Mutual Similarity Research Articles

Comparative analysis of the social component when assigning an ESG rating

Every year, Russian companies are using ESG (environmental, social, governance) ratings more actively as a tool to increase their investment attractiveness. At the same time, there are no clear standards in the world and domestic practice that should be considered when forming these ratings. In this regard, the ESG methodologies presented on the Russian market are based on extremely subjective criteria and approaches to their assessment. This can lead to dramatically different results of the ESG ratings from different analytical agencies. In order to verify this fact, the authors of the article have conducted a study the essence of which is to calculate and further compare the S-criterion based on the methodologies of several large Russian analytical agencies providing ESG assessment services. This approach has allowed us to assess in practice the degree of differences that underlie the formation of the ESG methodologies. The result of the work is that the results of the S-components of the domestic ESG agencies are highly similar, which has not confirmed the hypothesis made by the authors. Despite the fact that each of the ESG methodologies ha included different, often dissimilar indicators, the result of the study has revealed their remarkable efficiency and consistency with each other. Contrary to the concerns of regulators and researchers, the results of the agency ratings show a high degree of mutual similarity. Further research should be aimed at evaluating the E and G components of the ESG ratings of the Russian analytical agencies, and at summarising the findings obtained for the final refutation or acceptance of the made hypothesis.

GAP: A group-based automatic pruning algorithm via convolution kernel fusion

In recent years, the deployment and operation of convolution neural networks on edge devices with limited computing capabilities have become increasingly challenging due to the large network structure and computational cost. Currently, the mainstream structured pruning algorithms mainly compress the network at the filter or layer level. However, these methods introduce too much human intervention with large granularities, which may lead to unpredictable performance after compression. In this paper, we propose a group-based automatic pruning algorithm(GAP) via kernel fusion to automatically search for the optimal pruning structure in a more fine-grained manner. Specifically, we first adopt a novel nonlinear dimensionality reduction clustering algorithm to divide the filters of each convolution layer into groups of equal size. Afterwards, we encode the mutual distribution similarity of the kernels within each group, and its KL divergence is employed as an importance indicator to determine the retained kernel groups through weighted fusion. Subsequently, we introduce an intelligent searching module that automatically explore and optimize the pruned structure of each layer. Finally, the pruned filters are permutated to form a dense group convolution and fine-tuned. Sufficient experiments show that, on two image classification datasets, for five advanced CNN models, our GAP algorithm outperforms most extant SOTA schemes, reduces artificial intervention, and enables efficient end-to-end training of compact models.

Facial augmented reality based on hierarchical optimization of similarity aspect graph

BackgroundThe existing face matching method requires a point cloud to be drawn on the real face for registration, which results in low registration accuracy due to the irregular deformation of the patient's skin that makes the point cloud have many outlier points. MethodsThis work proposes a non-contact pose estimation method based on similarity aspect graph hierarchical optimization. The proposed method constructs a distance-weighted and triangular-constrained similarity measure to describe the similarity between views by automatically identifying the 2D and 3D feature points of the face. A mutual similarity clustering method is proposed to construct a hierarchical aspect graph with 3D pose as nodes. A Monte Carlo tree search strategy is used to search the hierarchical aspect graph for determining the optimal pose of the facial 3D model, so as to realize the accurate registration of the facial 3D model and the real face. ResultsThe proposed method was used to conduct accuracy verification experiments on the phantoms and volunteers, which were compared with four advanced pose calibration methods. The proposed method obtained average fusion errors of 1.13 ± 0.20 mm and 0.92 ± 0.08 mm in head phantom and volunteer experiments, respectively, which exhibits the best fusion performance among all comparison methods. ConclusionsOur experiments proved the effectiveness of the proposed pose estimation method in facial augmented reality.

