خوشامدگویی یا نه؟ - حس امنیت، تعلق خاطر و نگرش های افراد بومی نسبت به فرهنگ پذیری مهاجران
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 25
تنوع فرهنگی به دلیل مهاجرت تبدیل به یک وضوع کلیدی در بسیاری از جوامع امروزی شده است. این سوال که چگونه جمعیت بومی این توسعه ها را تجربه می کند دارای اهمیتی اساسی برای روابط بین فرهنگی است و پایه ای را برای فرهنگ پذیری مهاجران ایجاد می کند. ما با الهام از تحقیقات مربوط به تعلق خاطر و چند فرهنگی بودن، در اینجا فرض می کنیم که حس امنیت عمومی و خصوصی می تواند به نگرش های مثبت تر به تنوع فرهنگی مربوط شود درحالیکه حس تهدید می تواند به آزادی کمتر مربوط شود. به صورت صریح تر، مطالعه حاضر به بررسی این موضوع پرداخته است که چگونه تعلق خاطر عمومی افراد بومی (امن یا ترسان) و نیز حس امنیت (فرهنگی یا اقتصادی) خاص آنها می تواند به تجربیات آنها درباره فرهنگ پذیری مهاجران در کشور چند فرهنگه لوکزامبورگ مربوط شود. نمونه ما شامل 134 فرد لوکزامبورگی با میانگین سنی 02/45 (انحراف معیار 41/17) بود که یک پرسشنامه آنلاین را پر کردند. نتایج نشان داد که تعلق خاطر عمومی خود – گزارش شده توسط افراد ترسان رابطه ای مثبت با جهت گیری های غیر خوشامدگویانه ترِ فرهنگ پذیری دارد. روابط بین تعلق خاطر عمومی و جهت گیری های فرهنگ پذیری توسط حس امنیت فرهنگی وساطت می شود که تاثیراتی قوی روی جهت گیری های (غیر) خوشامدگویانه افراد بومی نسبت به دلستگی عمومی دارند. یافته ها بیانگر این هستند که جهت گیری های غیر خوشامدگویانه درقبال مهاجران که شامل آزادی برای برقراری رابطه و تبادل فرهنگی می شود، با حس امنیت فرهنگی و اقتصادی که به صورت جزئی توسط یک تعلق خاطر عمومی امن یا ترسان منحرف می شود، رابطه دارد. بنابراین به نظر می رسد که حس امنیت یک پایه امنی را برای دامنه و آزادی تنوع فرهنگی فراهم می کند که به منظور مواجهه موفقیت آمیز با چالش های جوامع چند فرهنگه امروزی مورد نیاز هستند.
|مقاله ترجمه شده|
Does higher unemployment lead to greater criminality? Revisiting the debate over the business cycle
آیا بیکاری بیشتر منجر به جنایت بیشتر می شود؟ بررسی مجدد بحث درباره چرخه تجارت-2019
A crime epidemic appears to have erupted in many advanced economies, raising questions on whether higher unemployment leads to greater incidence of crime. In this paper, we establish a robust connection between unemployment and crime, considering both violent and non-violent crimes, in a 4 variable time-varying VAR setting in identifying four shocks: unemployment, output, migration fear, and crime shocks. Using data from France and the UK over the time period 1975Q1 to 2013Q4 and 1983Q1 to 2018Q2 respectively, we find significant positive effects of unemployment shocks on crime rates in both countries, par- ticularly so during the times of economic contraction. We also find that crime rates de- cline during times of economic expansion (in response to a positive shock in GDP growth). Considering shocks in migration fears, we find that migration fears are fuelling higher in- cidence of crime in France, whereas unemployment shock continues to drive crime rates in the UK. Undertaking further analysis for a single country at quarterly frequency for the UK, and with panel data from 24 countries at annual frequency over the period 1998 to 2016, we support our main hypothesis that higher unemployment rate tends to increase non-violent crime. Our results imply that maintaining stable economic activity is critical in order to stabilize incidence of non-violent crime. These findings have political signifi- cance, given the recent unprecedented levels of concern and uncertainty about migration in European countries.
Keywords: Criminality | Unemployment | Immigration | Business cycles | Time-varying impulse responses | Wavelet
First do No harm: Medical legal violence and immigrant health in Coral County, USA
اول صدمه نبینید: خشونت حقوقی پزشکی و سلامت مهاجران در Coral County، USA-2019
Contemporary U.S. health and immigration policies exclude millions of noncitizens from healthcare coverage. Growing scholarship emphasizes legal status as a technology of social exclusion and determinant of health, but few studies capture the effects of recent policy uncertainty on noncitizen health. By examining the case of Coral County (a pseudonym), I highlight the challenges facing safety-net clinics and their noncitizen patients making life and death decisions amidst uncertainty before and after the 2016 presidential election. Observational and interview data with patients, clinic workers, and community partners (n=27) revealed that growing anxiety over federal immigration policies altered clinical risk calculations through a process I refer to as “medical legal violence” (MLV). Whereas previous risk negotiation strategies leveraged bureaucratic routines to elevate imminent threats of illness and/or injury in health decisions, heightened immigration enforcement under the Trump administration shifted the balance in clinical risk calculations toward social risks of detention, deportation, and family separation. This transformed clinical care in Coral County by turning trusted medical-legal bureaucracies into potential tools for federal biopolitical surveillance of immigrant patients, blocking healthcare pathways and increasing patients’ fear and anxiety.
