Criminalization of Refugees in the Age of Insecurity and Mass Migration: Zygmunt Bauman on Ethnicity, Asylum and the new ‘Criminal’

Immigrants and refugees continue to be a controversial political issue in Western countries.  Negative impacts of globalization on European labor markets, increasing presence of ethnic minorities in the West, and increasing fear of terrorism and crime, have made them easy targets for hate crimes, governmental mistreatment, and demonization by the media.  Western governments are determined to prevent or restrict influx of refugees.  They have been progressively deserting their time-honored liberal principles and have been governing their people through politics of fear of crime and foreigners, and insecurity.  Refugees are subject to growing bigotry, criminalization, and transfer to inhumane camps.  Developing forms of dislocation, prejudice and criminalization of refugees have been key points of Zygmunt Bauman’s for several years now.  This paper discusses Bauman’s evolving views on criminalization of refugees.  It will discuss novel sociopolitical processes that he has recently pointed to as processes that sustain it.  &nbsp

Criminals/Refugees in the Age of Welfareless States: Zygmunt Bauman on Ethnicity, Asylum and the new ‘Criminal’

Refugees have become a hotly debated political issue in the West. Adverse effects of globalization on European labor markets, the greater availability of ethnic minorities in this region, and fear of crime and terrorism, have made these groups convenient targets for waves of hate crimes, governmental escapegoating, and media-driven demonization since the end of the 1980s. Western governments are increasingly determined to restrict influx of refugees. They have been increasingly abandoning their liberal values and have been governing their population through politics of fear of crime and insecurity. Refugees are subject to increasing harassment, hatred, detention, discrimination, criminalization, and transfer to remote and dangerous places. Changing forms of displacement, racism and criminalization of refugees have increasingly become the focal points of Zygmunt Bauman’s work. This paper discusses Bauman’s views on criminalization of refugees. It will discuss the social processes that Bauman believes create and sustain it. I believe that Bauman’s conducive to a richer and a more coherent understanding of the new processes that create refugees

Yield Erosion Sediment (YES): A PyQGIS Plug-In for the Sediments Production Calculation Based on the Erosion Potential Method

The Erosion Potential Method is a model for qualifying the erosion severity and estimating the total annual sediment yield of a catchment. The method includes a diverse set of equations, which are influenced by different factors such as geology, morphology, climate and soil use. This study describes a PyQGIS YES plug-in, which allows a semiautomatized use of the Erosion Potential Method in Geographic Information System (GIS) environment. In detail, we developed a plug-in using Python programming language that is made up of a series of operations allowing one to estimate sediment production through a wizard procedure. The first stage consists of data preprocessing and involves: (i) loading of the layers (e.g., geological map); (ii) spatial selection of the catchment area; (iii) elaboration of loaded layers (e.g., clipping). During the second stage, the user assigns a relative coefficient to each factor either by selecting a preloaded value from bibliographic sources or by inserting a value inferred from field observations and data. The third stage includes the addition of rainfall and temperature values loaded as: average values, point shapefiles (the plug-in calculates the average monthly values) or tables (the plug-in creates the linear regression depending on altitude). During the final stage, the plug-in executes the equation of EPM Model obtaining the sediment yield value at basin scale. Additionally, the user can use the “squared cell” method choosing the appropriate option in the setting dialogue of the plug-in. This method divides the catchment area in a regularly-spaced grid which allows one to carry out the distribution map of the sediment production during the final stage

Dyadic Movement Synchrony Estimation Under Privacy-preserving Conditions

Movement synchrony refers to the dynamic temporal connection between the motions of interacting people. The applications of movement synchrony are wide and broad. For example, as a measure of coordination between teammates, synchrony scores are often reported in sports. The autism community also identifies movement synchrony as a key indicator of children's social and developmental achievements. In general, raw video recordings are often used for movement synchrony estimation, with the drawback that they may reveal people's identities. Furthermore, such privacy concern also hinders data sharing, one major roadblock to a fair comparison between different approaches in autism research. To address the issue, this paper proposes an ensemble method for movement synchrony estimation, one of the first deep-learning-based methods for automatic movement synchrony assessment under privacy-preserving conditions. Our method relies entirely on publicly shareable, identity-agnostic secondary data, such as skeleton data and optical flow. We validate our method on two datasets: (1) PT13 dataset collected from autism therapy interventions and (2) TASD-2 dataset collected from synchronized diving competitions. In this context, our method outperforms its counterpart approaches, both deep neural networks and alternatives.Comment: IEEE ICPR 2022. 8 pages, 3 figure

Semiotics of Animal Motifs in the Jewelry of the Achaemenid Era

Achaemenid art is a combination of different nations' art and a reflection of thought and religion of the era. Plant, human, animal motifs and a combination of them have been used in the art of the Achaemenid era. But the abundant use of animal motifs, especially in jewelry, has distinguished the Achaemenid art. The present paper aims to study the animal symbols in the jewelry of the Achaemenid era. Following a definition of the symbol, it evaluates the role and status of animal symbols in Achaemenid jewelry by categorizing and analyzing those motifs. Descriptive-analytic method has been used in this paper and data has obtained from library research. According to the results of the study, it can be found that the symbolic animal motifs were used in the jewelry of the Achaemenid era to express the power and majesty of the king and the sovereignty; they demonstrate the Iranians' particular viewpoint toward the world, religion and custom of the era and artistic qualities of the related nations

Pose Uncertainty Aware Movement Synchrony Estimation via Spatial-Temporal Graph Transformer

Movement synchrony reflects the coordination of body movements between interacting dyads. The estimation of movement synchrony has been automated by powerful deep learning models such as transformer networks. However, instead of designing a specialized network for movement synchrony estimation, previous transformer-based works broadly adopted architectures from other tasks such as human activity recognition. Therefore, this paper proposed a skeleton-based graph transformer for movement synchrony estimation. The proposed model applied ST-GCN, a spatial-temporal graph convolutional neural network for skeleton feature extraction, followed by a spatial transformer for spatial feature generation. The spatial transformer is guided by a uniquely designed joint position embedding shared between the same joints of interacting individuals. Besides, we incorporated a temporal similarity matrix in temporal attention computation considering the periodic intrinsic of body movements. In addition, the confidence score associated with each joint reflects the uncertainty of a pose, while previous works on movement synchrony estimation have not sufficiently emphasized this point. Since transformer networks demand a significant amount of data to train, we constructed a dataset for movement synchrony estimation using Human3.6M, a benchmark dataset for human activity recognition, and pretrained our model on it using contrastive learning. We further applied knowledge distillation to alleviate information loss introduced by pose detector failure in a privacy-preserving way. We compared our method with representative approaches on PT13, a dataset collected from autism therapy interventions. Our method achieved an overall accuracy of 88.98% and surpassed its counterparts by a wide margin while maintaining data privacy.Comment: Accepted by 24th ACM International Conference on Multimodal Interaction (ICMI'22). 17 pages, 2 figure