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The 'neuropunk revolution' represents an innovative approach focused on the integration of human–machine interfaces through nerve and neural pathways. This cutting-edge concept seeks to push the boundaries of human–machine interaction by exploring the intricate connections between the human nervous system and artificial technologies. Neuropunk delves into the realm of cyborg technologies, blurring the lines between humans and machines and opening up new possibilities for augmenting human capabilities. By tapping into nerve and neural pathways, neuropunk envisions a future where individuals can seamlessly interface with technology, leading to enhanced sensory perception, cognitive abilities, and physical performance. This concept encapsulates a rebellious and unconventional spirit, challenging traditional notions of the human body and its relationship with technology. This paper discusses the challenges and potential of neuropunk, exploring the fusion of scientific exploration, technological innovation, and a punk-inspired ethos in advancing human–machine integration. It highlights the emergence of real-time neurosimulations and neuromorphic computing as key tools to achieve these aspirations. Additionally, the integration of neurological and hormonal bioinspiration is examined as a promising approach to the design of neural interfaces, fostering more efficient and effective technologies that align better with the human body. Finally, the paper addresses ethical considerations and research tasks to responsibly navigate the neuropunk revolution, ensuring a future that maximizes the benefits of neural interfaces while mitigating potential risks and promoting ethical principles. [ABSTRACT FROM AUTHOR]

In this paper, we prove that there is a strongly universal cellular automaton on the heptagrid with five states under the relaxation of the assumption of rotation invariance for the rules. The result is different from that of a previous paper of the author with six states but with rotationally invariant rules. Here, the structures is more constrained than in the quoted paper with six states and rotation invariance of the rules. [ABSTRACT FROM AUTHOR]

We have developed environment-adaptive software that enables high-performance operation by automatically converting application code in accordance with the deployed environment and setting appropriate amounts of resources. We only considered conversions and settings before the start of operation. In this paper, we verify that the logic is reconfigured in accordance with the usage characteristics during operation. We propose a FPGA reconfiguration method during operation and confirm that the application running on the FPGA is reconfigured into another application in accordance with the usage characteristics. Through confirmation, performance improvement and break time are checked, and the effectiveness of method is shown. [ABSTRACT FROM AUTHOR]

Due to the widespread penetration of wind energy, large-capacity wind turbines (WTs) are becoming more conspicuous. In this paper, a linear control-oriented large-capacity wind turbine (WT) model is redesigned based on the 15 MW WT in openFAST. Compared with the original model, the redesigned model is more efficient and conducive to the design of linear controllers. To verify the validity of the redesigned WT model, we design a distributed control scheme with deloading operation of the wind farm (WF) to fairly share the load. Simulations are performed to verify the effectiveness of the application in the WF. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) aims to analyze the emotional color contained in sentences or documents in more detail by classifying and evaluating different aspects and emotions in the text. However, the current research methods cannot effectively analyze the relationship between aspect words and context and extract grammatical information about sentences. Additionally, the extracted syntactic information is insufficient, and the combination of syntactic and semantic information is inefficient, leaving the model unable to correctly determine aspects' emotional orientations. This paper proposes an aspect-level sentiment analysis based on Fusion Graph Double Convolutional Neural Networks (FGD-GCN) to address these issues. Firstly, FGD-GCN proposes a multi-feature extraction module. Using BERT and bidirectional long-short-term memory models, this module extracts the hidden context between words. In addition, the positional attention module is used to capture important features in sentences, reducing noise and bias. Then, a semantic enhancement module is proposed, which fuses attention-focused information and feature information extracted from graphs to emphasize aspect words and context, and uses CNN model to classify on feature vectors. According to experiment results on three benchmark datasets, the model outperforms previous GCN methods for context-based aspect-level sentiment analysis. [ABSTRACT FROM AUTHOR]

In this paper, we construct a weakly universal cellular automaton in the heptagrid, the tessellation $ \{7,3\} $ { 7 , 3 } which takes place in the hyperbolic plane. The cellular automation is not rotation invariant but which is truly planar. This result, under these conditions, cannot be improved for the tessellations $ \{p,3\} $ { p , 3 } of the hyperbolic plane. [ABSTRACT FROM AUTHOR]

Recent advances in quantum computing and machine learning have brought about a promising intersection of these two fields, leading to the emergence of quantum machine learning (QML). However, the integration of quantum computing and machine learning poses several challenges. One of the prominent challenges lies in the presence of barren plateaus (BP) in QML algorithms, particularly in quantum neural networks (QNNs). Recent studies have successfully identified the fundamental causes underlying the existence of BP in QNNs. This paper presents a framework designed to explore the interplay of multiple factors contributing to the BP problem in quantum neural networks (QNNs), which poses a critical challenge for the practical applications of QML. We focus on the combined influence of data encoding, qubit entanglement, and ansatz expressibility in hybrid quantum neural networks (HQNNs) for multi-class classification tasks. Our framework aims to empirically analyze the joint impact of these factors on the training landscape of HQNNs. Our results show that the occurrence of the BP problem in HQNNs is contingent upon the expressibility of the underlying ansatz and the type of the adopted data encoding technique. Additionally, we observe that qubit entanglement also plays a role in exacerbating the BP problem. Leveraging various evaluation metrics for classification tasks, we systematically evaluate the performance of HQNNs and provide recommendations tailored to different constraint scenarios. Our findings emphasize the significance of our framework in addressing the practical success of QNNs. [ABSTRACT FROM AUTHOR]

