bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study on Techniques and Challenges in Sign Language Translation
Authors: Prachi P. Waghmare Et al.
Published: January 2024

Sign Language Translation (SLT) plays a pivotal role in enabling effective communication for the Deaf and Hard of Hearing (DHH) community. This review delves into the state-of-the-art techniques and methodologies in SLT, focusing on its significance, challenges, and recent advancements. The review provides a comprehensive analysis of various SLT approaches, ranging from rule-based systems to deep learning models, highlighting their strengths and limitations. Datasets specifically tailored for SLT research are explored, shedding light on the diversity and complexity of Sign Languages across the globe. The review also addresses critical issues in SLT, such as the expressiveness of generated signs, facial expressions, and non-manual signals. Furthermore, it discusses the integration of SLT into assistive technologies and educational tools, emphasizing the transformative potential in enhancing accessibility and inclusivity. Finally, the review outlines future directions, including the incorporation of multimodal inputs and the imperative need for co-creation with the Deaf community, paving the way for more accurate, expressive, and culturally sensitive Sign Language Generation systems.
Impact of Feature Extraction Combined with Data Sampling Methods on Heartbeat Categorization
Authors: Sampath Kini K Et al.
Published: January 2024

Dealing with class-imbalanced datasets in data analytics poses challenges, especially when faced with high-dimensional data. In order to handle this issue, researchers often utilize preprocessed methods like feature selection. Feature selection attempts to create a more informative and condensed feature set, while data sampling helps alleviate class imbalance. In our study, aim is to explore the effectiveness of data sampling preprocessed techniques combined with feature extraction using a dataset on ECG Heartbeat. We evaluate ensemble classifiers: Decision Tree; Random Forests (RF), Gradient-Boosted Trees (GBT) for feature extraction. In terms of data sampling, we assess the effectiveness of two methods: Random Under sampling (RUS) and Synthetic Minority Oversampling (SMOTE). The performance of this feature extraction is measured using the sensitivity and the specificity, two important metrics used for accuracy. Our findings depict that the combination of the RUS and GBT method yields the highest performance for ECG Heartbeat detection.
Exploring Seasonal Dynamics: Physico-Chemical Traits of Thunga and Bhadra Rivers in Karnataka, India
Authors: Pushparaj Arunachalam, Senthil Nathan Sengottayan Muruganantham Manickam, Amutha Meenakshi Sundaram,
Published: January 2024

The aim of the present study is to investigate the water quality of the Thunga Bhadra River, a significant tributary of the Krishna River Basin in Karnataka, India.The surface water samples were put through a thorough Physico-Chemical investigation that included significant cations and anions in addition to general characteristics. The Water variables were plottedin spatial map using GIS and analysed by Pearson correlation. The temperature, pH, Dissolved oxygen, Electrical Conductivity, Hardness, Alkalinity, Turbidity, Total solids, Total suspended solids, Total dissolved solids, DO, BOD and COD.  Most of the parameters like pH, conductivity, Turbidity; Dissolved solids are high at Bhadra riverduring the Monsoon in India. It is suggested that the Bhadra river possess highly fertile undisturbed water environment value
Novel approach to integrate various feature extraction techniques for the Visual Question Answering System with skeletal images in the healthcare sector
Authors: Jinesh Melvin Y I Et al.
Published: January 2024

In the realm of medical science, one of the most challenging concepts to grasp is the Medical Imaging Query Response System. The comprehension and classification of the diverse representations of the human body require a significant degree of effort and expertise. Furthermore, it is imperative for users within the healthcare sector to rigorously validate the system. In the domain of human health, a plethora of imaging techniques, including MRI, CT, ultrasound, X-ray, PET-CT, and others, play a pivotal role in the identification of medical issues. These technologies are instrumental in supporting both patient engagement and clinical decision-making. However, the utilization of models, techniques, and datasets for processing textual and visual information introduces complexities that can at times impede the provision of pertinent clinical solutions. The overarching objective of the proposed approach is to conduct a comprehensive comparative analysis of various feature extraction methodologies for both visual and textual information within the Visual Question Answering (VQA) system, focusing on human skeletal images. This endeavor is aimed at enhancing the VQA system's performance with newer datasets and addressing any limitations inherent in existing models. In addition, this research initiative seeks to enable researchers to identify and optimize novel methods that enhance the accuracy of the VQA system. The models under scrutiny in this analysis encompass various methods of feature extraction that help to improve the model and quality of the healthcare industry. The researcher will find the proper methodology for different datasets. To gauge the efficacy of each model in delivering the desired outcomes, an array of metrics will be employed, including classification measurement accuracy, F-classification, C-true positive rate (CTPR), C-precision, C-recall, C-sensitivity, and C-false negative rate (FNR). These metrics are designed to enhance the accuracy of any dataset and optimize the performance of both visual and textual components to ensure accurate responses to the posed queries.
Optimizing Churn Identification in Telecommunications Using Natural Language Processing and XG Boost Machine Learning Paradigm
Authors: Abhinav S. Thorat Et. al
Published: January 2024