Keywords: United States | Immigration status | Social determinants of health | Legal violence | Health inequalities | Medicaid | Safety-net clinics | Biopolitics
Assessing the quantum-resistant cryptographic agility of routing and switching IT network infrastructure in a large-size financial organization
ارزیابی چابکی رمزنگاری مقاوم به کوانتومی در مسیریابی و تعویض زیرساخت های شبکه IT در یک سازمان مالی با اندازه بزرگ-2019
This paper provides exploratory research by determining the impact of quantum computing threat on modern routing and switching infrastructure components of IT network infrastructure in a large-size fi- nancial organization. We determine whether common routing and switching IT network infrastructure, including its hardware and software components, have enough cryptographic agility to accommodate the change of cryptographic algorithms to the ones that do not exhibit vulnerability to quantum computing and to the ones that are compliant with National Security Agency (NSA) Suite B set of protocols. We pinpoint upstream or downstream impacts of a change in the encryption algorithms across various IT network infrastructure components in terms of effort required to accomplish this transition. This study is among the first studies that investigate quantum-resistance cryptographic from the hard- ware perspectives of routing and switching technologies using diffusion of innovation theory. The study integrates enterprise governance to meet the challenges presented by quantum-computing with a focus on cryptographic agility. We offer an enterprise architecture framework for assessing the dependencies, costs, and benefits of IT infrastructure migration plan to meet the future challenges of quantum-resistant cryptographic. The analysis in this research can be used by IT managers to pre-plan for an upgrade of routing and switching infrastructure, assist in estimating the effort s required to perform these upgrades, and assist in selecting a vendor of routing and switching equipment from the perspective of cryptographic agility. With today’s supercomputers, a computational task involved in breaking modern asymmetric cryptographic algorithms would be infeasible due to the required computational complexity. However, with progress in the devel- opment of quantum computing technology, firms are facing an increasing risk of potential security threats to existing encrypted data and secured transmission channels as the processing power of quantum com- puters continue to increase.
Keywords: Quantum-resistance | Cryptography | Routing and switching | Enterprise architecture | IT governance
An efficient code-based threshold ring signature scheme
یک طرح امضای حلقه آستانه مبتنی بر کد-2019
Code based cryptography is a powerful and a promising alternative to the number theoretic cryptography as it is supposed to resist to the quantum computer. The migration to the post quantum hypothetical world requires the design of all the most important cryptographic primitives such as signature schemes. For this reason, we propose in this paper a ring signature and a threshold ring signature schemes that are based on coding theory assumptions. In the case of threshold ring signature scheme, we have a subset of users belonging to a group of users called a ring who collaborate in order to produce a signature on behalf of ring members while maintaining the anonymity of the identity of the real signers. In this paper, we use the AGS identification scheme, which is a coding theory zero-knowledge authentication protocol, to produce a ring signature and a threshold ring signature. By comparing our schemes with the best one in performance so far on 80 bits security level, we find an average 86% reduction of the signature size and also a reduction on the public key length at equivalent security level. The reduction is achieved for different values of the number of signers and ring’s size.
Keywords: Ring signature | Threshold ring signature | Code-based cryptography | AGS identification scheme | Post-quantum | Zero-knowledge
IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning
سیستم تشخیص نفوذ و استخراج ویژگی داده های اینترنت اشیا برای شهرهای هوشمند بر اساس یادگیری مهاجرتی عمیق-2019
With the development of information technology and economic growth, the Internet of Things (IoT) industry has also entered the fast lane of development. The IoT industry system has also gradually improved, forming a complete industrial foundation, including chips, electronic components, equipment, software, integrated systems, IoT services, and telecom operators. In the event of selective forwarding attacks, virus damage, malicious virus intrusion, etc., the losses caused by such security problems are more serious than those of traditional networks, which are not only network information materials, but also physical objects. The limitations of sensor node resources in the Internet of Things, the complexity of networking, and the open wireless broadcast communication characteristics make it vulnerable to attacks. Intrusion Detection System (IDS) helps identify anomalies in the network and takes the necessary countermeasures to ensure the safe and reliable operation of IoT applications. This paper proposes an IoT feature extraction and intrusion detection algorithm for intelligent city based on deep migration learning model, which combines deep learning model with intrusion detection technology. According to the existing literature and algorithms, this paper introduces the modeling scheme of migration learning model and data feature extraction. In the experimental part, KDD CUP 99 was selected as the experimental data set, and 10% of the data was used as training data. At the same time, the proposed algorithm is compared with the existing algorithms. The experimental results show that the proposed algorithm has shorter detection time and higher detection efficiency.
