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Electronic voting (e-voting) systems are gaining increasing attention as a means to modernize electoral processes, enhance transparency, and boost voters' participation. In recent years, significant developments have occurred in the study of e-voting and blockchain technology systems, hence reshaping many electoral systems globally. For example, real-world implementations of blockchain-based e-voting have been explored in various countries, such as Estonia and Switzerland, which demonstrates the potential of blockchain to enhance the security and transparency of elections. Thus, in this paper, we present a survey of the latest trends in the development of e-voting systems, focusing on the integration of blockchain technology as a promising solution to address various concerns in e-voting, including security, transparency, auditability, and voting integrity. This survey is important because existing survey articles do not cover the latest advancements in blockchain technology for e-voting, particularly as it relates to architecture, global trends, and current concerns in the developmental process. Thus, we address this gap by providing an encompassing overview of architectures, developments, concerns, and solutions in e-voting systems based on the use of blockchain technology. Specifically, a concise summary of the information necessary for implementing blockchain-based e-voting solutions is provided. Furthermore, we discuss recent advances in blockchain systems, which aim to enhance scalability and performance in large-scale voting scenarios. We also highlight the fact that the implementation of blockchain-based e-voting systems faces challenges, including cybersecurity risks, resource intensity, and the need for robust infrastructure, which must be addressed to ensure the scalability and reliability of these systems. This survey also points to the ongoing development in the field, highlighting future research directions such as improving the efficiency of blockchain algorithms and integrating advanced cryptographic techniques to further enhance security and trust in e-voting systems. Hence, by analyzing the current state of e-voting systems and blockchain technology, insights have been provided into the opportunities and challenges in the field with opportunities for future research and development efforts aimed at creating more secure, transparent, and inclusive electoral processes. [ABSTRACT FROM AUTHOR]

Energy management controllers (EMCs) are pivotal for optimizing energy consumption and ensuring operational efficiency across diverse systems. This review paper delves into the various control strategies utilized by energy management controllers and explores their coordination mechanisms. Additionally, it examines the architectures of energy management controllers and their real-world implementations. The paper surveys a spectrum of EMCs, including conventional-based, rule-based, optimization-based, hybrid methods, and commercial software-based approaches, highlighting their respective advantages and drawbacks. It investigates how these controllers are coordinated within complex energy systems to achieve optimal performance and adaptability. Furthermore, the review outlines different architectures of energy management controllers, ranging from centralized to decentralized designs, discussing their suitability for various applications and their impact on system performance. Real-world applications of energy management controllers in sectors such as smart grids, buildings, industrial processes, and transportation systems are examined. Case studies and examples demonstrate the efficacy of different control strategies and architectures in addressing specific energy management challenges and achieving desired outcomes. Overall, this review provides valuable insights into the current landscape of energy management controller design and implementation, offering direction for future research and development in the pursuit of energy optimization and sustainability. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) is the backbone of machine learning, specifically deep learning. The interpolating and learning ability of an ANN makes it an ideal tool for modelling, control and various other complex tasks. Fractional calculus (FC) involving derivatives and integrals of arbitrary non-integer order has recently been popular for its capability to model memory-type systems. There have been many attempts to explore the possibilities of combining these two fields, the most popular combination being the use of fractional derivative in the learning algorithm. This paper reviews the use of fractional calculus in various artificial neural network architectures, such as radial basis functions, recurrent neural networks, backpropagation NNs, and convolutional neural networks. These ANNs are popularly known as fractional-order artificial neural networks (FANNs). A detailed review of the various concepts related to FANNs, including activation functions, training algorithms based on fractional derivative, stability, synchronization, hardware implementations of FANNs, and real-world applications of FANNs, is presented. The study also highlights the advantage of combining fractional derivatives with ANN, the impact of fractional derivative order on performance indices like mean square error, the time required for training and testing FANN, stability, and synchronization in FANN. The survey reports interesting observations: combining FC to an ANN endows it with the memory feature; Caputo definition of fractional derivative is the most commonly used in FANNs; fractional derivative-based activation functions in ANN provide additional adjustable hyperparameters to the networks; the FANN has more degree of freedom for adjusting parameters compared to an ordinary ANN; use of multiple types of activation functions can be employed in FANN, and many more. [ABSTRACT FROM AUTHOR]