The body as the boundary between the hero and the world in non-classical lyrics

The object of research in this article is the lyrical subject in works related to the lyrics of the non-classical stage of the poetics of artistic modality (S. N. Broitman). The subject is bodily images that perform the function of the boundary between the hero and the world in the lyrical works. The purpose of the study is to identify and describe the value-semantic parameters of bodily images that perform the functions of the boundary between the hero and the world in the works of non-classical lyrics. The focus of this article is on the specifics of the relationship of the lyrical subject/character with the outside world, expressed by bodily images that carry the semantic meaning of the boundary between him and the world. Identifying and describing the features of these images, it is worth noting that the images of the similarity of the world to the human body and, conversely, the human body to the world are most directly related to them. The main method used in this work is historical and poetical analysis, namely, the identification in the texts under consideration of those features that are due to its belonging to a certain stage of the evolution of poetics. The scientific novelty of the study is determined by the fact that for the first time it theoretically summarizes observations on bodily images that perform the function of the boundary between the hero and the world in the lyrics of the non-classical stage of the poetics of artistic modality. In non–classical lyrics, the semantic emphasis in works depicting the world through the body and/or the body through the world, in our opinion, is placed on the external similarity of the lyrical subject and the phenomena of the external world (and more broadly, man and being), illustrating the relationship of the original connection between them with autonomy from each other. The images of the mutual similarity of man and the world in the lyrical works of this stage of poetics express the theme of the inseparability of these two spheres, the outer world of the hero and the inner world of the hero, with their a priori non-identity to each other.

Interval Type-2 Mutual Subsethood Cauchy Fuzzy Neural Inference System (IT2MSCFuNIS)

An interval type-2 (IT2) mutual subsethood Cauchy fuzzy neural inference system has been proposed in this paper. The network architecture consists of 3-layers with all connection weights being IT2 Cauchy fuzzy membership functions (CMFs). The crisp inputs to the system are fuzzified into IT2CMFs with fixed centers and uncertain spreads. The hidden layer represents the rule-based knowledge. The firing degree of the antecedent part of each rule at the hidden layer is computed by aggregating the product of the mutual subsethood similarity measures between the inputs and the connection weights. A volume defuzzification is used to compute the numeric output. A gradient descent back-propagation algorithm is used to train the model. The novelty of the proposed model is threefold. First, is enriching the theory of the mutual subsethood fuzzy neural models by adopting the Cauchy membership function (CMF) as another powerful fuzzy basis function (FBF) rather than the classical choice of Gaussian fuzzy membership functions (GMFs). Second, is the success of computing the mutual subsethood similarity measure between the IT2CMFs and all the model parameters’ updating equations in analytic closed-form formulas, not numerically or approximately. Third, is the ability to extract the type-1 (T1) mutual subsethood Cauchy fuzzy neural inference system (T1MSCFuNIS) with all its analytic closed-form formulas directly as a special case from the general formulas of IT2MSCFuNIS model. Such a novelty makes the proposed model a concrete and effective development of the theory of mutual subsethood fuzzy neural models. Both IT2MSCFuNIS and T1MSCFuNIS models have been tested using different examples from the domains of function approximation, classification, and prediction. The results ensure the efficacy of both models compared with other models reported in the literature.

Addressing Challenges of Incorporating Appearance Cues Into Heuristic Multi-Object Tracker via a Novel Feature Paradigm.

In the field of Multi-Object Tracking (MOT), the incorporation of appearance cues into tracking-by-detection heuristic trackers using re-identification (ReID) features has posed limitations on its advancement. The existing ReID paradigm involves the extraction of coarse-grained object-level feature vectors from cropped objects at a fixed input size using a ReID model, and similarity computation through a simple normalized inner product. However, MOT requires fine-grained features from different object regions and more accurate similarity measurements to identify individuals, especially in the presence of occlusion. To address these limitations, we propose a novel feature paradigm. In this paradigm, we extract the feature map from the entire frame image to preserve object sizes and represent objects using a set of fine-grained features from different object regions. These features are sampled from adaptive patches within the object bounding box on the feature map to effectively capture local appearance cues. We introduce Mutual Ratio Similarity (MRS) to accurately measure the similarity of the most discriminative region between two objects based on the sampled patches, which proves effective in handling occlusion. Moreover, we propose absolute Intersection over Union (AIoU) to consider object sizes in feature cost computation. We integrate our paradigm with advanced motion techniques to develop a heuristic Motion-Feature joint multi-object tracker, MoFe. Within it, we reformulate the track state transition of tracklets to better model their life cycle, and firstly introduce a runtime recorder after MoFe to refine trajectories. Extensive experiments on five benchmarks, i.e., GMOT-40, BDD100k, DanceTrack, MOT17, and MOT20, demonstrate that MoFe achieves state-of-the-art performance in robustness and generalizability without any fine-tuning, and even surpasses the performance of fine-tuned ReID features.

