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Web3.0, often cited to drastically shape our lives, is ubiquitous. However, few literatures have discussed the crucial differentiators that separate Web3.0 from the era we are currently living in. Via a thorough analysis of the recent blockchain infrastructure evolution, we capture a key invariant featuring the evolution, based on which we provide the first academic definition for Web3.0. Our definition is not the only way of understanding Web3.0, yet, it captures the fundamental and defining trait of Web3.0, and meanwhile it is has two desirable properties. Under this definition, we articulate three key infrastructural enablers for Web3.0: individual blockchains, federated or centralized platforms capable of publishing verifiable states, and an interoperability platform to hyperconnect those state publishers. While innovations in all categories are necessary to fully enable Web3.0, in this paper, we present a design for the third enabler, namely HyperService, that delivers interoperability and programmability across heterogeneous blockchains and state publishers. HyperService is powered by two innovative designs: a developer-facing programming framework that allows developers to build cross-chain applications in a unified programming model; and a secure blockchain-facing cryptography protocol that provably realizes those applications on blockchains. We implement a prototype of HyperService in 62,000 lines of code to demonstrate its practicality, usability, and scalability.

Noisy gate-model quantum processing units (QPUs) are currently available from vendors over the cloud, and digital quantum programming approaches exist to run low-depth circuits on physical hardware. These digital representations are ultimately lowered to pulse-level instructions by vendor quantum control systems to affect unitary evolution representative of the submitted digital circuit. Vendors are beginning to open this pulse-level control system to the public via specified interfaces. Robust programming methodologies, software frameworks, and backend simulation technologies for this analog model of quantum computation will prove critical to advancing pulse-level control research and development. Prototypical use cases for this include error mitigation, optimal pulse control, and physics-inspired pulse construction. Here we present an extension to the XACC quantum-classical software framework that enables pulse-level programming for superconducting, gate-model quantum computers, and a novel, general, and extensible pulse-level simulation backend for XACC that scales on classical compute clusters via MPI. Our work enables custom backend Hamiltonian definitions and gate-level compilation to available pulses with a focus on performance and scalability. We end with a demonstration of this capability, and show how to use XACC for pertinent pulse-level programming tasks.

As the scientific community prepares to deploy an increasingly complex and diverse set of applications on exascale platforms, the need to assess reproducibility of simulations and identify the root causes of reproducibility failures increases correspondingly. One of the greatest challenges facing reproducibility issues at exascale is the inherent non-determinism at the level of inter-process communication. The use of non-deterministic communication constructs is necessary to boost performance, but communication non-determinism can also hamper software correctness and result reproducibility. To address this challenge, we propose a software framework for identifying the percentage and sources of communication non-determinism. We model parallel executions as directed graphs and leverage graph kernels to characterize run-to-run variations in inter-process communication. We demonstrate the effectiveness of graph kernel similarity as a proxy for non-determinism, by showing that these kernels can quantify the type and degree of non-determinism present in communication patterns. To demonstrate our framework’s ability to link and quantify runtime non-determinism to root sources, demonstrate with present for an adaptive mesh refinement application, where our framework automatically quantifies the impact of function calls on non-determinism, and a Monte Carlo application, where our framework automatically quantifies the impact of parameter configurations on non-determinism.

Analog computing has been recovering its relevance in the recent years. Field-Programmable Analog Arrays (FPAAs) are the equivalent to Field-Programmable Gate Arrays (FPGAs) but in the analog and mixed-signal domain. In order to increase the amount of analog resources, in this brief a cluster of 40 FPAAs is proposed. As a use case, a 19-8-6-4 feedforward Neural Network has been implemented on such cluster. With the help of a DCT-based software framework, this NN is able to classify $28 \times 28$ images from MNIST. Results show that the analog network is able to obtain similar results as the software baseline network.

Aerial manipulation is an emerging field in robotics with various potential applications such as transport and delivery, agriculture, and, infrastructure inspection. To deploy aerial vehicles in the real world, the safety and reliability of these systems is paramount. Motivated by the need for safety and reliability, this work proposes a software framework that has built-in robustness to algorithmic failures and hardware faults. The framework allows users to build complex applications while reasoning about faults that can happen at different stages of an aerial manipulation task and specifying fallback actions to return to normal operating mode. The aerial manipulator is further endowed with a magnetic gripper that can handle positional errors arising from perception and control uncertainties. We also introduce a bias estimator for measuring the contact forces and sensor bias. We demonstrate how the estimator can be used to detect either completion or failures across several tasks. We demonstrate the reliability of the proposed framework on two tasks: package sorting task (e.g. as might be used in a distribution center) and sensor placement task (for infrastructure inspection). We show different failure modes that can occur and how our aerial manipulation system recovers from them.

Monitoring environmental conditions is an important application of cyber-physical systems. Typically, the monitoring is to perceive surrounding environments with battery-powered, tiny devices deployed in the field. While deep learning-based methods, especially the convolutional neural networks (CNNs), are promising approaches to enriching the functionalities offered by the tiny devices, they demand more computation and memory resources, which makes these methods difficult to be adopted on such devices. In this article, we develop a software framework, RAP , that permits the construction of the CNN designs by aggregating the existing, lightweight CNN layers, which are able to fit in the limited memory (e.g., several KBs of SRAM) on the resource-constrained devices satisfying application-specific timing constrains. RAP leverages the Python-based neural network framework Chainer to build the CNNs by mounting the C/C++ implementations of the lightweight layers, trains the built CNN models as the ordinary model-training procedure in Chainer, and generates the C version codes of the trained models. The generated programs are compiled into target machine executables for the on-device inferences. With the vigorous development of lightweight CNNs, such as binarized neural networks with binary weights and activations, RAP facilitates the model building process for the resource-constrained devices by allowing them to alter, debug, and evaluate the CNN designs over the C/C++ implementation of the lightweight CNN layers. We have prototyped the RAP framework and built two environmental monitoring applications for protecting endangered species using image- and acoustic-based monitoring methods. Our results show that the built model consumes less than 0.5 KB of SRAM for buffering the runtime data required by the model inference while achieving up to 93% of accuracy for the acoustic monitoring with less than one second of inference time on the TI 16-bit microcontroller platform.

