Reviewerly/exHarmony huggingface dataset:
A Deep Learning Based System to Estimate Crowd and Detect Violence in Videos

One of the major concerns throughout the world in all the places of large gatherings during an event is crowd control. The event can be of any form ranging from gatherings of few hundreds to millions. When large number of people assemble or move in an area which might not be suitable to handle the number it can create congestion which would lead to unexpected events such as stampede, riots or emergencies in an unfavourable situation. It would be extremely time consuming and hectic for manual monitoring. Henceforth an attempt is made to build an automated system using deep learning that would count the number of people assembled and compare it with the thresholds provided by the security management and also would try to identify physical violence in the region of interest. This deep learning system would also help in maintaining law and order during protests, curfew and imposed social distancing. These would alert the security personnel to take necessary actions. The system is built to estimate crowd and detect violence based on the street camera’s feed which are usually positioned at the strategic places of crowd gatherings. The crowd estimation model is built with Convolutional Neural Network using different custom layers. All the results which are provided by this system is based on various methods crowd estimation and violence detection. A real time alert system in practicality has it’s own challenges and limitations. Therefore, this paper focuses on the model which could help the system perform better.
È stato un mezzo disastro… Fenomeni di convergenza tra approssimazione e intensificazione: uno studio contrastivo su italiano e russo

Based on the case-study of the Italian downtoner mezzo, ‘half of’, the present work aims to highlight, through a usage-based approach, interesting points of semantic and functional convergence between approximation and intensification, which are traditionally considered divergent meaningconstruction strategies. Mezzo functions both on the semantic level, by modulating the referential force of nouns, verbs, and adjectives, and on the pragmatic level, where it acts as a discourse marker, enabling the speaker to reduce the illocutionary force of their assertions and helping the receiver to make the inferences necessary to recognise the speaker’s communicative intentions in cases of “loose talk” (in Relevance Theory terms), such as understatement and overstatement. In the second part of the article, the results of a contrastive analysis with Russian are discussed, based on the assumption that the identification of functional equivalences between different languages is a valid heuristic method to explain the semantic and pragmatic multifunctionality of certain lexical elements, also from a cross-linguistic perspective.
I prefissi verbali russi pri- e do-: semantica spaziale e possibili corrispondenze in italiano

In this paper, we analyse the spatial meaning of verbal prefixes pri- and do- and their semantic contribution to the lexicalisation of motion events. Our analysis is based primarily on the lexicographic description of these prefixes proposed in the Academic Grammar of the Russian Language (Švedova et al., 1980) and on the interpretation given to them in different works devoted to the study of the semantics of verbal prefixes in the Russian language. In different contributions, it is observed that the spatial meaning of the prefix pri- expresses the reaching of the goal of motion, including the crossing of its threshold (boundary-crossing). In contrast, the spatial semantics of the prefix do- is mostly described in relation to the progressive approaching to an endpoint along a spatial scale (boundary-reaching). In the course of the contrastive analysis, an attempt will be made to identify and describe possible Italian correspondences for Russian verbs of motion with prefixes pri- and do-. We also consider which semantic components of a motion event encoded in a Russian prefixed verb are expressed or omitted in the corresponding Italian constructions. The research has been conducted on the parallel Russian-Italian corpus, part of the National Corpus of the Russian Language (we consider just a few examples).
DeNKD: Decoupled <i>non-target</i> Knowledge Distillation for Complementing Transformer-based Unsupervised Domain Adaptation

There is a growing need to explore the potential of transformers in Unsupervised Domain Adaptation (UDA) due to their increasing success in various vision tasks. However, the application of transformers in UDA has yet to be thoroughly investigated and requires further research. In this study, our primary focus is to design a novel pipeline specifically tailored for transformer-based UDA, to address a crucial challenge: the overemphasis on the transfer of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target</i> -oriented information, mainly caused by the self-attention blocks in transformers and the cross-domain adversarial learning scheme. First, we show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-target</i> information, including semantic contextual information such as background features and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-target</i> classes, must be addressed in the domain adaptation process. Recognizing the importance of incorporating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-target</i> knowledge, we propose a decoupled <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-target</i> knowledge distillation method called DeNKD. DeNKD decouples <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-target</i> information across domains at both feature and logit levels. This decoupling is achieved through a bi-directional knowledge distillation approach that facilitates the interaction and exchange of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-target</i> knowledge to facilitate an effective transformer-based cross-domain knowledge transfer. We perform extensive evaluations on several well-established UDA benchmark datasets. The results consistently show that DeNKD outperforms other methods, achieving the best performance across the board. For example, on the Office-Home dataset, DeNKD achieves an accuracy of 85.54%, while on the VisDA-2017 dataset, it achieves an accuracy of 89.95%. These results highlight the effectiveness of DeNKD in transformer-based UDA and its potential for improving cross-domain adaptation performance.
Artificial Intelligence in Gaming

