Resilience Techniques Research Articles

Understanding Islamophobia through the eyes of American Muslim children: Religious bullying and school social work interventions

AbstractIslamophobia and related incidents of hate‐crimes, religious discrimination and profiling are increasingly being researched to have adverse effects on Muslim adults and children alike. Rising episodes of anti‐Muslim religious bullying are being reported from schools, classrooms and playgrounds across the globe and, hence, need to be accounted for, recognized and mitigated. This study uses child‐participatory research methodology to understand experiences of American Muslim children regarding Islamophobia. It was conducted with 26 children participants (N = 26) studying in Grades 5–8, at an Islamic School in the United States. Drawings and semi‐structured focus group discussions were used for data collection. Findings revealed that Islamophobia should be characterized through a unique child‐centric lens, to recognize how it affects the lives of Muslim children. Narratives and silences of female participants highlighted how gendered Islamophobia has become so common for young Muslim girls that it has become ‘invisible.’ It was seen that children's perception of Islamophobia often becomes a projection of parental influences. Participants put forward resilience techniques, which pave ways for social work interventions with Muslim children. The study ends with a school social work model directed towards prevention and reduction of Islamophobia, which educators, schools and social workers can implement.

Online Virtual Machine Evacuation for Disaster Resilience in Inter-Data Center Networks

With the risk of natural disaster occurrence rising globally, the interest in innovative disaster resilience techniques is greatly increasing. In particular, Data Center (DC) operators are investigating techniques to avoid data-loss and service downtime in case of disaster occurrence. In cloud DC networks, DCs host Virtual Machines (VM) that support cloud services. A VM can be migrated, i.e., transferred, across DCs without service disruption, using a technique known as “online VM migration”. In this article, we investigate how to schedule online VMs migrations in an alerted disaster scenario (i.e., for those disasters, such as tsunami and hurricanes, that grant an alert time to DC operators) where VMs are migrated from a risky DC, i.e., a DC at risk to be affected by a disaster, to a DC in safe locations, within a deadline set by the alert time of the incoming disaster. We propose a multi-objective Integer Linear Programming (ILP) model and heuristic algorithms for efficient online VMs migration to maximize number of VMs migrated, minimize service downtime and minimize network resource occupation. The proposed approaches perform scheduling, destination DC selection and assign route and bandwidth to VM migrations. Compared to baseline approaches, our proposed algorithms eliminate service downtime in exchange of an acceptable additional network resource occupation. Results also give insights on how to calculate the minimum amount of time required to evacuate all VMs with no service downtime. Moreover, since the proposed approaches exhibit different execution times, we design an ‘alert-aware VM evacuation’ tool to intelligently select the most suitable approach based on the number and size of VMs, alert time and available network capacity.

Voltage Over-Scaling-Based Lightweight Authentication for IoT Security

It is a challenging task to deploy lightweight security protocols in resource-constrained IoT applications. A hardware-oriented lightweight authentication protocol based on device signature generated during voltage over-scaling (VOS) was recently proposed to address this issue. VOS-based authentication employs the computation unit such as adders to generate the process variation dependent error, which is combined with secret keys to create a two-factor authentication protocol. In this article, machine learning (ML)-based modeling attacks to break such authentication is presented. We also propose a <u>c</u>hallenge <u>s</u>elf-<u>o</u>bfuscation <u>s</u>tructure (CSoS) which employs previous challenges combined with keys or random numbers to obfuscate the current challenge for the VOS-based authentication to resist ML attacks. Experimental results show that ANN, RNN, and CMA-ES can clone the challenge-response behavior of VOS-based authentication with up to 99.65 percent prediction accuracy, while the prediction accuracy is less than 51.2 percent after deploying our proposed ML resilient technique. In addition, our proposed CSoS also shows good obfuscation ability for strong PUFs. Experimental results show that the modeling accuracy is below 54 percent when 10⁶ challenge-response pairs (CRPs) are collected to model the CSoS-based Arbiter PUF with ML attacks based on LR, SVM, ANN, RNN, and CMA-ES.

