. The results of the study prove that despite the multidirectional trends, the role of cryptocurrencies will continue to grow steadily.
A Comparison of Natural Language Understanding Platforms to Build Arabic Chatbots

Chatbots are computer programs that serve as virtual assistants by conversing with users in their natural languages. Basically, every chatbot contains a Natural Language Understanding (NLU) component at its core . A lot of platforms for building chatbots are created recently with a variety of functionalities, capacities, and price schemes. Chatbot services are based on NLU engines which deal with sentences to recognize information such as entities and intents. Numerous NLU platforms are also recently made available to act like an NLU component for chatbots, however choosing the right NLU for Arabic chatbots is still an issue. In order to choose a suitable platform to develop an Arabic chatbot, the categorization, and core features of the most common conversational platforms that support Arabic language (Watson, LUIS, Pandora, RASA and Wit.ai), are highlighted and compared in this paper. Among the studied platforms, the Wit.ai NLU platform is chosen as an equivelant, popular, and free available platform to be evaluated in terms of generating the structured language from the unstructured Arabic language. In the proposed model, the standard Arabic dialect is used for training and validation. A virtual bank customer service department from Al-Rasheed Bank in Mosul is presented as a knowledge base. In the phase of intent classification and entity extraction, Wit.ai proved to possess high values of precision and F1 score with 0.96 and 0.948, respectively.
Improving FALCON’s Key Generation on ARMv8-A Platforms

This short paper proposes two implementation techniques that may speed up the key generation routine of FALCON, a lattice-based digital signature scheme recently standardized by NIST. The first is a change in the set of primes used for splitting polynomials in NTT+RNS representation: by mixing 31-bit and 63-bit primes, we postulate that operations may be computed concurrently using both the scalar ALU (for 64-bit) and SIMD ALU (computing four 32-bit operations in a single 128-bit register). The second uses arbitrary-precision floating-point operations to compute the polynomial reduction step, which we prototype and benchmark on both Apple M1 SoC and Cortex-A72, improving on the original implementation for deeper recursion levels.
Fast FPGA-Based Image Feature Extraction for Data Fusion in Autonomous Vehicles.

Computer vision plays a critical role in many applications, particularly in the domain of autonomous vehicles. To achieve high-level image processing tasks such as image classification and object tracking, it is essential to extract low-level features from the image data. However, in order to integrate these compute-intensive tasks into a control loop, they must be completed as quickly as possible. This paper presents a novel FPGA-based system for fast and accurate image feature extraction, specifically designed to meet the constraints of data fusion in autonomous vehicles. The system computes a set of generic statistical image features, including contrast, homogeneity, and entropy, and is implemented on two Xilinx FPGA platforms - an Alveo U200 Data Center Accelerator Card and a Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit. Experimental results show that the proposed system achieves high-speed image feature extraction with low latency, making it well-suited for use in autonomous vehicle systems that require real-time image processing. The presented system can also be easily extended to extract additional features for various image and data fusion applications.
Strengthening General Education to Prepare Students for Today and Tomorrow

There is increasing skepticism in the United States about the value of higher education, and some of this skepticism is focused on general education. General education is an extremely important part of the undergraduate experience, but in many cases, it has failed to adapt to current needs. Three challenges that many general education programs face include its disconnect from the institutional mission, a lack of opportunities for students to integrate what they have learned, and an unclear articulation of general education's benefits. This chapter highlights some ways that these challenges are being addressed.
Nuclear Power Organizations as Learning Organizations Around Cybersecurity, Public Health, Public Safety, and Critical Infrastructure Protection

In an era characterized by rapidly evolving threats to cybersecurity, public health, and public safety, the imperative for nuclear power organizations to adapt and excel has never been more pronounced. This inquiry delves into the critical journey of transforming nuclear power organizations into learning organizations that are resilient and highly effective in safeguarding against cybersecurity threats and adeptly managing incidents, planning, and recovery. Leveraging the rich insights derived from qualitative focus group research, this study unveils a comprehensive framework of best practices tailored to the unique challenges nuclear power organizations face. Moreover, the study highlights the significance of organizational commitment to the principles of organizational learning, the seamless integration of learning into daily operations, regular assessments of organizational capacity and competence, and the unequivocal expression of an organizational commitment to learning.
A Comparative Analysis of a Game Browsing Website using IGDB Database

Video gamers are an oversized and growing a part of our culture for a protracted time. whether or not you're thirstily awaiting successive decision of Duty unleash or attempting to beat successive level of Candy Crush on your lunch break, you're a part of the play world. Over the years this world has had to alter loads of stereotypes and false claims. one among the foremost damaging of those claims is that the media blaming violence and different crimes on the influence of games. a company, called Qutee, is seeking to try and do away with a number of these stereotypes by giving a voice to a bunch people that are principally silent through the years. Qutee may be a data-led discussion platform that's giving the gamers a voice and asking them for his or her opinions on the influence that games have had on their lives and therefore the answers can presumably surprise you. Around common fraction of gamers claim that they need met up to 5 friends whereas enjoying games and another thirty seventh say they need created even additional. As a 38-year-old gamer, I actually have created over a number of friendships through play. enjoying a game provides an honest icebreaker, basis, and the simplest way to pay time along. It’s solely natural that it'd result in sturdy friendships. The voice of gamers goes to play a giant role within the future as play culture continues to develop and grow. It’s time we tend to be started taking note of the those who truly play these games instead of those who simply wish somebody in charge for the violence they see on the news. play isn't an ideal world. There square measure flaws like play addiction that require to be self-addressed. except for the foremost half, it looks that play contains a positive impact and will be treated in and of itself.
The Relationship between Different Levels of Education and Income Earnings: A Case Study of Islamia College Peshawar