Artificial Intelligence in games is mainly used to control/generate the behavior or the actions of the Non-Player Character (NPC) to a Human Being. AI is a strong selling point of Commercial Games (Video Games). Since games are associated with entertainment, but there are many serious applications of games/gaming such as in medical field, military field. Many other games such as racing game, shooting games they all have different component of AI. This paper presents the usage of AI in gaming and shows their impact in different fields.
One Hot Encoding Synthesis of Quantum Automata from Flowcharts

We present a new approach to the synthesis of quantum automata. In previous research, reversible quantum automata were designed from tabular specifications or state graphs, and minimum length codes, which lead to circuits with Toffoli gates with high numbers of inputs and thus to high quantum costs. This paper is the first to present a method to synthesize Sequential Quantum Circuits directly from flowcharts. In this paper, we directly map flowcharts to reversible/quantum circuits, using only inverters, 2*2 Feynman gates and 3*3 Toffoli gates, and thus reducing quantum costs. Our method has been confirmed by experiments on several benchmarks of practical flowcharts.
Personalized E-learning System Using Linear Regression for Intelligent Tutoring Systems

An e-learning system allows the learners to attend courses online from any geographical location, at any time using the Internet. The standard e-learning system does not give learners an individualistic model as it is not personalized and is inappropriate for all users. Hence, the satisfaction level of learning a course online is low for many users. This problem can be solved by applying an adaptive learning model for e-learning systems. This type of learning combines intelligent teaching with machine learning techniques to personalize the learners’ learning experience. In adaptive e-learning, the learning content is delivered based on the learner's knowledge level, experience level related to the course, interests, and background. This kind of learning favours an effective way to deal with the self-paced learning. A framework for developing an adaptive system is explored here, based on the core concepts of adaptive/personalized e-learning systems or the so called intelligent tutoring systems (ITS).
Non-overlapping Indexing in BWT-Runs Bounded Space

We revisit the non-overlapping indexing problem for an efficient repetition-aware solution. The problem is to index a text T[1..n], such that whenever a pattern P[1..p] comes as a query, we can report the largest set of non-overlapping occurrences of P in T. A previous index by Cohen and Porat [ISAAC 2009] takes linear space and optimal $$O(p+\mathsf {occ_{no}})$$ query time, where $$\mathsf {occ_{no}}$$ denotes the output size. We present an index of size O(r), where r denotes the number of runs in the Burrows Wheeler Transform (BWT) of T. The parameter r is significantly smaller than n for highly repetitive texts. The query time of our index is $$O(p\log \log _w \sigma +\textsf{sort}(\mathsf {occ_{no}}))$$ , where $$\sigma $$ denotes the alphabet size, w denotes the machine word size in bits and $$\textsf{sort}(x)$$ denotes the time for sorting x integers within the range [1, n].
Implementing Digitalization in Square Blockchain Technology Using HDS Methodologies

A square chain is a digitized, decentralized, open record of all cryptographic cash trades. Persistently creating as “completed” squares are recorded and added to it in consecutive solicitation, it licenses exhibit individuals to screen propelled cash trades without central recordkeeping. Each center point gets a copy of the square chain, which is downloaded normally. At first made as the accounting methodology for the virtual cash Bitcoin, square chains—which use what’s known as appropriated record advancement. At this moment, the development is chiefly used to affirm trades, inside cutting edge financial gauges anyway it is possible to digitize, code, and expansion in every way that really matters any record into the square chain. Doing so makes a perpetual record that can’t be changed; in addition, the record's validness can be affirmed by the entire system using the square chain as opposed to a lone united force. The proposed system is trades that are irreversible and assented to by all people in the framework. Django is used to make the structures of the undertaking and python is used to make the classes Block, Chain, and Transactions. The correspondence between the center points that are made is practiced by Python-Data Base Communication. The yield of this undertaking makes chains that do a couple of trades and the squares are made. The centers made here are decentralized that is the passageway isn’t through only a solitary center point. All the centers are related and the affiliation is through taking care of the past hash an impetus in the current center nearby its hash. The security is given with the objective that the centers can’t be changed and the data set aside can’t be changed. This endeavor gives security and the lesser worth-based charges.
Identification of Future Cyberdefense Technology by Text Mining

