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With the rapid development of Internet of Things (IoT) and next-generation communication technologies, resource-constrained mobile devices fail to meet the demand of resource-hungry and compute-intensive applications. To cope with this challenge, with the assistance of Mobile Edge Computing (MEC), offloading complex tasks from mobile devices to edge cloud servers or central cloud servers can reduce the computational burden of devices and improve the efficiency of task processing. However, it is difficult to obtain optimal offloading decisions by conventional heuristic optimization methods, because the decision-making problem is usually NP-hard. In addition, there are shortcomings in using intelligent decision-making methods, e.g., lack of training samples and poor ability of migration under different MEC environments. To this end, we propose a novel offloading algorithm named MR-DRO, consisting of a Meta-Reinforcement Learning (meta-RL) model, which improves the migration ability of the whole model, and a Deep Reinforcement Learning (DRL) model, which combines multiple parallel Deep Neural Networks (DNNs) to learn from historical task offloading scenarios. Simulation results demonstrate that our approach can effectively and efficiently generate near-optimal offloading decisions in IoT environments with edge and cloud collaboration, which further improves the computational performance and has strong portability when making offloading decisions.

As a significant part of portable robots, quadruped robots have high adaptability, high versatility, and high elements, which give incredible mobility and ecological versatility to build up a Rapidly-exploring Random Trees (RRT)-based movement organizer that accomplished jumping in reproduction with the Little Dog robot over amazingly unpleasant landscape. Boston Dynamics is to build up another type of unpleasant territory robots that catch the portability, self-governance and speed of living animals. Such robots will go in outside territory that is excessively steep, uneven, rough, wet, sloppy, and frigid for traditional vehicles. Little Dog is a quadruped robot that has 12 actuators, and a 36-dimensional state space; the assignment of bouncing includes differential constraints due to under actuation and engine limits, which makes movement arranging very testing. RRTs are notable for quick kinematic way arranging in high dimensional arrangement spaces within the sight of hindrances, be that as it may, the presentation of the essential RRTs calculation quickly debases with expansion of differential limitations and expanding dimensionality. To accelerate the arranging, changed the essential RRT calculation by (1) biasing the inquiry in task space, (2) actualizing the Reach ability-Guided RRTs, which progressively changes the inspecting area, and (3) by executing a movement crude which decreases the dimensionality of the issue. With presented alterations, the arranging of calculation successfully created designs, exceptionally unpleasant landscape. Short directions were exhibited to work open-circle on a genuine robot.

The technological innovation among a conscience as well as configured accessibility guidance conceptual model process has been combined mostly with digital technologies that generate integrated immediate transportation with adaptability shielding mechanism. The Intelligent as well as Adaptive Accessibility Benefits Interface updates and transmits roadway data collected by roadside sensors to a powerful guide known as a dynamic map (DM), which increases programmed portability control with high accuracy and reliability. To confirm the updating and establishment of roadway data to automatic motilities, we have built up a Wireless Electronic Traffic Mirror (WETM) that procures street data, perceives street items and moves acknowledgment data with a remote correspondence framework. Furthermore, to meet the security prerequisites, the AMAS should give the remote association most appropriate for portability. To give ideal remote availability, continuous remote channel checking, which quantifies the nature of remote channels with obstruction, is a significant assignment in the AMAS. The continuous peripheral distract surveillance implementation throughout combination with the generated WETM is mentioned in this report, and unambiguous confirmation including its exactness of every situation occurrence is revealed to explain the thinking beside how some the phenomenon including its pavement was improved.

Containers have emerged as the most promising lightweight virtualization technology in providing cloud services due to its flexible deployment, portability, and scalability especially in micro-services, smart vehicles, IoTs, and fog/edge computing. An important and vital role in cloud container services is played by the scheduler’s component to optimize performance and reduce cost due to the diverse nature of the workload and cloud resources. Despite the immense traction of containers in cloud computing, there is no comprehensive survey that covers container scheduling techniques. In this timely survey, we investigate the landscape of the state-of-the-art container scheduling techniques aiming to inspire more research work in this active area of research. The survey is structured around classifying the scheduling techniques into four categories based on the type of optimization algorithm employed to generate the schedule namely mathematical modeling, heuristics, meta-heuristics and machine learning. Then for each class of scheduling algorithms, we analyze and identify key benefits and pitfalls, together with key challenges of the available techniques based on the performance metrics. Finally, this paper highlights fertile future research opportunities to realize the full potential of the emergent container technology.

Coarse-grained FPGA overlays built around the runtime programmable DSP blocks in modern FPGAs can achieve high throughput and improved scalability compared to traditional overlays built without detailed consideration of FPGA architecture. These overlays can be mapped to using higher level compilers, achieving fast compilation, software-like programmability and run-time management, and high-level design abstraction. OpenCL allows programs running on a host computer to launch accelerator kernels which can be compiled at run-time for a specific architecture, thus enabling portability. However, prohibitive hardware compilation times in traditional design flows mean that the tools cannot effectively use just-in-time (JIT) compilation or runtime performance scaling on FPGAs. We present a methodology for runtime compilation of dataflow graphs expressed as OpenCL kernels onto coarse-grained overlays. The methodology benefits from the high level of abstraction afforded by using the OpenCL programming model, while the mapping to the overlay significantly reduces compilation and load times. Key characteristics of this work include highly performant DSP-optimized functional units that scale to large overlays on modern devices and the ability to perform automatic resource-aware kernel replication up to the size of the overlay. We demonstrate place and route times orders of magnitude better than traditional HLS flows, even when running on an embedded processor in the Xilinx Zynq.

