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The digital transfer of the mind to a computer system (i.e., mind uploading) requires representing the mind as a finite sequence of bits (1s and 0s). The classic "stored-program computer" paradigm, in turn, implies the equivalence between program and data, so that the sequence of bits themselves can be interpreted as a program, which will be algorithmically executed in the receiving device. Now, according to a previous proof, on which this paper is based, a computational or algorithmic machine, however complex, cannot be free (in the sense of 'self-determined'). Consequently, a finite sequence of bits cannot adequately represent a free mind and, therefore, a free mind cannot be digitally transferred, quod erat demonstrandum. The impossibility of making this transfer, as demonstrated here, should be a concern especially for those who wish to achieve it. Since we intend this to be a rigorous demonstration, we must give precise definitions and conditions of validity. The most important part of the paper is devoted to explaining the meaning and reasonableness of these definitions and conditions (for example that being truly free means being self-determined). Special attention is paid, also, to the philosophical implications of the demonstration. Finally, this thesis is distinguished from other closely related issues (such as other possible technological difficulties to "discretize" the mind; or, whether it is possible to transfer the mind from one material support to another one in a non-digital way). [ABSTRACT FROM AUTHOR]

In this paper we reflect on a body of work to develop a simpler form of digital photography. We give three examples of ‘Less is More’ thinking in this area which are directed and inspired by naturalistic user behaviours and reactions to design ideas. Each example happens to review the place of an old technology in the new scheme of things, and challenges a technological trend in the industry. Hence, we consider the role of sound in photography to recommend audiophotographs rather than short video clips as a new media form. We look again at the role of paper in photo sharing and recommend its support and augmentation against the trend towards screen-based viewing. Finally, we consider the role of physical souvenirs and memorabilia alongside photographs, to recommend their use as story triggers and containers, in contrast to explicit multimedia presentations. The implications for simple computing are discussed. [ABSTRACT FROM AUTHOR]

The motivation of task scheduling in heterogeneous computing systems is the optimal management of heterogeneous distributed resources as well as the exploitation of system capabilities. Energy consumption is one of the most important issues in dealing with task scheduling in heterogeneous distributed systems. In addition to energy, the task completion time and the task cost have also been added to the concerns of the users. Since the nature of computing systems is heterogeneous and dynamic, task scheduling with traditional methods is inefficient. Meta-heuristic approaches for task scheduling in heterogeneous distributed systems are open problems that have attracted the attention of researchers. So far, many meta-heuristic approaches have addressed the task scheduling problem. However, most of these algorithms are developed for homogeneous systems and optimize only one of the quality-of-service parameters. With this motivation, this paper presents an optimization for energy-aware design of task scheduling in heterogeneous distributed systems using meta-heuristic approaches. We simultaneously consider several parameters such as energy, task completion time and task execution cost for task scheduling. The Harris Hawk Optimization (HHO) algorithm is considered for the optimization task due to its adaptability to large search spaces. We combine HHO with a greedy algorithm to avoid local optima and early convergence. The evaluation of the proposed method has been done through numerical simulations. Experimental results show promising performance of the proposed method in terms of energy consumption. [ABSTRACT FROM AUTHOR]

Sensor clouds are formed by IP-enabled wireless sensors and Internet of Things devices that are used for sensing and actuation in commercial and industrial applications. Data collected by the sensors are consolidated by distributed cloud data consolidation (DCS) servers to be utilized as raw sensory information by applications running data analytics and actuation functions. Alternatively, DC servers may feed sensor data to the cloud-hosted Big Data Analytics (BDS) servers. Sensor clouds and their respective DCS servers, as well as BDS servers, may form different security realms. These security realms' ownership structures are complicated and differ from standard database servers, necessitating a dependable authentication technique to provide trusted access to DC and BDS servers. This paper proposes a new multiparty authentication framework to authenticate applications requesting access to the DCS and BDS servers without direct human or application access to the sensors and actuators. Only DC servers are permitted to communicate with sensors/actuators, and only applications certified by a Session Authority Cloud are granted access to DCS/BDS servers via an authentication protocol that includes many information and key exchanges. This solution may assure the reliable deployment of sensor clouds in different critical application domains (i.e., industry, commercial, national security, and defense, etc.) while reducing the potential of direct espionage of sensed/actuated systems. Linear Temporal Logic is used to explicitly analyze and establish the correctness of the presented framework. OPNET modeling and simulations are used to illustrate the protocol's design and operations. The results demonstrate that multiparty authentication is conceivable for Sensor cloud computing systems. [ABSTRACT FROM AUTHOR]

This paper mainly concentrates on obtaining solutions and other exact traveling wave solutions using the generalized G-expansion method. Some new exact solutions of the coupled nonlinear Schrödinger system using the mentioned method are extracted. This method is based on the general properties of the nonlinear model of expansion method with the support of the complete discrimination system for polynomial method and computer algebraic system (AS) such as Maple or Mathematica. The nonparaxial solitons with the propagation of ultra-broad nonparaxial pulses in a birefringent optical waveguide is studied. To attain this, an illustrative case of the coupled nonlinear Helmholtz (CNLH) system is given to illustrate the possibility and unwavering quality of the strategy utilized in this research. These solutions can be significant in the use of understanding the behavior of wave guides when studying Kerr medium, optical computing and optical beams in Kerr like nonlinear media. Physical meanings of solutions are simulated by various Figures in 2D and 3D along with density graphs. The constraint conditions of the existence of solutions are also reported in detail. Finally, the modulation instability analysis of the CNLH equation is presented in detail. [ABSTRACT FROM AUTHOR]

Heterogeneous computing has been developing continuously in the field of high-performance computing because of its high performance and energy efficiency. More and more accelerators have emerged, such as GPU, FPGA, DSP, AI accelerator, and so on. Usually, the accelerator is connected to the host CPU as a peripheral device to form a tightly coupled heterogeneous computing node, and then, a parallel system is constructed by multiple nodes. This organization is computationally efficient, but not flexible. When new accelerators appear, it is difficult to join the system that has been built. At the hardware level, we create an array of accelerators and connect them to the existing system through a high-speed network. At the software level, we dynamically organize computing resources from various arrays to build a virtual heterogeneous computing node. This approach also includes a standard programming environment. Therefore, it is a more flexible, elastic, and scalable heterogeneous computing organization. In this paper, a supernode OpenCL implementation is proposed for hybrid parallel computing systems, in which virtual supernodes can be dynamically constructed between different computing arrays, and a standard OpenCL environment is implemented based on RDMA communication of high-speed interconnection, which can be combined with the system-level MPI programming environment, thereby realizing the large-scale parallel computing of the hybrid array. SNCL is compatible with existing MPI/OpenCL programs without the need for additional modifications. Experiments show that the runtime overhead of the supernode OpenCL environment is very low, and it is suitable for deploying applications with high computing density and large data scale between different arrays to utilize their computing power without affecting scalability. [ABSTRACT FROM AUTHOR]