With the increasing competition in the telecom sector, accurate churn prediction has become indispensable for service providers seeking to retain customers. This research paper introduces a novel approach that combines Machine Learning (ML) and Natural Language Processing (NLP) and specifically leveraging the XGBoost algorithm, to enhance the precision and efficiency of churn prediction in the telecom industry. The integration of NLP enables the extraction of meaningful insights from diverse data sources, while XGBoost, a powerful gradient boosting algorithm, is employed to build a robust predictive model for identifying potential churners. A machine learning method called the XGBoost churn prediction model is utilized in the telecom industry to forecast client churn. To construct a predictive model that can precisely identify consumers prone to churn, XGBoost is essentially an ensemble method based on gradient-enhanced trees. Several telecom carriers have used this model to understand their consumers better and identify issues that can contribute to churn. It has been used to predict churn in the telecom sector accurately. The model has been tested for accuracy and effectiveness in identifying factors and forecasting customer attrition. These evaluations' findings indicate that the XGBoost model is a trustworthy and precise method for forecasting customer attrition in the telecom industry.
An Empirical Paradigm on Cybersecurity Vulnerability Mitigation Framework
Authors: Abhilash Maroju, Srinivas A Vaddadi, Sravanthi Dontu Rohith Vallabhaneni,
Published: January 2024

Current cybersecurity vulnerability assessment tools were developed in accordance with guidelines established by entities like the National Institute of Standards and Technology (NIST) and the United States Department of Energy. When assessing their facility's cybersecurity maturity, owners and operators of critical infrastructure frequently use frameworks like the NIST Cybersecurity Framework (CSF) and the cybersecurity capability maturity model (C2M2). These frameworks are great at finding vulnerabilities and doing qualitative cybersecurity analysis, but they don't help you get to the level of cybersecurity maturity you want by letting you prioritise how you fix those flaws. Cyber dangers pose a significant risk to businesses and are becoming more pervasive in our everyday lives. In this way, businesses may devise a strategy and set of guidelines by simulating a breach attack. But these strategies are based on experts' tacit knowledge. In response to this problem, the authors of this study suggest an automated and formal process for creating prioritised action plans to enhance environmental transparency. An experiment proving the validity of the proposed method was conducted, yielding consistent and applicable results to the tested scenario. Through testing against a real-world cyberattack that targeted industrial control systems at a critical infrastructure facility, this article presents a thorough architecture of CyFEr and demonstrates its application to CSF.
The Empirical Analysis on Proposed Ids Models based on Deep Learning Techniques for Privacy Preserving Cyber Security
Authors: Srinivas A Vaddadi, Sravanthi Dontu, Abhilash Maroju Rohith Vallabhaneni,
Published: January 2024

In AI, the deep learning (DL) method of machine learning (ML) places an emphasis on large-scale, scalable models that can learn distributed representations from their input data. The scope and effectiveness of these techniques are demonstrated in this thesis through a number of case studies pertaining to cyber security. By the end of each study, the neural network models had been fine-tuned and expanded to provide better results. The key arguments presented and discussed in this thesis are as follows: 1) Creating an all-inclusive database for domain name detection using domain generation algorithms (DGAs) and a new architecture to improve DGA domain name detection overall performance. 2) Constructing a hybrid intrusion detection warning system that incorporates deep neural networks (DNNs) to examine host-level and network-level behaviours within an Ethernet LAN. thirdly, analysing data from social media platforms, email, and URLs to create a single DL-based framework for detecting spam and phishing. 4) ScaleMalNet, a novel hybrid framework proposal, is part four. This is a two-step process: first, we use static and dynamic analysis to determine if the executable file is malicious or not. Then, we categorise the malicious executable file into the appropriate malware family. Malware and ransomware analysis for Android is accomplished using a hybrid DL framework that is comparable to this one.
An Intrusion Detection System (Ids) Schemes for Cybersecurity in Software Defined Networks
Authors: Srinivas A Vaddadi, Abhilash Maroju, Sravanthi Dontu Rohith Vallabhaneni,
Published: January 2024

The process of analysing and improving network traffic is of tremendous relevance to network management and multimedia data mining techniques. Security in Software Defined Networks (SDNs), which rely on a programmable controller in the middle, has recently emerged as the most challenging aspect of SDNs. Network traffic monitoring is critical for detecting and exposing intrusion anomalies in an SDN context. Thus, this study offers a thorough assessment of the NSL-KDD dataset using five separate clustering algorithms: K-means, Farthest First, Canopy, Density-based method, and Exception-maximization (EM). The software used to conduct the comparisons is the Waikato Environment for Knowledge Analysis (WEKA). In addition, the article introduces a knowledge discovery in databases (KDD)–based deep learning (DL) model for intrusion detection that is SDN-based. Initially, the dataset that is being used is clustered into four main attack types and one normal category. We will next go over the steps necessary to build a deep learning intrusion detection system that is based on SDN. The results provide an objective assessment of the several attack types present in the KDD dataset. Just like other methods, the results demonstrate that the proposed deep learning strategy provides better intrusion detection performance. As an illustration, the tested dataset demonstrates a detection accuracy of 94.21% when using the suggested approach.
Study on the Recent Cyber Security-Attacks and the Economic Loss Due to the Growing of Cyber-Attacks
Authors: Srinivas A Vaddadi, Rohith Vallabhaneni, Sravanthi Dontu Abhilash Maroju,
Published: January 2024

