bluuebunny/crossref_metadata_2025 huggingface dataset:
Palliative medicine education—Bed Race, The End of Life Board Game in undergraduates
Authors: Harris Dylan G , Atkinson Clea
Published: September 2021

Background

Educational board games facilitate active learning to conceptualise knowledge, and, promote collaborative learning and team work. Despite increasing interest in them, use in palliative and end of life care has been very limited to date.

Method

In ‘Bed race, The End of Life Game’, participants are divided into four teams who move a model hospital bed around a board to collect items (syringe driver; Do Not Attempt Resuscitation form; oral hydration gel; a ‘heart’; Just In Case medicines). To obtain items at themed ‘checkpoints’, each team needs to answer quiz questions, which require application of clinical knowledge and/or communication skills. Pregame and postgame quiz scores and feedback were collected from 12 game sessions involving 251 year 5 medical students.

Results

169 (67%) of students completed pregame and postgame anonymous quiz questions and free-text feedback. Postgame quiz scores were higher for each topic, and the difference in the paired pregame and postgame questionnaires was statistically significant (p<0.05). Themes from the free-text feedback included ‘engaging and fun’; ‘relevant learning’; ‘peer learning and team work’.

Conclusions

Educational board games are not a new panacea for education, but the concept can be successfully applied in palliative care.
Late complication rates after aortic coarctation repair in patients with or without a bicuspid aortic valve
Authors: Lim Michelle S , Cordina Rachael , Kotchetkova Irina , Celermajer David S
Published: September 2021

Objective

Patients with previously repaired aortic coarctation (CoA) are at risk of developing late surgical complications. Many patients with CoA also have a bicuspid aortic valve (BAV). We sought to determine in patients with repaired CoA whether the presence of BAV is associated with more cardiovascular reinterventions during follow-up.

Methods

Adults with previously repaired simple CoA were recruited from our Adult Congenital Heart Disease database (Sydney, Australia). The incidence of complications relating to the ‘CoA-site’ (descending aortic aneurysm or dissection, or recoarctation) and the ‘AV/AscAo’ (aortic valve or ascending aortic pathology) that required intervention was compared between those with BAV (‘CoA-BAV’) and without BAV (‘CoA-only’).

Results

Of 146 patients with repaired CoA, 101 (69%) had BAV. Age at CoA repair was similar (median 6.0 (IQR 0.5–14.0) years vs 5.0 (IQR 0.5–11.0) years, p=0.44), as was the distribution of repair types, with end-to-end repair the most common in both groups (45.9% vs 45.6%). At a median of 28 years following initial repair, a significantly higher proportion of patients with CoA-BAV required cardiovascular reintervention (45.5% vs 20.0%, p=0.003). Whereas ‘CoA-site’ complications were more common than ‘AV/AscAo’ complications in patients with CoA only (13.3% and 0%, respectively), patients with CoA-BAV had a high prevalence of both ‘CoA-site’ as well as ‘AV/AscAo’ complications (19.8% and 21.8%, respectively). Overall survival was similar (p=0.42).

Conclusion

In adults with repaired CoA, patients with CoA-BAV are more than twice as likely to require cardiovascular reintervention by early-to-mid-adult life compared with those with CoA alone. Despite this, no difference in survival outcomes was observed.
Tuberculosis in times of COVID-19
Authors: Zimmer Alexandra Jaye , Klinton Joel Shyam , Oga-Omenka Charity , Heitkamp Petra , Nawina Nyirenda Carol , Furin Jennifer , Pai Madhukar
Published: September 2021

The COVID-19 pandemic has caused widespread disruptions to tuberculosis (TB) care and service delivery in 2020, setting back progress in the fight against TB by several years. As newer COVID-19 variants continue to devastate many low and middle-income countries in 2021, the extent of this setback is likely to increase. Despite these challenges, the TB community can draw on the comprehensive approaches used to manage COVID-19 to help restore progress and mitigate the impact of COVID-19 on TB. Our team developed the ‘Swiss Cheese Model for Ending TB’ to illustrate that it is only through multisectoral collaborations that address the personal, societal and health system layers of care that we will end TB. In this paper, we examine how COVID-19 has impacted the different layers of TB care presented in the model and explore how we can leverage some of the lessons and outcomes of the COVID-19 pandemic to strengthen the global TB response.
Synthesis, characterization, photoluminescence, and antibacterial activities of silica-graphene oxide composites
Authors: Khaleel Imad H. , Alkhafaji Abdulkareem A. , Miran Hussein A. , Jaf Zainab N.
Published: September 2021