Resilient and Secure Hardware Devices Using ASL

Due to the globalization of Integrated Circuit (IC) design in the semiconductor industry and the outsourcing of chip manufacturing, Third-Party Intellectual Properties (3PIPs) become vulnerable to IP piracy, reverse engineering, counterfeit IC, and hardware trojans. To thwart such attacks, ICs can be protected using logic encryption techniques. However, strong resilient techniques incur significant overheads. Side-channel attacks (SCAs) further complicate matters by introducing potential attacks post fabrication. One of the most severe SCAs is power analysis (PA) attacks, in which an attacker can observe the power variations of the device and analyze them to extract the secret key. PA attacks can be mitigated via adding large extra hardware; however, the overheads of such solutions can render them impractical, especially when there are power and area constraints. All Spin Logic Device (ASLD) is one of the most promising spintronic devices due to its unique properties: small area, no spin-charge signal conversion, zero leakage current, non-volatile memory, high density, low operating voltage, and its compatibility with conventional CMOS technology. In this article, we extend the work in Reference [1] on the usage of ASLD to produce secure and resilient circuits that withstand IC attacks (during the fabrication) and PA attacks (after fabrication), including reverse engineering attacks. First, we show that ASLD has another unique feature: identical power dissipation through the switching operations, where such properties can be effectively used to prevent PA and IC attacks. We then evaluate the proposed ASLD-based on performance overheads and security guarantees.

An Error Compensation Technique for Low-Voltage DNN Accelerators

Reducing supply voltages of deep neural network (DNN) accelerators has been of particular interest since it can achieve high energy efficiency for mobile/edge applications. To ensure reliable DNN operations at low voltage, improving the timing error resilience of DNN accelerator is highly required. In this article, we present an error resilient technique to support low-voltage DNN operations by detecting and compensating erroneous computations using the proposed compensation multiply-accumulate (CMAC) unit. First, the timing errors are detected using Razor flip-flops at critical data-path, and erroneous computations are identified and dumped. Using additional multiplier data-path with flip-flops, the dropped computations are compensated in the next CMAC unit without additional clock-cycle penalty. Various bit-precisions of error compensations are analyzed to efficiently tradeoff DNN accuracy and hardware overhead. To improve the DNN accuracy even at low bit-precision of compensation, two types of rounding techniques are presented to effectively reflect the actual distribution of DNN computation results. The low-voltage DNN accelerator based on the proposed error compensation scheme has been implemented using 65-nm CMOS. Post-layout simulations show that the proposed DNN accelerator for ResNet-18 achieves about 47% and 24% energy savings compared with baseline and state-of-the-art error resilient DNN accelerators, respectively.

Geochemical evolution and tracing of groundwater salinization using different ionic ratios, multivariate statistical and geochemical modeling approaches in a typical semi-arid basin

The vulnerability of semi-arid basin aquifers to long-term salinization due to the dissolution of groundwater chemical constituents is a major global problem. Despite this, resilient techniques of tracing the sources of groundwater salinization in semi-arid basin aquifers are still evolving due to the aquifer complexities. This study proves the effectiveness of the use of different ionic ratios, multivariate statistical, and geochemical modeling approaches to understand groundwater evolution and trace salinization in the semi-arid Pru Basin of Ghana. The basin is homogeneously composed of argillaceous sediments of the Oti/Pendjari Group of the Voltaian Supergroup. A total of 81 samples from hand-dug wells and boreholes within the Pru Formation of the Oti/Pendjari Group in the basin were collected for this study. Quantitative analysis of the data shows that the abundance of major ions follows the order: Na+ > Ca2+ > Mg2+ > K+ and Cl− > HCO3− > SO42−. The groundwater evolved from Na-HCO3, Na-HCO3-Cl, Na-Ca-HCO3 to Na-Mg-HCO3 water types in a decreasing order of abundance. Calculated meteoric genesis index (r2) indicates the dominance of deep meteoric water percolation effects on groundwater chemistry. Groundwater chemistry is principally controlled by water-rock interaction, ion exchange reactions, weathering (carbonate and silicate), salinization, and anthropogenic activities. Different ionic ratio plots and spatial distribution maps reveal the prevalence of salinization in the aquifer system, especially around the southwestern part of the basin. Revelle index assessment of the groundwater salinization level indicates that about 19.8% of the groundwater samples with RI values >0.5 is influenced by salinization. The groundwater salinization results from saline water intrusion from adjacent aquifers, mixing effects, ion exchange reactions, water-rock interaction, and anthropogenic activities. The geochemical modeling involving thermodynamic calculation of mineral saturation indices in PHREEQC indicates that groundwater is largely saturated with respect to majority of the carbonate and silicate mineral phases.

