Reviewerly/exHarmony huggingface dataset:
Insurance of Cyber Risks in International Transport

The international transport of goods, passengers and luggage is recently facing the threat of cyberattacks. The article is focused on the analysis of the possible cyber risks in the field of the international transport and their management created by the international governmental and non-governmental organisations. The international regulation of the cybersecurity has only recommendatory character and will be subject to future development. That’s the reason why should carriers pay greater attention to all possible cyber security measures. As the instrument of the reduction and mitigation of cyber risks could be used cyber-insurance. The insurance companies are offering insurance cover mainlyon individual base corresponding to the extent of protection required by the policyholder.
Document Classification And Automatic Grading

Computing similarity between documents plays a vital role in grade assessment. Methods like word-by-word comparison are available to compute similarity between documents. Automatic assessments have to deal with lots of unstructured data which may be the same in meaning but different in sentences. A new methodology has to be adapted to compute similarity between unstructured documents. This paper provides a faster and efficient way to make embeddings for documents and computes the similarity for grade calculation. The most widely used similarity measure cosine similarity is used for computing the similarity score. To make embeddings for documents, this paper uses two algorithms Word2Vec and Doc2Vec. An aggregate result of these two methods is used for calculating grade scores.
“Era il discrimen tra socio doc, elegantiarum magister…” or Translating Code-Mixing from Italian into Russian. the Case of 7 uomini 7. Peripezie di una vedova by Silvana Grasso

This paper examines the translation problems related to multilingual issues in the literary text. In particular, we will consider the translation from Italian into Russian of the short novel 7 uomini 7. Peripezie di una vedova by the contemporary Sicilan writer Silvana Grasso. The main issue lies in identifying, along the translation path, a continuous lexical and stylistic compromise for the purpose of not losing the expressive effect desired by the author through the use of code-mixing. Starting from the theoretical framework provided by Katharina Reiss e Hans Vermeer focused on the Skopostheorie, there will be an analysis of the translation of the mentioned book and, consequently, translation proposals in Russian, yet analyzing the results on their validity. Therefore, it will be set as a further objective to investigate the solutions related to the linguistic-cultural contrast generated by code-switching and the way to apply specific translation methods focusing on the target reader. The scarcity of documentation and studies pertaining to translating code-mixing highlight the importance of this research which arises from the need to fill the gap inherent in the diatopic aspect of the Italian language in the translation field.
Identification of women with high grade histopathology results after conisation by artificial neural networks

The aim of the study was to evaluate if artificial neural networks can predict high-grade histopathology results after conisation from risk factors and their combinations in patients undergoing conisation because of pathological changes on uterine cervix.We analysed 1475 patients who had conisation surgery at the University Clinic for Gynaecology and Obstetrics of University Clinical Centre Maribor from 1993-2005. The database in different datasets was arranged to deal with unbalance data and enhance classification performance. Weka open-source software was used for analysis with artificial neural networks. Last Papanicolaou smear (PAP) and risk factors for development of cervical dysplasia and carcinoma were used as input and high-grade dysplasia Yes/No as output result. 10-fold cross validation was used for defining training and holdout set for analysis.Bas eline classification and multiple runs of artificial neural network on various risk factors settings were performed. We achieved 84.19% correct classifications, area under the curve 0.87, kappa 0.64, F-measure 0.884 and Matthews correlation coefficient (MCC) 0.640 in model, where baseline prediction was 69.79%.With artificial neural networks we were able to identify more patients who developed high-grade squamous intraepithelial lesion on final histopathology result of conisation as with baseline prediction. But, characteristics of 1475 patients who had conisation in years 1993-2005 at the University Clinical Centre Maribor did not allow reliable prediction with artificial neural networks for every-day clinical practice.
Modified efficiency-based Bee Algorithm

A modification on the search strategy of the bee algorithm that improves its overall performance is presented in this work. We propose efficiency-based recruitment process of the bees that perform the local search. Both the original and the modified algorithms were tested on a benchmark optimization problems and an additional statistical analysis was performed. Although the advantage varies between different comparison modules, the modified algorithm always provides an advantage in terms of the calculation time. Further, both the original and the modified algorithms were implemented to solve the problem of optimal control of an unmanned aerial vehicle; the algorithms were used for synthesizing a linear-quadratic regulator LQR. The obtained simulation results showed that the proposed modification improved the overall performance of the bee algorithm and made it more time-efficient.
Comparison of the Accuracy of Sentiment Analysis on the Twitter of the DKI Jakarta Provincial Government during the COVID-19 Vaccine Time