In this research, a novel thin film Si-GO10 and nanopowders Si-GO30 of silica-graphene oxide (GO) composite were prepared via the sol–gel method and deposited on glass substrates using spray pyrolysis. X-ray diffraction (XRD) results showed a relatively strong peak in the graphite layer that corresponds to the (002) plane. Transmission electron microscope (TEM) images showed that SiO2 nanoparticles were randomly distributed on the surface of GO plates, and the particle size in these nanopowders was below 50 nm. Field emission scanning electron microscopy (FESEM) analysis demonstrated that silica nanoparticles on the surface of GO plates exhibited almost spherical and rod-like nanoparticle shape, which in turn confirmed the formation of SiO2–GO nano-hybrids. Photocatalytic investigations revealed that the composite materials exhibit high activity for dye adsorption and decomposition. Si-GO10 thin film did not undergo degradation after 120 min; however, for Si-GO30 nanopowder, the adsorption peak intensity was reduced to 665 nm, indicating a decrease in the dye concentration in the solution. Fourier transform infrared scan (FTIR) analysis demonstrated that carboxylic functional groups are decreased by increasing silica particles. Photoluminescence (PL) spectrum in Si-GO10 thin film showed a sharp emission peak at about 665 nm. This spectrum completely disappeared in Si-GO30 nanopowders. Results of the antibacterial properties emphasized that Si-GO30 nanoparticle would prevent Escherichia coli growth after 20 h. The presented methodology allows for the synthesis of GO supported silicon dioxide nanoparticles for promising applications in photocatalytic and antibacterial fields.
Image Scene Analysis Based on Improved FCN Model
Authors: Yang Weijie , Hui Yueting
Published: September 2021

Image scene analysis is to analyze image scene content through image semantic segmentation, which can identify the categories and positions of different objects in an image. However, due to the loss of spatial detail information, the accuracy of image scene analysis is often affected, resulting in rough edges of FCN, inconsistent class labels of target regions and missing small targets. To address these problems, this paper increases the receptive field, conducts multi-scale fusion and changes the weight of different sensitive channels, so as to improve the feature discrimination and maintain or restore spatial detail information. Furthermore, the deep neural network FCN is used to build the base model of semantic segmentation. The ASPP, data augmentation, SENet, decoder and global pooling are added to the baseline to optimize the model structure and improve the effect of semantic segmentation. Finally, the more accurate results of scene analysis are obtained.
Supporting Requirements to Code Traceability Creation by Code Comments
Authors: Shen Guohua , Wang Haijuan , Huang Zhiqiu , Yu YaoShen , Chen Kai
Published: September 2021

Requirements-to-code tracing is an important and costly task that creates trace links from requirements to source code. These trace links help engineers reduce the time and complexity of software maintenance. Code comments play an important role in software maintenance tasks. However, few studies have focused intensively on the impact of code comments on requirements-to-code trace links creation. Different types of comments have different purposes, so how different types of code comments provide different improvements for requirements-to-code trace links creation? We focus on learning whether code comments and different types of comments can improve the quality of trace links creation. This paper presents a study to evaluate the contribution of code comments and different types of code comments to the creation of trace links. More specifically, this paper first experimentally evaluates the impact of code comments on requirements-to-code trace links creation, and then divides code comments into six categories to evaluate its impact on trace links creation. The results show that the precision increases by an average of 15% (based on the same recall) after adding code comments (even for different trace links creation techniques), and the type of Purpose comments contributes more to the tracing task than the other five. This empirical study provides evidence that code comments are effective in tracing links creation, and different types of code comments contribute differently. Purpose comments can be used to improve the accuracy of requirements-to-code trace links creation.
A Survey on Problem Formulations and (Meta)Heuristic-Based Solutions in Automated Assembly of Parallel Test Forms
Authors: Ignjatović Miroslava M. , Bojić Dragan M. , Tartalja Igor I.
Published: September 2021

Parallel test forms are ubiquitous in the educational, aptitude, achievement, and licensure testing. The problem of automated assembling of parallel test forms has been extensively explored for almost 40 years. Many different mathematical models of the problem formulations and a plenty of different solutions have emerged over the last two decades, indicating that the problem has matured. However, its investigation is still challenging, especially today, when the importance of distance learning and remote knowledge testing is rapidly growing. The diversity of proposed approaches originated notably from the variety of scientific fields involved such as psychometrics, applied mathematics, operations research, and artificial intelligence. Majority of solutions of the problem are (meta)heuristics-based, since they consider the problem as a combinatorial optimization problem which is NP-hard. In this paper, a comprehensive review of this research field, referring to related works since 1985, is conducted. Problem formulations and solutions of the problem are separately classified. Possible avenues of future research are pointed out.
THE ROLE OF D-SUMMABLE INFORMATION DIMENSION IN DIFFERENTIATING COVID-19 DISEASE
Authors: RAMIREZ-ARELLANO ALDO , ORTIZ-VILCHIS PILAR , BORY-REYES JUAN
Published: September 2021