Given a graph G and an edge $ e\in E(G) $ e ∈ E (G) , let S be a vertex set of G. For any two vertices x, $ y\in S $ y ∈ S , if e belongs to all the shortest paths between x and y, then x and y can monitor the edge e. For each edge e of G, if there exists x and y in S such that x and y can monitor e, then the set S can be called a monitoring-edge-geodetic ( $ \operatorname {MEG} $ MEG for short) set of G. The $ \operatorname {MEG} $ MEG number, denoted by $ \operatorname {meg}(G) $ meg ⁡ (G) , is the size of the smallest $ \operatorname {MEG} $ MEG set of G. In this paper, we obtain the exact values of the $ \operatorname {MEG} $ MEG numbers for radix triangular mesh networks, Sierpiński graphs, Sierpiński gasket graphs and Sierpiński generalized graphs. [ABSTRACT FROM AUTHOR]

As distributed and multi-organization Machine Learning emerges, new challenges must be solved, such as diverse and low-quality data or real-time delivery. In this paper, we use a distributed learning environment to analyze the relationship between block size, parallelism, and predictor quality. Specifically, the goal is to find the optimum block size and the best heuristic to create distributed Ensembles. We evaluated three different heuristics and five block sizes on four publicly available datasets. Results show that using fewer but better base models matches or outperforms a standard Random Forest, and that 32 MB is the best block size. [ABSTRACT FROM AUTHOR]

For a set M of vertices and an edge e of a graph G, let $ P_G(M, e) $ P G (M , e) be the set of the pair $ (x, y) $ (x , y) with a vertex x of M and a vertex y of $ V(G) $ V (G) such that $ d_G(x, y)\neq d_{G-e}(x, y) $ d G (x , y) ≠ d G − e (x , y). For a vertex x, let $ EM(x) $ EM (x) be the edge set e such that there exists a vertex v in G with $ (x, v) \in P(\{x\}, e) $ (x , v) ∈ P ({ x } , e). A set M of vertices of a graph G is distance-edge-monitoring set if every edge e of G is monitored by some vertex $ v\in M $ v ∈ M , that is, for any $ e \in E(G) $ e ∈ E (G) , we have $ P_G(M, e)\neq \emptyset $ P G (M , e) ≠ ∅. The distance-edge-monitoring number of a graph G, denoted by $ \operatorname {dem}(G) $ dem ⁡ (G) , is defined as the smallest size of distance-edge-monitoring sets of G. In this paper, we study the distance edge monitoring number of pseudo wheel graphs, that is, some variants of wheel graph. [ABSTRACT FROM AUTHOR]

The fault tolerability of the network is usually measured by the classical or generalized connectivity of the graph. For any subset $ S \subseteq V\left (G \right) $ S ⊆ V (G) with $ \left | S \right | \ge 2 $ | S | ≥ 2 , a tree T is called an S-tree if $ S \subseteq V\left (T \right) $ S ⊆ V (T) . Furthermore, any two S-tree $ T_1 $ T 1 and $ T_2 $ T 2 are internally disjoint if $ E\left ({{T_1}} \right) \cap E\left ({{T_2}} \right) = \emptyset $ E ( T 1 ) ∩ E ( T 2 ) = ∅ and $ V\left ({{T_1}} \right) \cap V\left ({{T_2}} \right) = S $ V ( T 1 ) ∩ V ( T 2 ) = S. We denote by $ {\kappa _G}\left (S \right) $ κ G (S) the maximum number of pairwise internally disjoint S-trees in G. For an integer $ k \ge 2 $ k ≥ 2 , the generalized k-connectivity of a graph G is defined as $ {\kappa _k}\left (G \right) = \min \left \{ {{\kappa _G}\left (S \right) | S \subseteq V\left (G \right)} \right. $ κ k (G) = min { κ G (S) | S ⊆ V (G) and $ \left. {\left | S \right | = k} \right \} $ | S | = k } . In this paper, we establish the generalized 4-connectivity of the Cayley graph $ CT_n $ C T n generated by complete graphs. [ABSTRACT FROM AUTHOR]

The interplanetary File system (IPFS) provides data communication using plaintext over distributed and decentralized systems. Up till now, the security of data has mostly been established using keys. To facilitate classified data communication within a Closed User Group (CUG), services of IPFS private swarm are required to render additional content encryption. Currently, there is no inherent security built into IPFS. This paper presents an application DMaya on IPFS to provide keyless encryption on IPFS based on the Quasigroup-based Secret Sharing Scheme (QSS). IPFS is used as a storage medium, irrespective of the nature of data. Data security in decentralized mode with IPFS is provided with DMaya. The classified data of all types (i.e. video data, audio data, text data, and image data) when subjected to DMaya application, are fragmented into smaller fragments then processed by the probabilistic Quasigroup-based Secret Sharing (QSS) Scheme. DMaya keyless encryption generates the metadata tag file at the source node. The recipient node within the same private IPFS swarm, on receipt of metadata tag file reconstructs the original data with DMaya application. [ABSTRACT FROM AUTHOR]

Let G be a graph with vertex set $ V(G) $ V (G) and edge set $ E(G) $ E (G). For any $ e \in E(G) $ e ∈ E (G) and $ u,v\in V(G) $ u , v ∈ V (G) , the edge e is monitored by two vertices u and v in graph G if $ d_G(u, v) \neq d_{G-e}(u, v) $ d G (u , v) ≠ d G − e (u , v). A set M of vertices of G is a monitoring-edge-geodetic set of G if for any edge $ e \in E(G) $ e ∈ E (G) there exists a pair $ u, v\in M $ u , v ∈ M such that e is monitored by u, v. The monitoring-edge-geodetic number $ \operatorname {meg}(G) $ meg ⁡ (G) is the cardinality of the minimum MEG-set in G. In this paper, we obtain the exact values or bounds for the MEG numbers of graph products, including join, corona, cluster, lexicographic products and direct products. [ABSTRACT FROM AUTHOR]