The coronavirus pandemic abruptly halted all in-person clerkships, or clinical rotations, for clinical veterinary students across the United States. Online clerkships in radiology offered the opportunity to expand the student’s ability to interpret medical images but did not allow for the development of physical hands-on imaging skills recognized as core competencies in veterinary medicine. The present report highlights the value of providing veterinary students with a smartphone-associated Butterfly iQ point-of-care ultrasound during a 3-week self-driven virtual clerkship. During the virtual rotation, the student was able to develop the skills required to generate sufficient quality images using three horses residing on her property. The affordability, portability, ease of use of the Butterfly iQ and availability of animals made it possible to develop hands-on imaging skills when distance learning was required.

A huge variety of fields and industries depend upon cloud computing based microservice due to its high-performance capability. Also, the merit of container usage is enormous; it enable larger portability, easier and faster deployment and restricted overheads. However, the rapid evolution causes issues in terms of container automation and management, Till now, a number of research works has concentrated on solving the open issues in container automation and management. In fact, container resource allocation is the major key hole for cloud providers since it directly influences the resource consumption and system performance. In this manner, this paper introduces a new optimized container resource allocation model by proposing a new optimization concept. To make the possibility of optimal container resource allocation, a new hybridized algorithm is implanted; namely, Whale Random update assisted Lion Algorithm (WR-LA), which is the hybrid form of Lion Algorithm (LA) and Whale Optimization Algorithm (WOA) is introduced. Moreover, the solution of optimized resource allocation is made by considering objectives like Threshold Distance, Balanced Cluster Use, System Failure, and Total Network Distance, respectively. Finally, the performance of the proposed model is compared over other conventional models and proves its superiority.

Despite the constant efforts of coming up with new ideas and approaches, operating systems’ development has continued to be based on the same fundamental technologies of the 1970s, when the UNIX operating system and the C programming language were first developed. However, there are certain segments of the market, where companies and software engineers are fed up with C and its derivatives, and they are seeking something that promises more automation in the development of low-level systems. A classic layout of complex software, such as operating systems, usually involves a set of fine-tuned performance-optimized routines that are combined and controlled from an upper layer in a lightweight fashion. As the complexity grows, reliable operation, portability, and maintainability become a major concern. However, this can be tamed by abstracting away from the platform-dependent details by modelling the components and their relation on a higher level. Using a functional programming language combined with the technique of language embedding may be an answer to the question of how to implement such a solution. In this design, the component descriptions may be captured adequately by an embedded domain-specific language that compiles to a lower-level language but there also has to be mechanism for composition and thereby obtaining a complete working system out of them. The Flow language is introduced through a motivating example which is also featured in the thesis. It also describes the major elements in the resulting computation model, e.g. tasks, channels, and the execution of tasks. Though its focus is on the applicability of the approach in relation of arbitrary Haskell-embedded domain-specific language as that is one of the main concerns in the adaption among third-party developers. Thus, Feldspar (Functional Embedded Language for Digital Signal Processing and Parallelism) is used as prime example for a domain-specific language to be applied in conjunction with the Flow. During the work on the design and implementation of a coordination language framework for embedded domain-specific languages we briefly touch on the issue of executing specialized task graphs. In our model, we create a set of workers (executors) dynamically picked and executed DSL programs (tasks) as part of a dataflow graph. The number of workers matched the number of processing units of the given hardware in the ideal case. This last aspect may contribute to lowering the expectations from the supporting run-time environment, eventually making the compiled graphs standalone on top of the bare metal. However, during the preliminary performance tests, we observed that our first na¨ive stab at scheduling execution of graphs did not scale well for multiple workers – which is not surprising as relations between tasks induced by data dependencies in the dataflow graph are not respected. To provide guarantees for that, tasks are partitioned into pools, although the optimal organization of pools is hard to achieve automatically. Hence we decided to create another embedded domain-specific functional language to provide a way for the programmer to express heuristics on how dataflow graphs are scheduled, which lead us to the concept of “declarative scheduling”. Declarative scheduling allows the implementation of domain-specific scheduling constraints that helps to abstract away from the low-level scheduling details and rather focuses on the protocol implementation itself. Also, an extension is proposed to the above model, where a tuning for performance based on certain properties is enabled which shows how a practitioner may profit from describing the application on a higher level and leaving the details to an appropriate compiler.

Cloud service providers promote their new field programmable gate array (FPGA) infrastructure as a service (IaaS) as the new era of cloud product. This FPGA IaaS wraps virtualized compute resources with FPGA boards, e.g., Amazon AWS F1, and reserves acceleration capability for specific applications. Though this acceleration technique sounds promising, questions like real-world performance, best-fit scenarios, portability, etc., still need further clarification. In this paper, we present one of the first few empirical studies that take a close look at FPGA clouds from the tenants' perspective. We have conducted measurement studies on Amazon AWS, Alibaba, and Huawei clouds for over one year. The experimental results show that: (1) Tenants experience severe performance-cost anomaly on FPGA IaaS platforms; (2) The inter-communication performance in FPGA clouds is tightly constrained by hardware drivers, e.g., small optimization of DMA drivers for PCIe can harvest significant performance gain; (3) The virtualized FPGA clouds are far from mature, e.g., small-sized jobs can greatly degrade the performance of FPGA clouds due to underutilized PCIe bandwidth. Our study not only provides useful hints to help tenants with FPGA service selection, but also sheds some lights for cloud providers to improve the performance of FPGA clouds.

VPIC is a general purpose particle-in-cell simulation code for modeling plasma phenomena such as magnetic reconnection, fusion, solar weather, and laser-plasma interaction in three dimensions using large numbers of particles. VPIC's capacity in both fidelity and scale makes it particularly well-suited for plasma research on pre-exascale and exascale platforms. In this article, we demonstrate the unique challenges involved in preparing the VPIC code for operation at exascale, outlining important optimizations to make VPIC efficient on accelerators. Specifically, we show the work undertaken in adapting VPIC to exploit the portability-enabling framework Kokkos and highlight the enhancements to VPIC's modeling capabilities to achieve performance at exascale. We assess the achieved performance-portability trade-off through a suite of studies on nine different varieties of modern pre-exascale hardware. Our performance-portability study includes weak-scaling runs on three of the top ten TOP500 supercomputers, as well as a comparison of low-level system performance of hardware from four different vendors.