Virtual brain research is accelerating thedevelopment of inexpensive real-time Brain Computer Interface (BCI). Hardware improvements that increase the capability of Virtual brain analyse and Brain Computer wearable sensors have made possible several new software frameworks for developers to use and create applications combining BCI and IoT. In this paper, we complete a survey on BCI in IoT from various perspectives; including Electroencephalogram (EEG) based BCI models, machine learning, and current active platforms. Based on our investigations, the main findings of this survey highlights three major development trends of BCI, which are EEG, IoT, and cloud computing. Using this it is completely useful for finding the true state of whether the brain is alive or dead. If it is alive, then the activity of the brain is monitored and stored. Through this anyone can come to conclusion that whether the action done is legal or illegal. And this has an advantage for 2 scenarios. First is for AUTISM affected people and secondly Forgery in asset documents. And if any changes in the status of the brain then it will be send to the specific person in their relation using SMS & Email id.

Logistics 4.0 aims at enabling the sustainable satisfaction of customer demands with optimised costs of services with the use of emerging technologies, such as Internet of Things, streaming analyti...

Deep learning technology is widely used in many modern fields and a number of models and software frameworks have been proposed. However, it is still very difficult to process deep learning tasks efficiently on traditional high performance computing (HPC) systems. In this paper, we propose swFLOW: a large-scale distributed framework for deep learning on Sunway TaihuLight. Based on the performance analysis results of convolutional neural network (CNN), we optimize the convolutional layer , and get 10.42× speedup compared to the original version. As for distributed training, we use elastic averaging stochastic gradient descent (EASGD) algorithm to reduce communication. On 512 processes, we get a parallel efficiency of 81.01% with communication period τ = 8 . Particularly, a decentralized implementation of distributed swFLOW system is presented to alleviate bottleneck of the central server. By using distributed swFLOW system, we can scale the batch size up to 4096 among 1024 concurrent processes for cancerous region detection algorithm . The successful application on swFLOW reveals the great opportunity for joint combination of deep learning and HPC system.

Apache Spark programming framework provides an effective and vigorous open-source solution for Big data testing and management. It offers a rich set of big data applications through a programming interface. Testing procedures deliver optimum solutions to complex business problems like analyzing large data volumes. A typical test engineer demands manual to automatic testing which assists the testing strategy with minimal effort. Big data testing means, verification, and validation of data while storing and processing it. This research paper demonstrates the challenges with new high-performance analytics for simpler and faster testing processing of relevant data. It enables, timely and accurate insights using Big data testing predict analytics and can manage large quantities of Structured, Semi-structured, and Unstructured data forms with spark analysis. These methods are evaluated using Extract, Transformation, Loading, and Apache spark procedures. The proposed Big data testing method has statistically outperformed conventional procedures.

Online experiments are ubiquitous. As the scale of experiments has grown, so has the complexity of their design and implementation. In response, firms have developed software frameworks for designing and deploying online experiments. Ensuring that experiments in these frameworks are correctly designed and that their results are trustworthy---referred to as *internal validity*---can be difficult. Currently, verifying internal validity requires manual inspection by someone with substantial expertise in experimental design. We present the first approach for statically checking the internal validity of online experiments. Our checks are based on well-known problems that arise in experimental design and causal inference. Our analyses target PlanOut, a widely deployed, open-source experimentation framework that uses a domain-specific language to specify and run complex experiments. We have built a tool, PlanAlyzer, that checks PlanOut programs for a variety of threats to internal validity, including failures of randomization, treatment assignment, and causal sufficiency. PlanAlyzer uses its analyses to automatically generate *contrasts*, a key type of information required to perform valid statistical analyses over experimental results. We demonstrate PlanAlyzer's utility on a corpus of PlanOut scripts deployed in production at Facebook, and we evaluate its ability to identify threats to validity on a mutated subset of this corpus. PlanAlyzer has both precision and recall of 92% on the mutated corpus, and 82% of the contrasts it automatically generates match hand-specified data., 30 pages, hella long

This paper describes briefly about the concept of serverless cloud computing model, its usage in IT industries and its benefits. In the traditional model the developer is responsible for resource allocation, managing servers and owning of servers, and it included three models based upon the service such as IaaS, PaaS and SaaS. In IaaS (Infrastructure as a Service) the content storage and accessing of network is carried out by the cloud provider, SaaS (Software as a Service) here different software’s are provided to the user as a service, PaaS (Platform as a Service), the developer gets access to certain services for carrying out organizing process and run it accordingly. In serverless cloud computing, the developer need not worry about owning, management, and maintenance of servers as it is carried out by the cloud service provider. Hence by using this model, the time that is needed for a system to reach the market is very much reduced and is cost effective. Serverless architecture includes three categories namely, AWS Lambda, Azure, and Google cloud. It also includes certain challenges such as it cannot be used in the case where a process takes longer time to run and it is discussed below in this paper.