The current COVID-19 pandemic mainly affects the upper respiratory tract. People with COVID-19 report a wide range of symptoms, some of which are similar to those of common flu, such as sore throat and rhinorrhea. Additionally, COVID-19 shares many clinical symptoms with severe pneumonia, including fever, fatigue, dry cough, and respiratory distress. Several diagnostic strategies, such as the real-time polymerase chain reaction technique and computed tomography imaging, which are more costly than chest radiography, are employed as diagnostic tools. The purpose of this paper is to describe the role of the d-summable information dimension of X-ray images in differentiating several lesions and lung illnesses better than both fractal and information dimensions. The statistical analysis shows that the d-summable information dimension model better describes the information obtained from the X-ray images. Therefore, it is a more precise measure of complexity than the information and box-counting dimension. The results also show that the X-ray images of COVID-19 pneumonia reveal greater damage than those of tuberculosis, pneumonia, and various lung lesions, where the damage is minor or much focused. Because the d-summable information dimension increases as the image complexity decreases, it could pave the way to formulate a new measure to quantify the lung damage and assist the clinical diagnosis based on the area under the d-summable information model. In addition, the physical meaning of the [Formula: see text] parameter in the d-summable information dimension is given.
OPTIMIZATION OF HEAT CONDUCTION FOR TREELIKE NETWORK WITH ARBITRARY CROSS-SECTIONAL SHAPE
Authors: LIU FEI , JING DALEI
Published: September 2021

To minimize the thermal resistance of the fractal treelike heat conduction network and develop an optimization principle applicable for the network with an arbitrary cross-sectional shape, this paper first establishes a theoretical model regarding the total thermal resistance of the symmetric treelike network with arbitrary cross-sectional shapes and then studies the effects of the geometric and structural parameters of the network on its total thermal resistance. The numerical simulations are also performed to analyze the influences of the geometric and structural parameters of symmetric treelike networks with circular, rectangular and triangular cross-sectional shapes on the total thermal resistance. Both the theoretical model and the numerical simulation show that the total thermal resistance of the network with an arbitrary cross-sectional shape first decreases and then increases with increasing cross-sectional area ratio but always increases with increasing length ratio of branches at two successive branching levels when the total branch volume is constant. When the cross-sectional area ratio is equal to the reciprocal of the branching number, the treelike network has the minimum total thermal resistance. This scaling law is applicable for the treelike network with an arbitrary cross-sectional shape to achieve the minimum total thermal resistance.
NETWORK COHERENCE ANALYSIS ON A FAMILY OF NESTED WEIGHTED n-POLYGON NETWORKS
Authors: LIU JIA-BAO , BAO YAN , ZHENG WU-TING , HAYAT SAKANDER
Published: September 2021

In this paper, we propose a family of nested weighted [Formula: see text]-polygon networks, which is a kind of promotion of infinite fractal dimension networks. We study the coherence of the networks with recursive features that contain the initial states dominated by a weighted parameter. The network coherence is a consensus problem with additive noises, and it is known that coherence is defined by the eigenvalues of the Laplacian matrix. According to the structure of recursive nested model, we get the recursive expressions of Laplacian eigenvalues and further derive the exact results of first- and second-order coherence. Finally, we investigate the influential impacts on the coherence for different large parameters and discuss the relationship between Laplacian energy and network coherence. Furthermore, we obtain the expressions of Kirchhoff index, mean first-passage time and average path length of the networks.
STRANGE NONCHAOTIC ATTRACTORS IN A QUASIPERIODICALLY FORCED ARTICULATED MOORING TOWER MODEL
Authors: SHEN YUNZHU , ZHANG YONGXIANG , XU HUIDONG
Published: September 2021

Strange nonchaotic attractors (SNAs) are investigated in a quasiperiodically forced piecewise smooth articulated mooring tower model. The smooth torus becomes wrinkled and loses its continuity considerably with the change of the control parameter. Then SNAs emerged due to the fractal property of wrinkled torus. SNAs are identified by the largest Lyapunov exponent, phase diagrams and phase sensitivity exponents. Fractal (strange) properties of SNAs are further explored by the rational approximation, power spectra and spectral distribution function, recurrence analysis, the largest Lyapunov exponent and its variance.
Strategic Study for Managing the Portfolio of IT Courses Offered by a Corporate Training Company: An Approach in the Light of the ELECTRE-MOr Multicriteria Hybrid Method
Authors: de Araújo Costa Igor Pinheiro , Moreira Miguel Ângelo Lellis , de Araújo Costa Arthur Pinheiro , de Souza de Barros Teixeira Luiz Frederico Horácio , Gomes Carlos Francisco Simões , Santos Marcos Dos
Published: September 2021

The globalization of business and the consequent exposure to global competition, besides the economic and social changes caused by the COVID-19 pandemic made the Training & Development (T&D) sector increasingly important for professionals in the corporate environment. In this sense, managing stakeholders and a portfolio of clients, as well as analyzing the relationship between customer and service, are necessary and strategic for the success of professional training organizations. This paper aims to support the strategic process of portfolio formation of T&D courses offered by a company in the Information Technology (IT) training sector in Brazil, through the application of the ELECTRE-MOr multicriteria sorting method. We have obtained a categorization of several courses, aiming to define which ones should be prioritized, maintained, or discarded by the company’s management. The results showed that, among the analyzed courses, only 17% should be prioritized, 61% maintained, and 22% discarded by the company.
Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model
Authors: Iwendi Celestine , Ibeke Ebuka , Eggoni Harshini , Velagala Sreerajavenkatareddy , Srivastava Gautam
Published: September 2021