Currently, the Government is intensively utilizing social media, one of which is Twitter as a place of interaction with the community. The results of these interactions can be used as feedback to determine whether public opinion on public policies is positive or negative. Tweets from users can be a supporting parameter for the government in evaluating future policies and decision making by applying the sentiment analysis method. This study aims to determine positive or negative sentiments on user tweets against the official twitter account of the DKI Jakarta Provincial Government during the COVID19 vaccine period. The data obtained are 1658 lines from March 30 to April 5, 2021 with queries on tweets containing words or mentioning the username @dkijakarta, which will be grouped by sentiment class, namely negative and positive using the TF-IDF Vectorizer for word weighting and classification using several methods, namely, nave Bayes with accuracy values. 82.50% with class recall on positive sentiment 88% and negative 77% and in class precision showing positive at 79.28% and negative at 86.52% in the rapid miner application then k-NN with an accuracy value of 81.50% with class recall on positive sentiment 85% and negative 78% and class precision shows positive at 79.44% and negative at 83.87% in the rapid miner application. And the accuracy value of the best method in this training data classification comparison is nave Bayes, the results the end of testing the sample dataset using the nave Bayes method with 84.80% accuracy with class recall at 85.01% positive sentiment and 84.59% negative sentiment and at c lass precision shows positive at 85.21% and negative at 84.38% in rapid mining applications.
ZoDIAC: Zoneout Dropout Injection Attention Calculation

Abstract Recently the use of self-attention has yielded to state-of-the-art results in vision-language tasks such as image captioning as well as natural language understanding and generation (NLU and NLG) tasks and computer vision tasks such as image classification. This is since self-attention maps the internal interactions among the elements of input source and target sequences. Although self attention successfully calculates the attention values and maps the relationships among the elements of input source and target sequence, yet there is no mechanism to control the intensity of attention. In real world, when communicating with each other face to face or vocally, we tend to express different visual and linguistic context with various amounts of intensity. Some words might carry (be spoken with) more stress and weight indicating the importance of that word in the context of the whole sentence. Based on this intuition, we propose Zoneout Dropout Injection Attention Calculation (ZoDIAC) in which the intensities of attention values in the elements of the input sequence are calculated with respect to the context of the elements of input sequence. The results of our experiments reveal that employing ZoDIAC leads to better performance in comparison with the self-attention module in the Transformer model. The ultimate goal is to find out if we could modify self-attention module in the Transformer model with a method that is potentially extensible to other models that leverage on self-attention at their core. Our findings suggest that this particular goal deserves further attention and investigation by the research community. The code for ZoDIAC is available on www.github.com/zanyarz/zodiac.
Ink Mismatch Detection Using Unsupervised Learning

This paper tells about the system that makes the use of k-means clustering to detect Ink Mismatch in a handwritten document.
The Impact of Training on Employee Performance: An Empirical Study of Hospitals in North Lebanon

Nowadays, it’s a challenge to maintain high performance among nurses in the healthcare system worldwide, such a phenomenon continues to rise, and this is something that requires proper management. Nurses as caregivers are the products that hospitals deliver to the patients, so it is an indispensable necessity to retain this workforce through effective training programs. This in turn maximizes employee achievement. Such a strategy will surely have a positive impact on the bottom line of healthcare organizations. Therefore, this research paper attempts to examine the training impact has on employee performance. It’s plausible, especially since Lebanon is one of the countries where the healthcare system is suffering from low performance among nurses. To do so, a descriptive correlational study is conducted among 100 nurses in different hospitals in Tripoli. A statistical analysis program (SPSS) was used to analyze the questionnaire transcripts. This study disclosed that the absence of efficient strategies related to nurses’ training causes low performance. Thus, the creation of well-structured training strategies will positively affect nurses’ performance and thus maximize healthcare efficiency.
Beyond neural scaling laws: beating power law scaling via data pruning