During the implementation process of identification systems, multiple factors need to be considered at the same time. Due to the large amount of calculation and the randomness of the signal, the automatic identification capability of the machine is currently poor, especially for continuous signal. In order to solve this problem, a neuron fuzzy identification system was described based on a complex nonlinear mathematical model, which was designed from both hardware and software aspects of the system. The hardware architecture diagram was constructed based on the S3C:2440 microprocessor. The main modules include the power module, acquisition module, storage module, and output module. The DSP/BIOS system was used to construct the software framework diagram of the neuron fuzzy identification system to describe the identification process of video data stream, image data stream and control signal data. The software algorithm was designed based on the neuron fuzzy theory to establish a fuzzy similarity matrix and the best identification result was found by the maximum and minimum methods. The experimental results show that the designed system has higher recognition ability. When the frame length reaches 40 frames, the recognition rate increases to a larger value, the recognition rate is 75%, and the information is more accurate.

Aiming at the problem of DDoS attack detection in internet of things (IoT) environment, statistical and machine-learning algorithms are proposed to model and analyze the network traffic of DDoS attack. Docker-based virtualization platform is designed and configured to collect IoT network traffic data. Then the packet-level, flow-level, and second-level network traffic datasets are generated, and the importance of features in different traffic datasets are sorted. By SKlearn and TensorFlow machine-learning software framework, different machine learning algorithms are researched and compared. In packet-level DDoS attack detection, KNN algorithm achieves the best results; the accuracy is 92.8%. In flow-level DDoS attack detection, the voting algorithm achieves the best results; the accuracy is 99.8%. In second-level DDoS attack detection, the RNN algorithm behaves best results; the accuracy is 97.1%. The DDoS attack detection method combined with statistical analysis and machine-learning can effectively detect large-scale DDoS attacks on the internet of things simulation experimental environment.

Robotic manipulation is fundamental to many realworld applications; however, it is an unsolved problem that remains a very active research area. New algorithms for robot perception and control are frequently proposed by the research community. These methods must be thoroughly evaluated in realistic conditions before they can be adopted by industry. This process can be extremely time consuming, mainly due to the complexity of integrating different hardware and software components.

Using large, multi-national datasets for high-performance medical imaging AI systems requires innovation in privacy-preserving machine learning so models can train on sensitive data without requiring data transfer. Here we present PriMIA (Privacy-preserving Medical Image Analysis), a free, open-source software framework for differentially private, securely aggregated federated learning and encrypted inference on medical imaging data. We test PriMIA using a real-life case study in which an expert-level deep convolutional neural network classifies paediatric chest X-rays; the resulting model’s classification performance is on par with locally, non-securely trained models. We theoretically and empirically evaluate our framework’s performance and privacy guarantees, and demonstrate that the protections provided prevent the reconstruction of usable data by a gradient-based model inversion attack. Finally, we successfully employ the trained model in an end-to-end encrypted remote inference scenario using secure multi-party computation to prevent the disclosure of the data and the model. Gaining access to medical data to train AI applications can present problems due to patient privacy or proprietary interests. A way forward can be privacy-preserving federated learning schemes. Kaissis, Ziller and colleagues demonstrate here their open source framework for privacy-preserving medical image analysis in a remote inference scenario.

Deep learning has sparked a renaissance in computer systems and architecture. Despite the breakneck pace of innovation, there is a crucial issue concerning the research and industry communities at large: how to enable neutral and useful performance assessment for machine learning (ML) software frameworks, ML hardware accelerators, and ML systems comprising both the software stack and the hardware. The ML field needs systematic methods for evaluating performance that represents real-world use cases and useful for making comparisons across different software and hardware implementations. MLPerf answers the call. MLPerf is an ML benchmark standard driven by academia and industry (70+ organizations). Built out of the expertise of multiple organizations, MLPerf establishes a standard benchmark suite with proper metrics and benchmarking methodologies to level the playing field for ML system performance measurement of different ML inference hardware, software, and services.

Edge computing technologies facilitate the deployment of services on nearby edge servers with a large number of end users and their mobile devices to fulfill personalized demands. Owing to frequent changes in user mobility and demands, service systems deployed in an edge–cloud environment must continuously adapt to ensure that the quality of service (QoS) perceived by the end users is maintained at a stable and satisfactory level. As it is difficult for system operation engineers to manually deal with such frequent and large-scale evolution due to problems of cost and efficiency, self-adaptation of the system is essential. In this paper, we present a programming framework for microservices (EPF4M) and an infrastructure for self-adaptive microservice systems (EI4MS) for the cloud–edge environment based on microservice architecture. Our study follows a “monitoring–analyzing–planning–execution” control loop that empowers the service systems to redeploy the services according to changes in the QoS. A two-phase strategy is adopted to minimize the side effects of the loop on the performance of the service system. A prototype of this framework and infrastructure has been open-sourced and verified through experiments conducted in a real cloud–edge environment. The results demonstrate the usefulness and advantages of our approach.