The creation of digital marketing has enabled companies to adopt personalized item recommendations for their customers. This process keeps them ahead of the competition. One of the techniques used in item recommendation is known as item-based recommendation system or item–item collaborative filtering. Presently, item recommendation is based completely on ratings like 1–5, which is not included in the comment section. In this context, users or customers express their feelings and thoughts about products or services. This paper proposes a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users’ reviews and comments, without disrupting it. We have implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with [Formula: see text] accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of [Formula: see text], precision at [Formula: see text], recall at [Formula: see text] and F1-Score at [Formula: see text]. Our model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall. Results of this experiment indicate that the similarity recommendation algorithm performs better than the conventional algorithm and enhances recommendation accuracy.
Geometry of the Wiman–Edge monodromy
Authors: Stover Matthew
Published: September 2021

The Wiman–Edge pencil is a pencil of genus 6 curves for which the generic member has automorphism group the alternating group [Formula: see text]. There is a unique smooth member, the Wiman sextic, with automorphism group the symmetric group [Formula: see text]. Farb and Looijenga proved that the monodromy of the Wiman–Edge pencil is commensurable with the Hilbert modular group [Formula: see text]. In this note, we give a complete description of the monodromy by congruence conditions modulo 4 and 5. The congruence condition modulo 4 is new, and this answers a question of Farb–Looijenga. We also show that the smooth resolution of the Baily–Borel compactification of the locally symmetric manifold associated with the monodromy is a projective surface of general type. Lastly, we give new information about the image of the period map for the pencil.
Affective Automotive User Interfaces–Reviewing the State of Driver Affect Research and Emotion Regulation in the Car
Authors: Braun Michael , Weber Florian , Alt Florian
Published: September 2021

Affective technology offers exciting opportunities to improve road safety by catering to human emotions. Modern car interiors enable the contactless detection of user states, paving the way for a systematic promotion of safe driver behavior through emotion regulation. We review the current literature regarding the impact of emotions on driver behavior and analyze the state of emotion regulation approaches in the car. We summarize challenges for affective interaction in the form of technological hurdles and methodological considerations, as well as opportunities to improve road safety by reinstating drivers into an emotionally balanced state. The purpose of this review is to outline the community’s combined knowledge for interested researchers, to provide a focussed introduction for practitioners, raise awareness for cultural aspects, and to identify future directions for affective interaction in the car.
Centralized, Distributed, and Everything in between
Authors: Dramé-Maigné Sophie , Laurent Maryline , Castillo Laurent , Ganem Hervé
Published: September 2021

The Internet of Things is taking hold in our everyday life. Regrettably, the security of IoT devices is often being overlooked. Among the vast array of security issues plaguing the emerging IoT, we decide to focus on access control, as privacy, trust, and other security properties cannot be achieved without controlled access. This article classifies IoT access control solutions from the literature according to their architecture (e.g., centralized, hierarchical, federated, distributed) and examines the suitability of each one for access control purposes. Our analysis concludes that important properties such as auditability and revocation are missing from many proposals while hierarchical and federated architectures are neglected by the community. Finally, we provide an architecture-based taxonomy and future research directions: a focus on hybrid architectures, usability, flexibility, privacy, and revocation schemes in serverless authorization.
RiSA: A Reinforced Systolic Array for Depthwise Convolutions and Embedded Tensor Reshaping
Authors: Cho Hyungmin
Published: September 2021

Depthwise convolutions are widely used in convolutional neural networks (CNNs) targeting mobile and embedded systems. Depthwise convolution layers reduce the computation loads and the number of parameters compared to the conventional convolution layers. Many deep neural network (DNN) accelerators adopt an architecture that exploits the high data-reuse factor of DNN computations, such as a systolic array. However, depthwise convolutions have low data-reuse factor and under-utilize the processing elements (PEs) in systolic arrays. In this paper, we present a DNN accelerator design called RiSA, which provides a novel mechanism that boosts the PE utilization for depthwise convolutions on a systolic array with minimal overheads.

In addition, the PEs in systolic arrays can be efficiently used only if the data items (
tensors
) are arranged in the desired layout. Typical DNN accelerators provide various types of PE interconnects or additional modules to flexibly rearrange the data items and manage data movements during DNN computations. RiSA provides a lightweight set of tensor management tasks within the PE array itself that eliminates the need for an additional module for tensor reshaping tasks. Using this embedded tensor reshaping, RiSA supports various DNN models, including convolutional neural networks and natural language processing models while maintaining a high area efficiency.

