Spectacular memory: Zombie pasts in the themed shopping malls of Dubai

Memory invariably involves sifting and sorting historical traces and reassembling them into societal representations of the past. Usually this has been done by social groups of different kinds or the cultural institutions associated with them, and has provided materials for the construction and maintenance of group identity. In what I term “spectacular memory,” however, the sifting and sorting of memory traces is performed by commercial and media institutions within a globalized cultural framework to create spectacles for mass consumption. Spectacular memory is enabled by the progressive breakdown of Halbwach’s “social frameworks of memory”—the association of memory with face-to-face relations within social groups. In late modern societies, “memory” as a coherent body of representations which is the property of more-or-less bounded social groups has largely devolved into a globalized store of representations curated and diffused through the media, advertising, tourism and entertainment industries. This article uses the example of the history-themed shopping malls of Dubai to characterize this form of memory.

SHOPPING IN THE REPUBLIC OF KOREA

Large modern shopping malls are replacing smaller, traditional groceries in the Republic of Korea. The present paper analyzes this phenomenon and recommends a laissez-faire public policy response. Alterations in selling format to consumers are only the tip of the iceberg in terms of changes in the economy. They are always occurring, at least in healthy economies, and, always, roadblocks are placed in their way. For example, Wal-Mart is prohibited from opening stores in a few communities. Uber and Lyft have been met with great hostility from established taxicab services. Economists even offer a generic term for this phenomenon: restrictions on entry. The present paper is a case study of this occurrence. It focuses on the Republic of Korea, and mainly considers grocery stores. But this small story is emblematic of what takes place in numerous countries all around the world, and many industries. We recommend a laissez-faire public policy approach to this phenomenon. If the new ways of doing things do not violate anyone’s rights, now laws should be passed interfering with the new ways of engaging in commerce. But is this not unfair to the people engaged in the old industries that are withering away? Not a bit of it. The horse and buggy industry, for example, was populated by entrepreneurs who earned a good living before the advent of the horseless carriage. Why should they be guaranteed profits when their offerings are no longer accepted by the public? And the same applies to automobile manufacturers, should their products ever be supplanted by even better means of transportation.

Crowd Abnormality Detection Using Optical Flow and GLCM-Based Texture Features

Detection of abnormal crowd behavior is one of the important tasks in real-time video surveillance systems for public safety in public places such as subway, shopping malls, sport complexes and various other public gatherings. Due to high density crowded scenes, the detection of crowd behavior becomes a tedious task. Hence, crowd behavior analysis becomes a hot topic of research and requires an approach with higher rate of detection. In this work, the focus is on the crowd management and present an end-to-end model for crowd behavior analysis. A feature extraction-based model using contrast, entropy, homogeneity, and uniformity features to determine the threshold on normal and abnormal activity has been proposed in this paper. The crowd behavior analysis is measured in terms of receiver operating characteristic curve (ROC) & area under curve (AUC) for UMN dataset for the proposed model and compared with other crowd analysis methods in literature to prove its worthiness. YouTube video sequences also used for anomaly detection.

Mathematical Method to Assess the Yin-Yang Balance of Commercial Complexes’ Entrances

Shopping malls are important landmarks of modern and sustainable cities as they are substantial business and investment by themselves, and as they also facilitate the social activities of communities. Entrances to shopping malls provide a first impression to customers, thus affecting the business performance of the malls. This paper presents a method to assess the entrances of modern shopping malls by applying traditionally qualitative Feng shui practices quantitatively with an innovative mathematical model. The assessment is based on the manipulation of the yin-and-yang concept applied to the layout of Ming tang (bright court) as the focus of consideration. By applying this novel approach to three shopping malls in Guangzhou, China as a pilot study to match their commercial performance, our hypothesis appears workable. The ideology of balancing yin and yang may be practically meaningful to urban planning and the successful measurement of such balance could shed light on future studies.

Virtual Environment as a Space of Shopping Center Customer Engagement

Purpose: The aim of the article is to identify online customer engagement in shopping centers (SC). Design/methodology/approach: The research was based on secondary and primary sources. The secondary sources are the subject literature, while the primary sources were obtained through netnographic research carried out on the basis of the analysis of affiliate pages of the Facebook social platform and the Google review platform of the 25 largest Polish shopping centers. Findings: The frequency of publishing posts by shopping malls and areas of customer engagement were identified. The research results show large differences between shopping centers in terms of customer engagement in a virtual environment and identify areas of customer engagement in shopping centers on the internet. Research limitations/implications: The authors of the article are aware of the limitations of their research: the analysis of statements in social media does not have to overlap with oral statements in an offline real environment. Moreover, the research results presented should only be referred to the environment of Facebook and Google. However, the variety of social media is very large, and according to the literature on the subject, the type of medium can have a large impact on the CE phenomenon. The research could be expanded by making the analysis of the type of content published by shopping centers (e.g. news, entertainment posts, shopping posts, etc.), by dividing them into categories and drawing attention to the relationships between the type of content published and the level of engagement. It could also be interesting to identify the relationship between the level and areas of customer engagement and the generation of the shopping center. Originality/value: The analysis presented in the article is of great cognitive importance. As far as the authors of the article know, this is the first publication on the engagement of a shopping center customer. The obtained results may be helpful for managers of shopping centers: they draw attention to the scale and particular areas of this phenomenon

Fast flow algorithm prioritizing the most time-consuming source point by using a multi-source evacuation model

In many public spaces (e.g. colleges and shopping malls), people are frequently distributed discretely, and thus, single-source evacuation, which means there’s only one point of origin, is not always a feasible solution. Hence, this paper discusses a multi-source evacuation model and algorithm, which are intended to evacuate all the people that are trapped within the minimum possible time. This study presents a fast flow algorithm to prioritize the most time-consuming source point under the constraint of route and exit capacity to reduce the evacuation time. This fast flow algorithm overcomes the deficiencies in the existing global optimization fast flow algorithm and capacity constrained route planner (CCRP) algorithm. For the fast flow algorithm, the first step is to determine the optimal solution to single-source evacuation and use the evacuation time of the most time-consuming source and exit gate set as the initial solution. The second step is to determine a multi-source evacuation solution by updating the lower limit of the current evacuation time and the exit gate set continually. The final step is to verify the effectiveness and feasibility of the algorithm through comparison.

Software Implementation of the Algorithm for Recognizing Protective Elements on The Face

The quarantine restrictions introduced during COVID-19 are necessary to minimize the spread of coronavirus disease. These measures include a fixed number of people in the room, social distance, wearing protective equipment. These restrictions are achieved by the work of technological control workers and the police. However, people are not ideal creatures, quite often the human factor makes its adjustments. That is why in this work we have developed software for determining the protective elements on the face in real time using the Python scripting language, the open software libraries OpenCV v4.5.4, TensorFlow v2.6.0, Keras v2.6.0 and MobileNetV2 using the camera. The training program uses a prepared set of photos from KAGGLE — with a mask and without a mask. This set has been expanded by the authors to include different types of masks and their location. Using TensorFlow, Keras, MobileNetV2, a model is created to study the neural network by analyzing images. The generated neural network uses a model to determine the masks. You can preview the learning result of the network — it is presented as a graphic file. A program that uses the connected camera is then launched and the user can test the operation. This model can be easily deployed on embedded systems such as Raspberry Pi, Google Coral, and become a hardware and software automated system that can be used in crowded places — airports, shopping malls, stadiums, government agencies and more.