The rapid adoption of solid-state drives (SSDs) as a major storage component has been made possible thanks to their ability to export a standard block I/O interface to file system and application developers. Meanwhile, this high-level abstraction has been shown to limit the utilization of the devices and the performance of applications running on top of them. Indeed, many optimizations of performance-critical applications bypass the standard block interface and rely on low-level control over SSD internal processes. However, the need to directly manage the physical device significantly increases development complexity and cost, and reduces its portability. Thus, application developers must choose between two extreme options, either easy development or optimal performance, without a real possibility to balance between these two objectives. To bridge this gap, we propose a flexible storage interface that exports the SSD hardware in three levels of abstraction: as a raw flash media with its low-level details, as a group of functions to manage flash capacity, or as a configurable block device. This multi-level abstraction allows developers to choose the degree in which they desire to control the flash hardware in a manner that best suits the applications’ semantics and performance objectives. We demonstrate the usability of this new model with Prism-SSD—a prototype of this interface as a user-level library on the Open-Channel SSD platform. We use each of the interface’s three abstraction levels to modify the I/O module of three representative applications: a key-value cache system, a user-level file system, and a graph processing engine. Prism-SSD improves application performance by 5% to 27%, at varying development costs, between 200 and 3,500 lines of code.

Web services are moving toward a new emerging technology lead to the migration of SOAP to RESTful API, which is an Architectural Style that holds Lightweight, Stateless, Uniform Interface, etc., as its constraints. Various sources of clusters of resources, entity, database relations are access throughout the distributed environment across the internet. Generative Power of the RESTful API witnesses the emergence of many companies whose whole business process is based upon the building applications. Since the Syntactic essentials of RESTful Web Services are mainly concerned with the RESTful API, there is a need for evaluation of those essentials whether they are computable or not. The proposed work is carried out on taking resources as simple and effective using Primitive Recursive Function (PRF). Primitive Recursive Function (PRF) uses Turing Machine for REST API capability evaluation and the Service Invocation along with the Application Logic and AppState Logic in order to handle manageability of the RESTful resources via computability evaluation with or without security. To demonstrate the effectiveness of our evaluation process, we conduct a case study on the available REST web services using Primitive Recursive Resources (PRR). The results of our case study show that our evaluation process achieves greater portability, reliability, scalability, etc., which in turn results in high performance.

Graphical abstract, Highlights • A comprehensive analysis of modern object detection models • Study on models that can be used as detectors for defect detection applications in industry. • Study on YOLOv4 that can perform good defect detection with not much capital investment. • Analysis on the correlation between dataset, labeling and the data augmentation steps and accuracy and computations. • Analysis on the importance of correct data acquiring, augmentation and labeling in low cost applications. • Analysis of the rate of improvement of the mAP of defect detection and image classification systems in recent years. • Analysis of model compression and acceleration on embedded applications and smart factories., Background Over the last years Deep Learning has shown to yield remarkable results when compared to traditional computer vision algorithms, in a large variety of computer vision applications. The deeplearning models outperformed in both accuracy and processing time. Thus, once a deeplearning models won the Image Net Large Scale Visual Recognition Contest, it proved that this area of research is of great potential. Furthermore, these increases in recognition performance resulted in more applied research and thus, more applications where deeplearning is useful: one of which is defect detection (or visual defect detection). In the last few years, deeplearning models achieved higher and higher accuracy on the complex testing datasets used for benchmarking. This surge in accuracy and usage is also supported (besides swarms of researchers pouring into the race), by incremental breakthroughs in computing hardware: such as more powerful GPUs(Graphical processing units), CPUs(central processing units) and better computing procedures (libraries and frameworks). Aim of the review To offer a structured and analytical overview(stating both advantages and disadvantages) of the existing popular object detection models that can be re-purposed for defect detection: such as Region based CNNs(Convolutional neural networks), YOLO(You only look once), SSD(single shot detectors) and cascaded architectures. A further brief summary on model compression and acceleration techniques that enabled the portability of deeplearning detection models is included. Key Scientific Concepts of Review It is of great use for future developments in the manufacturing industry that many of the popular, above mentioned models are easy to re-purpose for defect detection and, thus could really contribute to the overall increase in productivity of this sector. Moreover, in the experiment performed the YOLOv4 model was trained and re-purposed for industrial cable detection in several hours. The computing needs could be fulfilled by a general purpose computer or by a high-performance desktop setup, depending on the specificity of the application. Hence, the barrier of computing shall be somewhat easier to climb for all types of businesses.

Both routine radiation surveys and incident response--as currently performed by human workers--are time-consuming and involve significant, potentially unanticipated radiation dose (especially for accident response where levels prohibit human presence entirely). Previous efforts addressed routine surveys using robotic systems, but they must also localize and characterize discrete sources when anomalies occur. To be effective, characterization must account for real-world complications, including multiple sources, environmental attenuation, spatial localization, and isotopic identification. Recursive Bayesian estimation (RBE) using grid-based estimators and particle filters has been previously investigated, but we eliminate previous open-space assumptions by including attenuation models and sensor position height in the sequential sampling strategy. The method also incorporates autonomous isotopic identification via gamma spectroscopy, which supports the attenuation modeling and improves the computational efficiency in multisource cases. In addition, survey points are autonomously optimized using the Fisher information. A radioactive decay model is implemented to generalize the method by addressing short-lived nuclides. The developed hardware is evaluated in multiple scenarios where no operator intervention is required, effectively eliminating operator dose uptake from localization/characterization in real-world scenarios. Two different mobile robots are used, demonstrating the portability of the data collection and analysis software. Also, the generalized complexity is documented to support planning in future scenarios.