This paper proposes an unconventional architecture and algorithm for implementing reservoir computing on FPGA. An architecture-oriented algorithm with improved throughput and architecture designed to reduce memory and hardware resource requirements are presented. The proposed architecture exhibits good performance in terms of benchmarks for reservoir computing. A prediction accelerator for reservoir computing that operates on 55.45 mW at 450 K fps with <3000 LEs is realized by implementing the architecture on FPGA. The proposed approach presents a novel FPGA implementation of reservoir computing focussing on both algorithms and architecture that may serve as a basis for applications of AI at network edge. [ABSTRACT FROM AUTHOR]

This paper investigates the coordination control of large-scale systems (LSSs) with unknown external disturbances. The disturbances exist in the channels from the controllers to the actuators. To compensate the impact of disturbances, an extended observer using the output states is constructed, and an observer-based event-triggered controller is designed to save communication resources. Moreover, based on the event-triggering strategy, a controller using dynamic information estimation is designed, and Zeno behavior is further excluded. Finally, a DC micro-grid model is used for simulations to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Analog computing is often considered to be an outdated technology only to be found and cared for in museums but not as the basis of modern cutting edge computing. Nothing could be further from reality. Together with quantum computers, analog computers complement today's stored program digital computers, acting as co-processors for certain types of computationally intensive tasks. They are highly energy efficient and exhibit a degree of parallelism that by far surpasses classic digital computers. In contrast to quantum computers, analog computers do not require exotic technologies, no intricate cooling systems, no elaborate shielding systems, etc. In addition to this there already exists a plethora of proven programs for analog computers to solve a wide variety of problems. This paper describes the necessity of analog computing for the twenty-first century, gives a short introduction to the structure and programming of analog computers, before discussing future application areas. Analog computing will drastically alter the way we think about computing in the years to come as it will become an integral part of every high performance computer. It will be an indispensable technology for future AI systems, it will revolutionize many medical applications, it might even replace or at least complement, certain types of quantum computers, etc. [ABSTRACT FROM AUTHOR]

It is well and widely known that sample pooling could provide an effective and efficient way for fast coronavirus testing among massive asymptomatic individuals. The method of multi-level acceleration for asymptomatic COVID-19 screening has been introduced, and for one and two levels, the optimal group sizes have been obtained. However, there are still multiple challenges. First, it is not clear how to find the optimal group sizes for three or more levels. Second, there is lack of closed-form expressions for the optimal group sizes for two or more levels. Third, it is not clear how to determine the optimal number of levels. And last, it is not known what the maximum achievable speedup is. The motivation of this paper is to address all the above challenges. The optimization of a hierarchical pooling strategy includes its number of levels and the group size of each level. In this paper, based on multi-variable optimization and Taylor approximation, we are able to derive closed-form expressions for the optimal number of levels d ∗ = ln ⁡ (1 / ln ⁡ (1 / q 0)) − 1 , the optimal group sizes m 1 ∗ = e d ∗ = 1 / (e p 0) , m 2 ∗ = e d ∗ − 1 = 1 / (e 2 p 0) ,..., m d ∗ ∗ = e = 1 / (e d ∗ p 0) , and the maximum possible speedup of a hierarchical pooling strategy of 1 / (e p 0 ln ⁡ (1 / p 0)) , where p 0 is the fraction of infected people. The above speedup is nearly a linear function of the reciprocal of p 0 , in the sense that it is asymptotically greater than any sub-linear function (1 / p 0) 1 − ϵ of the reciprocal of p 0 for any small ϵ > 0. Using the results in this paper, we can quickly and easily predict the performance of an optimal hierarchical pooling strategy. For instance, if the fraction of infected people is 0.0001, an 8-level hierarchical pooling strategy can achieve speedup of nearly 400. [ABSTRACT FROM AUTHOR]

This paper reviews memristive PUFs (Physical Unclonable Functions) reported in the literature. The paper explains the motivation for using memristor technology for implementing PUFs. It focuses on PUFs' applications, sizes, analysis, and physical variations. In addition, the paper presents the number of samples generated using Monte Carlo simulation for evaluating the PUF circuits. This paper also describes the protocols, functionality, and methodologies proposed in the memristive PUF literature. Although memristive PUFs are not commercialized yet, there is a high expectation of exploiting the memristors as fundamental elements in the next generation of hardware security primitives (e.g. PUF) due to their unique characteristics such as forming process, temporal drift, nonlinearity, bidirectionality, nonvolatility and model complexity. There have been some survey papers on memristor PUFs in the past, however, the field has continued to develop so a comprehensive survey including recent publications seemed in order at this time. Lately, memristor technology improvement has accelerated, therefore creating a need for an updated survey of the applications of memristors for PUFs. [ABSTRACT FROM AUTHOR]

