Processing Paradigm Research Articles

One Transistor One Electrolyte‐Gated Transistor Based Spiking Neural Network for Power‐Efficient Neuromorphic Computing System

AbstractNeuromorphic computing powered by spiking neural networks (SNN) provides a powerful and efficient information processing paradigm. To harvest the advantage of SNNs, compact and low‐power synapses that can reliably practice local learning rules are required, posing significant challenges to the conventional silicon‐based platform in terms of area‐ and energy‐efficiency, as well as computing throughput. Here, electrolyte‐gated transistors (EGTs) paired with transistors are employed to implement power‐efficient neuromorphic computing systems. The one‐transistor‐one‐EGT (1T1E) synapse not only alleviates the self‐discharging of EGT but also provides a flexible and efficient way to practice the important spike‐timing‐dependent plasticity learning rule. Based on that, an SNN with a temporal coding scheme is implemented for associative memory that can learn and recover images of handwritten digits with high robustness. Thanks to the temporal coding scheme and low operation current of EGTs, the energy‐efficiency of 1T1E‐based SNN is ≈30× lower than that of the prevalent rate coding scheme, and the peak performance is estimated to be 2 pJ/SOP (picojoule per synaptic operation) at the training phase and 80 TOPs−1 W−1 (tera operations per second per watt) at inference phase, respectively. These results pave the way for power‐efficient neuromorphic computing systems with wide applications for edge computing.

Example of Warehouse System Design Based on the Principle of Logistics

The warehouse process, as one of many logistics processes, currently holds an irreplaceable position in logistics systems in companies and in the supply chain. The proper function of warehouse operations depends on, among other things, the type of the used technology and their utilization. The research in this article is focused on the design of a warehouse system. The selection of a suitable warehouse system is a current research topic as the warehouse system has an impact on warehouse capacity and utilization and on the speed of storage activities. The paper presents warehouse system design methodology that was designed applying the logistics principle-systematic (system) approach. The starting point for designing a warehouse system represents of the process of design logistics systems. The design process consists of several phases: project identification, design process paradigm selection, system analysis, synthesis, and project evaluation. This article’s contribution is the proposed methodology and design of the warehouse system for the specified conditions. The methodology was implemented for the design of a warehouse system in a cold box, which is a part of a distribution warehouse. The technology of pallet racking was chosen in the warehouse to store pallets. Pallets will be stored and removed by forklifts. For the specified conditions, the warehouse system was designed for two alternatives of racking assemblies, which are served by forklifts. Alternative 1—Standard pallet rack with wide aisles and Alternative 2—Pallet dynamic flow rack. The proposed systems were compared on the basis of selected indicators: Capacity—the number of pallet places in the system, Percentage ratio of storage area from the box area, Percentage ratio of handling aisles from the box area, Access to individual pallets by forklift, Investment costs for 1 pallet space in EUR. Based on the multicriteria evaluation, the Alternative 2 was chosen as the acceptable design of the warehouse system with storage capacity 720 pallet units. The system needs only two handling aisles. Loading and unloading processes are separate from each other, which means that there are no collisions with forklifts. The pallets with the goods are operated on the principle of FIFO (first in, first out), which will facilitate the control of the shelf life of batches or series of products. The methodology is a suitable tool for decision-making in selecting and designing a warehouse system.

Linked Data Processing for Human-in-the-Loop in Cyber–Physical Systems

There are several kinds of smart devices, such as smartphones, sensors, and smart wearable devices, included in the Human-in-the-Loop (HITL) system, but different devices have their own data processing and programming paradigm. Programmers usually need to design the same data processing logic for different devices by using a different programming model. How to mapping the same code to different devices without any change is an emerging topic in the HITL system. Furthermore, the intelligent data processing for the smart CPS sector is experiencing significant growth in data volume, driven by a large number of smart devices that are anticipated in the near further. All these smart devices are expected to improve the overall HITL system performance marvelously. A large number of devices can also outstandingly increase the data volume, which needs to be processed in real time. How to process large-scale data on a smart device in real time is another challenge. Focused on these challenges, this article proposed a computing device-aware HITL CPS data processing framework, named Barge, aiming to map the regular code to the different hardware without any change. In Barge, a semantic model, an architecture-driven programming model, and a graph partition scheme are included. The semantic model is used to express the user-defined graph algorithms by using the domain-specific language. The architecture-driven programming model will execute the graph algorithms on a different device in parallel. Furthermore, the graph partition scheme will partition the large-scale graphs into suitable partitions by aware of the topology to make the partitioned data suitable for kinds of smart devices. We believe that our work would open a wide range of opportunities to improve the performance of large-scale graph processing for HITL systems.