An Interprofessional Course on Trauma-Informed Care.

Health care professionals need to recognize trauma exposure and provide trauma-informed care. There is a concomitant need to develop resilience when working in this context. We recognized the need to educate future health care professionals to provide trauma-informed care, develop resilience skills, and collaborate with other disciplines to provide this care. We used a systematic instructional design process and an interprofessional approach to design and deliver the course. We utilized a range of resources and approached the course from micro, meso, and macro perspectives. Through purposeful design, we developed a course that was well-aligned with our objectives. Assessments provided documentation that students achieved the learning outcomes. The course educated future health care professionals on trauma-informed care. Students gained valuable experience that will help them contribute to interprofessional teams in the future. Students also practiced resilience techniques essential for health care professionals.

A Curriculum to Teach Resilience Skills to Medical Students During Clinical Training.

IntroductionBurnout in medical students is extensive and a critical issue. It is associated with increased rates of depression, suicide, and poor perception of the educational environment. Enhancing resilience, the ability to adapt well in the face of adversity, is a potential tool to mitigate burnout and improve medical student wellness.MethodsOur resilience curriculum consisted of facilitated workshops to cultivate resilience in medical students during their core clerkship rotations. This curriculum served as an introduction to the concept of resilience and taught skills to cultivate resilience and promote wellness. The sessions allowed for identification of and reflection on stressors in the clinical learning environment, including straining team dynamics, disappointment, and uncertainty. Educational sessions included resilience skill-building exercises for managing expectations, letting go of negative emotions, dealing with setbacks, and finding meaning in daily work. Associated materials included lesson plans for small-group facilitators, learner pre- and postcurriculum surveys, and a social media activity guide.ResultsThis curriculum was delivered to 144 clerkship students at two academic institutions over the 2017–2018 academic year. Sessions were well received by medical students, with the majority of students stating that the sessions should continue. The majority of attendees found the sessions valuable and learned new ways to approach challenges.DiscussionStudents valued connecting with peers and feeling less alone through their participation. A challenge was constructing a setting conducive to comfortable reflection for all learners. Not all students found these sessions necessary. Sessions may have improved resilience levels.

Task failure resilience technique for improving the performance of MapReduce in Hadoop

Task failure resilience technique for improving the performance of MapReduce in Hadoop

Mecanismos de enfrentamento na saúde mental dos trabalhadores do CTI oncopediátrico: revisão integrativa

Os riscos psicossociais entre os trabalhadores de saúde têm sido alvo de pesquisas nacionais e internacionais. Os trabalhadores de terapia intensiva oncopediátrica lidam constantemente com situações-limite, tornando-os vulneráveis ao esgotamento emocional e a possíveis mecanismos de enfrentamento. Objetivo: Identificar produções que visam a valorização da qualidade de vida do trabalhador de terapia intensiva oncopediátrica, assim como intervenções que promovem sua saúde mental. Metodologia: Revisão integrativa realizada em agosto de 2019 através da busca por artigos originais e disponíveis, publicados nos últimos cinco anos, e que tenham como tema a valorização da qualidade de vida de trabalhadores de saúde com alta demanda emocional no ambiente de terapia intensiva oncopediátrica. Realizou-se pesquisa on-line no portal BVS e bases de dados: PubMed e CAPES. Resultados: A amostra resultou em nove publicações das quais apenas uma avalia o risco ocupacional sofrido em terapia intensiva pediátrica, enquanto as demais relatam a presença de sentimentos de carga emocional negativa e mecanismos de enfrentamento adotados pelos profissionais de saúde. São destacadas inúmeras estratégias como espiritualidade, resiliência, técnicas de apoio individual e coletivo. Conclusão: A assistência contínua aos que estão em sofrimento suscita sentimentos que influenciam a vida dos profissionais como um todo, tornando-os vulneráveis aos riscos psicossociais. Aprimorar a capacitação do trabalhador em lidar com situações de sofrimento de alto impacto emocional com equanimidade e compaixão é uma iniciativa que promove a saúde mental e, também, favorece o cuidado humanizado eficaz.