In this article we review the current serverless architectures, abstract and categorize their founding principles, and provide an in-depth security analysis. In particular, we: show the security shortcomings of the analyzed serverless architectural paradigms; point to possible countermeasures; and, highlight several research directions for practitioners, Industry, and Academia. [ABSTRACT FROM AUTHOR]

The work presented in this article underlines the embodiment design phase aiming at the determination of different possible architectures of a system according to the energy vision combined with the graph theory. Indeed, the embodiment design phase presents the difficulty of organizing the design alternatives. This difficulty is due to the fact that the various components of the system are not yet fully defined, and there are many arrangement possibilities. In this context, we seek to structure a methodology for the preliminary definition of the technical system architecture, while respecting the TRIZ law of completeness of the system parts. We consider the availability of the transmission elements of the principal energy flow (functional flow). The modeling of the system will concern the flow ensuring the positioning of the system principal component called (contact flow). The modeling of an optimal system composed of the main elements (converter, transmitter, operator, and reference) with a graph ensures the possibility to propose various architectures based on the combinations of these components. The proposed approach consists of three principal phases: appropriate graph-system model association, inappropriate solutions elimination, full system definition, and decision making. An illustration of the methodology is carried out on a wind system example. [ABSTRACT FROM AUTHOR]

Behavior analysts have long recognized the benefits of closely following their data; however, the data we are following may be moving faster than the tools we have to accurately analyze and predict future behaviors. This problem even saturates behavior-analytic investigations that focus on the evaluation of complex data related to public policy issues in areas such as poverty, geriatrics, and child welfare practice. In the face of this research enigma, there exists a more powerful and precise set of classification and prediction platforms for researchers in the behavioral sciences. In this article, we describe a combination of neural network strategies that predict child welfare professionals' decision making. Extending the data analysis from the Yelick and Thyer (2019) study, we employed our current version of the Kohonen self-organizing map in conjunction with our deep neural network as a strategy for identifying participants who were at high probability for making heuristic decisions. [ABSTRACT FROM AUTHOR]

Shakey the robot was a milestone of autonomous robots and artificial intelligence. Its design principles have dominated research until now. Tacit philosophical and architectural assumptions have impoverished the space of research topics and methods. I point out ways to overcome this impasse with sideglances to other scientific fields. [ABSTRACT FROM AUTHOR]

We present a technique for automatically acquiring operational knowledge on how to adapt assembly systems to new production demands or recover from disruptions. Dealing with changes and disruptions affecting an assembly station is a complex process which requires deep knowledge of the assembly process, the product being assembled and the adopted technologies. Shop-floor operators typically perform a series of adjustments by trial and error until the expected results in terms of performance and quality are achieved. With the proposed approach, such adjustments are captured and their effect on the station is measured. Adaptation knowledge is then derived by generalising from individual cases using a variant of the k-nearest neighbour algorithm. The operator is informed about potential adaptations whenever the station enters a state similar to one contained in the experience base, that is, a state on which adaptation information has been captured. A case study is presented, showing how the technique enables to reduce adaptation times. The general system architecture in which the technique has been implemented is described, including the role of the different software components and their interactions. [ABSTRACT FROM AUTHOR]

The Reconfigurable Computing Cluster (RCC) project has been investigating unconventional architectures for high end computing using a cluster of FPGA devices connected by a high-speed, custom network. Most applications use the FPGAs to realize an embedded System-on-a-Chip (SoC) design augmented with application-specific accelerators to form a message-passing parallel computer. Other applications take a single accelerator core and tessellate the core across all of the devices, treating them like a large virtual FPGA. The experimental hardware has also been used for basic computer research by emulating novel architectures. This article discusses the genesis of the over-arching project, summarizes results of individual investigations that have been completed, and how this approach may prove useful in the investigation of future Exascale systems. [ABSTRACT FROM AUTHOR]