Synaptic Learning With Augmented Spikes.

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements in efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this article, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insights into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness, and applicability to practical tasks, such as acoustic and visual pattern recognition. Our augmented approaches show several advanced learning properties and reliably outperform the baseline ones that use typical all-or-nothing spikes. Our approaches significantly improve the accuracies of a temporal-based approach on sound and MNIST recognition tasks to 99.38% and 97.90%, respectively, highlighting the effectiveness and potential merits of our methods. More importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them, including neuromorphic computing.

Investigating response time and accuracy in online classifier learning for multimedia publish-subscribe systems

The enormous growth of multimedia content in the field of the Internet of Things (IoT) leads to the challenge of processing multimedia streams in real-time. Event-based systems are constructed to process event streams. They cannot natively consume multimedia event types produced by the Internet of Multimedia Things (IoMT) generated data to answer multimedia-based user subscriptions. Machine learning-based techniques have enabled rapid progress in solving real-world problems and need to be optimised for the low response time of the multimedia event processing paradigm. In this paper, we describe a classifier construction approach for the training of online classifiers, that can handle dynamic subscriptions with low response time and provide reasonable accuracy for the multimedia event processing. We find that the current object detection methods can be configured dynamically for the construction of classifiers in real-time, by tuning hyperparameters even when training from scratch. Our experiments demonstrate that deep neural network-based object detection models, with hyperparameter tuning, can improve the performance within less training time for the answering of previously unknown user subscriptions. The results from this study show that the proposed online classifier training based model can achieve accuracy of 79.00% with 15-min of training and 84.28% with 1-hour training from scratch on a single GPU for the processing of multimedia events.

Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things

The Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. Building up such flexibility within the edge-to-cloud continuum consisting of a distributed networked ecosystem of heterogeneous computing resources is challenging. Furthermore, IoT traffic dynamics and the rising demand for low-latency services foster the need for minimizing the response time and balanced service placement. Load-balancing for fog computing becomes a cornerstone for cost-effective system management and operations. This paper studies two optimization objectives and formulates a decentralized load-balancing problem for IoT service placement: (global) IoT workload balance and (local) quality of service (QoS), in terms of minimizing the cost of deadline violation, service deployment, and unhosted services. The proposed solution, EPOS Fog, introduces a decentralized multi-agent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution. The agents locally generate possible assignments of requests to resources and then cooperatively select an assignment such that their combination maximizes edge utilization while minimizes service execution cost. Extensive experimental evaluation with realistic Google cluster workloads on various networks demonstrates the superior performance of EPOS Fog in terms of workload balance and QoS, compared to approaches such as First Fit and exclusively Cloud-based. The results confirm that EPOS Fog reduces service execution delay up to 25% and the load-balance of network nodes up to 90%. The findings also demonstrate how distributed computational resources on the edge can be utilized more cost-effectively by harvesting collective intelligence.

Systematization of the theories of corporate growth on the basis of the methodology of paradigms

Multiple theories and models that examine different aspects of corporate growth can be systematized on the basis of the methodology of paradigms. For this purpose, the author determines the three dominant paradigms of growth (causal, paradigm of the results of growth, and process paradigm); each of them forms the methodological principles of growth research and original representation on the process of corporate growth. The methodology of causal paradigm is based on orientation towards determining causal relations (laws) responsible for the growth. Paradigm of the results synthesizes the theories and models of growth that study the life cycle of the companies and changes taking place on different stages. The concepts of process paradigm of growth view the company as a complex, self-organizing system, and follow the organizational changes that emerge as a result of growth. The present time marks the establishment of integration paradigm, which describes growth as a process of integrating companies into supra-organizational structures. This article attempts to systematize the theoretical approaches towards understanding the mechanisms and distinctive features of corporate growth based on the general scientific methodology of paradigms. The author believes that the fundamental difference between the proposed typology consists in  the focus on “natural” systematization, which is based not on the highlighted characteristics, analogies, isomorphic manifestations of growth essential for classification, but on homologies, i.e. characteristics based on the theoretical and methodological grounds shared by one or another group of theories, which set the general vector of research and representations on the peculiarities of the object of study — corporate growth. Such general principles of research comprise the core of a particular paradigm that encompasses a group of concepts of corporate growth.