Widely observed neural scaling laws, in which error falls off as a power of the training set size, model size, or both, have driven substantial performance improvements in deep learning. However, these improvements through scaling alone require considerable costs in compute and energy. Here we focus on the scaling of error with dataset size and show how in theory we can break beyond power law scaling and potentially even reduce it to exponential scaling instead if we have access to a high-quality data pruning metric that ranks the order in which training examples should be discarded to achieve any pruned dataset size. We then test this improved scaling prediction with pruned dataset size empirically, and indeed observe better than power law scaling in practice on ResNets trained on CIFAR-10, SVHN, and ImageNet. Next, given the importance of finding high-quality pruning metrics, we perform the first large-scale benchmarking study of ten different data pruning metrics on ImageNet. We find most existing high performing metrics scale poorly to ImageNet, while the best are computationally intensive and require labels for every image. We therefore developed a new simple, cheap and scalable self-supervised pruning metric that demonstrates comparable performance to the best supervised metrics. Overall, our work suggests that the discovery of good data-pruning metrics may provide a viable path forward to substantially improved neural scaling laws, thereby reducing the resource costs of modern deep learning.
Waveform Data Augmentation Using Conditional VAE for Deep Learning Side-Channel Attacks against AES Circuit

Side-channel attacks (SCAs) have been reported to reveal secret keys by analyzing the power consumption or electromagnetic leakage on cryptographic circuits. Deep-learning SCAs (DL-SCAs) have been actively studied due to their superior performance over conventional attack methods. Conventional attacks use statistical models between power consumption and the internal register values (e.g. the Hamming distance (HD) over consecutive clock is exploited). The attacker can guess the secret key from the internal register values estimated from power consumption. Similarly, neural network (NN) models are constructed to predict the internal register values from the power consumption in the case of a DL-SCA. However, it has been reported that NN models cannot be trained well by using HD as labels because the frequency distribution on each HD is greatly biased. We propose a method of mitigating the class imbalance problem using a conditional variational encoder. We report the results of a DL-SCA using this method against hardware-implemented AES. The method was able to reveal all the partial keys in less than 400 waveforms. Attack performance in this result was better than previous work.
Using of turquoise organization’s concept to benchmark indicators of creative team management

The purpose of this study is to study the possibilities of applying the turquoise type organization model in creative industries, where there is a need for constant staff development to achieve maximum results. When analyzing the turquoise organization model, a number of factors should be taken into account, on which it relies, and also choose from a number of them those that really have a significant impact on the formation of the turquoise approach in creative industries. At a certain stage, from a non-standard creative management model, one way or another, it is necessary to return to traditional management methods while maintaining the basic principles of the turquoise organization, which can be considered as a standard. The introduction of such tools into the creative team/process management system qualitatively improves the mechanism of managerial decision-making, and also allows it to be debugged through unidirectional actions of both managers and the entire creative team. To implement the task of standardization, a universal model of quantitative assessment of qualitative indicators was developed, taking into account the dynamics of their changes. At the same time, a model was built for selecting indicators for the formation of a set of personnel characteristics in order to benchmark them relative to the level of the same characteristics of the turquoise organization, taking into account the possibility of their transversal change over time.
3D-Aware Video Generation

Generative models have emerged as an essential building block for many image synthesis and editing tasks. Recent advances in this field have also enabled high-quality 3D or video content to be generated that exhibits either multi-view or temporal consistency. With our work, we explore 4D generative adversarial networks (GANs) that learn unconditional generation of 3D-aware videos. By combining neural implicit representations with time-aware discriminator, we develop a GAN framework that synthesizes 3D video supervised only with monocular videos. We show that our method learns a rich embedding of decomposable 3D structures and motions that enables new visual effects of spatio-temporal renderings while producing imagery with quality comparable to that of existing 3D or video GANs.
Are Women a Soft Target for Cyber Crime in India

A growing global issue that might have significant social and economic repercussions is cyber violence against women. This is due to the growing popularity of social media, the spread of information via mobile devices, and the growth of the internet. Any form of sexual or gender-based violence against women that takes place through ICTs like the Internet, mobile devices, and video games is referred to as cyber violence. These technologies are ideal for using as weapons against women for a variety of reasons these include cyber stalking, sexual abuse (including sending sexually explicit or pornographic emails to the victim), cyber extortion, cyber bullying, cybersex trafficking, and phishing. Even if there are more crimes against women overall, a woman can find that experiencing a cybercrime is the most upsetting thing that has ever happened to her. More so in India, where women are devalued and cybercrimes aren't even given significant legal consideration. India's legal system's current rules and practises are shown to have gaps in Cyber Crimes against Women, as well as what can be done to keep individuals secure online. In this essay, I'd want to discuss the various cybercrimes that can affect women and how they can harm them. I also briefly discuss new laws that are being introduced in this field, such as the Criminal Amendment Bill, as well as the many statutes that protect women in these circumstances, such as the Information Technology Act of 2000. (2013). to reach a conclusion, I consider a number of well-known cybercrime cases. Additionally, we need to consider a couple of answers for the rising frequency of cyber crimes against ladies in India. At the end, we talk about the choices accessible to casualties of cyber crime and the changes that should be made to the overall set of laws to battle the rising tide of cyber crimes.
On two-variable guarded fragment logic with expressive local Presburger constraints