Recently, the unmanned aerial vehicle (UAV)-assisted edge computing is proposed to improve the quality of service in some scenarios within sparse or unavailable base stations (BSs). Meanwhile, the caching technology is adopted to reduce the wireless traffic load and the data transmission delay. However, due to the limited storage capacity of edge nodes, the edge nodes cannot store all of the contents required by user equipment (UE). So, how to select the reasonable contents for caching on edge nodes to reduce the content delivery delay becomes a challenge in the UAV-assisted edge computing environment. In this paper, the Dijkstra algorithm based cooperative caching strategy for UAV-assisted edge computing system is proposed. Specially, the content transmission delay between two nodes is computed. Then, for each requested content, the weighted edge-undirected graph (WEUG), in which one vertex represents one node, is built. Furthermore, Dijkstra algorithm is adopted to achieve the minimal content transmission delay from the edge node caching the requested content to UE. Finally, the optimization problem of content caching is built, and the corresponding cache strategy is achieved by solving the optimization problem. The experimental results imply that the proposed cooperative caching algorithm can achieve better performance on the average content transmission delay, the average cache hit rate, and the total of hops, respectively, comparing with the benchmark algorithms. [ABSTRACT FROM AUTHOR]

This paper aims to explore the application of cloud computing image system based on optical communication in museum virtual reality (VR) design. In the context of the gradual maturity of the current technology, the use of optical technology and cloud computing can provide a more realistic and immersive experience for the museum's VR display. This study summarizes the principle and technology of cloud computing image system based on optical communication, and elaborates the specific application method of cloud computing image system based on optical communication in this scene by constructing an application scene of VR exhibition in a museum. Based on museum exhibits, through high-resolution image transmission and real-time image processing, in the virtual reality environment, the audience can feel the cultural charm of the museum. The experimental results show that the application of cloud computing image system based on optical communication in museum VR design can greatly improve the audience's participation and experience. Through the use of optical transmission and cloud computing technologies, high-quality image transmission and processing can be achieved, making the viewer feel a more realistic and lifelike virtual environment. [ABSTRACT FROM AUTHOR]

At present, human translation is characterized by low efficiency, high cost, high consumption of resources and time, so it is urgent to study automatic translation technology to meet the needs of information interaction and language communication in various countries under the background of economic globalization. In order to improve the performance of Chinese-English translation system, this study aims to use optical network transmission technology to optimize the translation process. This research is based on deep learning model and uses optical network transmission technology to implement Chinese-English translation system. The neural network model is used to train the Chinese-English translation, and then the data is transmitted to the cloud for translation using optical network transmission technology. Finally, the translation result is returned to the user. The system can learn by itself on the basis of analyzing source documents, and predict target translation output by extracting features. The system is equipped with a translation engine based on the data source, which can realize interactive translation, and the final output can be achieved by processing the predicted translation output and based on the translator's understanding. Through the design of simulation experiments for system detection, the results show that the Chinese English translation model system has a high translation quality. By comparing it with the baseline system, it can be seen that both of them can translate sentences normally in 8 PR cycles (PR * 8, about 18% KSMR), and the data set improvement rate is about + 35 BLEU, that is, 76 BLEU is finally achieved. This proves that users can maintain the translation accuracy while reducing the number of effective interactions by using the optimized translation model to translate the target text, showing the effectiveness. In this paper, an interactive Chinese English translation system is designed by combining deep learning with cloud computing. [ABSTRACT FROM AUTHOR]

The automotive industry is increasingly dependent on computing systems with different critical requirements. The verification and validation methods for these systems are now leveraging complex AI methods, for which the decision algorithms introduce non-determinism, especially in autonomous driving. This paper presents a runtime verification technique agnostic to the target system, which focuses on monitoring spatio-temporal properties that abstract the evolution of objects' behavior in their spatial and temporal flow. First, a formalization of three known traffic rules (from the Vienna convention on road traffic) is presented, where a spatio-temporal logic fragment is used. Then, these logical expressions are translated to a monitoring model written in first-order logic, where they are processed by a non-linear satisfiability solver. Finally, the translation allows the solver to check the validity of the encoded properties according to an instance of a specific traffic scenario (a trace). The results obtained from our tool, which automatically generates a monitor from a formula, show that our approach is feasible for online monitoring in a real-world environment. [ABSTRACT FROM AUTHOR]

These days, cloud computing usage has been increasing with the rapid growth of Internet coverage all over the world to serve as a pay-per-use model using shared computing resources. Internet of Things (IoT) is a growing technology which is used in different applications and it needs cloud computing however the distance between cloud computing resources and the end system in IoT can cause a delay which is intolerable for delay-sensitive applications. Fog computing is a computing resource between cloud computing and end system to reduce the delay for the delay-sensitive applications in IoT. Therefore, load balancing functionality is a significant role to provide the required quality of service (QoS), quality of experience (QoE), and performance. Load balancing can be done based on response time, throughput, energy consumption, and utilization metrics. In this paper, the papers published in Elsevier, ACM, IEEE, Springer and Wiley between 2018 and 2022 have been examined to extract the load-balancing algorithms, system architecture, tools and applications, advantages and disadvantages. This review is useful for those working on load-balancing performance improvement. [ABSTRACT FROM AUTHOR]

This paper mainly concentrates on obtaining solutions and other exact traveling wave solutions using the generalized G-expansion method. Some new exact solutions of the coupled nonlinear Schrödinger system using the mentioned method are extracted. This method is based on the general properties of the nonlinear model of expansion method with the support of the complete discrimination system for polynomial method and computer algebraic system (AS) such as Maple or Mathematica. The nonparaxial solitons with the propagation of ultra-broad nonparaxial pulses in a birefringent optical waveguide is studied. To attain this, an illustrative case of the coupled nonlinear Helmholtz (CNLH) system is given to illustrate the possibility and unwavering quality of the strategy utilized in this research. These solutions can be significant in the use of understanding the behavior of wave guides when studying Kerr medium, optical computing and optical beams in Kerr like nonlinear media. Physical meanings of solutions are simulated by various Figures in 2D and 3D along with density graphs. The constraint conditions of the existence of solutions are also reported in detail. Finally, the modulation instability analysis of the CNLH equation is presented in detail. [ABSTRACT FROM AUTHOR]