This article systemizes grid systems of different types according to their main categories and features that determine their technical and functional possibilities. It considers intelligent grid systems and itemizes the directions by which the present-day models, algorithms, and tools of the software for grid systems, including those with elements of artificial intelligence, are developed. It supplies information about the implemented intelligent software-hardware systems for monitoring and the appraisals of the technical condition of objects in national and regional networks of the mainline power supply. [ABSTRACT FROM AUTHOR]

Recent interests from the research community in building digitized transmitters have led to numerous architectural and circuit-level innovations and developments towards the design of digital-to-analog converters (DACs) in the GHz space. However, several challenges exist in terms of interface overhead and process capabilities that fundamentally influence the accuracy, linearity and achievable speed. This paper aims to highlight some of the fundamental challenges in DACs and provide the reader with an insight into some of the emerging innovations at the systemic and circuit levels that address these challenges and aid in the transition of high-resolution DACs into the GHz regime. [ABSTRACT FROM AUTHOR]

Currently, the manufacturing domain is primarily characterized by the flexibility, adaptability and robustness of the production system. The manufacturing flow processes lead to shorter cycle times to efficiently meet customer needs. Mentioned features can be more easily achieved in a distributed system, such as holonic or multi-agent system, which becomes strongly influenced by the advancement of semantic technologies. In the majority of existing multi-agent based control systems, which are responsible for acting, sensing, computing and production planning, the ontology (necessary for knowledge bases and communication) is usually hard-coded directly in the agent code. In this case, the hard-coded system behavior can be hardly maintained-usually system reprogramming is needed from time to time to satisfy customer requirements. In this paper we discuss the necessity of explicit definition of both declarative and procedural knowledge and propose explicit procedural knowledge handling. Sharing and distribution of such knowledge is discussed and illustrated on an implemented transportation system example. We also introduce the utilization of discussed architecture for explicit specification of agent behavior in failures patterns handling and smart grid configuration scenario. Such a solution greatly increases the possibility of system integration, openness, flexibility, and extensibility, all without having to restart the running distributed system. The topic discussed in this paper shows the ability of the dynamic reconfigurable multi-agent system to participate in development of industrial control systems and solutions. [ABSTRACT FROM AUTHOR]

Recently, graphene has attracted numerous interests from both fundamental and applied fields due to its excellent mechanical, thermal, electrical conductivity and other novel properties. This review gives an overview of recent progress on hybridization modifications of graphene with carbon nanomaterials. Some example applications of graphene-based nanohybrids in polymer composites, optical and conducting materials, high performance electrolyte materials and as well as other functional materials are summarized and discussed. [ABSTRACT FROM AUTHOR]

Recently, General Purpose Graphical Processing Units (GP-GPUs) have been identified as an intriguing technology to accelerate numerous data-parallel algorithms. Several GPU architectures and programming models are beginning to emerge and establish their niche in the High-Performance Computing (HPC) community. New massively parallel architectures such as the Nvidia's Fermi and AMD/ATi's Radeon pack tremendous computing power in their large number of multiprocessors. Their performance is unleashed using one of the two GP-GPU programming models: Compute Unified Device Architecture (CUDA) and Open Computing Language (OpenCL). Both of them offer constructs and features that have direct bearing on the application runtime performance. In this paper, we compare the two GP-GPU architectures and the two programming models using a two-level character recognition network. The two-level network is developed using four different Spiking Neural Network (SNN) models, each with different ratios of computation-to-communication requirements. To compare the architectures, we have chosen the two extremes of the SNN models for implementation of the aforementioned two-level network. An architectural performance comparison of the SNN application running on Nvidia's Fermi and AMD/ATi's Radeon is done using the OpenCL programming model exhausting all of the optimization strategies plausible for the two architectures. To compare the programming models, we implement the two-level network on Nvidia's Tesla C2050 based on the Fermi architecture. We present a hierarchy of implementations, where we successively add optimization techniques associated with the two programming models. We then compare the two programming models at these different levels of implementation and also present the effect of the network size (problem size) on the performance. We report significant application speed-up, as high as 1095× for the most computation intensive SNN neuron model, against a serial implementation on the Intel Core 2 Quad host. A comprehensive study presented in this paper establishes connections between programming models, architectures and applications. [ABSTRACT FROM AUTHOR]