Smart device networks form the backbone of many Internet of Things (IoT) applications. The performance of these systems depended on many factors. This includes their ability to connect to each other and network latency. In this paper, two algorithms are discussed that build a Peer-to-peer (P2P) overlay network. The focus is on real-time end-to-end remote instrumentation for IoT applications. P2P overlay networks are used for various purposes from content distribution through to media sharing. In contrast to traditional P2P systems, IoT systems can be highly sensitive to network latency and require low bandwidth. The main contributions of this paper are two algorithms to create overlay networks that assign peer nodes to super-peer nodes. The network contains a large number of smart devices and the key aim is to minimize system wide network latency. Other constraints regarding the nodes ability include the ability to become a super-peer node such as connectivity and limited processing capacities of the smart devices are also considered in context of a P2P remote instrumentation system. Both algorithms create a 3-hop network where the latency is lowered as much as possible with the given set of nodes. The algorithms have been validated through simulation. [ABSTRACT FROM AUTHOR]

In Wireless Rechargeable Sensor Networks (WRSNs), wireless chargers can recharge batteries of sensor nodes so that they can operate sustainably. Since wireless chargers are costly and have limited charging distances and angles, how to apply as few as possible chargers to cover all sensor nodes and satisfy their energy requirements is thus an important and challenging problem. This paper introduces the PSCD (Particle Swarm Charger Deployment) algorithm and the IPSCD (Improved PSCD) algorithm using the Particle Swarm Optimization (PSO) concept to nearly optimize WRSN charger deployment. PSCD and IPSCD estimate charging efficiency according to the distance and angle between chargers and sensor nodes. They then, on the basis of PSO, utilize the local optimum and the global optimum to adjust locations and antenna orientations of chargers to make WRSNs sustainable. We perform experiments using practical wireless chargers to obtain charging efficiency data. Based on the data, PSCD and IPSCD are simulated for obtaining the best parameter setting, and compared with two related greedy algorithms to show their superiority. This paper introduces the PSCD (Particle Swarm Charger Deployment) algorithm and the IPSCD (Improved PSCD) algorithm, which utilize the Particle Swarm Optimization (PSO) concept to deploy as few as possible wireless chargers with adjusted positions and antenna directions to fulfill the energy requirement of sensor nodes of a WRSN (Wireless Rechargeable Sensor Network) inside a cuboid with length L, width W and height H. [ABSTRACT FROM AUTHOR]

This paper presents efficient data structures for the implementation of matrix-free finite element methods on block-structured, hybrid tetrahedral grids. It provides a complete categorization of all geometric sub-objects that emerge from the regular refinement of the unstructured, tetrahedral coarse grid and describes efficient iteration patterns and analytical linearization functions for the mapping of coefficients to memory addresses. This foundation enables the implementation of fast, extreme-scalable, matrix-free, iterative solvers, and in particular geometric multigrid methods by design. Their application to the variable-coefficient Stokes system subject to an enriched Galerkin discretization and to the curl-curl problem discretized with Nédélec edge elements showcases the flexibility of the implementation. Finally, the solution of a curl-curl problem with $ 1.6\cdot 10^{11} $ 1.6 ⋅ 10 11 (more than one hundred billion) unknowns on more than 32,000 processes with a matrix-free full multigrid solver demonstrates its extreme-scalability. [ABSTRACT FROM AUTHOR]

The connectivity and diagnosability are two important parameters to evaluate the fault-tolerability of network. The enhance hypercube is a variant of the hypercube which not only retains some of the favorable properties of the hypercube, but also possesses some embedding properties that the hypercube does not have. In this paper, we obtain the connectivity and the conditional diagnosability of enhanced hypercube based on structure faults. [ABSTRACT FROM AUTHOR]

This paper reports a special class of cellular automata machines that can solve graph theoretic problems, such as spanning tree problem and single source shortest path problem. The machine deals with planar graphs only. We first develop two-dimensional cellular automata to solve the graph theoretic problems for planar graphs. Next, we design the hardware for implementation of those cellular automata. [ABSTRACT FROM AUTHOR]

Hypermedia Multi-Agent System (MAS) Simulation is a novel approach to building agent-based simulations in which the environment is modelled as a set of linked hypermedia resources that are implemented using microservices. This paper discusses the implementation of an prototypical simulation system based on this concept and the lessons learnt in the process. [ABSTRACT FROM AUTHOR]

The rapid adoption of Cloud, Fog, and Edge computing paradigms, along with Digital Twin (DT) technology, is being observed across numerous domains. Smart Agriculture is one such domain, where diverse requirements exist for applications that range from Augmented Reality, Satellite, and auto harvesters to a multitude of agricultural devices and sensors. While the integration of Cloud, Fog, and Edge computing can address various challenges, it can also pose some difficulties, including resource allocation, resource scheduling, and task scheduling. To address these challenges and enhance the productivity and sustainability of Smart Agriculture, this paper presents a novel architecture based on Cloud, Fog, Edge computing, and Multi-Agent Systems (MAS), incorporating the concept of Digital Twin. This proposed architecture is implemented in Smart Agricultural farms and fields, opening up new contemporary applications in the Agriculture domain. [ABSTRACT FROM AUTHOR]

This paper presents a parallel solution based on the coarse-grained multicomputer (CGM) model using the four-splitting technique to solve the optimal binary search tree problem. The well-known sequential algorithm of Knuth solves this problem in O (n 2) time and space, where n is the number of keys used to build the optimal binary search tree. To parallelise this algorithm on the CGM model, the irregular partitioning technique, consisting in subdividing the dependency graph into subgraphs (or blocks) of variable size, has been proposed to tackle the trade-off of minimising the number of communication rounds and balancing the load of processors. This technique, however, induces a high latency time of processors (which accounts for most of the global communication time) because varying the blocks' sizes does not enable them to start evaluating some blocks as soon as the data they need are available. The four-splitting technique proposed in this paper solves this shortcoming by evaluating a block as a sequence of computation and communication steps of four subblocks. This CGM-based parallel solution requires O (n 2 / p) execution time with O (k p) communication rounds, where p is the number of processors and k is the number of times the size of blocks is subdivided. An experimental study conducted to evaluate the performance of this CGM-based parallel solution showed that compared to the solution based on the irregular partitioning technique where the speedup factor is up to ×10.39 on 128 processors with 40,960 keys when k = 2, the speedup factor of this solution is up to ×13.12 and rises up to ×14.93 when k = 5. [ABSTRACT FROM AUTHOR]