Vehicle routing problem with simulated annealing using python programming

Vehicle Routing Problems (VRP) are a type of linear programming problem. This paper tries to explain the completion of VRP using Python Programming with the Simulated Annealing algorithm. Python Programming is used as a tool by utilizing the wealth of packages in python. Python is a popular programming language. Python is easy to learn for beginners. However, there is still no example and guide for solving VRP using simulated annealing in python programming with a process paradigm. This design can be a reference and guide for beginners in dealing with similar problems or the development of existing problems.

Vermifiltration as a natural, sustainable and green technology for environmental remediation: A new paradigm for wastewater treatment process

Wastewater treatment and reuse is one of the most promising efforts to stem global water crisis. With the present shift towards sustainable development as a priority, the wastewater companies should respond to this requirement. To downturn, the negative impact of anthropogenic activities, advanced green technologies (AGTs) are being practiced. AGTs refer to a group of natural, green technologies that includes practical methodologies and materials based on non-toxic chemical processes, clean energies, and environmental monitoring to slow down the negative impact induced by human activities. The main motive of green technologies is to provide high end results without compromising on environmental sustainability. Among all the existing options, green technology such as vermifiltration is the natural & sustainable choice. Vermifiltration is an extension of the vermicomposting process. It is a biofilter with earthworms, where the earthworms digest the suspended particles screened on the filter bed, and degrade organic matter through enzymatic activity, and in the process of ingestion; they passively aerate the system by burrowing action and removes pathogens. In this context, the present graphical review envisages the current state of the technical knowledge of the vermifiltration process, factors affecting the process and performance of vermifilters under different scenarios related to the treatment mechanisms and effective applications and advantages of the technology. This technology is a stand-alone technology providing tremendous benefits such as decentralized solution and high value end products like vermicompost and can be considered as a new paradigm for wastewater treatment processes.

Biometry in plant breeding

Abstract In this manuscript we discuss the purpose and scope of biometry and its interactions, complementation, and even overlap with several other areas of genetics. We emphasize that biometry is an area of genetics that enables researchers to analyze, process, and interpret biological phenomena from data, usually obtained from experimental tests, in an improved way to guide strategies and decision making for optimization of resources. We also highlight the importance of the biometry professional in the context of breeding and the need for continual training, due to new demands for and challenges from inclusion of different types of information for processing and analysis paradigms to better interpret these paradigms.

A novel approach to determine residual stress field during FSW of AZ91 Mg alloy using combined smoothed particle hydrodynamics/neuro-fuzzy computations and ultrasonic testing

The faults in welding design and process every so often yield defective parts during friction stir welding (FSW). The development of numerical approaches including the finite element method (FEM) provides a way to draw a process paradigm before any physical implementation. It is not practical to simulate all possible designs to identify the optimal FSW practice due to the inefficiency associated with concurrent modeling of material flow and heat dissipation throughout the FSW. This study intends to develop a computational workflow based on the mesh-free FEM framework named smoothed particle hydrodynamics (SPH) which was integrated with adaptive neuro-fuzzy inference system (ANFIS) to evaluate the residual stress in the FSW process. An integrated SPH and ANFIS methodology was established and the well-trained ANIS was then used to predict how the FSW process depends on its parameters. To verify the SPH calculation, an itemized FSW case was performed on AZ91 Mg alloy and the induced residual stress was measured by ultrasonic testing. The suggested methodology can efficiently predict the residual stress distribution throughout friction stir welding of AZ91 alloy.