The objective of this paper is to find out that how different levels of education effect income earnings of the people at Islamia College Peshawar. For this purpose, cross sectional data was collected from 40 respondents employed in different departments at Islamia College Peshawar. The result shows that higher education leads to higher level of income. On the basis of study it is suggested that the public expenditures should be increase on education so that the quality as well as the quantity of education should increase which will leads to higher employment level as a result the living standards of the people will also be improved.
CYBER SECURITY: AN ATTACK GRAPHS PERSPECTIVE

Attack graphs are used to model system vulnerabilities and their potential exploits. Security metrics serve as a powerful tool for organizations to understand the effectiveness of protecting communications networks. The present paper presents a method for analyzing and assessing cyber security, based on attack graphs. It uses a representation of the relations between vulnerable resources and security incidents produced in networks and allows the quantification of their security level.
Efficiency of SDG Achievement in Muslim Countries

The United Nations adopted the 2030 Agenda for Sustainable Development and its Sustainable Development Goals (SDGs) to ensure that by 2030, the world addresses the numerous issues confronting humanity to promote well-being, economic prosperity, and environmental protection. Muslim countries, under the Organization of Islamic Cooperation (OIC), also actively participate in implementing the 2030 Agenda. This study aims to examine how efficient Muslim countries have been in achieving the SDGs between 2010 and 2017. The information indicates which countries have the highest and lowest SDG achievement rates, using the Data Envelopment Analysis (DEA) method to determine the relative efficiency level. According to the findings, four countries – Kuwait, Qatar, Malaysia, and Türkiye – achieved the maximum efficiency level across the period assessed. The country with the lowest efficiency is Comoros. The analysis also found regional variations of efficiency in SDG achievement, with the region with the highest efficacy value is Central Asia, while East Africa has the lowest.
Coaching Cybersecurity Project Managers and Cybersecurity Engineers

This inquiry emphasizes executive coaching as an indispensable leadership development tool in cybersecurity project management, encompassing cyber engineering and offensive operations. Effective cybersecurity project management is crucial in today's digital landscape, characterized by ever-evolving cyber threats and vulnerabilities. It extends beyond administrative tasks, serving as the linchpin for resource allocation, risk mitigation, and aligning security measures with organizational objectives. With the evolving cyber threat landscape, cybersecurity project management leaders require technical expertise, effective leadership skills, and a commitment to continuous professional development. This chapter underscores the pivotal role of executive coaching in developing leaders capable of navigating the cybersecurity landscape effectively and ensuring the resilience of critical organizational assets against sophisticated cyber threats.
Why Historically Black Colleges and Universities (HBCUs) Should Employ New Approaches to Cybersecurity Faculty Development

The demand for specialized computer science degrees, particularly cybersecurity, has surged within educational institutions, presenting a significant challenge in the quest for well-qualified instructors. These emerging dynamics underscore the critical role of instructors in shaping and delivering relevant and comprehensive course experiences to mold adept security technologists. This chapter delves into the contemporary educational landscape, marked by the rapid expansion of the digital domain and an escalating need for cybersecurity professionals. Historically Black Colleges and Universities (HBCUs) are at the epicenter of an imperative. To meet the burgeoning demand for cybersecurity expertise, they must cultivate a new generation of cybersecurity faculty and graduates equipped to navigate the challenges of the digital age. The chapter explores innovative faculty development approaches in cybersecurity, addressing the need for educators to align their teaching with industry practices and equip students with the skills to thrive in this dynamic field.
DEVELOPMENT OF SCIENTIFIC AND METHODOLOGICAL PRINCIPLES FOR PREVENTING ESG-MIMICRY BY BUSINESS ENTITIES

The scientific article examines the issues of destructive phenomena in society, the creation of information asymmetry and the distortion of the actual state of affairs, disinformation for obtaining illegal competitive advantages, image improvements and access to ESG-investments, which nullify efforts to achieve the goals of sustainable development (SDG), introduces misleading investors and discredits the essence of ESG activity of enterprises/companies and is a direct deception of consumers and other stakeholders. The concept of ESG-mimicry is defined from the point of view of the actions of enterprises/companies aimed at creating false, false information about its corporate responsibility, environmental and social activity. Studying the basics of ESG mimicry, as well as its application, gives enterprises/companies a clear understanding of the consequences of its influence. It is the awareness of this aspect that creates a fundamental basis for successful management of ESG-mimicry, which consists of certain elements: ensuring information transparency of the enterprise/company; active communications with stakeholders, since, as a rule, it is precisely in these areas of activity that ESG-mimicry appears most often, and here an important element is control over communications in the form of an ESG audit, internal (independent directors of enterprises/companies) and external mechanisms ( ESG labeling). The main methods of manipulation and forms of ESG mimicry are considered and characterized. Based on the results of the study, measures to prevent them were developed and proposed, as well as basic methods of combating ESG-mimicry in terms of stakeholders were proposed. The conducted research made it possible to propose scientific and methodological principles for combating ESG-mimicry, which include the definition of forms and methods of ESGmimicry, the main negative consequences of its use for the enterprise/company, and recommendations for preventing ESG-mimicry.