Keywords: Deep learning | Migration learning model | Sensor network | Smart City | Internet of things | Information feature extraction | Intrusion detection | machine learning
Rural entrepreneurship and migration
کارآفرینی روستایی و مهاجرت-2019
Using data for U.S. rural counties we examine the how the age profile of migrants from 1990 to 2000 impacts business start-ups in 2000. Depending on the industry classification, we find that younger and older migrants tend to have the largest impacts on rural business start-ups. The impacts tend to be larger for older migrants than younger. This result, which is consistent with the findings of the meta analysis of Akgün et al. (2011), has strong policy implications: from an entrepreneurial perspective, the loss of younger adults is likely out-weighed by the “retirement migration” of older persons. Rural communities should not overlook the in-migration of people who are either pre-retirement age or retirees when pursuing entrepreneurship strategies.
Keywords: Rural | Migration | Entrepreneurship | Economic developmentJEL classification:L26 | O15 | R11
Error assessment and optimal cross-validation approaches in machine learning applied to impurity diffusion
ارزیابی خطا و رویکردهای بهینه سازی متقابل بهینه در یادگیری ماشین اعمال شده برای انتشار ناخالصی-2019
Machine learning models have been widely utilized in materials science to discover trends in existing data and then make predictions to generate large databases, providing powerful tools for accelerating materials discovery and design. However, there is a significant need to refine approaches both for developing the best models and assessing the uncertainty in their predictions. In this work, we evaluate the performance of Gaussian kernel ridge regression (GKRR) and Gaussian process regression (GPR) for modeling ab-initio predicted impurity diffusion activation energies, using a database with 15 pure metal hosts and 408 host-impurity pairs. We demonstrate the advantages of basing the feature selection on minimizing the Leave-Group-Out (LOG) cross-validation (CV) root mean squared error (RMSE) instead of the more commonly used random K-fold CV RMSE. For the best descriptor and hyperparameter sets, the LOG RMSE from the GKRR (GPR) model is only 0.148 eV (0.155 eV) and the corresponding 5-fold RMSE is 0.116 eV (0.129 eV), demonstrating the model can effectively predict diffusion activation energies. We also show that the ab-initio impurity migration barrier can be employed as a feature to increase the accuracy of the model significantly while still yielding a significant speedup in the ability to predict the activation energy of new systems. Finally, we define r as the magnitude of the ratio of the actual error (residual) in a left-out data point during CV to the predicted standard deviation for that same data point in the GPR model, and compare the distribution of r to a normal distribution. Deviations of r from a normal distribution can be used to quantify the accuracy of the machine learning error estimates, and our results generally show that the approach yields accurate, normally-distributed error estimates for this diffusion data set.
Keywords: Machine learning | Diffusion | Gaussian process | Error assessment
A Joint Power Efficient Server and Network Consolidation approach for virtualized data centers
یک سرور توانی کارآمد مشترک و دیدگاه یکپارچه سازی شبکه برای مراکز داده ای مجازی-2018
Cloud computing and virtualization are enabling technologies for designing energy-aware resource management mechanisms in virtualized data centers. Indeed, one of the main challenges of big data centers is to decrease the power consumption, both to cut costs and to reduce the environmental impact. To this extent, Virtual Machine (VM) consolidation is often used to smartly reallocate the VMs with the objective of reducing the power consumption, by exploiting the VM live migration. The consolidation problem consists in finding the set of migrations that allow to keep turned on the minimum number of servers needed to host all the VMs. However, most of the proposed consolidation approaches do not consider the network related consumption, which represents about 10–20% of the total energy consumed by IT equipment in real data centers. This paper proposes a novel joint server and network consolidation model that takes into account the power efficiency of both the switches forwarding the traffic and the servers hosting the VMs. It powers down switch ports and routes traffic along the most energy efficient path towards the least energy consuming server under QoS constraints. Since the model is complex, a fast Simulated Annealing based Resource Consolidation algorithm (SARC) is proposed. Our numerical results demonstrate that our approach is able to save on average 50% of the network related power consumption compared to a network unaware consolidation.
keywords: Cloud| Virtualization| Power| Green computing| Simulated annealing
Practices of conformity and transgression in an out-of-school reading programme for ‘at risk’ children
روشهای تایید و سرپیچی در یک برنامه آموزشی خارج از مدرسه برای بچه های پرخطر-2018
A large body of research has demonstrated that the plurilingualisms and pluriliteracies that children and youth bring to classrooms are often not those required for school success. This is even more so for students from underprivileged backgrounds, a demographic where children and youth with family backgrounds of immigration are over-represented. This article reports on ethnographic research at an after-school reading programme for primary school children considered to be at risk of school failure in the old town of Barcelona. Results suggest that the practices of pluriliteracy supported by the programme often conform with those inherent to the childrens formal education; that is, with the very practices that have contributed to the children being placed in the programme to begin with. However, through the fine-grained analysis of child–volunteer interactions, certain practices that subtly transgress these norms are identified. It is in such practices that we see potential for educational transformation.
keywords: Plurilingualism| Literacies| Translanguaging| Non-formal education| Children| Collaborative research