Heterogeneous hardware other than a small-core central processing unit (CPU) such as a graphics processing unit (GPU), field-programmable gate array (FPGA), or many-core CPU is increasingly being used. However, to use heterogeneous hardware, programmers must have sufficient technical skills to utilise OpenMP, CUDA, and OpenCL. On the basis of this, I have proposed environment-adaptive software that enables automatic conversion, configuration, and high performance operation of once written code, in accordance with the hardware. However, although it has been considered to convert the code according to the offload devices, there has been no study where to place the offloaded applications to satisfy users’ requirements of price and response time. In this paper, as a new element of environment-adapted software, I examine a method to calculate appropriate locations using the linear programming method. I confirm that applications can be arranged appropriately through simulation experiments when some conditions such as application type and users’ requirements are changed. [ABSTRACT FROM AUTHOR]

One of the most important challenges in information networks is to gather data from a larger set of nodes to a smaller set of nodes. This can be done via the use of a concentrator architecture in the connection topology. This paper is a proof-of-concept that demonstrates a quantum-based controller in large interconnection networks can asymptotically perform this task faster. We specifically present quantum algorithms for routing concentration assignments on full capacity fat-and-slim concentrators, bounded fat-and-slim concentrators, and regular fat-and-slim concentrators. Classically, the concentration assignment takes O (n) time on all these concentrators, where n is the number of inputs. Powered by Grover's quantum search algorithm, our algorithms take O (n c ln ⁡ c) time, where c is the capacity of the concentrator. Thus, our quantum algorithms are asymptotically faster than their classical counterparts, when c ln 2 ⁡ c = o (n). In general, c = n μ , satisfies c ln 2 ⁡ c = o (n) , implying a time complexity of O (n 0.5 (1 + μ) ln ⁡ n) , for any μ , 0 < μ < 1. [ABSTRACT FROM AUTHOR]

The traditional parallel Sparse matrix vector multiplication (SpMV) method has been optimized by an application-specific or compression format-specific. However, a single compression format cannot deal with all sparse matrix types effectively in practical applications. To solve this problem, an adaptive compression format based on Bagging ensemble learning algorithm is proposed in this paper. Experiments show that the adaptive compression format has higher prediction and computational performance on NVIDIA V100 and NVIDIA RTX 2080Ti. Compared with SpMV of the four compression formats, SpMV based on adaptive compression format reduces the execution time of 1.5×, 6.6×, 9× and 1.1×, respectively. [ABSTRACT FROM AUTHOR]

One of the important issues in evaluating an interconnection network is to study the Hamiltonian cycle embedding problems. For a positive integer k, a graph G is said to be spanning k-cyclable if for k prescribed vertices x 1 , x 2 , ... , x k , there exist k disjoint cycles C 1 , C 2 , ... , C k such that the union of C 1 , C 2 , ... , C k spans G, and each C j contains exactly one vertex x j of x 1 , x 2 , ... , x k . According to the definition, the problem of finding hamiltonian cycle focuses on k = 1. The notion of spanning cyclability can be applied to the problem of identifying faulty processors and other related issues in interconnection networks. The n-dimensional augmented cube A Q n is an important node-symmetric variant of the n-dimensional hypercube Q n . In this paper, we prove that A Q n with n ≥ 3 is spanning k-cyclable for 1 ≤ k ≤ 2 n − 4. [ABSTRACT FROM AUTHOR]

Fog computing is a complementary computing paradigm to the existing cloud computing. A fundamental problem of fog computing is how to allocate the computing resources of fog nodes when scheduling tasks that arrive in an online manner. Other than task completion speed metrics, fairness of resource allocation between competing users is also an important metric to consider. One such metric is Dominant Resource Fairness (DRF), a fairness scheme that guarantees four key qualities: incentivised sharing, strategy-proof, Pareto-efficiency, and envy free. This paper examines the multi-resource, multi-server, and heterogeneous task resource allocation problem from a DRF perspective. Four different types of tasks are considered: ordered/unordered and splittable/unsplittable. Three low complexity heuristics are proposed to maximise fairness between users. Results show that the proposed heuristics are at least comparable to three baseline scheduling algorithms in terms of task completion speed while achieving higher fairness between users. [ABSTRACT FROM AUTHOR]

In this paper, we consider non-clairvoyant task offloading for random tasks in mobile edge computing within the framework of combinatorial optimisation. For offline non-clairvoyant task offloading, we propose a non-clairvoyant task offloading algorithm, which is able to determine a task offloading strategy without knowing the amount of computation and communication of any task. For online non-clairvoyant task offloading, we propose a randomised online task offloading algorithm, which is able to make an offloading decision for an arrival task without knowing anything about future tasks and other tasks. For both algorithms, we analyse the probability of certain performance guarantee. We also demonstrate numerical data. To the best of the author's knowledge, this is the first paper which considers both offline and online non-clairvoyant task offloading in mobile edge computing, together with analytical results on performance guarantee with high probability. [ABSTRACT FROM AUTHOR]

