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This work addressed efficient and effective implementation of honeypots (decoy devices) for industrial control systems in cloud services. The honeypots we investigated simulated control systems of a small electrical-power distribution system. Starting with two honeypot software frameworks called Conpot and GridPot, we increased their deceptiveness by adding new obfuscation techniques, new simulated features of an electric grid, and new control interfaces that mimicked the operator interface for an actual power plant. These deceptions were effective in our first experiments with a standalone honeypot. We then deployed the honeypot at three cloud sites in the U.S. and in Asia. Attacks we observed were mostly similar between the deployments with a few differences. We were concerned that deployment in the cloud could be detected by attackers and discourage them, but we saw no significant differences in activity between the two kinds of deployments; apparently enough systems are managed in the cloud today that such deployment is not suspicious. We conclude that honeypots for industrial control systems using cloud services are an effective tool for collecting attack intelligence.

IoT nodes are spread out everywhere in the world, collecting data and transmitting it to local or cloud storage for further analysis and processing using machine learning or artificial intelligence algorithms. These nodes are the base elements of IoT networks infrastructures that constructs the foundation of smart cities. Actually, IoT nodes, are installed to perform two essential tasks: measurements and data processing and broadcasting. This is achieved through a programmable hardware that is made mainly of a processing unit and a wireless communication stack. Full disclosure, back in 2005 or so when iphone got into market, everyone heared about smartphone. Fast forward 15 years and well, a lot has changed. There is a lot more hardware (Enhanced CPUs and sensors), operating systems, applications, and everything in-between out there now that wasn’t so available before. As the demand of this technology is exponentially rising, developing tools and frameworks for rapid production is a challenge to allow rapid prototyping and access to market. Through this chapter, we will use Simulink to develop a code generator for a dedicated hardware IoT node based on espressif ESP8266 microcontroller. Simulink gives the IoT nodes not only the ability to accomplish measurements, but also the power to make decisions and performs actions using various algorithms, such as PID control, fuzzy logic and neural networks.

In today’s society, the Internet has penetrated into all aspects of daily life, and has become an indispensable part of people’s life. In the teaching of vocal music, teachers and students constantly put forward problems that need to be solved in teaching. For example, due to the lack of teaching time, teachers do not have enough classroom time to teach comprehensive vocal music skills for students. The homework assigned by teachers needs to be checked and scored uniformly. The workload is heavy, and there is no communication channel between teachers and students except classroom time. In this paper, the application of software engineering, computer network, web site architecture and other aspects of knowledge, the requirements of the system are analyzed, and on this basis, the overall architecture of the system and its internal modules are designed in detail. In the process of implementation, with the help of ThinkPHP, the mature software framework, on the basis of which, the data persistence layer and presentation layer of the system are realized, and the business logic layer of the system is set up between the two layers to realize the hierarchy and module structure of the system design. After the implementation of the system, we also use the system testing method to test the system in an all-round way to ensure the correct use of the system.

Time series forecasting plays a vital role in system monitoring and novelty detection. However, commonly used forecasting methods are not suited for handling non-stationarity, while existing methods for forecasting in non-stationary time series are often complex to implement and involve expensive computations. We introduce a Gaussian process-based model for handling of non-stationarity. The warping is achieved non-parametrically, through imposing a prior on the relative change of distance between subsequent observation inputs. The model allows the use of general gradient optimization algorithms for training and incurs only a small computational overhead on training and prediction. The model finds its applications in forecasting in non-stationary time series with either gradually varying volatility, presence of change points, or a combination thereof. We implement the model in a probabilistic programming framework, evaluate on synthetic and real-world time series data comparing against both broadly used baselines and known state-of-the-art approaches and show that the model exhibits state-of-the-art forecasting performance at a lower implementation and computation cost, enabling efficient applications in diverse fields of system monitoring and novelty detection..

Recently, CUDA introduces a new task graph programming model, CUDA graph, to enable efficient launch and execution of GPU work. Users describe a GPU workload in a task graph rather than aggregated GPU operations, allowing the CUDA runtime to perform whole-graph optimization and significantly reduce the kernel call overheads. However, programming CUDA graphs is extremely challenging. Users need to explicitly construct a graph with verbose parameter settings or implicitly capture a graph that requires complex dependency and concurrency managements using streams and events. To overcome this challenge, we introduce a lightweight task graph programming framework to enable efficient GPU computation using CUDA graph. Users can focus on high-level development of dependent GPU operations, while leaving all the intricate managements of stream concurrency and event dependency to our optimization algorithm. We have evaluated our framework and demonstrated its promising performance on both micro-benchmarks and a large-scale machine learning workload. The result also shows that our optimization algorithm achieves very comparable performance to an optimally-constructed graph and consumes much less GPU resource.