Economic damages caused by cyberattacks have been on the rise recently, on a global scale. However, there has not been enough research on the economic damage caused by cyberattacks to other industries; most studies have concentrated on the attacked enterprises. As a means of better damage prediction and other national measures, this study examined the economic damages inflicted on Japan by cyberattacks by employing the production function and the input-output model. We begin by outlining a production function–based approach to estimating the yearly direct harm by industry. Working hours lost due to cyber events are the major input dataset. Second, we used the input-output model to create a model that could estimate the national spillover damage. Thirdly, we explained how to estimate direct and spillover harm in all Japanese industries, even if the data on cyber damage was confined to interview data from the JNSA and the IPA. Therefore, we think our approach of estimating is workable and efficient on a nationwide scale.
Study of Clustering Data Mining Techniques
Authors: Dinesh Bhardwaj, Et al.
Published: January 2024

Data mining's primary purpose is to take a massive records series and wreck it down right into a more plausible form for evaluation and alertness. Exploratory facts evaluation and information mining applications frequently center on clustering. The time period "clustering" refers back to the method of categorizing facts factors into groupings wherein the objects within every cluster have more similarities than differences (clusters). Each approach serves a completely unique motive, determined by using the nature of the records at hand and the needs of the software. Nonetheless, our research has led us to the realization that the K-way approach outperforms the options in a huge type of settings. In this look at, senior undergraduate and master's degree college students from the Faculty of Economics and Business Administration at Babe?-Bolyai University of Cluj-Napoca participated via the usage of questionnaires in a collaborative effort, with the gathered data being processed through information mining clustering techniques, graphical and percent representations, the use of algorithms applied in the software program Weka
Spatial Data Analysis Utilizing Grid Dbscan Algorithm in Clustering Techniques for Partial Object Classification Issues
Authors: Kaulage Anant Nagesh, Et al.
Published: January 2024

Clustering algorithms to solve problems with partial object categorization in spatial data analysis is the topic of this research, which explores the usefulness of these techniques. In order to do this, the Grid-DBSCAN method is offered as an effective clustering tool for the purpose of resolving issues involving partial object categorization. A grid-based technique is included into the Grid-DBSCAN algorithm, which is derived from the DBSCAN algorithm and is designed to increase its overall performance. A number of datasets taken from the real world are used to evaluate the method, and it is then compared to existing clustering techniques. The findings of the experiments indicate that the Grid-DBSCAN method is superior to the other clustering algorithms in terms of accuracy and resilience, and that it is able to locate the most effective solution for jobs involving partial object categorization. It is also possible to enhance the Grid-DBSCAN technique so that it can handle different kinds of complicated datasets. The purpose of this study is to offer an understanding of the efficiency of the suggested method and its potential to perform partial object categorization problems in spatial data analysis.
Early Predication of Diabetes Using Artificial Neural Network
Authors: Prosanjeet Jyotirmay Sarkar, Et al.
Published: January 2024

Nowadays, the diabetes mellitus disease is increasing rapidly across the world. It is a chronic metabolic disorder, where patients unable to manage their blood glucose levels will lead to heart attack, blindness, renal disease, kidney problems etc. and that causes a lot of deaths per year. Patients need to pay a lot of money to practitioners and medicine for the maintenance of blood glucose level.  So, it is necessary to develop a smart and intelligent system based on artificial neural network that can help network early detection of diabetes and be helpful to the practitioner for the early treatment so it can stop the progress of the disease. In this work, a dataset from Pima, India was taken to predict the possibility of diabetes. The objective of the paper was to minimize the error function in neural network training using a neural network model. The average error rate decreases during training and gave an accuracy of 95.93% for the predication of whether a person is diabetic or not.
Data Mining with Supervised Instance Selection Improves Artificial Neural Network Classification Accuracy
Authors: S. Srinivas Reddy, Et al.
Published: January 2024