This paper addresses the partition problem under the continuous domain in swarm robotics. In this problem, a swarm of n robots, randomly deployed over a rectangular workspace, are required to form K size-balanced groups within a finite amount of time. We aim to look into the problem in the presence of horizontal line obstacles. The obstacles are scattered randomly over the bounded region, and their positions remain unaltered throughout the process. In the proposed solutions, the robots are assumed to be identical, autonomous, and do not have any direct communication among themselves. The robots are memoryless, except they retain only the information of the two parameters n and K throughout the process and their states among three possible ones. Two different partition algorithms are proposed assuming full-compass axis agreement and half-compass axis agreement among the local coordinate systems of the robots. In the first case, the proposed algorithm works for both synchronous and semi-synchronous models, whereas, in the second case, the robots are assumed to be synchronous. As a pre-processing step of the partition algorithms, an assembling algorithm for the half-compass axis agreement model has also been proposed for synchronous and semi-synchronous robots. [ABSTRACT FROM AUTHOR]

The paper further refines the clan composition technique that is considered a way of matrix partitioning into a union of block-diagonal and block-column matrices. This enables solving the individual systems for each horizontal block on a separate computing node, followed by solving the composition system. The size of minimal clans, obtained as a result of matrix decomposition, varies considerably. For load balancing, early versions of ParAd software were using dynamic scheduling of jobs. The present paper studies a task of static balancing the clan size. Rather good results are obtained using a fast bin packing algorithm with the first fit on a sorted array which are considerably improved applying a multi-objective graph partitioning with software package METIS. Aggregation of clans allows us to obtain up to three times extra speed-up, including systems over fields of real numbers, on matrices from Model Checking Contest and Matrix Market. [ABSTRACT FROM AUTHOR]

Broadcast abstractions are among the most important concepts in the field of fault tolerant distributed computing. These abstractions are used by consensus algorithms as a fundamental building block for ensuring that all correct processes in the system decide the same value. The Timely Provable Reliable Send primitive is among these broadcast abstractions with which we guarantee that messages are delivered correctly and in a timely manner, even in the presence of faulty processes. In this paper, we present an authenticated algorithm implementing provable reliable send primitive with very few eventually synchronous links. In other words, this algorithm assumes that there is a ⋄ 〈 t + 1 〉 -sink in the system. A ⋄ 〈 t + 1 〉 -sink is a correct process where the number of incoming eventually timely links that connecting it with correct processes is (t + 1) (including itself). We also show that a ⋄ 〈 t + 1 〉 -sink is the minimal synchrony assumption for implementing this primitive in a Byzantine system where an authentication mechanism is available. [ABSTRACT FROM AUTHOR]

Serverless computing is a new way of delivering cloud services that abstracts a user from the burden of managing resources and infrastructure. Although a few serverless simulators have been developed, they do not compare to services that are offered by commercial providers. Developing a comprehensive serverless simulation framework is essential towards evaluating scenarios reliant on the serverless paradigm. This paper focuses on introducing a serverless computing model to the DIScrete event baSed Energy Consumption simulaTor for Clouds and Federations (DISSECT-CF) simulator to enable simulating realistic function-as-a-service (FaaS) solutions. This new model is capable of imitating serverless providers in terms of cost model, associating triggers, extracting performance metrics, and customising the configurations of functions. [ABSTRACT FROM AUTHOR]

Foucard, Krishna and Lekshmi introduced a new graph-theoretic concept in the area of network monitoring. Let G be a graph with vertex set V (G) , there exists a subset S ⊆ V (G) , if deleting any edge in G, one can find that there exist at least two vertices in S whose distance is changed, then we call S is a monitoring edge-geodetic set (MEG-set for short). The minimum size of the MEG-set of G is called the monitoring edge-geodetic number of G (meg(G) for short). In this paper, we study the monitoring edge-geodetic number of some well-known networks, including Ladder, butterfly, circulant and Benes networks and convex polytopes. [ABSTRACT FROM AUTHOR]

In his Turing Award Lecture, Jack Dongarra revealed a drastic problem of modern HPC – low efficiency on real-life task mixture, 0.8% for the best supercomputer Frontier. Born in Ukraine paradigm of Sleptsov net computing resolves this problem with the computing memory hardware implementation of an entirely graphical language of concurrent programming supplied with framework of formal methods for verification of concurrent programs. [ABSTRACT FROM AUTHOR]

This paper focuses on a mobile-crowd federated learning system that includes a central server and a set of mobile devices. The server, acting as a model requester, motivates all devices to train an accurate model by paying them based on their individual contributions. Each participating device needs to balance between the training rewards and costs for profit maximization. A Stackelberg game is proposed to model interactions between the server and devices. To match with reality, our model takes the training deadline and the device-side upload time into consideration. Two reward policies, i.e. the size-based policy and accuracy-based policy, are compared. The existence and uniqueness of Stackelberg equilibrium (SE) under both policies are analyzed. We show that there is a lower bound of 0.5 on the price of anarchy in the proposed game. We extend our model by considering the uncertainty in the upload time. We also utilize the blockchain technique to ensure a truthful, trust-free, and fair system. This paper also analyzes how devices maximize their utilities when making profits via training and blockchain mining in the fixed-upload-time setting. A blockchain-powered testbed is implemented, and experiments are conducted to validate our analysis. [ABSTRACT FROM AUTHOR]