The open-source hardware/software framework TaPaSCo aims to make reconfigurable computing on FPGAs more accessible to non-experts. To this end, it provides an easily usable task-based programming abstraction, and combines this with powerful tool support to automatically implement the individual hardware accelerators and integrate them into usable system-on-chips. Currently, TaPaSCo relies on the host to manage task parallelism and perform the actual task launches. However, for more expressive parallel programming patterns, such as pipelines of task farms, the round trips from the hardware accelerators back to the host for launching child tasks, especially when exploiting data-dependent execution times, quickly add up. The major contribution of this work is the addition of on-chip task scheduling and launching capabilities to TaPaSCo. This enables not only low-latency dynamic task parallelism, it also encompasses the efficient on-chip exchange of parameter values and task results between parent and child accelerator tasks. Our solution is able to handle recursive task structures and is shown to have latency reductions of over 35x compared to the prior approaches.

The given paper is aimed to investigation of the energy efficiency issues for IoT solutions, based on combined sensor and contactless systems. The combined energy-efficient solutions are namely the hierarchical organized infrastructure WSNs and self-organized ad-hoc WSNs, which are widely interoperable with the 4G/5G base stations and micro-cells, the backboned Wi-Fi access points as well as with inexpensive and energy-efficient RFID/NFC reader farms. In regard to provide the energy-efficient WSN protocols, a holistic multi-layered approach is used, which is based on Low-Duty-Cycle-Principle, energy-harvesting, LEACH Clustering and topology optimization, efficient OS and software frameworks, enabling data reduction.

Rucio is an open-source software framework that provides scientific collaborations with the functionality to organize, manage, and access their data at scale. The data can be distributed across heterogeneous data centers at widely distributed locations [1]. Since its commissioning in 2014, Rucio has become the de-facto standard for scientific data management, even outside CERN community [6]. The rich amount of data gathered about the transfers by Rucio presents a unique opportunity to better understand the complex mechanisms involved in file transfers across the Worldwide LHC Computing Grid (WLCG). This work focuses on the study of a recently published dataset [4] to reconstruct the lifetime of transfers and reveals important information that can be used to predict the Time To Complete (TTC) of transfers across the WLCG.

Computing and software for big science 5(1), 20 (2021). doi:10.1007/s41781-021-00065-z, Computing centres, including those used to process High-Energy Physics data and simulations, are increasingly providing significant fractions of their computing resources through hardware architectures other than x86 CPUs, with GPUs being a common alternative. GPUs can provide excellent computational performance at a good price point for tasks that can be suitably parallelized. Charged particle (track) reconstruction is a computationally expensive component of HEP data reconstruction, and thus needs to use available resources in an efficient way. In this paper, an implementation of Kalman filter-based track fitting using CUDA and running on GPUs is presented. This utilizes the ACTS (A Common Tracking Software) toolkit; an open source and experiment-independent toolkit for track reconstruction. The implementation details and parallelization approach are described, along with the specific challenges for such an implementation. Detailed performance benchmarking results are discussed, which show encouraging performance gains over a CPU-based implementation for representative configurations. Finally, a perspective on the challenges and future directions for these studies is outlined. These include more complex and realistic scenarios which can be studied, and anticipated developments to software frameworks and standards which may open up possibilities for greater flexibility and improved performance., Published by Springer International Publishing, Cham, Switzerland

There are competing priorities between creative freedom and the need for robust, stable software frameworks to facilitate the rapid implementation of creative ideas in game development. This may result in a disparity between system and user requirements. Qualitative data extracted from seminars at the Game Developers Conference informs the design of several interviews with veteran game-system designers to explore this phenomenon. A survey of modular software plug-ins from the Unity Asset Store then validates the interview findings and explores the benefits of modular software architectures. Findings indicate that modifications to the native user experience (UX) design of Unity and plug-ins that reengineer for different workflows are most popular. The most popular workflows provide for data, asset, and project management. Discussion reflects on how modular architecture can alleviate points of failure within a game engine’s architecture whilst providing customized usability for different user needs.