Compared to Eyeriss v2, RiSA improves the area and energy efficiency for MobileNet-V1 inference by 1.91× and 1.31×, respectively.
MARCO: A High-performance Task

M

apping

a

nd

R

outing

Co

-optimization Framework for Point-to-Point NoC-based Heterogeneous Computing Systems
Authors: Chen Hui , Zhang Zihao , Chen Peng , Luo Xiangzhong , Li Shiqing , Liu Weichen
Published: September 2021

Heterogeneous computing systems (HCSs), which consist of various processing elements (PEs) that vary in their processing ability, are usually facilitated by the network-on-chip (NoC) to interconnect its components. The emerging point-to-point NoCs which support single-cycle-multi-hop transmission, reduce or eliminate the latency dependence on distance, addressing the scalability concern raised by high latency for long-distance transmission and enlarging the design space of the routing algorithm to search the non-shortest paths. For such point-to-point NoC-based HCSs, resource management strategies which are managed by compilers, scheduler, or controllers, e.g., mapping and routing, are complicated for the following reasons: (i) Due to the heterogeneity, mapping and routing need to optimize computation and communication concurrently (for homogeneous computing systems, only communication). (ii) Conducting mapping and routing consecutively cannot minimize the schedule length in most cases since the PEs with high processing ability may locate in the crowded area and suffer from high resource contention overhead. (iii) Since changing the mapping selection of one task will reconstruct the whole routing design space, the exploration of mapping and routing design space is challenging. Therefore, in this work, we propose MARCO, the
m
apping
a
nd
r
outing
co
-optimization framework, to decrease the schedule length of applications on point-to-point NoC-based HCSs. Specifically, we revise the tabu search to explore the design space and evaluate the quality of mapping and routing. The advanced reinforcement learning (RL)algorithm, i.e., advantage actor-critic, is adopted to efficiently compute paths. We perform extensive experiments on various real applications, which demonstrates that the MARCO achieves a remarkable performance improvement in terms of schedule length (+44.94% ∼ +50.18%) when compared with the state-of-the-art mapping and routing co-optimization algorithm for homogeneous computing systems. We also compare MARCO with different combinations of state-of-the-art mapping and routing approaches.
Comparative Analysis and Enhancement of CFG-based Hardware-Assisted CFI Schemes
Authors: Tauner Stefan , Telesklav Mario
Published: September 2021

Subverting the flow of instructions (e.g., by use of code-reuse attacks) still poses a serious threat to the security of today’s systems. Various control flow integrity (CFI) schemes have been proposed as a powerful technique to detect and mitigate such attacks. In recent years, many hardware-assisted implementations of CFI enforcement based on control flow graphs (CFGs) have been presented by academia. Such approaches check whether control flow transfers follow the intended CFG by limiting the valid target addresses. However, these papers all target different platforms and were evaluated with different sets of benchmark applications, which makes quantitative comparisons hardly possible.

For this paper, we have implemented multiple promising CFG-based CFI schemes on a common platform comprising a RISC-V within FPGA. By porting almost 40 benchmark applications to this system we can present a meaningful comparison of the various techniques in terms of run-time performance, hardware utilization, and binary size. In addition, we present an enhanced CFI approach that is inspired by what we consider the best concepts and ideas of previously proposed mechanisms. We have made this approach more practical and feature-complete by tackling some problems largely ignored previously. We show with this fine-grained scheme that CFI can be achieved with even less overheads than previously demonstrated.
Heterogeneity-aware Multicore Synchronization for Intermittent Systems
Authors: Chen Wei-Ming , Kuo Tei-Wei , Hsiu Pi-Cheng
Published: September 2021

Intermittent systems
enable batteryless devices to operate through energy harvesting by leveraging the complementary characteristics of volatile (VM) and non-volatile memory (NVM). Unfortunately, alternate and frequent accesses to heterogeneous memories for accumulative execution across power cycles can significantly hinder computation progress. The progress impediment is mainly due to more CPU time being wasted for slow NVM accesses than for fast VM accesses. This paper explores how to leverage heterogeneous cores to mitigate the progress impediment caused by heterogeneous memories. In particular, a
delegable
and
adaptive synchronization
protocol is proposed to allow memory accesses to be delegated between cores and to dynamically adapt to diverse memory access latency. Moreover, our design guarantees
task serializability
across multiple cores and maintains
data consistency
despite frequent power failures. We integrated our design into FreeRTOS running on a Cypress device featuring heterogeneous dual cores and hybrid memories. Experimental results show that, compared to recent approaches that assume single-core intermittent systems, our design can improve computation progress at least 1.8x and even up to 33.9x by leveraging core heterogeneity.
Prepare:

P

owe

r

-Awar

e

A

p

proximate Re

a

l-time Task Scheduling for Ene

r

gy-Adaptiv

e

QoS Maximization
Authors: Chakraborty Shounak , Saha Sangeet , Själander Magnus , Mcdonald-Maier Klaus
Published: September 2021