Cognitive modeling tools have been widely used by researchers and practitioners to help design, evaluate, and study computer user interfaces (UIs). Despite their usefulness, large-scale modeling tasks can still be very challenging due to the amount of manual work needed. To address this scalability challenge, we propose CogTool+, a new cognitive modeling software framework developed on top of the well-known software tool CogTool. CogTool+ addresses the scalability problem by supporting the following key features: (1) a higher level of parameterization and automation; (2) algorithmic components; (3) interfaces for using external data; and (4) a clear separation of tasks, which allows programmers and psychologists to define reusable components (e.g., algorithmic modules and behavioral templates) that can be used by UI/UX researchers and designers without the need to understand the low-level implementation details of such components. CogTool+ also supports mixed cognitive models required for many large-scale modeling tasks and provides an offline analyzer of simulation results. In order to show how CogTool+ can reduce the human effort required for large-scale modeling, we illustrate how it works using a pedagogical example, and demonstrate its actual performance by applying it to large-scale modeling tasks of two real-world user-authentication systems.

In proton therapy, the knowledge of the proton stopping power, i.e. the energy deposition per unit length within human tissue, is essential for accurate treatment planning. One suitable method to directly measure the stopping power is proton computed tomography (pCT). Due to the proton interaction mechanisms in matter, pCT image reconstruction faces some challenges: the unique path of each proton has to be considered separately in the reconstruction process adding complexity to the reconstruction problem. This study shows that the GPU-based open-source software toolkit TIGRE, which was initially intended for X-ray CT reconstruction, can be applied to the pCT image reconstruction problem using a straight line approach for the proton path. This simplified approach allows for reconstructions within seconds. To validate the applicability of TIGRE to pCT, several Monte Carlo simulations modeling a pCT setup with two Catphan® modules as phantoms were performed. Ordered-Subset Simultaneous Algebraic Reconstruction Technique (OS-SART) and Adaptive-Steepest-Descent Projection Onto Convex Sets (ASD-POCS) were used for image reconstruction. Since the accuracy of the approach is limited by the straight line approximation of the proton path, requirements for further improvement of TIGRE for pCT are addressed.

The work is devoted to the research that was carried out within the framework of computer vision problems applicable to the analysis of images and video information with vehicles. We solve the problem of classifying vehicles. We analyze the drawbacks of Haar features and convolutional neural networks and test the obtained networks using the key point method; we construct an integral algorithm that includes several networks, and we further validate it on a large number of real photographs and types of vehicles. Next, we solve the task to develop a software framework for tracking vehicles by analyzing adjacent photographs from a video sequence. After that, we consider the tracking task in more detail. We analyze modern tracking algorithms using machine learning and describe our implemented tracker with support for the appearance of obstacles between the camera and a moving vehicle. As a result, we propose algorithms and open-source software that, after being configured for specific cameras, can be used in traffic analysis systems.

Big data is the high-volume, high-variety data which involves data storage, data management, and data analysis that presents a wide view of business possibility for real-time data, sensor data, and streaming data over the web. Big data relies on technology, analysis, and mythology where technology deals with computation power, accuracy, linking, and large datasets; analysis is to find patterns by analyzing large datasets to discover hidden information; and mythology is the wrong beliefs that large datasets give insight knowledge of data that is not obtained by small datasets. In this paper, the authors analyzed the major benefits the organization see from employing contract workers using map reduce programming framework.

As a part of the future upgrade program of the Multi-Purpose Detector (MPD) experiment at the Nuclotron-Based Ion Collider Facility (NICA) complex, an Inner Tracking System (ITS) made of Monolitic Active Pixel Sensors (MAPSs) is proposed between the beam pipe and the Time Projection Chamber (TPC). It is expected that the new detector will enhance the experimental potential for the reconstruction of short-lived particles—in particular, those containing the open charm particle. To study the detector performance and select its best configuration, a track reconstruction approach based on a constrained combinatorial search was developed and implemented as a software toolkit called Vector Finder. This paper describes the proposed approach and demonstrates its characteristics for primary and secondary track finding in ITS, ITS-to-TPC track matching and hyperon reconstruction within the MPD software framework. The results were obtained on a set of simulated central gold–gold collision events at sNN=9 GeV with an average multiplicity of ∼1000 charged particles in the detector acceptance produced with the Ultra-Relativistic Quantum Molecular Dynamics (UrQMD) generator.

This paper presents an extension to a software framework designed to evaluate the efficiency of different software and hardware-accelerated virtual switches, each commonly adopted on Linux to provide virtual network connectivity to containers in high-performance scenarios, like in Network Function Virtualization (NFV). We present results from the use of our tools, showing the performance of multiple high-performance networking frameworks on a specific platform, comparing the collected data for various key metrics, namely throughput, latency and scalability, with respect to the required computational power.

This paper presents a controller-agent-based design framework (named OROMACS) for the construction of hierarchically structured multi-agent control system (MACS). OROMACS uses the decentralized coordination mechanism to build MACS. Each MACS can be in the form of an elementary controller-agent or a composite controller-agent. The latter consists of a group of elementary and/or composite controller-agents and a coordinator; the coordinator has a role to coordinate the activity behaviour of the whole group. The main idea in creating composite controller-agents is to consider a group of coordinated controller-agents as if it is an individual controlleragent. Then, a composite controller-agent can be used in other groups, leading to hierarchical structures of MACS. To provide real-time features for MACS, the OROMACS framework has used the real-time OROCOS software framework as running environment. The OROMACS-based design method is illustrated through a design of MACS for a single-axis electro-mechanical motion system using a linear motor with the dominant compliance in the transmission. The process starts with the design of an Operation Controller Agent and then reusing its whole design to develop a more complex system called Safe-Guarded MACS. Software-In-the-Loop (SIL) co-simulation results have demonstrated the applicability of the design method.