We live in an era full of unprecedented opportunities, and deep learning technology can help us achieve new breakthroughs. Deep learning plays a pivotal role in the exploration of exponents, the development of new drugs, the diagnosis of diseases, and the detection of subatomic particles. It can fundamentally enhance our understanding of biology (including genomics, proteomics, metabolomics, immunohisics, etc.).This era of our lives is also facing severe challenges. Climate change threatens food production, and may even one day explode because of limited resources. The challenge of environmental change will also be further exacerbated by the growing population, with a global population expected to reach 9 billion by 2050. Coupled with the ever-evolving ability of biological neural networks to process visual information, vision provides animals with a map of their surroundings, improving their ability to perceive the outside world. Today, the combination of artificial eye cameras and neural networks that can handle the visual information captured by these artificial eyes detonates the explosion of data-driven artificial intelligence applications. Just as vision plays a key role in the evolution of Earth's life, deep learning and neural networks will enhance the capabilities of robots. The ability of robots to understand the surrounding environment will become stronger and stronger, and they can make decisions on their own, collaborate with humans, and enhance human capabilities.

Due to the technological advances, the reference frameworks to carry out web and mobile applications primarily focus on improving Human-Computer Interaction (HCI). This document compiles a series of current trends both in framework and in developing systems that enhance HCI in the new paradigm of cognitive cities. For this, a Systematics Literature Review (SLR) methodology has been applied, based on an exhaustive search in Scientific libraries of the Informatics field. This research presents new frameworks that improve HCI in medicine, education, and urban planning; based on the development of Cognitive Cities.

Schematic diagrams are used in graph-based engineering systems. They focus mainly on the structure of the design object. Graph-based engineering systems help to solve a concrete design task. This is primarily realized by the application of domain-specific languages. The layout of schematic diagrams is of particular importance, and a neat representation is desirable. But automatically generated layouts cannot always fully match the intention of a modeler. To improve automatic layouts and enable a user-specific representation, an algorithm that allows interactive changes of the orthogonal hyperedge geometry was implemented. In this paper, we present this algorithm and give an overview of such interactions. Additionally, several reductions of the hyperedge geometry are shown. Furthermore, a local, automatic routing considering interactions on the hyperedge geometry is presented. The consideration of domain-specific semantics and the possibility of interactive changes is a new approach. All algorithms were implemented in a self-developed software framework.

Designing neural networks for classification or regression can be considered a search problem, and, as such, can be approached using different optimization procedures, all of them with several design challenges: The first and more important is to constrain the search space in such a way that proper solutions can be found in a reasonable amount of time; the second is to take into account that, depending on how the optimization procedure is formulated, the fitness score used for it can have a certain degree of uncertainty. This means that creating a framework for evolving neural networks for classification implies taking a series of decisions that range from the purely technical to the algorithmic at different levels: neural or the optimization framework chosen. This will be the focus of this paper, where we will introduce DeepGProp, a framework for genetic optimization of multilayer perceptrons that efficiently explores space of neural nets with different layers and layer size.

Knowledge Graphs are a critical source for Question Answering, but their potential may be threatened due to the complexity of their query languages, such as SPARQL. On the opposite side, Virtual Assistants have witnessed an extraordinary interest as they enable users to pose questions in natural language. Many companies and researchers have combined Knowledge Graphs and Virtual Assistants, but no one has provided end-users with a generic methodology to generate extensions for automatically querying knowledge graphs. Thus, we propose a community shared software framework to create custom extensions to query knowledge graphs by virtual assistants, unlocking the potentialities of the Semantic Web technologies by bringing knowledge graphs in the “pocket” of everyone, accessible from smartphones or smart speakers.

We present Phoenix, a scalable hypergraph analytics framework for data analytics and knowledge discovery that was implemented on the leadership class computing platforms at Oak Ridge National Laboratory (ORNL). Our software framework comprises a distributed implementation of a streaming server architecture which acts as a gateway for various hypergraph generators/external sources to connect. Phoenix has the capability to utilize diverse hypergraph generators, including HyGen, a very large-scale hypergraph generator developed by ORNL. Phoenix incorporates specific algorithms for efficient data representation by exploiting hidden structures of the hypergraphs. Our experimental results demonstrate Phoenix’s scalable and stable performance on massively parallel computing platforms. Phoenix’s superior performance is due to the merging of high-performance computing with data analytic.