Achieving high result-accuracy in approximate computing (AC) based real-time applications without violating power constraints of the underlying hardware is a challenging problem. Execution of such AC real-time tasks can be divided into the execution of the
mandatory part
to obtain a result of acceptable quality, followed by a partial/complete execution of the
optional part
to improve accuracy of the initially obtained result within the given time-limit. However, enhancing result-accuracy at the cost of increased execution length might lead to deadline violations with higher energy usage. We propose
Prepare
, a novel hybrid offline-online approximate real-time task-scheduling approach, that first schedules AC-based tasks and determines operational processing speeds for each individual task constrained by system-wide power limit, deadline, and task-dependency. At runtime, by employing fine-grained DVFS, the energy-adaptive processing speed governing mechanism of
Prepare
reduces processing speed during each last level cache miss induced stall and scales up the processing speed once the stall finishes to a higher value than the predetermined one. To ensure on-chip thermal safety, this higher processing speed is maintained only for a short time-span after each stall, however, this reduces execution times of the individual task and generates slacks.
Prepare
exploits the slacks either to enhance result-accuracy of the tasks, or to improve thermal and energy efficiency of the underlying hardware, or both. With a 70 - 80% workload,
Prepare
offers 75% result-accuracy with its constrained scheduling, which is enhanced by 5.3% for our benchmark based evaluation of the online energy-adaptive mechanism on a 4-core based homogeneous chip multi-processor, while meeting the deadline constraint. Overall, while maintaining runtime thermal safety,
Prepare
reduces peak temperature by up to 8.6 °C for our baseline system. Our empirical evaluation shows that constrained scheduling of
Prepare
outperforms a state-of-the-art scheduling policy, whereas our runtime energy-adaptive mechanism surpasses two current DVFS based thermal management techniques.
Intermittent-Aware Neural Architecture Search
Authors: Mendis Hashan Roshantha , Kang Chih-Kai , Hsiu Pi-cheng
Published: September 2021

The increasing paradigm shift towards
i
ntermittent computing has made it possible to intermittently execute
d
eep neural network (DNN) inference on edge devices powered by ambient energy. Recently,
n
eural architecture search (NAS) techniques have achieved great success in automatically finding DNNs with high accuracy and low inference latency on the deployed hardware. We make a key observation, where NAS attempts to improve inference latency by primarily maximizing data reuse, but the derived solutions when deployed on intermittently-powered systems may be inefficient, such that the inference may not satisfy an end-to-end latency requirement and, more seriously, they may be unsafe given an insufficient energy budget. This work proposes iNAS, which introduces intermittent execution behavior into NAS to find accurate network architectures with corresponding execution designs, which can safely and efficiently execute under intermittent power. An intermittent-aware execution design explorer is presented, which finds the right balance between data reuse and the costs related to intermittent inference, and incorporates a preservation design search space into NAS, while ensuring the power-cycle energy budget is not exceeded. To assess an intermittent execution design, an intermittent-aware abstract performance model is presented, which formulates the key costs related to progress preservation and recovery during intermittent inference. We implement iNAS on top of an existing NAS framework and evaluate their respective solutions found for various datasets, energy budgets and latency requirements, on a Texas Instruments device. Compared to those NAS solutions that can safely complete the inference, the iNAS solutions reduce the intermittent inference latency by 60% on average while achieving comparable accuracy, with an average 7% increase in search overhead.
Hardware Performance Counters: Ready-Made vs Tailor-Made
Authors: Kuruvila Abraham Peedikayil , Mahapatra Anushree , Karri Ramesh , Basu Kanad
Published: September 2021

Micro-architectural footprints can be used to distinguish one application from another. Most modern processors feature hardware performance counters to monitor the various micro-architectural events when an application is executing. These ready-made hardware performance counters can be used to create program fingerprints and have been shown to successfully differentiate between individual applications. In this paper, we demonstrate how ready-made hardware performance counters, due to their coarse-grain nature (low sampling rate and bundling of similar events, e.g., number of instructions instead of number of add instructions), are insufficient to this end. This observation motivates exploration of tailor-made hardware performance counters to capture fine-grain characteristics of the programs. As a case study, we evaluate both ready-made and tailor-made hardware performance counters using post-quantum cryptographic key encapsulation mechanism implementations. Machine learning models trained on tailor-made hardwareperformance counter streams demonstrate that they can uniquely identify the behavior of every post-quantum cryptographic key encapsulation mechanism algorithm with at least 98.99% accuracy.
HW-FlowQ: A Multi-Abstraction Level HW-CNN Co-design Quantization Methodology
Authors: Fasfous Nael , Vemparala Manoj Rohit , Frickenstein Alexander , Valpreda Emanuele , Salihu Driton , Doan Nguyen Anh Vu , Unger Christian , Nagaraja Naveen Shankar , Martina Maurizio , Stechele Walter
Published: September 2021