The "MyFairShare" project develops fair CO2 mobility budgets for individuals. Here, "fairness" mainly depends on the people's location as everyone should be able to access all destinations necessary to perform everyday tasks. Therefore, a basic understanding about the accessibility of facilities to visit within an area is needed, regarding all activities that must be performed. Given this, the amount of emitted CO2 is computed, assuming the use of sustainable modes while regarding reasonable ranges, i.e. a travel time of 15 min. In order to achieve this, a software system for computing this metric has been developed. It is based on open source applications and uses data that—besides public transport data in GTFS format—is freely available throughout Europe. This paper describes the method and presents the results of applying it to the project's five Living Labs Berlin, Jelgava, London, Sarpsborg, and Vienna. The results show that besides population density, the possibility to use sustainable modes of transport highly depend on the land use mix, i.e. the allocation of facilities daily activities may be performed at in the vicinity of places of inhabitancy. [ABSTRACT FROM AUTHOR]

In 5G, the next generation of technologies like the Internet of Medical Things (IoMT) cooperate with networking artifacts to provide seamless connectivity. To provide effective information sharing, the Internet infrastructure integrates various components of medical information systems such as computing devices, software tools, and application services. The information systems access a massive Internet of Medical Things (mIoMT) to discover a series of network infrastructures with technological innovations to offer remote diagnosis and treatment. In collecting healthcare information, innovative applications utilize medical sensor networks to drive extensive information processing including mobility support. However, the convergence of IoT and the cloud addresses the security gap while centralizing the devices into edge computing systems to protect the transmission flow through the device gateway. Considering this fact, an authentication scheme with privacy preservation was proposed using a key exchange protocol and digital signature by Soleymani et al. to guarantee data confidentiality and integrity. However, this scheme is still incapable of validating the integrity of data with the edge systems properly. Thus, this paper presents privacy-preserving authentication with device verification (PP-ADV) for securing healthcare systems in 5G networks. To protect the device privacy and server identities with registered users, the proposed PP-ADV utilizes two cryptographic primitives namely elliptic curve arithmetic and a collision-free hash function to achieve the significant properties of key agreement protocol including authenticity and confidentiality. Formal and informal analyses show that the proposed PP-ADV can restrict vulnerabilities such as privileged insider and forgery to achieve high-level privacy protection with seamless verification to improve system efficiency compared with other state-of-the-art approaches. Also, a board simulation testbed was designed using Raspberry Pi with Arduino IDE to examine the quality metrics including data transmission ratio, authentication delay, and throughput rate. The testbed analysis demonstrates that the proposed PP-ADV gains less delay ≈ 292.28 s and improved throughput ≈ 87.73 to meet the design criteria of 5G-enabled remote healthcare systems. [ABSTRACT FROM AUTHOR]

Owing to the enormous advancement in miniature hardware, modern smart mobile devices (SMDs) have become computationally powerful. Mobile crowd computing (MCC) is the computing paradigm that uses public-owned SMDs to garner affordable high-performance computing (HPC). Though several empirical works have established the feasibility of mobile-based computing for various applications, there is a lack of comprehensive coverage of MCC. This paper aims to explore the fundamentals and other nitty–gritty of the idea of MCC in a comprehensive manner. Starting with an explicit definition of MCC, the enabling backdrops and the detailed architectural layouts of different models of MCC are presented, along with categorising different types of MCC based on infrastructure and application demands. MCC is compared extensively with other HPC systems (e.g. desktop grid, cloud, clusters and supercomputers) and similar mobile computing systems (e.g. mobile grid, mobile cloud, ad hoc mobile cloud, and mobile crowdsourcing). MCC being a complex system, various design requirements and considerations are extensively analysed. The potential benefits of MCC are meticulously mentioned, with special discussions on the ubiquity and sustainability of MCC. The issues and challenges of MCC are critically presented in light of further research scopes. Several real-world applications of MCC are identified and propositioned. Finally, to carry forward the accomplishment of the MCC vision, the future prospects are briefly elucidated. [ABSTRACT FROM AUTHOR]

Mobility is a fundamental feature of mobile edge computing. Due to the mobility of users, the contextual attributes of cloudlets such as server resources and network state will dynamically change with time during offloading, showing time-varying and fuzzy characteristics. To this end, how to make efficient offloading decision to provide low-latency, low-power and highly reliable services in MEC has become a critical issue. In this paper, we propose a time-varying context-aware cloudlet decision algorithm based on neutrosophic set, TConNS (The Code of TConNS is available at https://github.com/zhengLabs/NSO) . Firstly, we establish a representation model of the multi-dimensional time-varying context of candidate cloudlets, including the mobile residence time. Secondly, we adopt the backward generator of cloud model theory to transform the contextual raw data into a single-valued neutrosophic set with the expression ability for fuzzy information. Finally, we use a series of appropriate operations under the own unique computing system of neutrosophic set to obtain the best cloudlet. Extensive experiments show that TConNS reduces the average response time by about 49% and the average energy consumption by about 46%, and also reduces the number of task failures. [ABSTRACT FROM AUTHOR]

CitcomCu is a numerical simulation software for mantle convection in the field of geodynamics, which can simulate thermo-chemical convection in a three-dimensional domain. Due to the increasing demand for high-precision simulations and larger application scales, larger-scale computing systems are needed to solve this problem. However, the parallel efficiency of CitcomCu on large-scale heterogeneous parallel computing systems is difficult to improve, especially it cannot adapt to the current mainstream heterogeneous high-performance computing architecture with CPUs and accelerators. In this paper, we propose an geodynamics numerical simulation parallel computing framework using heterogeneous computing architecture based on the Tianhe new-generation high-performance computer. Firstly, the data partitioning mode of CitcomCu was optimized based on the large-scale heterogeneous computing architecture to reduce the overall communication overhead. Secondly, the iterative solution algorithm of CitcomCu was improved to speed up the solution process. Finally, the NEON instruction set based on SIMD is used for the sparse matrix operations in the solution process to improve parallel efficiency. Based on our parallel computing framework, the optimized CitcomCu was deployed and tested on the Tianhe new-generation high-performance computer. Experimental data showed that the performance of the optimized program was 3.3975 times higher than that of the unoptimized program on a single node. Compared with 50,000 computational cores, the parallel efficiency of the unoptimized program on one million computational cores was 36.75%, while the parallel efficiency of the optimized program was improved by 16.22% and reached 42.71%. In addition, the optimized program can be executed on 40 million computational cores, with a parallel efficiency of 36.54%. [ABSTRACT FROM AUTHOR]