An obstacle perception system for intelligent vehicle is proposed. The proposed system combines the stereo version technique and the deep learning network model, and is applied to obstacle perception tasks in complex environment. In this paper, we provide a complete system design project, which includes the hardware parameters, software framework, algorithm principle, and optimization method. In addition, special experiments are designed to demonstrate that the performance of the proposed system meets the requirements of actual application. The experiment results show that the proposed system is valid to both standard obstacles and non-standard obstacles, and suitable for different weather and lighting conditions in complex environment. It announces that the proposed system is flexible and robust to the intelligent vehicle.

The large amount of memory usage in recent machine learning applications imposes a significant system burden with respect to power and processing speed. To cope with such a problem, Processing-In-Memory (PIM) techniques can be applied as an alternative solution. Especially, the recommendation system, which is one of the major machine learning applications used in data centers, requires a large memory capacity and therefore represents a suitable candidate application that could be helped by the PIM technique. In this paper, we introduce a machine learning framework, PIMCaffe, designed for in-memory neural processing units and its evaluation environment. PIMCaffe consists of two components: a Caffe2-based deep learning framework that supports PIM acceleration and a PIM-emulating hardware platform. We develop a suite of functions, libraries, application programming interfaces, and a device driver to support the framework. In addition, we implement a prototype Neural Processing Unit (NPU) in PIMCaffe to evaluate the performance of our platform with machine learning applications. Our prototype NPU design includes a vector processor for parallel vector processing and a systolic array unit for matrix multiplication. Using the proposed software framework, we perform a detailed analysis of the in-memory neural processing unit. PIMCaffe supports evaluations of recommendation systems and various convolutional neural network models on the in-memory neural processing unit. PIMCaffe with the NPU shows up to $2.26\times $ , $5.99\times $ , and $1.71\times $ speedup, compared to the ARM Cortex-A53 CPU, for the recommendation system, AlexNet, and ResNet-50, respectively.

We present a new voice-based programming approach and software framework for collaborative robots (cobots) based on the Web Speech API, which is now supported by most modern browsers. The framework targets human programmable interfaces (HPIs), which can be used by people with little or no programming experience. The framework follows a meta-programming approach by enabling users to program cobots by voice in addition to using a mouse, tablet or keyboard. Upon a voice instruction (such as move, pick, release, etc.), the framework automates the manual tasks required to manipulate the vendor-provided HPI. The main advantages of this approach are simplified, guided programming, which only requires the knowledge of 5–10 voice instructions; increased programming speed by up to 46% compared to the manual approach; and the possibility of sharing programs as videos. The open-source framework was evaluated using two application scenarios.

The standardization of the physical aspects of nanosatellites (also known as CubeSats) and their wide adoption in academia and industry has made the mass production and availability of off-the-shelf components possible. While this has led to a significant reduction in satellite development time, the fact remains that a considerable amount of mission development time and effort continues to be spent on flight software development. The CubeSat’s agile development environment makes it challenging to utilize the advantages of existing software frameworks. Such an adoption is not straightforward due to the added complexity characterized by a steep learning curve. A well-designed flight software architecture mitigates possible sources of failure and increases mission success rate while maintaining moderate complexity. This paper presents a novel approach to a flight software framework developed specifically for nanosatellites. The software framework is characterized by simplicity, reliability, modularity, portability, and real-time capability. The main features of the proposed framework include providing a standardized and explicit skeleton for each module to simplify their construction, offering standardized interfaces for all modules to simplify communication, and providing a collection of ready-to-use common services open for further enhancement by CubeSat software developers. The framework efficiency was demonstrated through a software developed for the MeznSat mission that was successfully launched into Low Earth Orbit in September 2020. The proposed software framework proved to simplify software development for the application developer while significantly enhancing software modularity.

The modern distribution automation suggests to enable decentralized self-healing of distribution networks using advance metering and controlling infrastructure. Decentralized fault location and isolation, as an essential and vital component of self-healing, has attracted considerable attention over the years. In this paper, an integrated fault location and isolation strategy based on fully decentralized multi-agents system (FDMAS) is presented for distribution system with distributed generators (DGs) by combining the primary protection with device failure protection (i.e., backup protection). The proposed FDMAS strategy can locate and isolate electrical fault rapidly even under device failures with minimum fault clearance time and range by using expert logical rules, meanwhile can detect and identify device failure adaptively. Furthermore, a unified programming framework is developed for generalization and application of the proposed strategy. The simulation studies are carried out on 22-bus distribution system by using dynamic model test platform. The test results show that the proposed strategy has an excellent performance on fault clearance time, fault isolation range and device failure detection.

There are currently rising applications of battery electric buses in public transportation system, given their energy efficiency and societal benefits. However, limited range and time-consuming charging processes significantly reduce the operational efficiency of battery electric buses system. Thanks to fast charging technology, battery electric buses can run continuously through rational planning of fast charging system. Therefore, to achieve an effective and economic electric transit route network, electric transit route network and fast charging stations should be simultaneously and systematically deployed. This study develops a bi-level programming framework to formulate the electric transit route network design problem. Where, the upper-level model is to determine the route structure and charging station location to optimize the total costs of both user and operator, the lower-level passenger assignment model is to calculate user cost for upper-level model. Then, the proposed model is solved by a modified genetic algorithm (GA), which can search all possible route structures through specifically designed genetic operators. A numerical example from Swiss road network is employed to demonstrate the applicability of bi-level model and performance of modified GA.