CLUSTERING ALGORITHM BASED ON OBJECT SIMILARITY

The article examines the issue of drug clustering. Initially, k classes are arbitrarily formed and the resulting training sample is pre-processed, then the similarities between the objects of each class are evaluated based on the proximity function and the criterion for evaluating the contribution of objects to the formation of their own class. Usually, it is in percentage and is the degree of mutual similarity of objects of each class. In the next steps of the algorithm, first, one object is taken from the first class, and by adding it to all k classes, the contribution of this object to this class is measured. The object will be left in the class which has the most contribution. This process is repeated several times in a row for all objects of the class. The process is stopped when the location of objects does not change and the degree of similarity exceeds the required percentage. As a result, the required clusters are formed.

Cadet Ladies

The article is devoted to the phenomenon of women in the Constitutional Democratic Party from its very foundation up to the preparation of the organization for the elections to the Constituent Assembly in 1917. The author seeks to reconstruct a socio-cultural portrait of “cadet ladies”, to find out the factors that could have influenced their entry into the party and to characterize their activities in the organization. Special attention is paid to the position in the party hierarchy that women could occupy, as well as to the opportunities for advancement in it. Cadet ladies were mainly responsible for maintaining the organizationʹs welfare, housekeeping, logistics, events and charitable institutions, but at the same time were actively involved in key decision-making, although they did not have the same political rights as men. Moreover, women gained the right to vote after the February Revolution, the number of Cadet ladies in key party positions increased dramatically, which confirms their importance to constitutional democrats. Characteristic features of the socio-cultural portrait of the women of the Peopleʹs Freedom Party are also highlighted. Cadet ladies grew up in post-reform Russia, when social and economic changes in the country and society had a huge impact on the process of womenʹs emancipation. The opportunity to receive a quality education, acquire a profession and work, to choose a spouse out of mutual respect and similarity of views — all these served as factors for acquiring the subjectivity that later allowed the cadet ladies to become the political phenomenon discussed in this article.

The Poetic Theory of Al-Waleed Al-Bohtory and William Wordsworth: A Comparative Study

This study delivers a comparative theoretical study between the Arabic Abbasid classic poet A-Waleed Al-Bohtory and the English poet William Wordsworth. This study relies on analytical and descriptive methods that focus on analyzing the theoretical concepts and studying them based on these two poets’ literary legacy. The significance of this study is to provide a comparative investigation between these Western and Eastern poets to find some mutual similarities and theoretical background of both, to help the researchers of the discipline in the future. Al-Bohtory and Wordsworth present imaginative, descriptive, analytical, rustic and simple poetic stanzas to simulate and enjoy their surrounding objects, for instance Nature. They depict nature as a place of tranquility, peace, and happiness at the end. They portray similar imaginative and descriptive images of Spring, pools, birds, animals, and forests. These two poets were influenced by other contemporary poets at their time as Coleridge and Abu-Tammam. These contemporary poets for Wordsworth and Al-Bohtory are like their poetic spark and inspiration.

Strategic and Planning Documents as Response to Adaptation to Climate Change

Although the impact of climate change cannot be certainty predicted, research shows that extreme weather events, such as droughts or heavy rainfall and floods, have become significantly more frequent in recent decades. Therefore, climate change is increasingly a risk and can significantly affect human health, ecosystems, the local and global economy, as well as social, political and cultural patterns. In response to the mentioned challenges that climate change brings, activities related to adaptation appear in the world. One of the first steps is the adoption of national strategic and planning documents that will establish guidelines and a roadmap to a climate-resilient society. In the first part of the paper, we dealt with determining the current strategic and legislative direction of the development of the system of adaptation to climate change of the Republic of Serbia and the surrounding countries ‒ Republic of Srpska, Montenegro, North Macedonia and Croatia. Mutual similarities and differences in the strategic and legislative frameworks of the mentioned countries were determined, while in the second part of the paper, the AAA model was used to examine the prevalence of climate change in the adopted national planning documents. The results of the paper indicate a relatively good (in the Spatial Plan of the Republic of Serbia and the Spatial Development Strategy of the Republic of Croatia) to insufficiently (in the Spatial Plan of the Republic of Srpska, Montenegro and North Macedonia) represented problems of climate change in the analyzed planning documents. The least represented category is Analysis, which is, among other things, the result of an underdeveloped system of monitoring and projection of future greenhouse gas emissions, as well as assessing the sensitivity of certain systems (traffic, sanitary, water) to climate change. The Action component is the most represented in all analyzed plans. The reason is that the planning documents defined development guidelines that are indirectly related to climate change (revitalization and reuse of brownfield sites, renewable sources and solar energy, energy efficiency, management, control and treatment of wastewater, development of multimodal transport systems) but as such, an integral part of climate change adaptation policies, and therefore significant.