This report summarizes the presentations and discussions at the 1st IFIP/IEEE International Workshop on Management of the Future Internet (ManFI 2009). This report provides a broad, high-level view of key requirements, challenges, strategies and R&D results associated with the current state-of-the-field in Future Internet management. [ABSTRACT FROM AUTHOR]

Object-Oriented Information System (OOIS) is an information system that employs object-oriented technologies in system design and implementation. Recent research advances and industrial innovations in distributed system modeling and Internet applications have enabled OOIS design and implementation to be carried out on the basis of new technologies and platforms. This special section onModeling Object-Oriented Information Systemspresents readers with a set of best papers selected from the 7th International Conference on OOIS. Reviews of theories and applications of OOIS’s are also provided for predicating trends in OOIS modeling. [ABSTRACT FROM AUTHOR]

A novel hardware architecture for extracting region boundaries in two raster scan passes through a binary image is presented. The first pass gathers statistics regarding the size of each object contour. This information is used by the hardware to allocate dynamically off-chip memory for storage of boundary codes. In the second raster pass the same architecture constructs lists of grid-joint codes to represent the perimeter pixels of each object. These codes, referred to variously as 'crack' codes or 'raster-chain' codes in the literature, are later decoded by the hardware to reproduce the ordered sequence of coordinates surrounding each object. This list of coordinates is useful for a variety of shape recognition and manipulation algorithms that utilize boundary information. We present results of software simulations of the VLSI architecture, along with measurements on the coding efficiency of the basic algorithm, and estimates of the overall complexity of a proposed VLSI chip. [ABSTRACT FROM AUTHOR]

An image-recognition system was implemented with a set of eight algorithmically dedicated integrated circuits to recognize two-dimensional objects that are characterized by their closed outer contours. The complete system operates at rates up to 15 frames/second using a standard workstation as a controller. The recognition system achieved a 97% recognition rate for over 10,000 trials of recognition of eight objects over a wide range of orientation and size variations. A 100% recognition rate was achieved if size variations were eliminated. The set of 4-micron NMOS image processor chips operates on 10-megahertz 8-bit video data (512 × 512 images) in real time. The processors include: a 3 × 3 linear convolver, a 3 × 3 sorting filter, a 7 × 7 logical convolver, a contour tracer, a feature extractor, a look-up table ROM, and two post-processors for the linear convolver. Each chip was designed using an architecture that is dedicated to the particular image processing task being performed. The design time for all of the chips was kept to 1.5 man-years by developing a set of design guidelines. The relationship between the algorithms that were implemented and the silicon implementation is discussed. [ABSTRACT FROM AUTHOR]

Present work addresses the guidelines that have been followed to construct basic behavioral agents for visually guided navigation within the framework of a hierarchical architecture. Visual and motor interactions are described within this generic framework that allows for an incremental development of behavior from an initial basis set. Basic locomotion agents as, Stop&Backward, Avoid, and Forward are implemented by means of fuzzy knowledge bases to deal with the uncertainty and imprecision inherent to real systems and environments. Basic visual agents as, Saccadic, Find_Contour, and Center are raised under a space-variant representation pursuing an anthropomorphic approach. We illustrate how a complex behavior results from the combination of lower level agents always connected to the basic motor agents. The proposed methodology is validated on a caterpillar mobile robot in navigation tasks directed by an object description. [ABSTRACT FROM AUTHOR]

In the recent investigations of reducing the relational join operation complexity several hash-based partitioned-join stategies have been introduced. All of these strategies depend upon the costly operation of data space partitioning before the join can be carried out. We had previously introduced a partitioned-join based on a dynamic and order preserving multidimensional data organization called DYOP. The present study extends the earlier research on DYOP and constructs a simulation model. The simulation studies on DYOP and subsequent comparisons of all the partitioned-join methodologies including DYOP have proven that space utilization of DYOP improves with the increasing number of attributes. Furthermore, the DYOP based join outperforms all the hash-based methodologies by greatly reducing the total I/O bandwidth required for the entire partitioned-join operation. The comparison model is independent of the architectural issues such as multiprocessing, multiple disk usage, and large memory availability all of which help to further increase the efficiency of the operation. [ABSTRACT FROM AUTHOR]