As more and more intelligent devices are put into the field of power system, the number of connected nodes in the power network is increasing exponentially. Under the background of smart grid cooperation across power areas and voltage levels, how to effectively process the massive data generated by smart grid has become a difficult problem to ensure the stable operation of power system. In the complex calculation process of power system, the operation time of complex calculation can not be shortened to the greatest extent, and the execution efficiency can not be improved. Therefore, this paper proposes a two-phase heuristic algorithm based on edge computing. In solving the virtual machine sequence problem, for the main partition and the coordination partition, the critical path algorithm is used to sort the virtual machines to minimize the computing time. For other sub-partitions, the minimum cut algorithm is used to reduce the traffic interaction of each sub-partition. In the second stage of the virtual machine placement process, an improved best fit algorithm is used to avoid poor placement of virtual machines across physical machine configurations, resulting in increased computing time. Through the experiment on the test system, it is proved that the calculation efficiency is improved when the coordinated partition calculation belongs to the target partition. Because the edge computing is closer to the data source, it can save more data transmission time than cloud computing. This paper provides an effective algorithm for power system distributed computing in virtual machine configuration in edge computing, which can effectively reduce the computing time of power system and improve the efficiency of system resource utilization. [ABSTRACT FROM AUTHOR]

This paper is on the concept of quantum steering and quantum entanglement of two observers. The concept is applied to a multi-view computer vision system that incorporates two cameras. Three separate multi-view static camera setups are used to compare the recognition accuracy and to improve the arrangement of cameras in gesture recognition and classroom organisation applications. Prominent features which are partially hidden from a viewpoint can increase performance if given attention. In view of that, this paper proposes an entanglement-based ranking technique that updates the weights of attentive features to improve the classification rate. Principles of entanglement are also used to optimise the position of cameras such that the authoritative hidden features are visible. The proposed technique has a high recognition rate with static cameras. It also shows a low error rate in the field of view when switching to other applications. The results are validated with derangement and Bland–Altman agreement test. The entanglement approach for determining the fine-tuned position of static cameras in a recognition task outperforms many active camera networks. [ABSTRACT FROM AUTHOR]

This special section is devoted to a selection of papers that originally appeared in two thematic sessions on high-level testing of complex systems at IDPT 2002 and 2003, the 6th and 7th World Conference on Integrated Design and Process Technology, which took place in Pasadena, CA in June 2002 and in Austin, TX in December 2003, respectively. This collection of papers spans a wide panoramic view on the development of testing and validation technology along several dimmensions. It touches on issues such as All seven papers present methods, tools, and case studies that aim at using diverse formal techniques for testing complex systems. [ABSTRACT FROM AUTHOR]

Focused on city-scale automation, and using self-driving cars (SDCs) as a case study, this article reflects on the role of AI—and in particular, computer vision systems used for mapping and navigation—as a catalyst for urban transformation. Urban research commonly presents AI and cities as having a one-way cause-and-effect relationship, giving undue weight to AI's impact on cities and overlooking the role of cities in shaping AI. Working at the intersection of data science and social research, this paper aims to counter this trend by exploring the reverse perspective: how do cities affect the development, and expose the present limits, of SDCs? The contribution of this paper is threefold. First, by comparing urban and nonurban environments and thoroughly examining the relationship between computer vision and city-specific sociality and form, it defines machine autonomy/automation as a function of the sociotechnical milieu in which an AI system operates. Second, and related, the paper problematizes the notion of SDCs as autonomous technologies and the role it plays in envisioning contending policy arrangements and technical solutions for achieving full driving automation. Finally, the article offers insight into a materialist and spatialized understanding of AI—namely, not as an abstract quality susceptible to replication within discrete machines, but rather as a distributed property emerging through embodied interactions among a multiplicity of agents (human, non-human, and technological) within/with their environments. [ABSTRACT FROM AUTHOR]

Int this paper, we study the process of changing the polarization of hafnium oxide crystals in the orthorhombic phase associated with the gradual weakening of polarization effects in FeRAM elements based on thin films of hafnium oxide HfO2. To solve the problem, quantum mechanical calculations of the structure of orthorhombic hafnium oxide are performed, a possible way of crystal restructuring during a change in polarization when a voltage is applied is identified, and its optimization is carried out using the elastic band method. The values of the change in polarization and the energy barrier of the corresponding transition are obtained. The stability of this transition is studied. The results of a series of computational experiments using high-performance computing systems of hybrid architecture based on the Center for Collective Use at the Federal Research Center "Computer Science and Control" are presented. An analysis of the results shows that, despite the low energy barrier of the transition, the probability of a spontaneous change in polarization is low due to the impossibility of changing the polarization of an individual cell with no allowance for the effect of the polarizations of the neighboring cells. [ABSTRACT FROM AUTHOR]

Numbers of the form k · p n + 1 with the restriction k < p n are called generalized Proth numbers. For a fixed prime p we denote them by T p . The underlying structure of T 2 (Proth numbers) was investigated in [2]. In this paper the authors extend their results to all primes. An efficiently computable upper bound for the reciprocal sum of primes in T p is presented. All formulae were implemented and verified by the PARI/GP computer algebra system. We show that the asymptotic density of ⋃ p ∈ P T p is log 2 . [ABSTRACT FROM AUTHOR]