IDSs may monitor intrusion logs, traffic control packets, and assaults. Nets create large amounts of data. IDS log characteristics are used to detect whether a record or connection was attacked or regular network activity. Reduced feature size aids machine learning classification. This paper describes a standardised and systematic intrusion detection classification approach. Using dataset signatures, the Naive Bayes Algorithm, Random Tree, and Neural Network classifiers are assessed. We examine the feature reduction efficacy of PCA and the fisheries score in this study. The first round of testing uses a reduced dataset without decreasing the components set, and the second uses principal components analysis. PCA boosts classification accuracy by 1.66 percent. Artificial immune systems, inspired by the human immune system, use learning, long-term memory, and association to recognise and v-classify. Introduces the Artificial Neural Network (ANN) classifier model and its development issues. Iris and Wine data from the UCI learning repository proves the ANN approach works. Determine the role of dimension reduction in ANN-based classifiers. Detailed mutual information-based feature selection methods are provided. Simulations from the KDD Cup'99 demonstrate the method's efficacy. Classifying big data is important to tackle most engineering, health, science, and business challenges. Labelled data samples train a classifier model, which classifies unlabeled data samples into numerous categories. Fuzzy logic and artificial neural networks (ANNs) are used to classify data in this dissertation.
IMPLEMENTATION of AT-LEACH protocol in WSN to Improve the system Performance
Authors: Mhamane Sanjeev Chandrashekhar, Et al.
Published: January 2024

Wireless sensor networks are those that are set up in places that are off-limits to people. Sensors must find the data and send it to sink nodes. Data can be routed from one source to another sink by using a variety of routing rules. An example of such a communication system is an information-sending hierarchical routing protocol. One hierarchical system where communication takes place in two stages is Low Energy Adaptive Clustering Hierarchy (LEACH): the setup phase and the normal phase. Enhancing LEACH performance is necessary to prolong the network's lifespan. To improve outcomes, the LEACH method is coupled with the Adaptive LEACH (AT-LEACH) algorithm. However, since the board selection would transmit the recording to the sink, it should be considered on a regular basis. It ought to be adequate. In order to offer a complete solution for maximizing data aggregation in wireless sensor networks, this study introduces the AT-LEACH algorithm in conjunction with the LEACH protocol. Adaptive thresholding is used by the protocol to pick the cluster head in response to shifting network conditions. The threshold value is dynamically modified in response to network characteristics, including node density and energy levels, to guarantee the protocol's continued efficacy under various conditions.
Performance Evaluation of Nature-Inspired Metaheuristic Approaches for Single Document Text Summarization
Authors: Pravesh Patel Et. al
Published: January 2024

In today era, day by day huge amount of data is collected on internet. The reading of text document or retrieving important information are time consuming process, so there is need for introducing effective text summarization technique. Text summarization, is the process of retrieving key information from lengthy document, its plays an essential role in information retrieval and content extraction. The paper we presented a comprehensive examination of nature-inspired metaheuristic algorithms, such as firefly, Cuckoo Search(CS) and Particle Swarm Optimization (PSO) to improve text summarization with an emphasis on single document datasets such as DUC-2001 and DUC-2002. The measurement of generated text summaries quality, generated summaries of datasets are compared with existing golden summaries and evaluated using ROUGE score. Our results show that nature-inspired metaheuristic-based approaches show potential for enhancing text summary of individual documents, metaheuristics methods improve summarizing effectiveness while offering a fresh viewpoint on how to handle the process within the confines of a single document dataset.
Priority radiomic parameters for computed tomography of head and neck malignancies: A systematic review
Authors: Vasilev Yuriy A. , Nanova Olga G. , Blokhin Ivan A. , Reshetnikov Roman V. , Vladzymyrskyy Anton V. , Omelyanskaya Olga V.
Published: January 2024

BACKGROUND: Radiomics is the newest and most promising direction in modern radiographic diagnostics. The number of head and neck cancer studies employing radiomics is increasing annually. A systematic review of recent publications (2021–2023) on computed tomography (CT) of head and neck malignancies was performed.
AIM: To present systematized data on parameters for radiomic analysis for head and neck malignancies identified by CT data.
MATERIALS AND METHODS: The literature search was carried out in PubMed. The basic characteristics of the selected articles were extracted, and their quality was assessed using RQS 2.0 and the modified QUADAS-CAD questionnaire. The reproducibility level of radiomic parameters selected for predictive models in different studies was assessed. Eleven articles were selected for the review. In most cases, a high risk of systematic error associated with data imbalance in terms of demographic parameters and level of pathologies was noted.
RESULTS: The range of RQS 2.0 scores for the included articles varied from 19.44% to 50.00% of the maximum possible score. The decreasing research quality was mainly caused by the lack of external result validation (73% of the analyzed articles) and data accessibility and transparency (82%). Inter-study reproducibility of radiomic parameters was low owing to the wide variety of techniques used for image acquisition, image post-processing, extraction, and statistical processing of radiomic parameters.
CONCLUSION: A set of stable radiomic parameters must be successfully introduced into clinical practice. The standardization of radiomics method and creation of an open radiomics database are necessary for this purpose.
Legal Clinics in the Nonstate System of Pro Bono: Problems and Prospects (on the Example of the Legal Clinic of the Voronezh State University)
Authors: Katinskaya Elena S. , Katinskiy Vladislav G.
Published: January 2024