An Enhanced Filtering-Based Information Granulation Procedure for Graph Embedding and Classification

Granular Computing is a powerful information processing paradigm for synthesizing advanced pattern recognition systems in non-conventional domains. In this article, a novel procedure for the automatic synthesis of suitable information granules is proposed. The procedure leverages a joint sensitivity-vs-specificity score that accounts the meaningfulness of candidate information granules for each class considered in the classification problem at hand. Only statistically relevant granules are retained for a graph embedding procedure towards a geometric space, in which standard classification systems can be used without alterations. Performance tests have been carried out by considering open access datasets of fully labelled graphs with arbitrarily complex nodes and/or edges attributes that, by definition, must rely on inexact graph matching procedures to quantify dissimilarities. Two variants of the procedure are investigated: a standard variant, which aims at automatically finding suitable information granules for solving the classification problem as a whole, and a class-specific metric learning variant, in which the optimization procedure is performed in a class-aware fashion. In the latter case, each class will have its own set of information granules, along with the corresponding parameters defining distinct instances of the dissimilarity measure. Computational results show that the proposed algorithm is able to outperform the vast majority of current approaches for graph classification, while at the same time returning a grey-box model, interpretable by field-experts.

Sparse Analysis Recovery via Iterative Cosupport Detection Estimation

Cosparse analysis model (CAM) provides a new signal processing paradigm for recovering cosparse signals with respect to a given analysis operator from the undersampled linear measurements in the context of emerging theory of compressed sensing (CS). The sparse analysis recovery/cosparse recovery is a key one brought up by this new paradigm. In this paper, we propose a new family of analysis pursuit algorithms for the sparse analysis recovery problem when the signals obey the cosparse analysis model, termed as iterative cosupport detection estimation (ICDE). ICDE is an algorithmic framework, which alternates between detecting a cosupport set of the unknown true signal and estimating the underlying signal by solving a truncated analysis pursuit problem on the detected cosupport. Further, we propose effective implementations of ICDE equipped with an efficient thresholding strategy for cosupport detection. Empirical performance comparisons show that ICDE is favorable in comparison with the state-of-the-art sparse analysis recovery algorithms. Source code of ICDE has been made publicly available on Github: https://github.com/songhp/ICDE.

Research of User-Centered Intelligent Technology in China’s Cultural and Creative Product Design

With the development of computer science, “user-centered” design assisted by intelligent technology is a new trend in China’s cultural and creative design. To clarify the research progress, the author reviewed the representative predecessor’s works in China, summarized a design process paradigm from their works, and classified the various design methods into a reasonable framework by manual content analysis. However, some problems still exist. The first is lack of intelligent design method promotion, and the other is the researchers are unaware that cultural and creative design has developed to a state with blurred boundary, in which the design not only means the design of cultural and creative products but also includes the design of exhibitions and education activities.

Blockchain-Enabled HMM Model for Sports Performance Prediction

The historical training or exam data of an athlete produced in the past sport exercise or test activities have provided a promising way to objectively and accurately evaluate the real-time sport performance of the athlete. However, the continuous generation of sport training or exam data has placed a heavy transmission and processing burden on the traditional centralized data processing paradigm (e.g., cloud platform). Considering this drawback, a decentralized blockchain-based athlete sport data transmission and utilization solution is proposed in this research work. Moreover, the available athlete sport data produced in past sport exercise or test activities is often sparse and time-related, which call for a robust and time-aware data fusion and processing solution. In this situation, HMM model is employed in this article to cope with the data sparsity and dynamics and further make accurate sports performance prediction for athletes accordingly. Finally, we design a set of experiments on a real-world dataset to validate the feasibility of our proposal in terms of effectiveness and efficiency.