We consider the extension of the two-variable guarded fragment logic with local Presburger quantifiers. These are quantifiers that can express properties such as "the number of incoming blue edges plus twice the number of outgoing red edges is at most three times the number of incoming green edges" and captures various description logics with counting, but without constant symbols. We show that the satisfiability problem for this logic is EXP-complete. While the lower bound already holds for the standard two-variable guarded fragment logic, the upper bound is established by a novel, yet simple deterministic graph-based algorithm.
The Symbols of the Divine: Approaching a Post-human Ontology of Digital Design via the Study of Discards

Thinking about design in post-human terms (Forlano, 2017) challenges designers to consider the effects of their designs on the more-than-human world, and in particular to consider non-human actors as users of our designs (Forlano 2017, Cruickshank &amp; Trivedi 2017). However, the post-human turn also calls into question the modernist assumption that “good design” is predicated on notions of instrumental use, which underpins user-centred design, the dominant perspective in digital and interaction design. I propose an alternative, based on Willis’s ontological designing (Willis 2006) and Yuk Hui’s digital ontology (Hui 2016), which highlights the role of ongoing care and maintenance in the ontology of the digital, and argue that discards, and in particular digital discards, have a peculiar ontological status within this theory which offer promise for future study of digital design beyond instrumental use, and it’s entanglements with the more-than-human.
Multi-sensor Tracking with Partly Overlapping FoV Using Detection Field of Probability Modeling and the GLMB Filter

Abstract In this paper, we consider multi-sensor with partly overlapping field of view (FoV) in the labeled random finite set (L-RFS) framework. This is different from most existing multi-sensor tracking algorithms, where the sensors are assumed to have the same FoV. We describe the partly overlapping FoV by modeling probability field of detection (PFoD) for individual sensors in whole observation area and can be seen as the same range of FoV. We consider all these using generalized labeled multi-Bernoulli (GLMB) filter in labeled RFS framework. Besides, a multi-sensor measurement driven of birth model is proposed. Finally, the effectiveness of the proposed algorithm is verified by experiments.
Attitude of Nursing Students towards Artificial Intelligence

The present study was aimed to assess the attitude of nursing students towards artificial intelligence. The objectives of the study were to assess the attitude of nursing students on artificial intelligence &amp; to find the association between attitude towards artificial intelligence and socio demographic variables. The study was conducted among nursing students from selected nursing colleges, Bengaluru with sample size of 189, selected using convenience sampling method. A quantitative non-experimental approach was chosen for the study. The attitude of nursing students regarding artificial intelligence was assessed using structured questionnaire. The result with regard to attitude revealed that 63% of nursing students had unfavorable attitude and 37% had favorable attitude towards artificial intelligence. The Chi-square test revealed that the association between demographic variables and attitude on artificial intelligence. It showed that there was a no significant association between nursing course, age, previous knowledge and source of information with the attitude of the nursing students on artificial intelligence. Keywords: attitude, nursing students, artificial intelligence.
3D-SECURITY IN DIGITAL INDIA

Digital India is not just a dream, but it is the need of today’s world so that each and every online transaction can be transparent, and secure. Digitalization is the process of translating information into a digital format to improve productivity and efficiency. Complex information can be represented in a digital format to reach more customers automatically. However, recent cyber-attacks have raised awareness within the Indian digital world for a more pragmatic, anti-cyber terrorism course of action. These may encompass techniques like machine learning and sentiment analysis. As people in India are less educated and do not have technical knowledge, taking advantage of which hackers do more fraud to them. This paper presents the need for the implementation of 3D-Security and implementing various policies and techniques which assure a safe cyber future and a smooth digital transformation in India. Through this 3D security, the customers web browser encrypts the information sent between the browser and the merchants web server, using Secure Socket Layer (SSL) encryption. SSL provides high data privacy using a series of protocols. So, confidential details of the user, such as plastic cards, and login details, can be transmitted securely. It also provides data integrity. Unauthorized users can’t modify the data. Even if the message is intercepted unauthorized users can’t decode the message.