The Internet of Things (IoT) is a rapidly growing system of physical sensors and connected devices, enabling advanced information gathering, interpretation, and monitoring. The realization of a versatile IoT edge computing framework will accelerate seamless integration of the cyber-world with new physical IoT devices, and will fundamentally change and empower the way humans interact with the world. While there are many cloud-based IoT computing frameworks, they cannot support the needs of IoT applications that require local processing and guarantee of consumer’s privacy. This paper presents experimentation with the open source plug and play IoT middleware called Cordova Accesor Host. We demonstrated that Cordova Accessor Host supports the essential ingredients of the composition and re-usability of IoT services using accessor as the basic building block and adopting an accessor-module-plugin design pattern. The portability is demonstrated by using the same accessor for collecting sensor data from radically different IoT devices such as wearables (e.g., smartwatches) and micro-controllers (e.g., Arduino). Our energy profiling experiments show that IoT services deployed using Cordova Accessor Host consume around 35% less battery power than the same IoT services deployed in the native Android operating system.

Biosensors for sensitive and specific detection of foodborne and waterborne pathogens are particularly valued for their portability, usability, relatively low cost, and real-time or near real-time response. Their application is widespread in several domains, including environmental monitoring. The main limitation of currently developed biosensors is a lack of sensitivity and specificity in complex matrices. Due to increased interest in biosensor development, we conducted a systematic review, complying with the PRISMA guidelines, covering the period from January 2010 to December 2019. The review is focused on biosensor applications in the identification of foodborne and waterborne microorganisms based on research articles identified in the Pubmed, ScienceDirect, and Scopus search engines. Efforts are still in progress to overcome detection limitations and to provide a rapid detection system which will safeguard water and food quality. The use of biosensors is an essential tool with applicability in the evaluation and monitoring of the environment and food, with great impact in public health.

The message-passing programming model is based on the abstraction of a parallel computer with a distributed address space where each processor has a local memory to which it has exclusive access, see Sect. 2.3.1. There is no global memory. Data exchange must be performed by message passing: to transfer data from the local memory of one processor \(A\) to the local memory of another processor \(B\), \(A\) must send a message containing the data to \(B\), and \(B\) must receive the data in a buffer in its local memory. To guarantee portability of programs, no assumptions on the topology of the interconnection network is made. Instead, it is assumed that each processor can send a message to any other processor.

The portability of pension rights is an increasingly important issue in the Caribbean. The large and increasing flows of migrant workers, including both permanent and temporary migrants, the small size of the domestic economies and the process of regional integration and economic openness call for effective means to make pensions portable. This document presents a select survey of the literature on pension portability and reviews the progress made by the Caribbean countries as well as some remaining challenges in the light of the international experience.

Recent advances have been made in applying convolutional neural networks to achieve more precise prediction results for medical image segmentation problems. However, the success of existing methods has highly relied on huge computational complexity and massive storage, which is impractical in the real-world scenario. To deal with this problem, we propose an efficient architecture by distilling knowledge from well-trained medical image segmentation networks to train another lightweight network. This architecture empowers the lightweight network to get a significant improvement on segmentation capability while retaining its runtime efficiency. We further devise a novel distillation module tailored for medical image segmentation to transfer semantic region information from teacher to student network. It forces the student network to mimic the extent of difference of representations calculated from different tissue regions. This module avoids the ambiguous boundary problem encountered when dealing with medical imaging but instead encodes the internal information of each semantic region for transferring. Benefited from our module, the lightweight network could receive an improvement of up to 32.6% in our experiment while maintaining its portability in the inference phase. The entire structure has been verified on two widely accepted public CT datasets LiTS17 and KiTS19. We demonstrate that a lightweight network distilled by our method has non-negligible value in the scenario which requires relatively high operating speed and low storage usage., Accepted by IEEE TMI, Code Avalivable

The advantages of the second-generation AMD EPYC Rome processors can be successfully used in the race to Exascale. However, the novel architecture’s complexity makes it challenging to adapt demanding scientific codes - like stencil ones - to platforms with Rome CPUs. This article tackles this challenge by exploring the adaptation of the stencil-based CFD (computational fluid dynamics) application called MPDATA to these processors’ influential features. We show that the previously proposed parametric adaptation methodology can be profitably applied to extend the performance portability of the memory-bound MPDATA on the AMD EPYC architecture. The extension of the parametric adaptation on the novel architecture requires careful consideration of two relevant aspects that reflect splitting the Rome architecture into multiple dies – features of the cache hierarchy and partitioning cores into work teams. The article also investigates the correlation between the performance optimizations and energy efficiency for a ccNUMA platform powered by top-of-the-line 64-core AMD Rome 7742 CPUs, comparing the results against two servers with Intel Xeon Scalable processors of different generations. Even without appealing to prices, the achieved performance and energy efficiency results are a solid argument confirming the competitiveness of AMD Rome processors against Intel Xeon CPUs in scientific applications.