Improved Broad Learning System for Birdsong Recognition

Birds play a vital and indispensable role in biodiversity and environmental conservation. Protecting bird diversity is crucial for maintaining the balance of nature, promoting ecosystem health, and ensuring sustainable development. The Broad Learning System (BLS) exhibits an excellent ability to extract highly discriminative features from raw inputs and construct complex feature representations by combining feature nodes and enhancement nodes, thereby enabling effective recognition and classification of various birdsongs. However, within the BLS, the selection of feature nodes and enhancement nodes assumes critical significance, yet the model lacks the capability to identify high quality network nodes. To address this issue, this paper proposes a novel method that introduces residual blocks and Mutual Similarity Criterion (MSC) layers into BLS to form an improved BLS (RMSC-BLS), which makes it easier for BLS to automatically select optimal features related to output. Experimental results demonstrate the accuracy of the RMSC-BLS model for the three construction features of MFCC, dMFCC, and dsquence is 78.85%, 79.29%, and 92.37%, respectively, which is 4.08%, 4.50%, and 2.38% higher than that of original BLS model. In addition, compared with other models, our RMSC-BLS model shows superior recognition performance, has higher stability and better generalization ability, and provides an effective solution for birdsong recognition.

Culturable airborne fungi communities in naturally ventilated indoor spaces of old residential buildings in Poland

The fungal communities inhabiting apartments in multifamily buildings ( N = 33 apartments) with natural ventilation were determined during seasonal variation in the south part of Poland (Silesia region). It was found that the average level of CO2 concentration usually not exceeded 1000 ppm. The minimal value was noted in November – 402 ppm, however the maximal concentration was equal 6156 ppm and obtained in June. The average fungal colony forming units (CFU/m3) was calculated as follows: 1.06 × 102 (February), 1.17 × 102 (June) and 2.98 × 102 (November). The most frequently occurred species/genera: yeast and yeast-like fungi, Aspergillus niger, Penicillium spp., Cladosporium cladosporioides, Rhodotorula rubra; Culvaraia lunata, Dematiaceae family, Ulocladium consortiale, Verticillium spp.; Fusarium spp., Alternaria alternata; Oidiodendron griseum. The cluster analysis showed no mutual similarity between communities of air fungi isolated from bathrooms of all studied apartments as well as between communities isolated from other indoor spaces (rooms, kitchens, halls). This suggests that natural ventilation has no (or minimal effect) on the propagation of fungal spores between apartments. Statistically significant positive correlation between the concentration of fungi in the air and external humidity, carbon dioxide levels (February, November), and external temperature (November) were found. This study provides that natural ventilation has no effect on the spread of fungal spores inside the building and are not contributed to increase of fungal airborne health risk transmission. It also shows evidence of seasonal changes in carbon dioxide in multi-family homes with natural ventilation. This may give input to establish CO2 benchmark data in such type of buildings.

SimVGNets: Similarity-Based Visibility Graph Networks for Carbon Price Forecasting

In response to global warming, carbon trading market emerges to reduce carbon emissions. However, uncertain fluctuations and complicated price mechanisms in the market have changed the characteristics of carbon data. Existing methods cannot track underlying structures among carbon data streams, failing to explain high variations and predict carbon prices accurately. In this case, how to predict carbon price is a problem demanding new and prompt solutions. Unlike existing predictive mechanisms with fixed structure, in this work, we investigate carbon price from a novel perspective of visibility graph networks, developing a variant forecasting framework based on visible relationships. Firstly, carbon prices are mapped into networks where data are transformed into corresponding nodes utilizing visibility algorithm. Then a novel similarity index is proposed to measure similarity distribution of nodes converted from carbon data, which extracts past and recent price information rather than only considering recent input, resulting in the loss of past knowledge. The forecasting model is finally established based on mutual visibility machamism and similarity of constructed carbon network. To verify the proposed method, experiments are conducted on real carbon datasets, and the results demonstrate the method is superior to existing models, suggesting the predictive potentials of graph network science. It is hoped that this work can act as a bridge between carbon data and networks, motivating further research on carbon price forecasting from graph network views.