We propose a reproducible, automated, scalable, and free method for automated bibliometric analysis that requires little computing power. We explain how firms can use this method with open source data from public repositories to generate unbiased insights about future technology developments, and to assess the maturity, security and likely future development of particular technology domains. The method is demonstrated by systematic text mining of more than 400,000 e-prints from the arXiv repository.
Introduction and Overview

Contemporary cyberdefense is too slow, it lacks foresight, and it is often ineffective. This introductory chapter explains these problems and proposes how the contributions in this volume can help to overcome them.
rOpenSci News Digest, December 2022

Dear rOpenSci friends, it’s time for our monthly news roundup! You can read this post on our blog.Now let’s dive into the activity at and around rOpenSci!&lt;strong&gt;rOpenSci HQ&lt;/strong&gt;&lt;strong&gt;One-page HTML package manuals on R-universe!&lt;/strong&gt; We have recently started building HTML reference manuals for each package in the R-universe!
Become a Mentor for rOpenSci Champions!

&lt;strong&gt;rOpenSci is seeking mentors to support our inaugural cohort of rOpenSci Champions!&lt;/strong&gt; The rOpenSci Champions Program is for &lt;em&gt;people from historically and systematically excluded groups&lt;/em&gt; who are interested in contributing to rOpenSci and the broader ecosystem of open source and open science communities.
The Effect of Using Scrabble Games towards Students’ Achievement in Vocabulary

The objective of this research was to find out the significant effect of using scrabble games towards students’ achievement in vocabulary at the eighth grade of SMP Negeri 2 Deli Tua. The type of research was quantitative research, and it was conducted by using experimental design with two classes, in experimental class and control class consisted of 26 students. In technique of collecting data, the researcher conducted pre-test, treatment and post-test by vocabulary test. The data were analyzed by using mean, standard deviation and t-test formula. The result of the data showed that t-observe (12.19) value was higher than the t-table in which t-observe &gt; t-table (12.19 &gt;1.68). The finding showed that the hypothesis was accepted. It can be concluded that there was significant difference score in vocabulary achievement for the students who were taught by using scrabble games and who were not taught by using scrabble games. Based on the explanation above, the researcher concluded that scrabble games is effective for teaching vocabulary of the eighth grade students at SMP Negeri 2 Deli Tua Academic Year 2021/2022
Masking the Backgrounds to Produce Object-Focused Images and Comparing that Augmentation Method to Other Methods

Image augmentation is a very powerful method to expand existing image datasets. This paper presents a novel method for creating a variation of existing images, called Object-Focused Image (OFI). This is when an image includes only the labeled object and everything else is made white. This paper elaborates on the OFI approach, explores its efficiency, and compares the validation accuracy of 780 notebooks. The presented testbed makes use of a subset of ImageNet Dataset (8,000 images of 14 classes) and incorporates all available models in Keras. These 26 models are tested before augmentation and after applying 9 different categories of augmentation methods. Each of these 260 notebooks is tested in 3 different scenarios: scenario A (ImageNet weights are not used and network layers are trainable), scenario B (ImageNet weights are used and network layers are trainable) and scenario C (ImageNet weights are used and network layers are not trainable). The experiments presented in this paper show that using OFI images along with the original images can be better than other augmentation methods in 16.4% of the cases. It was also shown that OFI method could help some models learn although they could not learn when other augmentation methods were applied. The conducted experiments also proved that the Kernel filters and the color space transformations are among the best data augmentation methods.
Literature Classics Reading and College Chinese Teaching Research

Under the background of modern education, which takes moral education as the fundamental task, College Chinese course should pay more and more attention to the cultivation of humanistic influence and cultural literacy. AS the key content of Chinese teaching, literary classics can let students gain growth in reading, so as to treat learning more actively. Based on this, this paper will make a detailed discussion on the core literacy of literary classics reading and College Chinese teaching, hoping to provide some new teaching ideas for teachers and colleagues.
Reducing the Impact of Team Process Conflict on Commitment from the Perspective of Positive Emotions

There is a subtle link between the intensification of the conflict and the team identity. Process conflict is the most frequent in team cooperation, and plays an important role in determining task strategic cooperation and power and responsibility allocation. The exploration of team process conflict and team commitment has attracted the interest of some scholars. But the internal process of the two researches is still lacking. This paper will carry out an empirical study in the perspective of two positive emotions: Team Trust Perception and Team Emotional Intelligence. The results of this study show that: (1) team process conflict has a negative effect on team trust perception and team commitment. (2) Team trust perception plays a mediating role in the impact of team process conflict on team commitment. (3) Team emotional intelligence can regulate the negative impact of team process conflict on team trust perception.
A Personal Values-Based Approach to Understanding Users’ Co-Creative and Co-Destructive Gaming Experiences in Augmented Reality Mobile Games