The exchanged 3-ary n-cube E 3 C (r , s , t) , proposed by Lv et al. in 2021, is obtained by removing edges from a 3-ary n-cube Q n 3 , where r + s + t + 1 = n. The topological interconnection network of a multiprocessor system can be modeled as a connected graph. Analyzing the fault tolerance of its topological structure is critical in the course of design and maintenance of it. Given a connected graph G, let F be an edge subset of G. F is called an h-edge-cut of G, if G−F is disconnected and each remaining component has the minimum degree of at least h. The h-edge-connectivity λ h (G) is the minimum cardinality of all h-edge-cuts of G. For 1 ≤ r ≤ s ≤ t and 0 ≤ h ≤ r , in this paper, we determine the 2 h -edge-connectivity of exchanged 3-ary n-cubes, E 3 C (r , s , t) , and prove the exact values λ 2 h (E 3 C (r , s , t)) = 2 ⋅ 3 h (r + 1 − h). [ABSTRACT FROM AUTHOR]

Increased chip densities offer massive computation power to deal with fundamental big data operations such as searching and sorting. At the same time, the proliferation of processing elements (PEs) in such multicore chips together with the employment of more aggressive parallel algorithms cause the amount of space needed for interprocessor communications to dominate the overall chip space, potentially resulting in reduced computational efficiency. To overcome this issue, this paper introduces a new architecture that uses simple crosspoint switches to pair PEs instead of a complex interconnection network. This new architecture may be viewed as a 'quadratic' array of processors as it uses O (n 2) PEs rather than O (n) PEs as in linear array processor models. The switches between adjacent PEs are created using a cyclic permutation wiring idea with n (n − 1) / 2 + 1 PEs and as many crosspoints. We demonstrate the versatility of this new parallel architecture by designing fast algorithms to sort and search a list of n elements with it. In particular, we show that finding a minimum, maximum, and searching a list of n elements can all be performed on this parallel architecture in O (1) time with additional O (n) elementary logic gates with O (n) fan-in and in O (lg ⁡ n) time with O (1) fan-in. We further show that sorting a list of n elements can also be carried out in O (1) time using O (n) additional elementary logic gates with O (n) fan-in and threshold logic gates on the same parallel architecture. The sorting time increases to O ((lg ⁡ n) lg ⁡ lg ⁡ n) if only elementary logic gates with O (1) fan-in are used. In addition, we establish how similar queries can be handled within the same order of time complexities. We use this new parallel architecture to perform sorting and searching on big data on three different models. The first of these models provides an efficient implementation of enumeration sorting and searching for moderate size big data sets. The second model offers increased parallelism by replication of the new parallel architecture but its hardware complexity limits its use to moderate size big data sets as well. The third model removes this limitation by introducing a tradeoff parameter between the time and hardware complexity of the overall computation, thereby providing an optimal use of available resources within a given chip-set space. [ABSTRACT FROM AUTHOR]

Modern Cyber Physical Systems (CPS) applications require hardware capable of optimized performance-per-watt efficency. This is usually obtained through massively parallel accelerators such as the GPU. Recent research is therefore investigating novel designs to optimize GPU energy consumption and performance for various applications in the Internet-of-things, autonomous navigation, and industrial robotics domains. This paper presents a survey of the current state-of-the-art approaches for optimizing GPU performance metrics; we present a complete and up-to-date summary of ideas, mechanisms, and potential improvements for next-generation GPU devices. [ABSTRACT FROM AUTHOR]

A parallel version of Ant Lion Optimizer algorithm using the CUDA platform is introduced in this paper. Efficient kernel, memory and thread management approaches have been developed to maximize its performance. The new algorithm was tested against the canonical algorithm on 15 scalable problems of different sizes, with a total of 172 experiments. The solution costs and the execution times were compared using relative percentage difference and significance tests. The results showed the CUDA antlion algorithm significantly improves upon the execution time, while retaining the same solution quality. [ABSTRACT FROM AUTHOR]

The task of looking for the optimal allocation of resources in an economy is fraught with a number of severe restrictions. This is manifested in the complexity of the technical implementation of the solution even in the case of a low dimension of the problem. In this paper, we consider two approaches, analytical and numerical, for deriving the dynamical optimal allocation of resources in a three-sector economy and show that the use of modern artificial intelligence (AI) technologies such as genetic algorithms (GA), can be useful for expanding the range of effective tools and new contributions to this problem. [ABSTRACT FROM AUTHOR]

Reliability evaluation of interconnection networks is of significant importance to the design and maintenance of interconnection networks. The component connectivity is an important parameter for the reliability evaluation of interconnection networks and is a generalization of the traditional connectivity. Let g ≥ 0 be an integer and G be a connected graph. A g-component cut of G is a vertex set S such that G−S has at least g components. The g-component connectivity c κ g (G) of G is the size of the smallest g-component cut. Determining the g-component connectivity is still an unsolved problem in many interconnection networks. In this paper, we prove the lower bound of the g-component connectivity of any n-dimensional hypercube-like networks. We also determine the g-component connectivity of varietal hypercubes and crossed cubes which are the members of hypercube-like networks. As a by-product, we characterize the optimal g-component cut under the condition that any two vertices have exactly two common neighbors if they have of any n-dimensional hypercube-like networks. [ABSTRACT FROM AUTHOR]