어휘 등급의 검증 방안 연구

In this study, a new method was designed and verified to verify the validity of the 'vocabulary grading list', which selected and graded educational vocabulary to support systematic vocabulary teaching and learning in school education. As a verification method, the following two-way method was devised. First, as a way to expand language life beyond school education, it is a way to verify validity by checking whether the vocabulary grade to be verified is a level that appropriately reflects general language life in the wider world than school education. To implement this plan, a hierarchy of vocabulary extracted from a large-scale corpus, which is a new basic data, was set up and a method of comparative analysis with the grade of this study was designed. Second, it is a convergent method to verify the validity by focusing more on the educational purpose of the vocabulary grade and checking whether it is a level that can explain specific teaching-learning aspects of school education. To implement this plan, a method was designed to reveal the degree of linkage with textbook text readability levels of various subjects through correlation analysis and regression analysis. As a result of the verification, the vocabulary grade of this study was quite consistent with the lexical hierarchy extracted based on the large-scale corpus and the level of readability evaluation of textbook texts, but showed a tendency to show a high degree of concordance, especially in low-level easy vocabulary, and the verification result index was poor. If not, they showed mutual similarity. Through this, it was confirmed that the verification method of this study was partially effective, and it could be said that it was a useful verification result in that it consistently informs the direction of supplementation of the vocabulary ranking list. The vocabulary grade verification method explored in this study is meaningful in that it improved the empirical quality of vocabulary grade setting and grade verification.

디지털 플랫폼 기업의 기술 역량 다각화에 관한 연구

In the era of digital transformation, digital platform companies that started as start-ups or in-house ventures have a huge influence in various fields of our economy and life today. Their rapid growth is due not only to exogenous factors such as the development of IT technology and infrastructure, but also to the combination of their active technological innovation and diversification strategy. However, research on patent development trends of Korea's representative digital platform companies has received relatively little attention. In particular, since the 2010s, when digital transformation began in earnest, their patent trends are very important in that they show the direction and strategy of exploratory activities, but the reality is that there is still a lack of research from an integrated perspective on these areas. Therefore, this study aims to broaden the understanding of the patent strategy of digital platform companies by analyzing the patent development trends of Naver, LINE, Baemin, Kakao, Coupang, and Toss, which are representative digital platform companies in Korea. To achieve this, this study conducted text-based analysis of the patent data filed by each company. Specifically, Word Cloud analysis and Text Similarity analysis were performed to identify the key technological directions pursued by each company and analyze any similarities in their technological strategies. As a result, in the case of companies that had not yet implemented diversification, the direction of new diversification strategies could be examined through keyword analysis of filed patents. Matured companies with diversification showed a high level of similarity in patent information compared to other companies. As a result of analyzing patent information filed after 2015 compared to the entire period, it was confirmed that all companies have increased mutual similarity, suggesting that in the future, an intersection will be formed in the businesses pursued by all companies, and competition may intensify. It was confirmed that the establishment of a new strategy was required.

Spatiotemporal variation in aboveground biomass in a tropical grass pasture over a 22‐year period

AbstractThis study monitored spatiotemporal variation in aboveground biomass of a paddock (1.1 ha) in a sown tropical grass pasture under cattle grazing over a 22‐year period (1995–2016) by nondestructively estimating biomasses of 182 fixed locations (0.5 × 0.5 m each) in the paddock at about monthly intervals during the annual stocking season (May to October or November). The mean aboveground biomass of the paddock varied seasonally and interannually between 33 and 605 g DM/m2 and was explained by a regression model including positive effects of mean air temperature, N fertilizer rate and total precipitation during the preceding 60 days (R2 = 0.311, P < 0.001). The coefficient of variation of aboveground biomass, as an indicator of spatial (within‐paddock) variability, varied seasonally and interannually between 0.16 and 0.94, and was described by a model incorporating a positive effect of cumulative stocking density during the preceding 40 days and negative effects of mean air temperature, total precipitation during the preceding 20 days and N fertilizer rate during the preceding 60 days (R2 = 0.277, P < 0.001). The spatial distribution pattern of aboveground biomass in the paddock was persistent (P < 0.05) or not persistent over the study period, almost without being reversed. In detail, patterns in the mid stocking season tended to maintain mutual similarity in the entire range of the intervals between the measurements (364–7,694 days), whereas those at the beginning and near end of the stocking season lost mutual similarity as the interval increased. There was no indubitable evidence that the changes in botanical composition destabilized the spatial distribution pattern of aboveground biomass. The results indicate the need for assessing how the long‐term stability of the within‐paddock distribution pattern of aboveground biomass affects agronomic and ecological performance of the grazing system.