On Resilience in Cloud Computing

Cloud infrastructures are highly favoured as a computing delivery model worldwide, creating a strong societal dependence. It is therefore vital to enhance their resilience, providing persistent service delivery under a variety of conditions. Cloud environments are highly complex and continuously evolving. Additionally, the plethora of use-cases ensures requirements for persistent service delivery vary. As a contribution to knowledge, this work surveys resilience techniques for cloud environments. We apply a novel perspective using a layered model of traditional and emerging cloud paradigms. Works are then classified according to the Resilinets model. For each layer, the most common techniques with limitations are derived including an actor’s strength in influencing resilience in the cloud with each technique. We conclude with some future challenges to the field of resilient cloud computing.

CoREC

The dramatic increase in the scale of current and planned high-end HPC systems is leading new challenges, such as the growing costs of data movement and IO, and the reduced mean time between failures (MTBF) of system components. In-situ workflows, i.e., executing the entire application workflows on the HPC system, have emerged as an attractive approach to address data-related challenges by moving computations closer to the data, and staging-based frameworks have been effectively used to support in-situ workflows at scale. However, the resilience of these staging-based solutions has not been addressed, and they remain susceptible to expensive data failures. Furthermore, naive use of data resilience techniques such as n-way replication and erasure codes can impact latency and/or result in significant storage overheads. In this article, we present CoREC, a scalable and resilient in-memory data staging runtime for large-scale in-situ workflows. CoREC uses a novel hybrid approach that combines dynamic replication with erasure coding based on data access patterns. It also leverages multiple levels of replications and erasure coding to support diverse data resiliency requirements. Furthermore, the article presents optimizations for load balancing and conflict-avoiding encoding, and a low overhead, lazy data recovery scheme. We have implemented the CoREC runtime and have deployed with the DataSpaces staging service on leadership class computing machines and present an experimental evaluation in the article. The experiments demonstrate that CoREC can tolerate in-memory data failures while maintaining low latency and sustaining high overall storage efficiency at large scales.

Testing the blade resilient asynchronous template

As VLSI design moves into ultra-deep-submicron technologies, timing margins added to the clock period are mandatory, to ensure correct circuit behavior under worst-case conditions. Timing resilient architectures emerged as a promising solution to alleviate these worst-case timing margins. These architectures allow improving system performance and reducing energy consumption. Asynchronous systems, on the other hand, have the potential to improve energy efficiency and performance. Blade is an asynchronous timing resilient template that leverages the advantages of both asynchronous and timing resilient techniques. However, Blade still presents challenges regarding its testability, which hinders its commercial or large-scale application. This paper demonstrates that scan chains can be prohibitive for Blade due to their high silicon costs., which can reach more than 100%. Then, it proposes an alternative test approach that allows concurrent testing, stuck-at, and delay testing. The test approach is based on the reuse the Blade features to provide testability, with silicon area overheads between 4 and 7%.

A resilient and secure two-stage ITA and blockchain mechanism in mobile crowd sourcing

Mobile crowd sourcing offers a way for an organization to gain afar their “base of minds” of workforce offers in the form of idea competitions or innovation contests where task is divided among individuals to achieve a pooled result using mobile devices. However, mobile crowd sourcing consequence to an urban bias in addition to other key challenging issues such as safety, privacy and connectivity or data fault tolerance. Fault tolerance in mobile crowd sourcing is a property to tolerate any fault related to data collected or the working of the system. Further, it is needed to ensure a secure resilient mechanism due to de-centralized behavior of the network. In this paper, we have proposed a resilient-improved two-stage auction (ITA) and blockchain mechanism in mobile crowd sourcing beneficial for the organizations. The resilient technique and secure scheme is beneficial for the organizations that select the workers according to their budget and time. Further, an akka API tool is implemented upon already existing mobile crowd sourcing technique i.e. ITA. The proposed resilient mechanism is initially implemented over. The proposed resilient ITA mechanism is analyzed over further crowd sourcing mechanisms needed for workers selection in order to benefit the organizations. Further, the resilient-ITA is measured against database connection loss that is considered as a promising type of fault occurring during the workers selection.