The ARBITRARY PATTERN FORMATION (APF) is widely studied in distributed computing for swarm robots. This paper deals with the APF problem in an infinite grid under an asynchronous scheduler. In [Bose K, Adhikary R, Kundu MK, et al. Arbitrary pattern formation on infinite grid by asynchronous oblivious robots. Theor Comput Sci. 2020;815:213–227], the authors proposed an algorithm for APF problem in O B L O T model under an asynchronous scheduler, but the proposed algorithm was neither time optimal nor move optimal. This work provides two algorithms that solve APF problem in an asynchronous scheduler. The first algorithm is move optimal considering O B L O T model and the second algorithm is move and time optimal considering the L U M I model, where each robot has one light having three distinct colours. [ABSTRACT FROM AUTHOR]

The reason of significantly higher transmissibility of SARS Covid (2019 CoV-2) compared with SARS Covid (2003 CoV) and MERS Covid (2012 MERS) can be attributed to mutations reported in structural proteins, and the role played by non-structural proteins (nsps) and accessory proteins (ORFs) for viral replication, assembly, and shedding. Envelope protein E is one of the four structural proteins of minimum length. Recent studies have confirmed critical role played by the envelope protein in the viral life cycle including assembly of virion exported from infected cell for its transmission. However, the determinants of the highly complex viral–host interactions of envelope protein, particularly with host Golgi complex, have not been adequately characterized. CoV-2 and CoV Envelope proteins of length 75 and 76 amino acids (AAs) differ in four AA locations. The additional AA Gly (G) at location 70 makes CoV length 76. The AA pair EG at locations 69–70 of CoV in place of amino acid R in location 69 of CoV-2, has been identified as a major determining factor in the current investigation. This paper concentrates on the analysis of envelope proteins of SARS covid and MERS covid based on Cellular Automata enhanced Machine Learning (CAML) model developed for study of biological strings. This computational model compares deviation of structure–function of CoV-2 from that of CoV employing CAML model parameters derived out of CA evolution of AA chains of envelope proteins. We hypothesize that large differences of CAML model parameter of CoV-2 and CoV characterize the deviation in structure and function of envelope proteins in respect of interaction of virus with host Golgi complex. This difference gets reflected in the contribution of envelope protein towards overall large difference of transmissibility of CoV-2 and CoV. The hypothesis has been validated from single-point mutational study on- (i) human HBB beta-globin hemoglobin protein associated with sickle cell anemia, (ii) mutants of envelope protein of COVID-2-infected patients reported in recent publications. [ABSTRACT FROM AUTHOR]

Field programmable gate arrays (FPGAs) have become widely prevalent in recent years as a great alternative to application-specific integrated circuits (ASIC) and as a potentially cheap alternative to expensive graphics processing units (GPUs). Introduced as a prototyping solution for ASIC, FPGAs are now widely popular in applications such as artificial intelligence (AI) and machine learning (ML) models that require processing data rapidly. As a relatively low-cost option to GPUs, FPGAs have the advantage of being reprogrammed to be used in almost any data-driven application. In this work, we propose an easily scalable and cost-effective cluster-based co-processing system using FPGAs for ML and AI applications that is easily reconfigured to the requirements of each user application. The aim is to introduce a clustering system of FPGA boards to improve the efficiency of the training component of machine learning algorithms. Our proposed configuration provides an opportunity to utilise relatively inexpensive FPGA development boards to produce a cluster without expert knowledge in VHDL, Verilog, or the system designs related to FPGA development. Consisting of two parts – a computer-based host application to control the cluster and an FPGA cluster connected through a high-speed Ethernet switch, allows the users to customise and adapt the system without much effort. The methods proposed in this paper provide the ability to utilise any FPGA board with an Ethernet port to be used as a part of the cluster and unboundedly scaled. To demonstrate the effectiveness of the proposed work, a two-part experiment to demonstrate the flexibility and portability of the proposed work – a homogeneous and heterogeneous cluster, was conducted with results compared against a desktop computer and combinations of FPGAs in two clusters. Data sets ranging from 60,000 to 14 million, including stroke prediction and covid-19, were used in conducting the experiments. Results suggest that the proposed system in this work performs close to 70% faster than a traditional computer with similar accuracy rates. [ABSTRACT FROM AUTHOR]

The spreading of information in social networks can be modelled as a process of diffusing information with a probability from its source to its neighbours. There is a challenge in the real world where competing companies implement their strategies to gain influence in the same social network at the same time. To effectively control the spreading of processes within the network, the effective use of limited resources is of prime importance. When budgets are fixed, competitors will search for a set of seed members to diffuse influence and maximise the number of members that are affected. Each competitor seeks to maximise its influence by investing in the most influential members in the given social network. In this paper, we utilise the Colonel Blotto game to help competitors figure out how many resources should be allocated to influential nodes to increase the influences on nodes. This is done while also taking into account that competing campaigns are trying to do the same thing. We propose a Max-Influence-Independent-Set (MIIS) algorithm to determine the most influential independent set and find the optimal investment to gain maximum influence in the given social network. The effectiveness of this approach is evaluated under different parameter values, namely probability distributions, topologies, and density. [ABSTRACT FROM AUTHOR]

In many practical situations, deep neural networks work better than the traditional ‘shallow’ ones; however, in some cases, the shallow neural networks lead to better results. At present, deciding which type of neural networks will work better is mostly done by trial and error. It is therefore desirable to come up with some criterion of when deep learning is better and when shallow is better. In this paper, we argue that this depends on whether the corresponding situation has natural symmetries: if it does, we expect deep learning to work better, otherwise we expect shallow learning to be more effective. Our general qualitative arguments are strengthened by the fact that in the simplest case, the connection between symmetries and effectiveness of deep learning can be theoretically proven. [ABSTRACT FROM AUTHOR]