The Hadoop platform provides a powerful software framework for distributed storage and processing of massive amounts of data. It is at the heart of big data processing and has found numerous applications in diverse areas, ranging from environmental monitoring to security analysis. To facilitate the storage and processing of big data, a Hadoop platform typically runs on a cluster of servers and may scale up to process big data over thousands of hardware nodes. However, the growing scale and complexity of the Hadoop platform also make it increasingly challenging to manage and operate. In this paper, we present a framework called LogM that leverages not only the deep learning model, but also the knowledge graph technology for failure prediction and analysis of the Hadoop cluster. In particular, we first develop a CAB net (Convolutional Neural Network (CNN) with attention-based Bi-directional Long Short Term Memory (Bi-LSTM)) architecture to effectively learn the temporal dynamics from the sequential log data, which allows us to predict system failures. We then adopt a knowledge graph approach for failure analysis and diagnosis. Extensive experiments have been carried out to assess the performance of the proposed approach. It is seen that LogM is highly effective in predicting and diagnosing system failures.

We present the scope, concepts, data structures and application programming models of the open-source Lattice Boltzmann library Palabos. Palabos is a C++ software platform developed since 2010 for Computational Fluid Dynamics simulations and Lattice Boltzmann modeling, which specifically targets applications with complex, coupled physics. The software proposes a very broad modeling framework, capable of addressing a large number of applications of interest in the Lattice Boltzmann community, yet exhibits solid computational performance. The article describes the philosophy of this programming framework and lists the models already implemented. Finally, benchmark simulations are provided which serve as a proof of quality of the implemented core functionalities.

To improve the programming efficiency of automatic assembly system, a novel skill programming framework based on task learning is proposed for modular assembly system in this paper. In this framework, the motion sequence of assembly skills can be modeled by demonstration data. And the assembly task is represented hierarchically. A complete assembly process of a part is divided into several skills, and each skill is divided into several sequential assembly motion primitives (AMP) of multiple modules. Then, a learning method of assembly motion sequence based on Hidden Markov Model is proposed, and the maximum probability method is used to generate the optimal sequential AMP. Each AMP is input to the assembly system in the form of instruction to complete the assembly. Aiming at the problem of accurate positioning and trajectory planning, visual guidance and direct teaching method are used to settle this problem. To evaluate the viability of the proposed framework, a customized modular assembly system is used to acquire the demonstration data, and a graphical user interface (GUI) software is designed. Five assembly skills are learned. Experimental are conducted to validate the effectiveness of the proposed method.

Traditional computing systems based on the von Neumann architecture are fundamentally bottlenecked by data transfers between processors and memory. The emergence of data-intensive workloads, such as machine learning (ML), creates an urgent need to address this bottleneck by designing computing platforms that utilize the principle of colocated memory and processing units. Such an approach, known as “in-memory computing,” can potentially eliminate data movement costs by computing inside the memory array itself. Crossbars based on resistive nonvolatile memory (NVM) devices have shown immense promise in serving as the building blocks of in-memory computing systems for ML workloads. This is because their high density can lead to higher on-chip storage capacity, while they can also perform massively parallel, in situ matrix–vector multiplication (MVM) operations, thereby accelerating the main computational kernel of ML workloads. However, resistive crossbar-based analog computing is inherently approximate due to the device- and circuit-level nonidealities. Furthermore, the area and energy costs of peripheral circuits for conversions between the analog and digital domains can greatly diminish the intrinsic efficiency of crossbar-based MVM computation. We present a comprehensive overview of the emerging paradigm of computing using NVM crossbars for accelerating ML workloads. We describe the design principles of resistive crossbars, including the devices and associated circuits that constitute them. We discuss intrinsic approximations arising from the device and circuit characteristics and study their functional impact on the MVM operation. Next, we present an overview of spatial architectures that exploit the high storage density of NVM crossbars. Furthermore, we elaborate on software frameworks that effectively capture device–circuit–architecture characteristics to evaluate the performance of large-scale deep neural networks (DNNs) using resistive crossbar-based hardware. Finally, we discuss open challenges and future research directions that need to be explored in order to realize the vision of resistive crossbars as the building blocks of future computing platforms.

In clinical scenarios, there is an increasing interest in complex computational experiments, as for example the training of Deep Learning models. Reproducibility is an essential property of such experiments, especially if the result contributes to a patient’s treatment. This paper introduces Curious Containers, a software framework for computational reproducibility that treats data, software and runtime environment as decentralized network resources. All experiment resources are described in a single file, using a new format that is compatible with a subset of the Common Workflow Language. Docker is used to deploy the experiment software in a container image, including arbitrary data transmission programs to connect with existing storage solutions. The framework supports Deep Learning applications, that have a high demand in storage and processing capabilities. Large datasets can be mounted inside containers via network filesystems like SSHFS based on the filesystem in user-space technology. The Nvidia-Container-Toolkit enables GPU usage. Curious Containers has been tested in two biomedical scenarios. The first use case is a Deep Learning application for tumor classification in images that requires a large dataset and a GPU. In this context, a prototypical integration of the framework with the existing Data Version Control system for exploratory Deep Learning modeling has been developed. The second use case extends an existing container image, including a scientific workflow for detection and comparison of human protein in mass spectrography data. The container image was originally developed for an archiving platform and could be extended to be compatible with both Curious Containers and cwltool, the Common Workflow Language reference implementation. The presented solution allows for consistent description and execution of computational experiments, while trying to be both flexible and interoperable with existing software and standards. Support for Deep Learning experiments is gaining importance as such systems are increasingly validated as medical decision support systems.