The article discusses the main directions for improving the legal regulation of the provision of free legal assistance by legal clinics. Topical issues related to the definition of categories of persons entitled to apply within the framework of the nonstate system of free legal assistance to a legal clinic are highlighted, with no grounds for refusing to provide it. In addition, the requirements for clinicians established by legal acts, the possibility of providing legal assistance in the form of representation, and the expediency of consulting citizens by students with the obligatory presence of supervising teachers are evaluated. It also substantiates the need to introduce local regulations and regulations in each legal clinic. A significant gap is the question of unity in understanding the content of the activities of legal clinics, uniform standards of activity, and guarantees of the quality of such assistance, which are closely related to the mechanisms of control by the state. The issue of regulation and financing of the activities of legal clinics is raised, including the procedure for the functioning of automated systems and their processing of personal data. The authors propose to amend the current legislation regarding the grounds for refusing to provide free legal assistance by legal clinics. Also, focusing on the example of the Legal Clinic of Voronezh State University, attention is paid to approaches to entrance examinations and training under the program «Model Legal Clinic» and cooperation with government authorities and nonprofit organizations. The article concludes that it is necessary to further develop and support legal clinics in the system of free legal aid and solve existing problems to improve the quality of assistance provided and achieve greater accessibility for the population.
Decentralized Finance: Issues of Financial and Legal Theory
Authors: Karaseva Marina V.
Published: January 2024

The article examines the current problems of the financial law system. It highlights that currently legal institutions are not distinguished in the system of the industry. Regulating decentralized finance. Meanwhile, the financial and legal institutes of the state and municipal unitary enterprises have existed in the system of science since Soviet times. However, at present, new financial and legal institutions regulate centralized finance. Nevertheless, in the system of the branch of law, they stand out very rarely. The latter include the Institute of Finance of State Corporations and the Institute of Finance of Public Law Companies. The author underlines a critical difference between these legal institutions, which is crucial for financial law because it affects the characteristics of financial legal relations.
Administrative Proceedings as a Way to Protect Subjective Public Rights and Legitimate Interests
Authors: Rogacheva Olga S.
Published: January 2024

The article analyzes the subject of judicial protection in administrative proceedings. The article examines subjective rights, legitimate interests and public interests as the subject of judicial protection, outlines the prospects for expanding the subject of judicial protection in administrative proceedings.
Clinical tests in the diagnosis of scapholunate ligament injuries
Authors: Golubev Igor O. , Gazimieva Bella M. , Sautin Maksim E. , Korolev Andrey V.
Published: January 2024

BACKGROUND:Non-invasive instrumental methods of scapholunate ligament injuries, one of the main wrist joint stabilisers, are complicated by frequent false results of examination, and therefore clinical testing is essential in making the diagnosis. At the same time, the limited number of existing specific tests requires evaluation of their prognostic accuracy.
AIM:Evaluating the diagnostic significance of alternative specific tests in the diagnosis of scapholunate ligament injuries.
MATERIALS AND METHODS:In this study, we performed clinical examination and testing of both carpal joints in 50 subjects who had no complaints of carpal joint dysfunction or pain and denied a history of trauma. In clinical testing, the presence or absence of pain during palpation in the projection of the scapholunate ligament was analysed, as well as the specific Watson test, the navicular balloting test and the Kleinman test, for which specificity was assessed.
RESULTS:The specificity of scapholunate ligament palpation as a diagnostic test was 84.0%, Watson test — 96.0%, navicular balloting test — 98.0%, Kleinman test — 87.0%. The overall specificity of clinical tests was 79.0%. A combination of Watson test, navicular balloting test and Kleinman test achieved the highest specificity (86.0%).
CONCLUSIONS:Clinical testing is an important diagnostic tool in suspected scapholunate ligament injuries, however the benefit of isolated clinical tests is not absolute and therefore the best algorithm for clinical examination of the wrist joint may be the use of several specific clinical tests in combination.
The Cultural Otherness of the Indians as a Just Cause of the Spanish Conquest of the Americas
Authors: Vyšný Peter
Published: January 2024

The Spanish conquest of the Americas has been interpreted – and at the same time legitimized – in a number of ways. One of them was passing it off as a just war. There were two basic views on what should be considered a just cause of the conquest. For some, this cause was the cultural, especially religious, otherness of the Indians and its main manifestation – the extreme sinfulness of the Indians. Others, however, denied that the cultural/religious otherness or the extreme sinfulness of the Indians was a just cause for waging war against them. Both views are briefly explored in the study. The article also shows that their contradiction was only partial. Supporters of the first and second views differed on the question of the methods of carrying out the Spanish conquest of the Americas or the establishment of Spanish rule over America, but not in the belief that the Spanish conquest and rule could have positive effects for the Indians – the ‘imperfect’ Indian societies would become an integral part of the ‘perfect’ European Christian civilization, represented by the Spanish monarchy. The goal of the conquest was so important that even Francisco de Vitoria, who otherwise rejected the cultural/religious otherness of the Indians as a just cause for the war against them, allowed it to be conducted by the Spaniards for certain elements of the Indian way of life or forms of behaviour that resulted from the cultural/religious otherness of the Indians.
“Housing Poverty” in Poland as a Consequence of World War II: In Search of Legal Forms of Its Eradication – Allocation of Dwellings as an Institution of the Housing Law (1944–1959)
Authors: Fermus-Bobowiec Anna
Published: January 2024