STANDARDIZATION OF LANGUAGE UNITS: STEREOTYPE, CLICHE, STAMP

The paper is dedicated to the study of the concepts of stereotypes, cliches, stamps, and standards widely used in linguistic science. In world linguistics, these terms are used concerning the problems of word culture and stylistics, affecting the issues of cognitive linguistics, ethnolinguistics, linguoculturology and word usage. In linguistic research, definitions such as a model of stable stereotypical thinking or ready-made speech turns, the reuse of language units in a ready-made form in relation to the concepts of a stereotype, cliche, and stamp in the language are often used. The main reason for this is that in the course of communication, public experience tries to simplify the process of information exchange, standardizing the form of content, transmitting information, and fixing it as a speech form. In this regard, the views and conclusions of some scholars in world linguistics are described and analyzed to determine the complex nature of these concepts and their representation in language. Based on them, an attempt is made to differentiate the mutual similarities and differences between the concepts of stereotype, cliche, and stamp in the language. The features of their use in functional speech styles are also analyzed to identify the positive or negative impact of the phenomena of stereotypes, cliche, and stamps on the speech process.

Analiza doniesień prasowych z wykorzystaniem technik przetwarzania języka naturalnego

The dynamic development of natural language processing results in a growing number of products utilizing so-called speech and language technologies. On the one hand this refers to the possibility of interacting with a computer using a language that people naturally use in speech and writing; one the other – making the information contained in all sorts of texts accessible for a computer. We present how methods for gathering and extracting information can be applied to news releases, to possibly reduce the overhead generated by republishing the same news by numerous internet information portals. We present how web syndication can be used to gather press releases; how to process those texts in order to determine mutual similarity; and how to visualize those. We present preliminary results of an experiment with application of the above-mentioned methods to selected Polish internet portals.

Ensemble-NQG-T5: Ensemble Neural Question Generation Model Based on Text-to-Text Transfer Transformer

Deep learning chatbot research and development is exploding recently to offer customers in numerous industries personalized services. However, human resources are used to create a learning dataset for a deep learning chatbot. In order to augment this, the idea of neural question generation (NQG) has evolved, although it has restrictions on how questions can be expressed in different ways and has a finite capacity for question generation. In this paper, we propose an ensemble-type NQG model based on the text-to-text transfer transformer (T5). Through the proposed model, the number of generated questions for each single NQG model can be greatly increased by considering the mutual similarity and the quality of the questions using the soft-voting method. For the training of the soft-voting algorithm, the evaluation score and mutual similarity score weights based on the context and the question–answer (QA) dataset are used as the threshold weight. Performance comparison results with existing T5-based NQG models using the SQuAD 2.0 dataset demonstrate the effectiveness of the proposed method for QG. The implementation of the proposed ensemble model is anticipated to span diverse industrial fields, including interactive chatbots, robotic process automation (RPA), and Internet of Things (IoT) services in the future.

Tipovi stanovanja na području Generalnog urbanističkog plana Niša

The paper analyzes the types of housing in the area of the General Urban Plan of Niš. There are 5 types of housing singled out: city-type family housing, suburban-type family housing, mixed housing zones, multi-family housing from the pre-war and post-war period, and newly built multi-family housing. These types of housing were analyzed based on 18 previously defined criteria. The criteria took into account the characteristics of the macro-location (location, traffic matrix, availability of contents, quality of the environment, etc.), as well as urban parameters at the block level (size and geometry of the plot, number of floors, parking, architecture, construction quality, etc.). Mutual similarities and differences of these housing types were determined, as well as essential changes that occurred in the post-socialist transition period, in order to define possible directions for the improvement of planning regulations and housing construction.