A modern Defence Force consists of a diverse range of capabilities to support missions at the tactical, operational and strategic levels. Designing a balanced and affordable force structure to meet Government strategic objectives and assure national security has always been a challenge. Force design is a centralized and enduring process within the Australian Defence Organisation that seeks to translate Government strategic objectives into a coherent force structure within specified time and budget envelopes. This process increasingly relies on analytical approaches and tools such as wargaming, simulation and optimization techniques. This paper investigates evolutionary algorithms (EAs) as a potential tool for generating and evaluating force structure options. EAs can evaluate an extremely large solution space of force mixes at a much faster rate than human cognition to determine a balanced and affordable force structure option according to an objective function. This paper also discusses the implementation of a software framework, dubbed “FORCESIGHT”, which can be customized by developers to model any scenario where the use of EAs is appropriate. Based on the outcomes of a trial of FORCESIGHT, it is clear that the EAs approach could provide a result of respectable quality. It is demonstrated that EAs can lead to large increases in efficient evaluation of potential improvements to the Force-in-Being and Future Force.

Today, the energy resources used by machine learning methods, especially those based on deep neural networks, pose a serious climate problem. To reduce the energy footprint of these systems, the study and development of energy-efficient neural networks is increasing enormously. Among the different existing proposals, spiking neural networks are a promising alternative to achieve this goal. These methods use activation functions based on sparse binary spikes over time that allow for a significant reduction in energy consumption. However, one of the main drawbacks of these networks is that these activation functions are not derivable, which prevents their direct training in traditional neural network development software. Due to this limitation, the community has developed different training methods for these networks, together with different libraries that implement them. In this paper, different libraries for the development and training of these networks are analysed. Their main features are highlighted with the aim of helping researchers and practitioners in the decision making process regarding the development of spiking neural networks according to their needs.

International audience; Parallel programming is much more complex and difficult than sequential programming, and it is therefore more challenging to achieve the same software quality in a parallel context. High-level parallel programming models, if implemented as software frameworks, could increase productivity and reliability. Important requirements such as extensibility and adaptability for different platforms are required for such a framework, and this paper reflects on these requirements and their relation to the software engineering methodologies that could put them in practice. All these are exemplified on a Java framework-JPLF; this is a high-level parallel programming approach being based on the model brought by the PowerLists associated theories, and it respects the analysed requirements. The design of JPLF is analysed by explaining the design choices and highlighting the design patterns and design principles applied.

We present a software framework that integrates Arduino-enabled remote devices with the UNITY game engine through Internet-of-Things technology. The framework allows a technology stack where networked physical devices become digital-twin game objects in UNITY. This makes it possible for game developers without competence in electronics or Arduino programming to develop hybrid pervasive games directly in UNITY. The framework was implemented in C# on the UNITY side and C++ on the Arduino side, using MQTT as a message broker. The first version of the framework was evaluated empirically with UNITY game developers, resulting in requirements for an updated version. The updated version was used to develop a hybrid pervasive exergame for elderly, which is presented in the paper. The overall positive feedback from the evaluation with game developers and our own experience using the framework encourages us to develop the framework further and integrating it as much as possible with the UNITY development environment. Our aim is to make developing hybrid pervasive games possible for UNITY game developers, without requiring them to concern themselves with the troubles of networks, electronics, or low-level microcontroller programming.

The physics programs of the experiments at the LHC heavily rely on detailed simulations that predict the response of the complex detectors and make the comparison between experimental results and theoretical predictions possible. The simulation process in ATLAS is controlled by Athena [1], a software framework which takes care of the particle interaction and propagation in the detector, as well as the physics object reconstruction, described in Chap. 5. Athena uses the Geant4 [2] simulation toolkit to propagate particles. The simulation chain can be divided into fundamental steps: the event generation, the detector simulation, the digitisation of the energy deposited in the sensitive material of the detector and finally the reconstruction of the physics objects [3]. The flow of the ATLAS simulation chain is sketched in Fig. 6.1, which reports the main stages (in boxes) and the data objects produced (arrows), and is briefly outlined below.

Robots are being used for an increasing number and range of tasks in the fields of nuclear energy, mining, petrochemical processing, and sub-sea. This is resulting in ever more complex robotics installations being deployed, maintained, and extended over long periods of time. Additionally, the unstructured, experimental, or unknown operational conditions frequently result in new or changing system requirements, meaning extension and adaptation is necessary. Whilst existing frameworks allow for robust integration of complex robotic systems, they are not compatible with highly efficient maintenance and extension in the face of changing requirements and obsolescence issues over decades-long periods. We present CorteX that attempts to solve the long-term maintainability and extensibility issues encountered in such scenarios through the use of a standardised, self-describing data representations and associated communications protocols. Progress in developing and testing the CorteX framework, as well as an overview of current and planned deployments, will be presented.