The article discusses the allocation of dwellings as an institution of the housing law in the People’s Republic of Poland, which was established in 1944 and was solidified by 1959, eventually becoming a central institution, typical of the entire period of the People’s Republic. The emergence of the allocation of dwellings was caused, on the one hand, by the housing situation in Poland after World War II, which the Manifesto of the Polish Committee of National Liberation described emphatically as “housing poverty” and, on the other hand, was shaped by the solutions present in the Soviet Union, or more broadly, in the housing law of the Eastern Bloc states. Thus, as a legal institution, it became a part of the political transformation of Poland after World War II and of the housing policy implemented by the government of the People’s Republic. The article deals with the following issues: the housing policy of the Soviet Union and the Eastern Bloc states, the housing situation in Poland after World War II, the emergence of the allocation of dwellings and its development until 1959. Furthermore, in the description of the allocation of dwellings as an institution, the following normative solutions, inherently connected with it, are discussed: allocation of dwellings as a source of a tenancy relation (distribution of apartments based on administrative law), housing standards, regulated rent, special protection of the tenant against termination of tenancy. The analysis leads to the conclusion that the origin of the institution should be sought in the Soviet Union, and that it fits into the realities of the communist regime and the transformation of the civil law present therein, i.e. publicization of private law, its instrumentalization and restrictions on private property.
The Draft of the Military Criminal Code and the Law of the Czechoslovak Legion in Russia
Authors: Novák Jakub
Published: January 2024

The article presents the reasons for the creation, content and fate of the draft of the military criminal code developed for the needs of the Czechoslovak Legion in Siberia at the time of its involvement in the Russian Civil War. After introducing the historical background of the Legion’s activity in Russia, it summarizes the functioning of the improvised legal system that the legionnaires, cut off in Siberia from their command in Europe, had to create and maintain. Subsequently, the paper deals with the reasons for the necessity of creating a new criminal code in the middle of a civil war and the process of its creation, as well as a brief summary of the content of the individual parts of the draft. The last chapter sheds light both on the reasons why the code did not come into effect in the end, and on the “second life” that was breathed into it by the Corps Court (the Supreme Court of the Czechoslovak Army in Siberia) in its resolution S-2/19. In addition to general history books dealing with the Czechoslovak legions, the sources for this article were, in particular, archival materials of the Legal Department of the Branch of the Czechoslovak National Council in Russia, containing, among other things, the original draft of the code, and the book-published memoirs of Viktor Svoboda, chairman of the Legal Department.
The Law of Revolution Time: The Case of Russia
Authors: Lityński Adam
Published: January 2024

Among the many revolutions in the world history, the most significant ones were the American, French and Russian. The autocracy and the entire system of governance in Russia led to the rebellion. The roots of the Russian Revolution can be traced back to before the 20th century. The ideological foundation was based on the teachings of Marx and Engels. According to these concepts, communism would not have a state or, consequently, laws at all. After seizing power in 1917, the Bolsheviks systematically abolished the entire existing legal system. They promised to create a new law completely different from the bourgeois one – a typical utopia. However, Soviet scholars were unable to create entirely original legal constructs; they operated within the framework of commonly known legal concepts. As time passed, the Bolshevik legal system increasingly resembled bourgeois law in its solutions. Particularly, the Soviet state continued to strengthen, contradicting the idea of the gradual withering away of the state.
Military Testaments in the Second Polish Republic
Authors: Koredczuk Józef
Published: January 2024

Military testaments in the Second Polish Republic gained particular practical significance due to the unique circumstances resulting from Poland’s existence in the short interwar period. Their regulation was based on post-partition legal provisions, including the 1811 Austrian Civil Code with the Military Service Regulations, the 1874 Military Law of the Reich (Reichsmilitärgesetz), and in the case of Russia the provisions of Volume 10 Part I of the Digest of Laws of the Russian Empire. It wasn’t until 1933 that Poland passed its own Law regarding the last wills of military personnel. Military testaments were treated as a privileged form of last will and testament, characterized by certain simplifications compared to the ordinary form.
Artists Embedded in Government: Expanding the Cultural Policy Toolkit
Authors: Taylor Johanna K.
Published: January 2024

Artists have long worked in fields that are not related to art, instigating innovative ways of working in different institutional contexts from health to business to technology. Civic systems are no exception, with artists spearheading new ways of operating within government agencies. Some artists work covertly, bringing creative methods to traditional civic roles, while others are invited into government agencies in temporary positions as artists in residence addressing specific concerns. As local governments are increasingly challenged to provide services and operate equitably amid eroding public trust, opportunities for cross-sector collaboration uniting artists with government staff are compelling mechanisms for them to cocreate the necessary conditions for systemic change.