Background: Understanding how users evaluate their experiences has been recognized as being fundamental to designing services that meet the users’ needs and support the emergence of positive rather than negative value outcomes in service use. Still, the current literature does not explicitly describe how the users’ value determination unfolds or how the levels of experienced value could be measured to support service design. We address this gap in the context of augmented reality (AR) mobile games by scrutinizing users’ personal values as a potential basis for achieving such an understanding. Method: Through a qualitative content analysis of 43 in-depth laddering interviews with active Pokémon Go gamers in Finland, we uncover the focal personal values associated with the game. Furthermore, we determine the connection of these values to the users’ co-creative and co-destructive gaming experiences. Results: Our study defines eight personal values highlighted in Pokémon Go. The focal co-created values include pleasure, a sense of belonging, ambition, activity, and a healthy life. The most co-destroyed values in the game include social recognition and responsibility. Interestingly, the value of sociality is highlighted in both the co-creative and the co-destructive gaming experiences. While the findings may not be generalizable beyond the studied AR mobile game context, this study explains how users’ personal values may serve as a basis for understanding the value structures of other digital service users to support service design. Conclusion: Our study contributes to the literature by introducing personal values as a potential basis for understanding users’ value-based drivers and service experiences to support the design of digital services. We theoretically conceptualize the users’ dynamic value creation process based on personal values and, using empirical findings, offer novel insights into the value co-creation and co-destruction phenomena in AR mobile games.
Path Planning Algorithms: An Evaluation of Five Rapidly Exploring Random Tree Methods

Path planning is one element of mobile robot navigation.It helps a robot arrive at its destination safely by defining its route from the start to the end locations.The Rapidly Exploring Random Tree (RRT) algorithm is a frequently employed type of path planning technique because it is uncomplicated, even though it does not come up with the optimal route.For this reason, variations of RRT algorithms have been developed to improve its effectiveness.This paper evaluates the performance of five variations of RRT algorithms.These include the RRT, Rapidly Exploring Random Tree Star (RRT*), Informed RRT*, Linear Quadratic Regulator Rapidly Exploring Random Tree Star (LQR RRT*), and the Batch Informed Tree Star (BIT*).
Tertiary Students’ Perception of Mobile Legend Game on Vocabulary Mastery

The phenomenon of mobile legend is quite widespread among tertiary students,because all of this game this game is using English, unique, and everyone interested to play this game. . The purpose of this research was to investigate students' perceptions of mobile legends on vocabulary. A qualitative research method with thematic analysis was applied in this research and the subjects of this research were four students of the Universitas Muslim Nusantara Al-Washliyah. Coming to different departments namely; 2 English students with higher experience in learning English, and 2 non English students with less experience in learning English. Based on the result of interview, there were four themes found, namely; Curiosity,Interaction,Obstacle,Confidence, it is implied that all of the EFL students’ have the same level in the curiosity and confidence theme, therefore all of the EFL students’ had the different level in interaction and obstacle. It turned out that the educational background had an impact on this research.
Professional Mobility Strategies as Perceived by IT Graduates (Pilot Study)

This pilot study is part of an initiative project dedicated to studying the professional mobility of Russian IT specialists. The relevance of the project is due to the problems of forming an adequate understanding of IT graduates’ professional mobility strategies, since such representations guide professional development from the very beginning of a career. An objective view of these strategies contributes to the successful adaptation and socialization of novice IT professionals in the information technology field. The scientific substantiation of methodological and methodical approaches to the study of IT graduates’ perceptions of professional mobility strategies, considered in the totality of objective (socio-structural, activity) and subjective (personal-motivational) factors. At the empirical level, methods for studying ideas about mobility strategies have been developed and tested. The conclusions about the peculiarities of graduate students’ perceptions of IT specialists’ professional mobility strategies are concretized.
2 Months in 2 Minutes - rOpenSci News, June 2019

&lt;strong&gt;rOpenSci HQ 👨🏽‍💻👩🏼‍💻 🏗️&lt;/strong&gt;Join our next Community Call on Involving Multilingual Communities June 28th.Video of our Community Call on Security for R is up, with a long list of resources.Our Community Manager, Stefanie Butland, spoke at R-Ladies Seattle and Fred Hutch about rOpenSci, Learning R, and Building Community May 22nd.&lt;strong&gt;Software Peer Review ✔&lt;/strong&gt; 6 community-contributed packages passed software peer reviewnbaR - R Pack