Model compression through quantization is commonly applied to convolutional neural networks (CNNs) deployed on compute and memory-constrained embedded platforms. Different layers of the CNN can have varying degrees of numerical precision for both weights and activations, resulting in a large search space. Together with the hardware (HW) design space, the challenge of finding the globally optimal HW-CNN combination for a given application becomes daunting. To this end, we propose HW-FlowQ, a systematic approach that enables the co-design of the target hardware platform and the compressed CNN model through quantization. The search space is viewed at three levels of abstraction, allowing for an iterative approach for narrowing down the solution space before reaching a high-fidelity CNN hardware modeling tool, capable of capturing the effects of mixed-precision quantization strategies on different hardware architectures (processing unit counts, memory levels, cost models, dataflows) and two types of computation engines (bit-parallel vectorized, bit-serial). To combine both worlds, a multi-objective non-dominated sorting genetic algorithm (NSGA-II) is leveraged to establish a Pareto-optimal set of quantization strategies for the target HW-metrics at each abstraction level. HW-FlowQ detects optima in a discrete search space and maximizes the task-related accuracy of the underlying CNN while minimizing hardware-related costs. The Pareto-front approach keeps the design space open to a range of non-dominated solutions before refining the design to a more detailed level of abstraction. With equivalent prediction accuracy, we improve the energy and latency by 20% and 45% respectively for ResNet56 compared to existing mixed-precision search methods.
Data Pattern Aware Reliability Enhancement Scheme for 3D Solid-State Drives
Authors: Nie Shiqiang , Wu Weiguo , Zhang Chi
Published: September 2021

3D charge-trap (CT) NAND flash-based SSD has been used widely for its large capacity, low cost per bit, and high endurance. One-shot program (OSP) scheme, as a variation of incremental step pulse programming (ISPP) scheme, has been employed to program data for CT flash, whose program unit is the Word-Line (WL) instead of the page. The existing program optimization schemes either make trade-offs among program latency and reliability by adjusting the program step voltageon demand; or remap the most error-prone cell states to others by re-encoding programmed data. However, the data pattern, which represents the ratio of 1s in data values, has not been thoroughly studied. In this paper, we observe that most small files do not contain uniform 1s and 0s among these common file types (i.e., image, audio, text, executable file), leading to programming WL cells in different states unevenly. Some cell states dominate over the WL, while others are not. Based on this observation, we propose a flexible reliability enhancement scheme based on the OSP scheme. This scheme programs the cells into different states with varied, i.e., these cells in one state, whose number is the largest in one WL, are programmed with a fine-grained(namely slow write). In contrast, the minority are programmed with a coarse-grained(namely fast write). So the reliability is improved due to averaging the major enhanced cells with the minor degraded cells without program latency overhead. A series of experiments have been conducted, and the results indicate that the proposed scheme achieves 34% read performance improvement and 16% lifetime elongation on average.
REPAIR: Control Flow Protection based on Register Pairing Updates for SW-Implemented HW Fault Tolerance
Authors: Sharif Uzair , Mueller-Gritschneder Daniel , Schlichtmann Ulf
Published: September 2021

Safety-critical embedded systems may either use specialized hardware or rely on Software-Implemented Hardware Fault Tolerance (SIHFT) to meet soft error resilience requirements. SIHFT has the advantage that it can be used with low-cost, off-the-shelf components such as standard Micro-Controller Units. For this, SIHFT methods apply redundancy in software computation and special checker codes to detect transient errors, so called soft errors, that either corrupt the data flow or the control flow of the software and may lead to Silent Data Corruption (SDC). So far, this is done by applying separate SIHFT methods for the data and control flow protection, which leads to large overheads in computation time.

This work in contrast presents REPAIR, a method that exploits the checks of the SIHFT data flow protection to also detect control flow errors as well, thereby, yielding higher SDC resilience with less computational overhead. For this, the data flow protection methods entail duplicating the computation with subsequent checks placed strategically throughout the program. These checks assure that the two redundant computation paths, which work on two different parts of the register file, yield the same result. By updating the pairing between the registers used in the primary computation path and the registers in the duplicated computation path using the REPAIR method, these checks also fail with high coverage when a control flow error, which leads to an illegal jumps, occurs. Extensive RTL fault injection simulations are carried out to accurately quantify soft error resilience while evaluating Mibench programs along with an embedded case-study running on an OpenRISC processor. Our method performs slightly better on average in terms of soft error resilience compared to the best state-of-the-art method but requiring significantly lower overheads. These results show that REPAIR is a valuable addition to the set of known SIHFT methods.
Algorithm-hardware Co-design of Attention Mechanism on FPGA Devices
Authors: Zhang Xinyi , Wu Yawen , Zhou Peipei , Tang Xulong , Hu Jingtong
Published: September 2021