Edge–fog–cloud computing system can be divided into edge or IoT layer (tier 1), fog layer (tier 2) and cloud layer (tier 3). The devices at the edge layer generate different types of tasks which may be computation-intensive or communication intensive or having a combination of these properties. Depending on the characteristics of tasks, those may be scheduled to run at the edge or fog or cloud layers. There are many advantages of offloading some of the computationally intensive workloads, which includes improved response time, satisfying the deadlines of delay-sensitive tasks and overall reduced make span of the workloads. In this context, there is a need for designing a scheduling algorithm with the goal to minimize the overall execution time while satisfying the deadlines of the tasks and maximizing the resource utilization at fog layer. In this paper, we are proposing a task offloading and scheduling algorithm based on the osmotic approach. In the osmotic approach, the devices and tasks are classified, and the tasks are assigned to the most suitable devices based on their dynamically available capacity. The proposed scheduling algorithm is compared with traditional random task offloading and round robin task offloading algorithms using synthetic data sets and found that the proposed algorithm performance is significantly better than other algorithms. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting-Multi Operating System Installation Toolkit (WRF-MOSIT) is a set of scripting packages primarily developed for the automation, configuration, and installation of the standard Weather Research and Forecasting model (WRF), and other WRF applications: hurricane (HWRF), hydrological (WRF-Hydro) and chemical (WRF-Chem). The package consists of many tools for importing, configuring, and installing libraries, and undertaking a wide range of analyses to enhance scalability, performance, interoperability, and ease of use within a parallel computing environment. WRF-MOSIT is a cross-platform modular tool simplifies the installation process, automates configuration, and reduces the risk of errors during installation. It provides a simple and intuitive interface, which makes it easier for users who are not familiar with the installation process. This toolkit can be installed on the four main computer systems used in the atmospheric community: Debian Kernels, Darwin Kernels, Fedora & CentOS Kernels, and Windows Sub-System Linux Kernels, allowing users to install WRF on different systems without having to manually configure the system. The WRF-MOSIT toolkit is freely available on GitHub (https://github.com/HathewayWill/WRF-MOSIT). The toolkit improves efficiency by automating many of the steps involved in the WRF installation process, enabling users to spend less time on the installation process and more time on their research. Finally, insights are given for future developments. The purpose of the WRF-MOSIT is to alleviate the installation hurdles of installing all the requirements and supplementary files, software packages, and pre/post-processing tools to run the different WRF models. The WRF-MOSIT toolkit simplifies the installation process, automates configuration, and reduces errors when installing the WRF model. It is a cross-platform tool that can be installed on multiple operating systems, including Debian Kernels, Darwin Kernels, Fedora & CentOS Kernels, and Windows Sub-System Linux Kernels. The WRF-MOSIT toolkit provides an intuitive interface that makes it easier for users who are not familiar with the installation process to install the WRF model. This toolkit is freely available on GitHub and improves efficiency by automating many of the steps involved in the installation process, enabling users to spend more time on their research. The paper provides insights into future developments of the WRF-MOSIT toolkit, including the addition of more features and support for other coupled model packages. [ABSTRACT FROM AUTHOR]

Water treatment using RO is one of the most cost-effective methods. This study investigates how temperature affects pressure drop and NDP in SWRO and BWRO units. For each membrane used in hybrid and simple single-stage configurations of the RO systems, the results showed a reduction in pressure drop and NDP at higher temperatures. Based on the simulations performed, the average pressure drop and NDP of the hybrid single-stage configuration were less than the simple single-stage design of the BWRO system. Besides, the NDP in the hybrid single-stage configuration was lower than in the simple single-stage configuration, and the value of NDP in the two configurations differed by 0.32 bar. Each membrane induced a lower pressure drop at higher temperatures and required less driving pressure. As a result, the total pressure drop of hybrid and simple two-stage configurations was lower at higher temperatures. The average driving pressure of the hybrid two-stage configuration was less than the simple two-stage configuration, and the average driving pressure differed by a minimum of 0.11 bar. At higher temperatures, each membrane induced lower pressure drops in two-stage configurations. Also, a reduction of the driving pressure in each membrane in the SWRO system was observed. According to observations, the simple single-stage configuration of the SWRO system induced higher pressure drops than the hybrid single-stage configuration. Also, the total average driving pressure in the hybrid single-stage configuration was more significant than in the simple single-stage configuration, and the two differed by 0.18 bar. However, the hybrid single-stage configuration experienced smaller total pressure drops than the simple single-stage configuration, and the two differed by a minimum of 0.06 bar. The results of this paper can be used to improve and optimize the design of RO systems based on operating temperatures. [ABSTRACT FROM AUTHOR]

Continuing the previous initiatives [1-2], we pursue the exploration of operator growth and Krylov complexity in dissipative open quantum systems. In this paper, we resort to the bi-Lanczos algorithm generating two bi-orthogonal Krylov spaces, which individually generate non-orthogonal subspaces. Unlike the previously studied Arnoldi iteration, this algorithm renders the Lindbladian into a purely tridiagonal form, thus opening up a possibility to study a wide class of dissipative integrable and non-integrable systems by computing Krylov complexity at late times. Our study relies on two specific systems, the dissipative transverse-field Ising model (TFIM) and the dissipative interacting XXZ chain. We find that, for the weak coupling, initial Lanczos coefficients can efficiently distinguish integrable and chaotic evolution before the dissipative effect sets in, which results in more fluctuations in higher Lanczos coefficients. This results in the equal saturation of late-time complexity for both integrable and chaotic cases, making the notion of late-time chaos dubious. [ABSTRACT FROM AUTHOR]

Cloud storage provides convenience for data owners. But it faces serious challenges from data tampering and abuse. Flexible access control method is an effective way to protect shared data security. Existing attribute-based access control methods attempt to improve the access flexibility and security of shared data. But there are some problems for resource-limited lightweight devices as performance and security to be solved. Blockchain can construct a trusted network for data owner to deal with data validity and digital forensics. This paper proposes a novel blockchain-based lightweight access control scheme. The proposed scheme uses blockchain to construct a trusted sharing network by consensus mechanism. The lightweight attribute-based sharing scheme is used to support fine-grained access control of data. In this scheme, it obfuscates the access control policy with fuzzy attribute set to improve system security, and reduces the computing complexity of system users by outsourcing complex operations to semi-trusted proxy servers. The security analysis shows that the scheme is S , ρ , n - 1 , ε -secure against collusion attack between users and attribute authorities. The performance analysis results show that the proposed scheme reduces the computational complexity of user devices and provides faster response time compared with benchmark and state-of-the-art technologies. [ABSTRACT FROM AUTHOR]

This work aims to estimate the execution time of data processing tasks (specific executions of a program or an algorithm) before their execution. The paper focuses on the estimation of the average-case execution time (ACET). This metric can be used to predict the approximate cost of computations, e.g. when resource consumption in a High-Performance Computing system has to be known in advance. The presented approach proposes to create machine learning models using historical data. The models use program metadata (properties of input data) and parameters of the run-time environment as their explanatory variables. Moreover, the set of these variables can be easily expanded with additional parameters of the specific programs. The program code itself is treated as a black box. The response variable of the model is the execution time. The models have been validated within a Large-Scale Computing system that allows for a unified treatment of programs as computation modules. We present the process of training and validation for several different computation modules and discuss the suitability of the proposed models for ACET estimation in various computing environments. [ABSTRACT FROM AUTHOR]

In this paper, electronic and magnetic properties of monolayers and bilayers of Vanadium-based transition metal dichalcogenides VX2 (X = S, Se, Te) in the H phase are investigated theoretically using methods based on DFT calculations as well as analytical methods based on effective spin Hamiltonians. The band structure has been computed for all systems, and then the results have been used to determine exchange parameters and magnetic anisotropy constants. These parameters are subsequently used for the determination of the Curie temperatures, hysteresis curves, and energy of spin-wave excitations. In the latter case, we compare analytical results based on effective spin Hamiltonian with those determined numerically by Quantum ATK software and find a good agreement. The determined Curie temperature for VTe2 monolayers and bilayers is below the room temperature (especially that for bilayers), while for the other two materials, i.e. for VS2 and VSe2, it is above the room temperature, in agreement with available experimental data. [ABSTRACT FROM AUTHOR]