To remain competitive, companies must face the changes occurring in their environment and adapt their business processes accordingly. Those processes are implemented in business process management systems (BPMS), which mostly support manual adaptations. That means that the process users have to detect what changes in the environment require process adaptation, and what adaptation operations have to be performed. Such manual adaptations of processes are costly, time-consuming and error prone tasks. For this reason, some contributions of the literature have tried to address the issue of self-adaptations of processes. But these contributions suffer from shortcomings: isolated use of adaptation techniques, non-coverage of the process dimensions and of the adaptation types, etc.; the adaptation issue remains partially addressed. Thus we recommend in this paper a hybrid approach to ensure autonomic adaptations of running processes. According to this approach, the Plan component tries to find an appropriate model version of the concerned process. Then, if such a version does not exist, it looks to reuse an adaptation case that was applied in the past under a similar situation (context). Finally, if necessary, it applies rules, as an artificial intelligence planning technique, to define an ad hoc adaptation. Moreover, the recommended approach takes advantage of the IBM MAPE-K (Monitor, Analyze, Plan, Execute—Knowledge) control loop from autonomic computing, recognized as a prominent solution for self-adaptation at run-time. More precisely the paper addresses the resolution of adaptation needs while covering three process dimensions and all adaptation types and ensuring the separation of concerns for better portability and wide usability through the BPMN standard. It presents both the required Knowledge and the Plan component of the control loop for this resolution. It also shows the effectiveness of the approach by illustrating self-adaptation of a process from the crisis domain, and demonstrates its feasibility by reporting about its implementation and qualitative and quantitative evaluation.

Virtual keyboards could potentially replace traditional physical keyboards for text input and human-computer-interface due to their portability and comfortable usage characteristics. However, typical virtual keyboards such as wearable keyboards and camera-based keyboards have unsatisfactory input recognition accuracy, and hence, preventing them from gaining pervasive acceptance by users. In this study, we demonstrated that the layout optimization of keyboards can potentially improve recognition accuracy for ring-type motion sensing virtual keyboards, and analyzed several layout designs for the configuration of typing keys. Moreover, we proved that the new layout designs have significant advantages in reducing muscle oppression for the users. The layout designs were mainly improved through three aspects: arc and split design, key position adjustment, and letter arrangement. Three new layouts were tested, which show significant improvements ranging from 5.74% to 7.51% in recognition accuracy, i.e., achieving up to 97.57% accuracy for ring-type virtual keyboards. Additionally, muscle oppression test results (using surface myoelectricity data) indicate that our new keyboard layouts could provide a significant difference ( $\text{p} ) in reducing muscle oppression from traditional QWERTY keyboards. As for the moving distance of finger tips, our customized layouts could even reduce half of the movement distance compared with traditional keyboards. The experimental results demonstrate that our motion sensing rings combined with customized keyboard layouts could greatly overcome several disadvantages of traditional QWERTY keyboards.

Handling the ever-increasing complexity of mesh generation codes along with the intricacies of newer hardware often results in codes that are both difficult to comprehend and maintain. Different facets of codes such as thread management and load balancing are often intertwined, resulting in efficient but highly complex software. In this work, we present a framework which aids in establishing a core principle, deemed separation of concerns, where functionality is separated from performance aspects of various mesh operations. In particular, thread management and scheduling decisions are elevated into a generic and reusable tasking framework. The results indicate that our approach can successfully abstract the load balancing aspects of two case studies, while providing access to a plethora of different execution back-ends. One would expect, this new flexibility to lead to some additional cost. However, for the configurations studied in this work, we observed up to $$13\%$$ speedup for some meshing operations and up to $$5.8\%$$ speedup over the entire application runtime compared to hand-optimized code. Moreover, we show that by using different task creation strategies, the overhead compared to straight-forward task execution models can be improved dramatically by as much as $$1200\%$$ without compromises in portability and functionality.

“Go Green Campus” is a university program that requires all elements on campus to minimize the use and waste of paper and plastic. This encourages the transition from paper data collection to digitizing data storage to computer media. The Guest Book web application for Secretariat Lab of Faculty of Computer Science (FIK) Universitas Pembangunan Nasional Veterans Jakarta (UPNVJ) is a website-based software to facilitate the process of collecting visitor’s data who visit the FIK lab secretariat room. This application can replace the previous conventional guestbook to digital. The application built using the waterfall method and using the PHP programming language with the Laravel framework and MySQL database running on a local server. The Guestbook Web-based system for the Secretariat of the Laboratory of the FIK, UPNVJ can gather student’s data who visit the FIK lab secretariat room and the information can be downloaded in pdf and excel form. This web application also provides features such as covid-19 self-screening survey, news information, critics and suggestions, and the activity log list. The system that has been developed has also passed the security aspect test, reliability aspect test, portability aspect test, usage aspect test and is able to carry out the designed process.

The CPqPy framework coupling COMSOL and PHREEQC based on Python was developed. This framework can achieve the simulation of diversified situations including multi-physics coupling and geochemical reactions of soil and groundwater. The multi-physics coupling models are calculated in COMSOL, whereas PHREEQC was applied to calculate the geochemical models through the Phreeqpy library in Python. Feasibility and accuracy of CPqPy were verified and applied to two cases, including a solute transport model considering equilibrium reaction and ion exchange as well as a reactive transport model of a variable saturation soil considering kinetic reaction. The results show a high degree of credibility of CPqPy. The framework has the advantages of strong portability, and it can be further used in conjunction with multiple Python calculation libraries, which greatly extends the application of the reactive transport model.

A challenge that heterogeneous system programmers face is leveraging the performance of all the devices that integrate the system. This paper presents Sigmoid, a new load balancing algorithm that efficiently co-executes a single OpenCL data-parallel kernel on all the devices of heterogeneous systems. Sigmoid splits the workload proportionally to the capabilities of the devices, drastically reducing response time and energy consumption. It is designed around several features; it is dynamic, adaptive, guided and effortless, as it does not require the user to give any parameter, adapting to the behaviourof each kernel at runtime. To evaluate Sigmoid's performance, it has been implemented in Maat, a system abstraction library. Experimental results with different kernel types show that Sigmoid exhibits excellent performance, reaching a utilization of 90%, together with energy savings up to 20%, always reducing programming effort compared to OpenCL, and facilitating the portability to other heterogeneous machines. This work has been supported by the Spanish Science and Technology Commission under contract PID2019-105660RB-C22 and the European HiPEAC Network of Excellence.