Time- and Computation-Efficient Data Localization at Vehicular Networks’ Edge

As Vehicular Networks rely increasingly on sensed data to enhance functionality and safety, efficient and distributed data analysis is needed to effectively leverage new technologies in real-world applications. Considering the tens of GBs per hour sensed by modern connected vehicles, traditional analysis, based on global data accumulation, can rapidly exhaust the capacity of the underlying network, becoming increasingly costly, slow, or even infeasible. Employing the edge processing paradigm, which aims at alleviating this drawback by leveraging vehicles’ computational power, we are the first to study how to localize, efficiently and distributively, relevant data in a vehicular fleet for analysis applications. This is achieved by appropriate methods to spread requests across the fleet, while efficiently balancing the time needed to identify relevant vehicles, and the computational overhead induced on the Vehicular Network. We evaluate our techniques using two large sets of real-world data in a realistic environment where vehicles join or leave the fleet during the distributed data localization process. As we show, our algorithms are both efficient and configurable, outperforming the baseline algorithms by up to a $40\times $ speedup while reducing computational overhead by up to $3\times $ , while providing good estimates for the fraction of vehicles with relevant data and fairly spreading the workload over the fleet. All code as well as detailed instructions are available at https://github.com/dcs-chalmers/dataloc_vn .

Customer Value of Smart Grid Application

The authors investigate customer value of smart grid application in smart city from the perspective of main research paradigms of customer value. Data is based on questionnaire for customers (N=131), deep interviews among specialists (7=N), and two months of observation. The results show that the typical user of smart grid technology is a male aged between 30 and 69 who considers that using the application is interesting because of the perceived benefits. Developing strong customer relationship is formed through the provision of e-service quality that has a key role in maintaining customer trust, satisfaction, and loyalty. End-product and service process paradigms to measure customer value do not fully take the complex context of smart cities into consideration, and the ecosystem paradigm must be developed to analyze customer value in smart cities in interactive dynamic decentralized environment where cumulative big data is used to match the customer needs with new digital services.

Mapping the Big Data Landscape: Technologies, Platforms and Paradigms for Real-Time Analytics of Data Streams

The `Big Data' of yesterday is the `data' of today. As technology progresses, new challenges arise and new solutions are developed. Due to the emergence of Internet of Things applications within the last decade, the field of Data Mining has been faced with the challenge of processing and analysing data streams in real-time, and under high data throughput conditions. This is often referred to as the Velocity aspect of Big Data. Whereas there are numerous reviews on Data Stream Mining techniques and applications, there is very little work surveying Data Stream processing paradigms and associated technologies, from data collection through to pre-processing and feature processing, from the perspective of the user, not that of the service provider. In this article, we evaluate a particular type of solution, which focuses on streaming data, and processing pipelines that permit online analysis of data streams that cannot be stored as-is on the computing platform. We review foundational computational concepts such as distributed computation, fault-tolerant computing, and computational paradigms/architectures. We then review the available technological solutions, and applications that pertain to data stream mining as case studies of these theoretical concepts. We conclude with a discussion of the field of data stream processing/analytics, future directions and research challenges.

A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process

Textile manufacturing is a typical traditional industry involving high complexity in interconnected processes with limited capacity on the application of modern technologies. Decision-making in this domain generally takes multiple criteria into consideration, which usually arouses more complexity. To address this issue, the present paper proposes a decision support system that combines the intelligent data-based models of random forest (RF) and a human knowledge-based multi-criteria structure of analytical hierarchical process (AHP) in accordance with the objective and the subjective factors of the textile manufacturing process. More importantly, the textile chemical manufacturing process is described as the Markov decision process (MDP) paradigm, and a deep reinforcement learning scheme, the Deep Q-networks (DQN), is employed to optimize it. The effectiveness of this system has been validated in a case study of optimizing a textile ozonation process, showing that it can better master the challenging decision-making tasks in textile chemical manufacturing processes.

Wire Arc Additive Manufacturing Perspectives and Recent Developments

The outstanding performance of producing complex three dimensional moulded components, which could be difficult or impossible to accomplish through traditional production processes. Additive processing in recent years has revolutionised the processing paradigm. Due to the high deposition speeds, which are substantially higher than powdered techniques, the wire and arc additive manufacturing (WAAM) is distinct among various additive manufacturing techniques that are suitable for producing large metal components. WAAM’s efficiency is growing rapidly, and thus substantial study is ongoing. This research work will include an overview of the greatest advances in WAAM, outlining the innovations and variants in processes to monitor the microstructure, mechanical properties and defect production in the as-built components along with the most important technological materials used, and the variants of WAAM.