Wave-based analog computing has recently emerged as a promising computing paradigm due to its potential for high computational efficiency and minimal crosstalk. Although low-frequency acoustic analog computing systems exist, their bulky size makes it difficult to integrate them into chips that are compatible with complementary metal-oxide semiconductors (CMOS). This research paper addresses this issue by introducing a compact analog computing system (ACS) that leverages the interactions between ultrasonic waves and metasurfaces to solve ordinary and partial differential equations. The results of our wave propagation simulations, conducted using MATLAB, demonstrate the high accuracy of the ACS in solving such differential equations. Our proposed device has the potential to enhance the prospects of wave-based analog computing systems as the supercomputers of tomorrow. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the granularity in the free layer of the magnetic tunnel junctions (MTJ) and its potential to function as a reservoir for reservoir computing where grains act as oscillatory neurons while the device is in the vortex state. The input of the reservoir is applied in the form of a magnetic field which can pin the vortex core into different grains of the device in the magnetic vortex state. The oscillation frequency and MTJ resistance vary across different grains in a non-linear fashion making them great candidates to be served as the reservoir's outputs for classification objectives. Hence, we propose an experimentally validated area-efficient single granular vortex spin-torque nano oscillator (GV-STNO) device in which pinning sites work as random reservoirs that can emulate neuronal functions. We harness the nonlinear oscillation frequency and resistance exhibited by the vortex core granular pinning of the GV-STNO reservoir computing system to demonstrate waveform classification. [ABSTRACT FROM AUTHOR]

High-performance computing systems conduct extensive logging of resource usage data and system logs, and parsing these data is an often advocated basis for failure diagnosis. Major page faults are known to be one of the most common cause of performance problems in large cluster systems. We conduct an empirical study of major page faults on two large cluster systems. We set up three regression algorithms including the LASSO, Ridge and Elastic Net regression techniques. To the best of our knowledge, there is no work that studied different regression models to diagnose major page faults in a large cluster system. In this paper, we (a) propose an approach for diagnosing major page faults, and (b) evaluate the LASSO, Ridge and Elastic Net regression algorithms on real resource use data and system logs. As part of our contributions, we (a) compare the accuracy of the three regression algorithms, (b) identify the resource use counters which are correlated to major page faults and the system events which are correlated to page fault events, and (c) provide insights into major page faults and page fault events. Our work highlights empirical observations that could facilitate better handling of node failures in cluster systems. [ABSTRACT FROM AUTHOR]

As multi/many-core processors are widely deployed in high-performance computing systems, efficient intra-node communication becomes more important. Intra-node communication involves data copy operations to move messages from source to destination buffer. Researchers have tried to reduce the overhead of this copy operation, but the copy operation performed by CPU still wastes the CPU resources and even hinders overlapping between computation and communication. The copy engine is a hardware component that can move data between intra-node buffers without intervention of CPU. Thus, we can offload the copy operation performed by CPU onto the copy engine. In this paper, we aim at exploiting copy engines for MPI blocking collective communication, such as broadcast and gather operations. MPI is a messaging-based parallel programming model and provides point-to-point, collective, and one-sided communications. Research has been conducted to utilize the copy engine for MPI, but the support for collective communication has not yet been studied. We propose the asynchronism in blocking collective communication and the CE-CPU hybrid approach to utilize both copy engine and CPU for intra-node collective communication. The measurement results show that the proposed approach can reduce the overall execution time of a microbenchmark and a synthetic application that perform collective communication and computation up to 72% and 57%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the granularity in the free layer of the magnetic tunnel junctions (MTJ) and its potential to function as a reservoir for reservoir computing where grains act as oscillatory neurons while the device is in the vortex state. The input of the reservoir is applied in the form of a magnetic field which can pin the vortex core into different grains of the device in the magnetic vortex state. The oscillation frequency and MTJ resistance vary across different grains in a non-linear fashion making them great candidates to be served as the reservoir's outputs for classification objectives. Hence, we propose an experimentally validated area-efficient single granular vortex spin-torque nano oscillator (GV-STNO) device in which pinning sites work as random reservoirs that can emulate neuronal functions. We harness the nonlinear oscillation frequency and resistance exhibited by the vortex core granular pinning of the GV-STNO reservoir computing system to demonstrate waveform classification. [ABSTRACT FROM AUTHOR]

This paper presents an original package for investigating numerical solutions of ordinary differential equations, which is built in the Sage computer algebra system. This project is focused on a closer integration of numerical and symbolic methods while primarily aiming to create a convenient tool for working with numerical solutions in Sage. The package defines two new classes: initial problems and approximate solutions. The first class defines tools for symbolic computations related to initial problems, while the second class defines tools for interpolating values of symbolic expressions on an approximate solution and estimating the error with the use of the Richardson method. An implementation of the Runge–Kutta method is briefly described, with its main feature being the possibility of working with arbitrary Butcher tableaux and arbitrary numeric fields. [ABSTRACT FROM AUTHOR]

RISC-V set architecture is playing an increasingly important role in processor technology due to its open instructions which allow researchers to build and improve computing systems. However, many RISC-V architectures exist in multi-core architecture with complex designs, large area, and high-power consumption. This paper studies an open-source multi-core RISC-V processor in a simple design with less power consumption. The processor depends on an open-source single RISC-V core processor, Taiga. Two cores of Taiga are integrated on a single chip while addressing issues related to cache coherence, interconnect, and memory design. A solution has been developed to achieve data coherence between implemented caches and the main memory; its architecture depends on the snoopy protocol. A hardware-customized peripheral unit has been implemented to achieve the management process among operated cores' tasks. For more consistent and highly controlled memory storage, the main memory unit has been designed in dual-port based on a specific protocol in the interface, and 8192 lines and word addressable unit. As the UART is common in several devices and processors for communication, the UART as a peripheral customized device has been appended for communication with other devices. The processor has been implemented in System Verilog HDL and extensively tested on various testbenches to ensure correct functionality. Hence, the system performance has been evaluated using the CoreMark benchmark, and achieved 4.605 CoreMark/MHz on Zedboard (FPGA Xilinx family) with a maximum operating frequency 98 MHz. The results indicate that the processor performs comparably to state-of-the-art multi-core processors, while offering a simpler and more power-efficient design. Overall, the research demonstrates the potentialof RISC-V architecture in creating a simple and power-efficient multi-core RISC-V processor. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for real-time terrain recognition-based navigation for mobile robots. Mobile robots performing tasks in unstructured environments need to adapt their trajectories in real-time to achieve safe and efficient navigation in complex terrains. However, current methods largely depend on visual and IMU (inertial measurement units) that demand high computational resources for real-time applications. In this paper, a real-time terrain identification-based navigation method is proposed using an on-board tapered whisker-based reservoir computing system. The nonlinear dynamic response of the tapered whisker was investigated in various analytical and Finite Element Analysis frameworks to demonstrate its reservoir computing capabilities. Numerical simulations and experiments were cross-checked with each other to verify that whisker sensors can separate different frequency signals directly in the time domain and demonstrate the computational superiority of the proposed system, and that different whisker axis locations and motion velocities provide variable dynamical response information. Terrain surface-following experiments demonstrated that our system could accurately identify changes in the terrain in real-time and adjust its trajectory to stay on specific terrain. [ABSTRACT FROM AUTHOR]