This article describes the development of a software architectural framework for implementing compute-aware control systems, where the term “compute-aware” describes controllers that can modify existing low-level computing platform power managers in response to the needs of the physical system controller. This level of interaction means that high-level decisions can be made as to when to operate the computing platform in a power-savings mode or a high-performance mode in response to situation awareness of the physical system. The framework is demonstrated experimentally on a mobile robot platform. In this example, a situation-aware governor is developed that adjusts the speed of the processor based on the physical performance of the robot as it traverses a path through obstacles. The results show that the situation-aware governor results in overall power savings of up to 38.9 percent with 1.3 percent degradation in performance compared to the static high-power strategy.

Economic nonlinear model predictive control (NMPC) is a variant of NMPC that directly optimizes an economic performance index instead of a tracking error. Although economic NMPC can achieve excellent closed-loop performance, the associated computational effort as well as the difficulty of guaranteeing stability in practice are its main drawbacks. Motivated by these difficulties, a formal procedure was developed that tunes a tracking (non)linear MPC scheme so that it is first-order equivalent to economic NMPC. This letter introduces TuneMPC, a new open-source software framework that closes the gap between the underlying theory and practical application of this tuning procedure. For user-provided system dynamics, constraints and economic objective, TuneMPC enables automated computation of optimal steady states and periodic trajectories, and returns the corresponding tuned stage cost matrices. To demonstrate the potential of the tool, we apply the technique to the challenging example of an autonomous tethered aircraft flying periodic orbits for airborne wind energy harvesting.

X-ray ptychography imaging at synchrotron facilities like the Advanced Photon Source (APS) involves controlling instrument hardwares to collect a set of diffraction patterns from overlapping coherent illumination spots on extended samples, managing data storage, reconstructing ptychographic images from acquired diffraction patterns, and providing the visualization of results and feedback. In addition to the complicated workflow, ptychography instrument could produce up to several TB's of data per second that is needed to be processed in real time. This brings up the need to develop a high performance, robust and user friendly processing software package for ptychographic data analysis. In this paper we present a software framework which provides functionality of visualization, work flow control, and data reconstruction. To accelerate the computation and large datasets process, the data reconstruction part is implemented with three algorithms, ePIE, DM and LSQML using CUDA-C on GPU., Comment: X-Ray Nanoimaging: Instruments and Methods V

The microservice architectural style has gained much attention from both academia and industry recently as a novel way to design, develop, and deploy cloud-native applications. This concept encourages the decomposition of a monolith into multiple independently deployable units. A typical microservices-based application is formed of two service types: functional services, which provide the core business logic, and infrastructure services, which provide essential functionalities for a microservices ecosystem. To improve developers' productivity, many software frameworks have been developed to provide those reusable infrastructure services, allowing programmers to focus on implementing microservices in arbitrary ways. In this work, we made use of four open source frameworks to develop a cloud-based application in order to compare and evaluate their usability and practicability. While all selected frameworks promote asynchronous microservice design in general, there are differences in the ways each implements services. This leads to interoperability issues, such as message topic naming convention. Additionally, a key finding is the long startup times of JVM-based services that might reduce application's resiliency and portability. Some other advantages come directly from the programming language, such as the ability of Go to generate native binary executables, which results in very small and compact Docker images (up to 78\% smaller compared to other languages).

We present Korali, an open-source framework for large-scale Bayesian uncertainty quantification and stochastic optimization. The framework relies on non-intrusive sampling of complex multiphysics models and enables their exploitation for optimization and decision-making. In addition, its distributed sampling engine makes efficient use of massively-parallel architectures while introducing novel fault tolerance and load balancing mechanisms. We demonstrate these features by interfacing Korali with existing high-performance software such as Aphros, LAMMPS (CPU-based), and Mirheo (GPU-based) and show efficient scaling for up to 512 nodes of the CSCS Piz Daint supercomputer. Finally, we present benchmarks demonstrating that Korali outperforms related state-of-the-art software frameworks., Computer Methods in Applied Mechanics and Engineering, 389, ISSN:0045-7825, ISSN:1879-2138

SUMMARYThis paper proposes an approach for the high-level programming of co-designed robots that reduces programming complexity. Particularly, the work presented focuses on the programming framework of an intelligent system, based on the IEEE Standard Ontologies for Robotics and Automation, which allows users the automatic design of robots and the automatic implementation of controllers in the Robot Operating System (ROS). In our approach, the co-designed robot functionalities are automatically translated into visual programming blocks allowing non-expert users an easy robot programming by means of a visual programming language. Several robot configurations and three case studies are provided as a proof of concept. The validation, in terms of usability, of the framework has been carried out with inexperienced users showing promising results.

Sistem absensi online dengan pemanfaatan Raspberry sebagai server lokal untuk sinkron data sistem pada server dan pengolahan data absensi, nodemcu sebagai alat yang dapat memancarkan sinyal hostpot untuk pengambilan absensi dan android yang terhubung ke hostpot nodemcu dengan aplikasi android untuk masing - masing perserta acara dalam pengambilan absensi. Pada sistem ini Absensi yang dapat diambil pada jarak kurang dari 30 meter dengan pemanfaatan sinyal hostpot nodemcu, alat absensi dengan daya tambahan baterai sebagai atisipasi daya utama listrik mati, server lokal Raspberry pi yang selalu sinkron dengan server publik sehingga pada saat jaringan internet mati request absensi masih dapat diolah dan di simpan, aplikasi yang menyimpan mac address peserta acara sehingga hanya terapat satu perangak pada satu user untuk minimalisir kecurangan penitipan absensi, pengambilan absensi dan izin acara dapat dilakukan pada aplikasi android, pengambilan absensi memungkinkan dapat diambil secara bersamaat untuk mengatasi pengambilan absensi. Sistem absensi ini memanfaatkan teknologi Restfult API dengan framework Express pemograman JavaScript ,mikrokontroller dengan Arduino IDE pemrograman C, website dengan framework React.js pemograman JavaScript, dan aplikasi android dengan React Native pemograman JavaScript. Dalam perancangan sistem absensi ini memiliki beberapa metode yaitu identifikasi permasalahan, studi literatur, konsultasi, analisi sistem, perancangan sistem, dan implementasi. Hasil dari sistem ini berupa sistem yang dapat mengefesienkan pengambilan dan minimalisir kecurangan absensi