The research reported herein explores how artists are engaged in cross-sectoral collaborative models of cultural policy within local governments. Governments already recognize the potential of formal artist residency programs conducted over set periods of time to advance civic goals. In addition, artists have been engaged in this work through informal government partnerships in departments including transportation, parks, and public health. Some collaborations act as applied research enacted internally, bringing more complex understandings of government operations, while others become deep processes of external engagement to expand awareness of local concerns.

This paper presents a framework categorizing the structures in which artists work within government agencies to advance civic goals. The framework is based on research conducted on artists who were embedded in the government across the US from 2020 through 2022. Describing these structures highlights new ways of working within local government that center artists as agents that promote change; this paper lays the foundation for understanding how they operate and delineates opportunities and challenges for the artists and the governments that initiate collaboration. The framework is a foundation for cultural policy makers to evaluate artist in residence in government programs as a cultural policy tool and enable evaluation of the impact of their implementation.
Participatory Governance and Community-Based Research at Mass Culture
Authors: Dubuc Laurence D.
Published: January 2024

This article uses the national arts research network Mass Culture (MC) as a case study for assessing the strengths and limitations of participatory governance and community-based research for reimagining and enacting better futures in the Canadian arts sector. MC is currently the only digital network that takes such an approach to promote the equitable mobilization of arts research in Canada, which falls in line with broader trends and values associated with the participatory turn of cultural policy. At MC, this orientation is first reflected in the governance structure, which grew out of both grassroots processes and formal consultations involving key actors in the Canadian arts community. Here, I draw inspiration from Rosenau’s (Rosenau & Czempiel, 1992) definition of governance to refer to MC’s system of rule, which includes informal mechanisms such as intersubjective meanings, along with formally sanctioned regulations such as charters, terms of reference, etc. MC’s approach is also activated by the methods through which it designs, implements, and evaluates cross-sectoral collaborative projects at the national level. By experimenting with various community-engaged methods tailored to each of its initiatives, MC seeks to build the relational and data infrastructures that are needed to ensure that the research it produces is both relevant and easily accessible to potential users, from practitioners, artists, academics, arts funders, and policymakers, to those working at the intersection of several professional roles. By providing an in-depth account of MC’s emergence as a networked organization and by elaborating on its community-based approach to research, this article aims to contribute new knowledge about the value of various models of collaboration in the fields of cultural policy and cultural management.
Every Picture Tells a Story: Framing and Understanding the Activism of Convict Criminology
Authors: Ross Jeffrey Ian , Tietjen Grant
Published: January 2024

Convict Criminology (CC) consists of three major initiatives. Although scholarship and mentoring have been dominant activities, understanding the activism/policymaking of CC is less well known. This paper reviews the primary United States based activities that CC has done in this area and suggests what it needs to do to assist the interests of individuals who arebehind bars and those who are formerly incarcerated, as well as work towards the mission of the CC organization as a whole. Some of the areas where CC has participated politically include the news-making we have done (i.e. interviews with the news media) and the periodic statements released on social media by the American Society of Criminology’s Division of Convict Criminology. This paper will also consider the notion of praxis as applied to CC, in that some members consider their research, public speaking, and mentorship to be political actions worthy to be considered political activity.
De l’insécurité à la sécurité linguistique : vers une meilleure prise en compte des phénomènes d’hypocorrection
Authors: Moussion Frédéric
Published: January 2024

Cet article reviendra, tout d’abord, sur les travaux fondateurs de l’insécurité linguistique (IL) en dressant un constat : le manque, voire l’absence, de conceptualisation des phénomènes d’hypocorrection. Puis, nous proposerons une définition renouvelée de ces phénomènes, comme étant caractéristiques, via un processus de conscientisation, d’un agir avec prise de risques, révélateur d’un sujet parrèsiaste. Enfin, nous illustrerons, à travers l’entretien de recherche biographique (ERB) d’Olivier, la prise en compte de ces phénomènes d’hypocorrection. Cet ERB montrera le pouvoir d’agir qu’un individu, devenu sujet, est parvenu à exercer sur son IL grâce à la mise en oeuvre d’un pacte parrèsiastique, et d’une IL, que nous avons nommée critique.
Overcoming barriers to access in French immersion: Special education needs and socioeconomic status
Authors: Burchell Diana Marie , Dobrin-De Grace Roksana , Kay-Raining Bird Elizabeth , Chen Xi
Published: January 2024

Canadian French immersion (FI) programs have increased in popularity due to the social and developmental benefits of English–French bilingualism. However, several studies have shown that FI programs do not provide equitable access to diverse students, nor do they provide adequate support to these students once they gain access to the program. To examine these issues in finer detail, the current study interviewed eight parents on their child(ren)’s enrollment and participation in FI programs. We specifically queried the impact of special education needs (SEN) and socioeconomic status (SES) on these families’ experiences. Results showed that parents encountered barriers on multiple levels while supporting their children in the FI program. These barriers increased significantly for children who had SEN, and/or for children and families from low SES communities.
Résilience langagière et émancipation
Authors: Dalley Phyllis , Sutherland Hannah
Published: January 2024