The quantitative evaluation of sports motions is a precondition for scientific training and data-driven professional athletic motion analyses. However, the high accuracy and the portability of the evaluation schemes are contradictory and it is difficult to simultaneously improve both metrics in one device. In this work, we propose an effective gymnastics motion recognition and evaluation system to solve this problem. The system is a sensor network composed of 11 acceleration and angular velocity sensors. The gymnastics data obtained from each sensor node can be uploaded in real-time via Wi-Fi. The data from different sensor nodes on the body is used to form the corresponding gymnastics motion recognition data sets. Using these sensor node data and the linear discriminant analysis (LDA) algorithm, 6 different sets gymnastics motions could be recognized at an accuracy of 99.18%. Experimental results also showed that, by combining only the data from the left arm sensor node and the right foot sensor node as the gymnastic motion evaluation data set, the LDA algorithm could be used to evaluate three gymnastics motions, all of which delivered an accuracy of no less than 96.0%. This evaluation system meets the needs for accuracy and portability at the same time, and potentially can be extended to evaluate other physical activities, such as yoga and Tai Chi.

There is a need for simple, fast, and memory-efficient multidimensional data structures for dense and sparse storage that arise with numerical methods and in software applications. The data structures must perform equally well across multiple computer architectures, including CPUs and GPUs. For this purpose, we developed MATAR, a C++ software library that allows for simple creation and use of intricate data structures that is also portable across disparate architectures using Kokkos. The performance aspect is achieved by forcing contiguous memory layout (or as close to contiguous as possible) for multidimensional and multi-size dense or sparse MATrix and ARray (hence, MATAR) types. Our results show that MATAR has the capability to improve memory utilization, performance, and programmer productivity in scientific computing. This is achieved by fitting more work into the available memory, minimizing memory loads required, and by loading memory in the most efficient order. This document describes the purpose of the work, the implementation of each of the data types, and the resulting performance both in some simple baseline test cases and in an application code.

Today’s scientific data analysis very often requires complex Data Analysis Workflows (DAWs) executed over distributed computational infrastructures, e.g., clusters. Much research effort is devoted to the tuning and performance optimization of specific workflows for specific clusters. However, an arguably even more important problem for accelerating research is the reduction of development, adaptation, and maintenance times of DAWs. We describe the design and setup of the Collaborative Research Center (CRC) 1404 “FONDA -– Foundations of Workflows for Large-Scale Scientific Data Analysis”, in which roughly 50 researchers jointly investigate new technologies, algorithms, and models to increase the portability, adaptability, and dependability of DAWs executed over distributed infrastructures. We describe the motivation behind our project, explain its underlying core concepts, introduce FONDA’s internal structure, and sketch our vision for the future of workflow-based scientific data analysis. We also describe some lessons learned during the “making of” a CRC in Computer Science with strong interdisciplinary components, with the aim to foster similar endeavors.

This article focuses on two users of early portable computers – the ‘road warrior’ and ‘digital nomad’ of the 1980s and 90s – to illustrate how portability has served an imperialist function in eng...

STUFISH partner MAciej Woroniecki explains some of the parameters that entertainment architects need to consider. They include aspects such as the definition of the brief, the materiality of the proposition, its programmatic appropriateness, its portability and weight and its cost. Above all there must be a clear project narrative that drives the special and spectacular. Copyright © 2021 John Wiley & Sons, Ltd.

With the rapid development of intelligent technology, people’s lives have gradually entered the era of information and intelligence. Traditional vision-based and sensor-based human motion detection methods have shortcomings such as high illumination requirements, difficulty in preserving privacy, and poor portability. The wireless sensor detection method based on a wireless network can overcome the shortcomings of these methods, so it has become a hot research direction. Human motion parameters are very important data. If leaked, it may cause unimaginable harm to individuals. Blockchain technology is an emerging technology in the field of information technology. Its decentralized nature, distributed storage, and data is not easy. The modification provides a feasible solution for us to realize the data upload. Combining the advantages of wireless sensing and blockchain technology, this paper proposes a method of wireless sensing collection of human motion parameters based on blockchain, and designs a human motion recognition system. The experiment found that under the experiments given in this article, the traditional accuracy rate of sensor data collection is 71.82%, and the accuracy rate of the wireless sensing collection method of human motion parameters based on the blockchain in this article has reached 95.31%, which verifies the effectiveness and superiority of the system designed in this article.

PO Puspa Jaya is a company that is growing rapidly and is a company that is taken into account in land transportation services in Lampung. but in its service puspa jaya still perform manually, such as ticket booking, departure schedule information, and payment transactions carried out at pull puspa jaya. For that, there is a need for supporting facilities to book tickets at pull puspa jaya. From this problem came the idea to create an android-based application, which can make bus ticket reservations in po puspa jaya. The method used in the creation of applications is the Waterfall method. Programming language used by Android studio. With a MySQL support database. The testing method used is ISO 9126 (Usability, Fungtionality, Portability, Efficiency). Supported by the internet connection in the agency. This application will later be used as a medium of ticket booking and passenger data control. This application is for several features, namely determining the time and destination of departure, choosing the desired seat, tray information, contact information and information about. The ticket booking application in po puspa jaya has been tested using ISO 9126 which consists of four aspects of testing namely Usability, Fungtionality, Portability, Efficiency. The Usability aspect obtained an 80.50% result with a decent record. Fungtionality gets 100% results

Exposure science is fundamental to the field of occupational safety and health. The measurement of worker exposures to hazardous agents informs effective workplace risk mitigation strategies. The modern era of occupational exposure measurement began with the invention of the personal sampling device, which is still widely used today in the practice of occupational hygiene. Newer direct-reading sensor devices are incorporating recent advances in transducers, nanomaterials, electronics miniaturization, portability, batteries with high-power density, wireless communication, energy-efficient microprocessing, and display technology to usher in a new era in exposure science. Commercial applications of new sensor technologies have led to a variety of health and lifestyle management devices for everyday life. These applications are also being investigated as tools to measure occupational and environmental exposures. As the next-generation placeable, wearable, and implantable sensor technologies move from the research laboratory to the workplace, their role in the future of work will be of increasing importance to employers, workers, and occupational safety and health researchers and practitioners. This commentary discusses some of the benefits and challenges of placeable, wearable, and implantable sensor technologies in the future of work.