As a rising technology in recent years, edge computing has the characteristics of low latency and low energy consumption, which makes it possible for intelligent interconnection of things based on the Internet of Things(IoT) to meet people's intelligent service needs anytime and anywhere. However, due to its wide geographical distribution, limited node resources, and vulnerability to network attacks, the operating environment of edge computing systems is in jeopardy, and user data privacy disclosure, malicious data tampering, identity authentication, and other security issues are increasingly prominent. As a distributed data-sharing technology, blockchain's hash encryption transmission, consensus mechanism, and other characteristics determine that it has tamper-proof and traceable performance characteristics, which can well solve the security problems in edge computing systems. Although there have been quite many studies on the application of blockchain technology to the edge computing-enabled IoT system, there has been no systematic and mathematical research and analysis on the security mechanism of the blockchain technology architecture itself. From the perspective of relative security in the field of information security, this paper uses stochastic differential game theory to model the gains of both attack and defense sides under the edge computing system based on blockchain technology and obtain the Nash equilibrium solution. Runge–Kutta algorithm is used to solve the numerical solution of the revenue game model of both sides. From the perspective of architecture theory, it discusses and studies the security characteristics brought by blockchain technology architecture itself. Through simulation comparison with relevant research work and existing baseline scheme, the simulation results show the effectiveness and superiority of our proposed scheme. At the same time, the difficulty coefficient and block size are also revealed as security factors in systems based on blockchain technology. [ABSTRACT FROM AUTHOR]

In the blockchain environment, smart contracts are computer programs that run on the blockchain platform. However, the development of smart contracts is a major challenge for developers, since blockchain platforms are still evolving. Owing to the inherited nature of blockchain, developing smart contracts without introducing vulnerabilities is not an easy task, as the deployed code is immutable and can be invoked by anyone with access to the network. Smart contracts have proved to be error-prone in practice due to the complexity of programming. Additionally, non-functional requirements, such as service cost, security, performance, authorization, and authentication, should be well implemented and defined in computer systems. In this paper, we aim to present a systematic literature review to outline in detail different approaches of smart contracts generation. Furthermore, we present a comparison of the existing approaches based on a classification according to automation paradigm and a set of defined criteria. Finally, we discuss the gaps in the literature, as well as identify a set of potential challenges which can significantly strengthen the existing work. The study shows that the examined works focused only on a limited number of specific features, such as authorization, asset control, and security. Additionally, formal verification of smart contracts and data privacy are poorly addressed. [ABSTRACT FROM AUTHOR]

Conscious selection is the mental process by which lottery players select numbers nonrandomly. In this paper, we show that the number 19, which has been heard, read, seen, and googled countless times since March 2020, has become significantly less popular among Belgian lottery players after the World Health Organization named the disease caused by the coronavirus SARS-CoV-2 "COVID-19". We argue that the reduced popularity of the number 19 is due to its negative association with the COVID-19 pandemic. Our study triangulates evidence from field data from the Belgian National Lottery and survey data from a nationally representative sample of 500 Belgian individuals. The field data indicate that the number 19 has been played significantly less frequently since March 2020. However, a potential limitation of the field data is that an unknown proportion of players selects numbers randomly through the "Quick Pick" computer system. The survey data do not suffer from this limitation and reinforce our previous findings by showing that priming an increase in the salience of COVID-19 prior to the players' selection of lottery numbers reduces their preference for the number 19. The effect of priming is concentrated amongst those with high superstitious beliefs, further supporting our explanation for the reduced popularity of the number 19 during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

With versatility and complexity of computer systems, warning and errors are inevitable. To effectively monitor system's status, system logs are critical. To detect anomalies in system logs, deep learning is a promising way to go. However, abnormal system logs in the real world are often difficult to collect, and effectively and accurately categorize the logs is an even time-consuming project. Thus, the data incompleteness is not conducive to the deep learning for this practical application. In this paper, we put forward a novel semi-supervised dual branch model that alleviate the need for large scale labeled logs for training a deep system log anomaly detector. Specifically, our model consists of two homogeneous networks that share the same parameters, one is called weak augmented teacher model and the other is termed as strong augmented student model. In the teacher model, the log features are augmented with small Gaussian noise, while in the student model, the strong augmentation is injected to force the model to learn a more robust feature representation with the guidance of teacher model provided soft labels. Furthermore, to further utilize unlabeled samples effectively, we propose a flexible label screening strategy that takes into account the confidence and stability of pseudo-labels. Experimental results show favorable effect of our model on prevalent HDFS and Hadoop Application datasets. Precisely, with only 30% training data labeled, our model can achieve the comparable results as the fully supervised version. [ABSTRACT FROM AUTHOR]

Wave-based analog computing has recently emerged as a promising computing paradigm due to its potential for high computational efficiency and minimal crosstalk. Although low-frequency acoustic analog computing systems exist, their bulky size makes it difficult to integrate them into chips that are compatible with complementary metal-oxide semiconductors (CMOS). This research paper addresses this issue by introducing a compact analog computing system (ACS) that leverages the interactions between ultrasonic waves and metasurfaces to solve ordinary and partial differential equations. The results of our wave propagation simulations, conducted using MATLAB, demonstrate the high accuracy of the ACS in solving such differential equations. Our proposed device has the potential to enhance the prospects of wave-based analog computing systems as the supercomputers of tomorrow. [ABSTRACT FROM AUTHOR]