Analyzing real-world software is challenging due to complexity of the software frameworks or APIs they depend on. In this paper, we present a tool, PROMPT, that facilitates the analysis of software components using API model guided symbolic execution. PROMPT has a specification component, PROSE, that lets users define an API model, which consists of a set of data constraints and life-cycle rules that define control-flow constraints among sequentially composed API functions. Given a PROSE model and a software component, PROMPT symbolically executes the component while enforcing the specified API model. PROMPT has been implemented on top of the KLEE symbolic execution engine and has been applied to Linux device drivers from the video, sound, and network subsystems and to some vulnerable components of BlueZ, the implementation of the Bluetooth protocol stack for the Linux kernel. PROMPT detected two new and four known memory vulnerabilities in some of the analyzed system software components.

This paper describes a cloud-based software framework to predict cement strength for 2 days, 7 days and 28 days. Levenbarg-Marquardt back-propagation-artificial neural network (LMBP-ANN) is used to...

Emergent paradigms of Industry 4.0 and Industrial Internet of Things expect cyber-physical systems to reliably provide services overcoming disruptions in operative conditions and adapting to changes in architectural and functional requirements. In this paper, we describe a hardware/software framework supporting operation and maintenance of software-controlled systems enhancing resilience by promoting a Model-Driven Engineering (MDE) process to automatically derive structural configurations and failure models from reliability artifacts. Specifically, a reflective architecture developed around digital twins enables representation and control of system Configuration Items properly derived from SysML Block Definition Diagrams, providing support for variation. Besides, a plurality of distributed analytic agents for qualitative evaluation over executable failure models empowers the system with runtime self-assessment and dynamic adaptation capabilities. We describe the framework architecture outlining roles and responsibilities in a System of Systems perspective, providing salient design traits about digital twins and data analytic agents for failure propagation modeling and analysis. We discuss a prototype implementation following the MDE approach, highlighting self-recovery and self-adaptation properties on a real cyber-physical system for vehicle access control to Limited Traffic Zones.

Today, the number of interconnected devices and the amount of personal information gathered by them increases tremendously resulting in the need for development tools to harness its potential. New devices are continually being introduced in the daily life of people, and they are already producing an unprecedented amount of data related to people's well-being. However, taking advantage of such information to create innovative Internet of Bodies solutions heavily relies on manually gathering the needed information from several sources on services and the devices involved. In this paper, we present a novel Human Data Model - a new tool to combine personal information from several sources, perform computations over that information, and proactively schedule computer-human interactions. Developers that use the proposed model would obtain an opportunity to create the Internet of Bodies solutions using high-level abstractions of the users' personal information and taking advantage of the distributed approach of the model. acceptedVersion

Recent years have witnessed an exponential increase in the use of mobile and embedded devices. With the great success of deep learning in many fields, there is an emerging trend to deploy deep learning on mobile and embedded devices to better meet the requirement of real-time applications and user privacy protection. However, the limited resources of mobile and embedded devices make it challenging to fulfill the intensive computation and storage demand of deep learning models. In this survey, we conduct a comprehensive review on the related issues for deep learning on mobile and embedded devices. We start with a brief introduction of deep learning and discuss major challenges of implementing deep learning models on mobile and embedded devices. We then conduct an in-depth survey on important compression and acceleration techniques that help adapt deep learning models to mobile and embedded devices, which we specifically classify as pruning, quantization, model distillation, network design strategies, and low-rank factorization. We elaborate on the hardware-based solutions, including mobile GPU, FPGA, and ASIC, and describe software frameworks for mobile deep learning models, especially the development of frameworks based on OpenCL and RenderScript. After that, we present the application of mobile deep learning in a variety of areas, such as navigation, health, speech recognition, and information security. Finally, we discuss some future directions for deep learning on mobile and embedded devices to inspire further research in this area.

Technology has changed our life and the way we work; however, technology has affected several methods of working in Small and Medium Enterprises (SME)s. Human Resource (HR) is one of the core components in businesses, and nowadays most businesses are using technology for daily basis tasks. However, it still is not used all over the world. In Kurdistan Region-Iraq (KRI), most of the SMEs still use the old way of working and follow the paper-based method for their daily basis tasks. According to a survey, more than seventy percent of SMEs in Kurdistan are not using software to manage human resource management tasks. However, some big companies are using HRMS; but even then, there is a lack of use of Cloud Technology. In this study, a model of the Enterprise Human Resource Management System (EHRMS) is proposed and implemented to solve the HR problems in this area using Cloud Technology. The proposed system consists of sixteen standard modules which used usually with famous HRM systems. The system has been developed by using several technologies such as CodeIgniter as a software framework. The system is launched and deployed on Amazon Web Service (AWS) Elastic Compute Cloud (EC2).