A supervised learning-based simple three class freshness detection algorithm is presented in this paper for prediction of freshness of Amla samples. Six major features from the red–green–blue (RGB) and hue–saturation–vital component (HSV) colourspace and ten other minor features have been studied here with the proposed artificial neural network (ANN) model. The proposed freshness classifier is computationally light due to the use of only ANN as the major classifying tool. More importantly, the analysis is based on images captured on smart phone only, which enables portability and hence, wide acceptability of the scheme. Accuracy of classification higher than 96.5% is achieved using the hue histogram of the image, followed by green layer histogram and others. All the major features were able to produce more than 83% efficiency in freshness class determination; whereas, minor features could achieve a highest classification accuracy of about 77%; clearly suggesting advantage of the major set of features. High efficient freshness categorization, combined with ease of computation, simple feature analysis and use of smart phones for image acquisition ensures its high possibility of real life implementation, especially incorporating within mobile application-based software development.

In the digital era, innovation continues to emerge, as can be seen from the number of startups, especially in the financial technology sector, which are considered to be able to accelerate human work. SMK YPT 1 Purbalingga is a Vocational High School located in Purbalingga Regency, Central Java. SPP transaction activities are carried out manually from payments, recordings, financial transaction reports, and often information is not conveyed to the guardians of students. SMK YPT 1 Purbalingga also has a vision and mission, one of which is to implement good school governance so that it requires a system that can assist SPP administration until transaction reports become efficient and effective. The method for designing the system is the prototype method. The system was created using the PHP programming language and MySQL database, which were analyzed using White Box, Black Box, Hypothesis Testing, Normalization Testing, Validity Testing, Reliability Testing and Benefit Testing. The result of the system created is the Website-based Mindtrans Payment Gateway Payment and Registration Information System. Based on the results of the benefit test, it shows the average value of Efficiency 92.43%, Reliability 86.36%, Correctness 92.04%, Usability 90.90%, Maintenance 84.09%, Integrity 97.73%, Portability 87.05 %. The results of hypothesis testing with two variables before and after using the system with Paired Sample T-Test resulted in Sig. (2-tailed) with an average time before 8.9 minutes and after 2.1 minutes so it can be concluded that this system is more helpful for the financial admin in the SPP administration process to be more efficient and effective

In this work, we demonstrate the process for porting the cloud resolving model (CRM) used in the Energy Exascale Earth System Model Multi-Scale Modeling Framework (E3SM-MMF) from its original Fortran code base to C++ code using a portability library. This porting process is performed using the Yet Another Kernel Library (YAKL), a simplified C++ portability library that specializes in Fortran porting. In particular, we detail our step-by-step approach for porting the System for Atmospheric Modeling (SAM), the CRM used in E3SM-MMF, using a hybrid Fortran/C++ framework that allows for systematic reproduction and correctness testing of gradually ported YAKL C++ code. Additionally, analysis is done on the performance of the ported code using OLCF’s Summit supercomputer.

Purpose Making a diagnosis from clinical genomic sequencing requires well-structured phenotypic data to guide genotype interpretation. A patient's phenotypic features can be documented using the Human Phenotype Ontology (HPO), generating terms used to prioritize genes potentially causing the patient's disease. We have developed GenomeDiver to provide a user interface for clinicians that allows more effective collaboration with the clinical diagnostic laboratory, with the goal of improving the success of the diagnostic process. Methods GenomeDiver uses genomic data to prompt reverse phenotyping of patients undergoing genetic testing, enriching the amount and quality of structured phenotype data for the diagnostic laboratory, and helping clinicians to explore and flag diseases potentially causing their patient's presentation. Results We show how GenomeDiver communicates the clinician's informed insights to the diagnostic lab in the form of HPO terms for interpretation of genomic sequencing data. We describe our user-driven design process, the engineering of the software for efficiency, security and portability, and examples of the performance of GenomeDiver using genomic testing data. Conclusion GenomeDiver is a first step in a new approach to genomic diagnostics that enhances laboratory–clinician interactions, with the goal of directly engaging clinicians to improve the outcome of genomic diagnostic testing. Graphical Abstract Unlabelled Image

Soft pneumatic actuators (SPAs) are highly desirable for interactive robotics applications owing to their unconventional properties like high compliance, safe human-machine interaction and adaptable design space. The SPA dynamic behaviour is governed by pneumatic supply systems (PSSs) consisting of source, valve, tubing and fittings . Although their functionality is well known, the inter-dependence of PSS components and their impact on the SPA behaviour has not been quantified, especially in the context of performance and portability. Using the metrics of maximum actuation frequency, air and energy consumption per actuation cycle, here we systematically investigate the effect of five parameters: SPA size, tubing diameter and length, source pressure and valve flow capacity. We model the SPA pressure dynamics model using first principles and define a large study set of 162 model parameter combinations based on commonly used SPAs and PSS components. We then simulate and experimentally measure the maximum actuation frequency in these parameter combinations, while additionally defining experimental protocols for flow characterization of PSS components and valve control strategy for maximizing actuation frequency. Results from experiments and simulations show good agreement, depicting the trends of how different parameters affect SPA performance. An interesting observation was that for a set of SPA size, tubing length and valve flow capacity, there exists a unique optimal tubing diameter that maximizes the actuation frequency. By further analyzing air and energy consumption, this work allows us to define and solve a multi-objective optimization problem to select and control PSS components to optimize SPA performance and portability simultaneously.