Secure Energy Efficient Attack Resilient Routing Technique for Zone based Wireless Sensor Network

Security and Energy efficiency are two key factors to be contemplated in the design of applications based on wireless sensor networks (WSNs). Optimization of energy consumption is obligatory for an increased life time of the network. Without security, attackers can disrupt the entire operation of sensor network by instigating diverse attacks like message tampering, message dropping either in partial or whole and message flooding etc. This work proposes a secure energy efficient attack resilient routing technique for zone based wireless sensor network with proactive detection of malicious zones and mitigation from the attacks. Different from earlier works on detecting each malicious node, this work cleaves the network to zones and allots a probabilistic fuzzy score to model the success ratio of packet propagation through the zone. The routing is adaptive to ongoing residual energy and security risks. A Firm decision cannot be made on the frameworks influencing the life time of the network considering it may influence the operations of network. Experimentation of the proposed solution is done in NS2 and contrasted with the existing solutions to prove the effectiveness of the approach.

Self-Care and Resilience Techniques to Combat Diabetes Care and Education Specialist Burnout

Self-Care and Resilience Techniques to Combat Diabetes Care and Education Specialist Burnout

A survey on modeling and improving reliability of DNN algorithms and accelerators

Abstract As DNNs become increasingly common in mission-critical applications, ensuring their reliable operation has become crucial. Conventional resilience techniques fail to account for the unique characteristics of DNN algorithms/accelerators, and hence, they are infeasible or ineffective. In this paper, we present a survey of techniques for studying and optimizing the reliability of DNN accelerators and architectures. The reliability issues we cover include soft/hard errors arising due to process variation, voltage scaling, timing errors, DRAM errors due to refresh rate scaling and thermal effects, etc. We organize the research projects on several categories to bring out their key attributes. This paper underscores the importance of designing for reliability as the first principle, and not merely retrofit for it.

Bounded perturbation resilience and superiorization techniques for a modified proximal gradient method

ABSTRACTIn this article, we combine the viscosity approximation method and proximal operator to propose modified proximal gradient methods for solving the unconstrained convex optimization problems. The bounded perturbation resilience of these methods is investigated in the general Hilbert space H. Under reasonable parameter conditions, we prove that our algorithms strongly converge to the unique solution of a variational inequality problem. Our results improve and extend the corresponding results reported by many authors recently.

A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis

Nanoscale CMOS technology has encountered severe reliability issues especially in on-chip memory. Conventional word-level error resilience techniques such as Error Correcting Codes (ECC) suffer from high physical overhead and inability to correct increasingly reported multiple bit flip errors. On the other hands, state-of-the-art applications such as image processing and machine learning loosen the requirement on the levels of data protection, which result in dedicated techniques of approximated fault tolerance. In this work, we introduce a novel error protection scheme for memory, based on feature extraction through Principal Component Analysis and the modular-wise technique to segment the data before PCA. The extracted features can be protected by replacing the fault vector with the averaged confinement vectors. This approach confines the errors with either single or multi-bit flips for generic data blocks, whilst achieving significant savings on execution time and memory usage compared to traditional ECC techniques. Experimental results of image processing demonstrate that the proposed technique results in a reconstructed image with PSNR over 30 dB, while robust against both single bit and multiple bit flip errors, with reduced memory storage to just 22.4% compared to the conventional ECC-based technique.

Sensitivity-Based Error Resilient Techniques With Heterogeneous Multiply–Accumulate Unit for Voltage Scalable Deep Neural Network Accelerators

With inherent algorithmic error resilience of deep neural networks (DNNs), supply voltage scaling could be a promising technique for energy efficient DNN accelerator design. In this paper, we present an error resilient technique to enable aggressive voltage scaling by exploiting the asymmetric error resilience (sensitivity) with respect to DNN layers, filters, and channels. First-order Taylor expansion is used to evaluate the filter/channel-level weight sensitivities of large scale DNNs which accurately approximates weight sensitivities from actual error injection simulations. We also present the heterogeneous multiply-accumulate (MAC) unit based design approach where some of the MAC units are designed larger with shorter critical path delays for robustness to aggressive voltage scaling while other MAC units are designed relatively smaller. The sensitivity variations among filter weights can be leveraged to design DNN accelerator such that the computations with more sensitive weights are assigned to more robust (larger) MAC units while the computations with less sensitive weights are assigned to less robust (smaller) MAC units. Using dynamic programming, the sizes of MAC units are selected to achieve best DNN accuracy under ISO area constraint. As a result, the proposed voltage scalable DNN accelerator can achieve 34% energy savings in post layout simulations using 65 nm CMOS process with ImageNet dataset using ResNet-18 compared to state-of-the-art timing error recovery technique.