Si l’insécurité linguistique est un sujet courant dans le discours des minorités francophones, il est également important d’examiner d’où elle provient et comment elle interagit avec d’autres contextes d’adversité linguistique, tels que la glottophobie et le linguicisme. L’ensemble de ces contextes (re)produisent des hiérarchies sociolinguistiques fondées sur des idéologies de la norme unique, et donc de la domination symbolique. Nous proposons que la résilience langagière sert d’expression à la capacité des individus minorisés à agir en contextes de domination symbolique, notamment en contexte de linguicisme, de glottophobie ou d’insécurité linguistique. Ce processus nécessite la mobilisation de ressources internes et externes à l’individu et la participation de systèmes concomitants. Son but ultime est d’arriver à un état de bien-être ou de mieux-être langagier. Nous proposons de penser à une résilience langagière émancipatrice afin de contrer la domination symbolique, par et contre des francophones, et d’éviter que l’atteinte du bien-être langagier des uns se fasse au coût du bien-être des autres francophones.
L'insécurité linguistique comme objet de discours : une comparaison Québec-Acadie
Authors: Violette Isabelle , Hébert Shayna-Eve
Published: January 2024

Cet article analyse l’expression insécurité linguistique comme objet de discours dans la presse francophone au Canada de 1990 à 2022, en comparant un corpus québécois (La Presse) et un corpus acadien (Acadie Nouvelle). À partir d’une démarche historicisante, les autrices montrent que l’insécurité linguistique reçoit une plus forte attention médiatique à des périodes différentes : dans les années 1990 et au début des années 2000 au Québec et à partir de 2018 en Acadie. Elles constatent que, dans le corpus québécois, l’insécurité linguistique est traitée comme un enjeu politique en lien avec une inquiétude sur l’avenir du français au Québec ; dans le corpus acadien, il s’agit plutôt d’un enjeu public qui se rapporte à l’illégitimité des variétés de français acadien. La comparaison des deux espaces discursifs montre que la thématisation de l’expression est liée à des conditions sociopolitiques et à des rapports intergroupes.
Adhoc Apparatus Quality Improvement Model: Recruitment And Selection System
Authors: Sultan Zulkifli , Rahim Abdul Rahman , Fitri Dian , Klimko Roman
Published: January 2024

The quality of human resources has an important role in the organization's sustainability, so the recruitment and selection process is carried out according to existing procedures, especially supported by technology-based systems. This research aims to analyze the relationship between recruitment and selection in producing quality human resources using a model utilizing an online-based system. Researchers used a mixed method approach to complete what had been formulated, reaching 114 samples employing simple random sampling. Data was obtained by distributing questionnaires and conducting interviews using the FGD method with the implementing committee and selected District Panwaslu members. Data from the questionnaire research were analyzed utilizing structural equation modeling (SEM), while data from interviews were analyzed using data triangulation. The results found that information and communication technology systems significantly influenced recruitment, selection, and quality of human resources. Meanwhile, the recruitment and selection process did not significantly influence the production quality of human resources since optimal recruitment and selection did not guarantee that the selected human resources were of good quality. However, the role of selection could mediate the relationship between technological systems in creating quality human resources, while recruitment cannot.
Marketing Tactics and Corporate Image: The Key to Maintaining Loyalty
Authors: Pahmi Pahmi , Tasrim Tasrim , Jayanti Ansri , Rachmadana Satria Lintang , Munzir Munzir
Published: January 2024

Sektor perbankan merupakan salah satu bisnis yang sangat memerlukan perhatian dalam hal mempertahankan nasabah. Sebab, sektor ini umumnya menawarkan jenis layanan yang relatif sama. Penelitian ini bertujuan untuk menganalisis taktik pemasaran yang terdiri dari kualitas layanan, kenyamanan layanan dan nilai tambah terhadap loyalitas. Variabel citra perusahaan sebagai variabel mediasi. Populasi penelitian adalah nasabah bank umum syariah di Indonesia. Sampel penelitian sebanyak 360 pelanggan. SEM PLS digunakan untuk analisis data dengan bantuan WarpPLS 7.0. Hasil penelitian menunjukkan bahwa taktik pemasaran dan citra perusahaan menjadi faktor penentu dalam meningkatkan loyalitas pelanggan. Hal ini menunjukkan bahwa kualitas layanan, kenyamanan layanan dan nilai tambah dapat menjadi bagian dari taktik pemasaran untuk meningkatkan loyalitas. Sedangkan citra perusahaan tidak berhasil memberikan efek mediasi antara taktik pemasaran dengan loyalitas. Sebab, perusahaan umumnya hanya melihat gambarannya secara parsial. Hasil penelitian ini memberikan referensi tambahan penggunaan teori utilitas ekonomi untuk menjelaskan hubungan taktik pemasaran perusahaan dan loyalitas.