The continual development of mobile edge computing efficiently solves the problem that mobile devices are unable to handle computation-intensive tasks due to their computation capacity and battery restrictions. In this paper, we consider mobile awareness and dynamic battery charging in a multi-user and multi-server mobile edge computing system, where various tasks are generated successively on the user devices. Servers act as learning agents and collaborate with user devices to develop task partitioning and computation resource allocation strategies. With the purpose of decreasing task failure rate and improving system utility in the long term, which is closely related to latency, energy consumption, and server cost, optimal strategies are demanded by the system. We model the joint optimization problem as a multi-agent Markov decision process game. And a deep reinforcement learning method based on the multi-agent deep deterministic policy gradient algorithm is proposed, which employs neural networks and works in a centralized training and decentralized execution manner to optimize the strategies. Finally, simulation results demonstrate the effectiveness of our proposed algorithm in terms of reducing task failure rate and improving system utility. [ABSTRACT FROM AUTHOR]

Cooperative computation offloading (CCO) is a technique to improve computation offloading performance in edge networks through collaboration between edge nodes. CCO can achieve better resource utilization, balance the computational load and further reduce delay to improve the service experience of user equipment (UE). In this paper, we investigate the problem of collaborative computing task offloading scheme and computing resource allocation in mobile edge computing and propose a data compression cooperation computing offloading (DCCO) scheme. To reduce the amount of data transmitted on the UE offloading link, we introduce a Data Compression into CCO and present the computational offloading strategy, collaborative offloading and computational resource allocation problems with the goal of minimizing the weighted sum of delay and energy consumption of the UE under the constraints of UE delay and energy consumption. And an improved genetic algorithm is proposed to solve the problem which is a non-convex mixed-integer problem with binary and continuous variables. The offloading strategy and computational resource allocation correspond to the genes in the genetic algorithm chromosome. The simulation results show that the DCCO scheme can reduce the offloading cost up to 11% compared with the existing schemes. It effectively improves the computation offloading performance of edge network computing. [ABSTRACT FROM AUTHOR]

Application with a set of dependent distributed tasks is generally regarded as a direct acyclic graph or an out-tree. Tree-shaped task graphs are widely applied in a variety of computational domains, including electronic structure computations and sparse matrix factorization. Efficient algorithms for tree-shaped task partition and allocation can dominate the performance of heterogeneous computing systems, as most relevant publications have pointed out. This paper presents efficient algorithms for partitioning and allocating tree-shaped tasks on heterogeneous multiprocessor systems with limited memory to improve task-parallel computing. The proposed main algorithm consists of two stages: partition and allocation. During partition, an algorithm is provided for partitioning a task tree into multiple subtrees. It iteratively partitions the subtrees on the critical path of the quotient tree. During allocation, two algorithms are proposed for task allocation to minimize the task tree's execution time. One is to preferentially allocate the largest subtree of the whole tree, and the other is to preferentially allocate the subtree located on the quotient tree's critical path. Experimental results show that the proposed algorithms significantly improve the latest works in terms of average makespan, both on randomly generated trees and on a real-world dataset. On a real-world dataset, the average makespan of existing work is approximately 6.28 × 10 8 . However, it is approximately 2.13 × 10 8 for our proposed algorithm. This results in a reduction of 64.33%. On randomly generated trees, the average makespan of existing work is approximately 8973. However, it is approximately 4040 for our proposed algorithm. This results in a reduction of 54.96%. [ABSTRACT FROM AUTHOR]

The integration and remoteness of mobile terminals, as well as local area network-based office issues, have basically been solved, that is, a solution established on the basis of a cloud service collaborative platform, which can be implemented between various service systems. Each place enters the office system at all times to process related business, which improves the overall efficiency of our company's collaboration, and each information management system is no longer an information island. Technically, this article provides the possibility for the implementation of the system, and analyzes in detail the business requirements, performance requirements and functional modules of the system. In addition, it briefly introduces the requirements for the key technical means used. The system designed in this paper can effectively process a large amount of data and improve the operation efficiency of the system by separating the front-end management and back-end management. More importantly, the collaborative office platform designed by this system increases the sharing degree of information to the maximum, which can effectively reduce useless work, maximize work efficiency, and stimulate the enthusiasm of workers. At the same time, the operating costs of the system are reduced. It realizes the integration of information processing of data and efficient management of data. [ABSTRACT FROM AUTHOR]

Mathematical education, both mass education, and university education of non-mathematicians, are in an abominable state, and rapidly degrading. We argue that the instruction of non-mathematicians should be dramatically reformed both as substance and style. With traditional approach, such a transformation would take decades, with unclear results. But we do not have this time. The advent of Computer Algebra Systems gives the mathematics community a chance to reverse the trend. We should make a serious attempt to seize this opportunity. In the present paper we describe one such project of reform implemented at the St Petersburg State University. [ABSTRACT FROM AUTHOR]

Multi-layer perceptron (MLP) is a class of Artificial Neural Networks widely used in regression, classification, and prediction. To accelerate the training of MLP, more cores can be used for parallel computing on many-core systems. However, with the increasing number of cores integrated into the chip, the communication bottleneck in the training of MLP on electrical network-on-chip (ENoC) becomes severe, degrading MLP training performance. Replacing ENoC with optical network-on-chip (ONoC) can break the communication bottleneck in MLP training. To facilitate the development of ONoC for MLP training, it is necessary to compare and model the MLP training performance of ONoC and ENoC in advance. This paper first analyzes and compares the differences between ONoC and ENoC. Then, we formulate the performance and energy model of MLP training on ONoC and ENoC by analyzing the communication and computation time, static energy, and dynamic energy consumption, respectively. Furthermore, we conduct extensive simulations to compare their MLP training performance and energy consumption with our simulation infrastructure. The experimental results show the MLP training time of ONoC has been reduced by 65.16% and 52.51% on average in different numbers of cores and batch sizes compared with ENoC. The results also exhibit that ONoC overall has 54.86% and 43.13% on average energy reduction in different numbers of cores and batch sizes compared with ENoC. However, with a small number of cores (e.g., less than 50) in MLP training, ENoC consumes less energy than ONoC. These experiments confirm that generally ONoC is a good replacement for ENoC when using a large number of cores in terms of performance and energy consumption for MLP training. [ABSTRACT FROM AUTHOR]

The detection of differential item functioning (DIF) is a central topic in psychometrics and educational measurement. In the past few years, a new family of score-based tests of measurement invariance has been proposed, which allows the detection of DIF along arbitrary person covariates in a variety of item response theory (IRT) models. This paper illustrates the application of these tests within the R system for statistical computing, making them accessible to a broad range of users. This presentation also includes IRT models for which these tests have not previously been investigated, such as the generalized partial credit model. The paper has three goals: First, we review the ideas behind score-based tests of measurement invariance. Second, we describe the implementation of these tests within the R system for statistical computing, which is based on the interaction of the R packages mirt, psychotools and strucchange. Third, we illustrate the application of this software and the interpretation of its output in two empirical datasets. The complete R code for reproducing our results is reported in the paper. [ABSTRACT FROM AUTHOR]