Multi-head self-attention (attention mechanism) has been employed in a variety of fields such as machine translation, language modeling, and image processing due to its superiority in feature extraction and sequential data analysis. This is benefited from a large number of parameters and sophisticated model architecture behind the attention mechanism. To efficiently deploy attention mechanism on resource-constrained devices, existing works propose to reduce the model size by building a customized smaller model or compressing a big standard model. A customized smaller model is usually optimized for the specific task and needs effort in model parameters exploration. Model compression reduces model size without hurting the model architecture robustness, which can be efficiently applied to different tasks. The compressed weights in the model are usually regularly shaped (e.g. rectangle) but the dimension sizes vary (e.g. differs in rectangle height and width). Such compressed attention mechanism can be efficiently deployed on CPU/GPU platforms as their memory and computing resources can be flexibly assigned with demand. However, for Field Programmable Gate Arrays (FPGAs), the data buffer allocation and computing kernel are fixed at run time to achieve maximum energy efficiency. After compression, weights are much smaller and different in size, which leads to inefficient utilization of FPGA on-chip buffer. Moreover, the different weight heights and widths may lead to inefficient FPGA computing kernel execution. Due to the large number of weights in the attention mechanism, building a unique buffer and computing kernel for each compressed weight on FPGA is not feasible. In this work, we jointly consider the compression impact on buffer allocation and the required computing kernel during the attention mechanism compressing. A novel structural pruning method with memory footprint awareness is proposed and the associated accelerator on FPGA is designed. The experimental results show that our work can compress Transformer (an attention mechanism based model) by 95x. The developed accelerator can fully utilize the FPGA resource, processing the sparse attention mechanism with the run-time throughput performance of 1.87 Tops in ZCU102 FPGA.
Chauffeur: Benchmark Suite for Design and End-to-End Analysis of Self-Driving Vehicles on Embedded Systems
Authors: Maity Biswadip , Yi Saehanseul , Seo Dongjoo , Cheng Leming , Lim Sung-Soo , Kim Jong-Chan , Donyanavard Bryan , Dutt Nikil
Published: September 2021

Self-driving systems execute an ensemble of different self-driving workloads on embedded systems in an end-to-end manner, subject to functional and performance requirements. To enable exploration, optimization, and end-to-end evaluation on different embedded platforms, system designers critically need a benchmark suite that enables flexible and seamless configuration of self-driving scenarios, which realistically reflects real-world self-driving workloads’ unique characteristics. Existing CPU and GPU embedded benchmark suites typically (1) consider isolated applications, (2) are not sensor-driven, and (3) are unable to support emerging self-driving applications that simultaneously utilize CPUs and GPUs with stringent timing requirements. On the other hand, full-system self-driving simulators (e.g., AUTOWARE, APOLLO) focus on functional simulation, but lack the ability to evaluate the self-driving software stack on various embedded platforms. To address design needs, we present Chauffeur, the first open-source end-to-end benchmark suite for self-driving vehicles with configurable representative workloads. Chauffeur is easy to configure and run, enabling researchers to evaluate different platform configurations and explore alternative instantiations of the self-driving software pipeline. Chauffeur runs on diverse emerging platforms and exploits heterogeneous onboard resources. Our initial characterization of Chauffeur on different embedded platforms – NVIDIA Jetson TX2 and Drive PX2 – enables comparative evaluation of these GPU platforms in executing an end-to-end self-driving computational pipeline to assess the end-to-end response times on these emerging embedded platforms while also creating opportunities to create application gangs for better response times. Chauffeur enables researchers to benchmark representative self-driving workloads and flexibly compose them for different self-driving scenarios to explore end-to-end tradeoffs between design constraints, power budget, real-time performance requirements, and accuracy of applications.
COVID-19 Contact Tracing and Privacy: A Longitudinal Study of Public Opinion
Authors: Simko Lucy , Chang Jack , Jiang Maggie , Calo Ryan , Roesner Franziska , Kohno Tadayoshi
Published: September 2021

There is growing use of technology-enabled contact tracing, the process of identifying potentially infected COVID-19 patients by notifying all recent contacts of an infected person. Governments, technology companies, and research groups alike have been working towards releasing smartphone apps, using IoT devices, and distributing wearable technology to automatically track “close contacts” and identify prior contacts in the event an individual tests positive. However, there has been significant public discussion about the tensions between effective technology-based contact tracing and the privacy of individuals. To inform this discussion, we present the results of seven months of online surveys focused on contact tracing and privacy, each with 100 participants. Our first surveys were on April 1 and 3, 2020, before the first peak of the virus in the US, and we continued to conduct the surveys weekly for 10 weeks (through June), and then fortnightly through November, adding topical questions to reflect current discussions about contact tracing and COVID-19. Our results present the diversity of public opinion and can inform policy makers, technologists, researchers, and public health experts on whether and how to leverage technology to reduce the spread of COVID-19, while considering potential privacy concerns.
Gang Research in the Twenty-First Century
Authors: Moore Caylin Louis , Stuart Forrest
Published: September 2021

For nearly a century, gang scholarship has remained foundational to criminological theory and method. Twenty-first-century scholarship continues to refine and, in some cases, supplant long-held axioms about gang formation, organization, and behavior. Recent advances can be traced to shifts in the empirical social reality and conditions within which gangs exist and act. We draw out this relationship—between the ontological and epistemological—by identifying key macrostructural shifts that have transformed gang composition and behavior and, in turn, forced scholars to revise dominant theoretical frameworks and analytical approaches. These shifts include large-scale economic transformations, the expansion of punitive state interventions, the proliferation of the Internet and social media, intensified globalization, and the increasing presence of women and LGBTQ individuals in gangs and gang research. By introducing historically unprecedented conditions and actors, these developments provide novel opportunities to reconsider previous analyses of gang structure, violence, and other related objects of inquiry.