To solve the problems of difficult control law design, poor portability, and poor stability of traditional multi-agent formation obstacle avoidance algorithms, a multi-agent formation obstacle avoidance method based on deep reinforcement learning (DRL) is proposed. This method combines the perception ability of convolutional neural networks (CNNs) with the decision-making ability of reinforcement learning in a general form and realizes direct output control from the visual perception input of the environment to the action through an end-to-end learning method. The multi-agent system (MAS) model of the follow-leader formation method was designed with the wheelbarrow as the control object. An improved deep Q netwrok (DQN) algorithm (we improved its discount factor and learning efficiency and designed a reward value function that considers the distance relationship between the agent and the obstacle and the coordination factor between the multi-agents) was designed to achieve obstacle avoidance and collision avoidance in the process of multi-agent formation into the desired formation. The simulation results show that the proposed method achieves the expected goal of multi-agent formation obstacle avoidance and has stronger portability compared with the traditional algorithm.

The use of digital wallet or also known as e-wallet is widely spreading across world and specifically among Muslim in Malaysia without people actually knowing and understanding the operational mechanism of an e-wallet. Research has shown that the advancements in term of technology that are brought by e-wallet claimed to give a lot of benefits and advantages towards its users. E-wallet have the ability to make transactions easier, faster and quicker than before. The use of e-wallet also creates improvements in payments, portability, and cashless transaction. This study aims to identify the concept of e-wallet in term of its definition, operational mechanism and the contracts that are involved within e-wallet. The main instrument used for data collection was journals, library research and Internet. The construction of the instrument is the result of modification of the instruments that have been used by previous researchers and researchers’ own design based on the objectives to be achieved. The result shows the right concept of an e-wallet based on its definition as electronic money is a payment instrument that contains monetary value that is paid in advance by the user to the e-money issuer. The user of e-money can make payments for purchases of items and services to vendors who offer the e-money as payment. Besides, writer also finds the contracts involved within an e-wallet namely, wadi’ah contract and qardh contract. The results indicate that e-wallet operational mechanism is not contradict and in accordance to Sharia, hence allowed and permissible. However, further research is needed to identify other factors that could strengthen the effectiveness of this study.

Objective In the wake of the coronavirus pandemic, teleneuropsychology utilization has increased. There is a need to characterize the first-hand experiences of individuals using teleneuropsychology, identify the common teleneuropsychology challenges, and devise practical strategies for mitigating/resolving these challenges. Method Survey data were collected from U.S. based neuropsychologists and other individuals (e.g., graduate students and research assistants) who were engaged in remote cognitive assessment with adults (n = 87). Frequency analyses were conducted to examine: how respondents used teleneuropsychology (e.g., duration of use, types of measures and devices/technology platforms used); challenges that were encountered with different technology platforms and teleneuropsychology use; and advice for navigating these challenges. Results Most respondents began using teleneuropsychology relatively recently in the context of the coronavirus pandemic, with home-to-home or clinic-to-home settings being the most frequently reported teleneuropsychology settings. Zoom®, Doxy.Me®, and MyChart® were the most frequently used platforms, largely due to workplace mandates and/or Health Insurance Portability and Accountability Act-compliant features. Common challenges with teleneuropsychology included internet connection issues and environmental distractions in examinees’ homes, and examinees having limited-to-no access to technologies requisite for teleneuropsychology. Providing clear instructions to the examinee prior to the teleneuropsychology visit was the most common advice for ensuring a successful teleneuropsychology evaluation. Similar response patterns were generally noted for those who used TeleNP for clinical or research purposes. Conclusions These survey results reflect common experiences and challenges with teleneuropsychology and identify priority targets for increasing its feasibility, reliability, and validity. Findings provide context for the development of formal teleneuropsychology competencies.

The rapid growth of high-throughput technologies has transformed biomedical research. With the increasing amount and complexity of data, scalability and reproducibility have become essential not just for experiments, but also for computational analysis. However, transforming data into information involves running a large number of tools, optimizing parameters, and integrating dynamically changing reference data. Workflow managers were developed in response to such challenges. They simplify pipeline development, optimize resource usage, handle software installation and versions, and run on different compute platforms, enabling workflow portability and sharing. In this Perspective, we highlight key features of workflow managers, compare commonly used approaches for bioinformatics workflows, and provide a guide for computational and noncomputational users. We outline community-curated pipeline initiatives that enable novice and experienced users to perform complex, best-practice analyses without having to manually assemble workflows. In sum, we illustrate how workflow managers contribute to making computational analysis in biomedical research shareable, scalable, and reproducible.

The traditional mouse has been used as a main tool for human-computer interaction for more than 50 years. However, it has become unable to cater to people’s need for mobile officing and all-weather use due to its reliance on the support of a two-dimensional plane, poor portability, wearisomeness, and other problems. In this paper, we propose a portable ring-type wireless mouse scheme based on IMU sensors and a multi-level decision algorithm. The user only needs to operate in the air with a smart ring worn on the middle finger of their right hand to realize the interactive function of a mouse. The smart ring first captures changes in the finger’s attitude angle to reflect how the cursor position changes. And, it captures the rapid rotation of the user’s palm to the left and right to achieve mouse clicking. In addition, a multi-level decision algorithm is developed to improve the response speed and recognition accuracy of the virtual mouse. The experimental results show that the virtual mouse has a target selection accuracy of over 96%, which proves its practicability in real-world applications. This virtual mouse is expected to be used as a portable and reliable tool for multi-scenario human-computer interaction applications in the future.