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The rapidly advancing fields of machine learning and mathematical modeling, greatly enhanced by the recent growth in artificial intelligence, are the focus of this special issue. This issue compiles extensively revised and improved versions of the top papers from the workshop on Mathematical Modeling and Problem Solving at PDPTA'23, the 29th International Conference on Parallel and Distributed Processing Techniques and Applications. Covering fundamental research in matrix operations and heuristic searches to real-world applications in computer vision and drug discovery, the issue underscores the crucial role of supercomputing and parallel and distributed computing infrastructure in research. Featuring nine key studies, this issue pushes forward computational technologies in mathematical modeling, refines techniques for analyzing images and time-series data, and introduces new methods in pharmaceutical and materials science, making significant contributions to these areas. [ABSTRACT FROM AUTHOR]

Using attribute graphs for node embedding to detect community structure has become a popular research topic. However, most of the existing algorithms mainly focus on the network structure and node features, which ignore the higher-order relationships between nodes. In addition, only adopting the original graph structure will suffer from sparsity problems, and will also result in suboptimal node clustering performance. In this paper, we propose a hypergraph network embedding (HGNE) for community detection to solve the above problems. Firstly, we construct potential connections based on the shared feature information of the nodes. By fusing the original topology with feature-based potential connections, both the explicit and implicit relationships are encoded into the node representations, thus alleviating the sparsity problem. Secondly, for integrating the higher-order relationship, we adopt hypergraph convolution to encode the higher-order correlations. To constrain the quality of the node embedding, the spectral hypergraph embedding loss is utilized. Furthermore, we design a dual-contrast mechanism, which draws similar nodes closer by comparing the representations of different views. This mechanism can efficiently prevent multi-node classes from distorting less-node classes. Finally, the dual-contrast mechanism is jointly optimized with self-training clustering to obtain more robust node representations, thus improving the clustering results. Extensive experiments on five datasets indicate the superiority and effectiveness of HGNE. [ABSTRACT FROM AUTHOR]

The gazelle optimization algorithm (GOA) is an iterative optimization method inspired by the agile movements of gazelles, employing adaptive step sizes and velocity adjustments for rapid convergence in continuous search spaces. However, GOA tends to lack diversity, leading to issues like local minima trapping and premature convergence. This paper addresses these limitations by introducing dynamic opposition-based learning (OBL) and incorporating a balanced sine-control-logistic chaotic mapping system, resulting in the improved GOA (IGOA). Dynamic OBL accelerates the search process, improving learning and selecting superior candidate solutions, while chaotic mapping in chaotic local search widens ranges for exploration and local optima escape. Evaluating IGOA against seven algorithms, including GOA and others, across 31 general test functions, the results consistently showcase IGOA's superior efficiency in achieving solutions closest to optima, early convergence, and hit rate when compared to alternative algorithms. [ABSTRACT FROM AUTHOR]

The sparse triangular solver (SpTRSV) is mostly used for scientific and engineering applications. The structured-grid triangular solver of regular dependencies (STRSV) is a special kind of SpTRSV. Some general SpTRSVs that disregards the regularity of the matrix are unsuitable for solving this problem. This paper proposes an efficient parallel algorithm for STRSV on the SW26010 (a kind of China independently designed many-core processors), namely swStructTRSV. The algorithm makes full use of the fine-grained and low latency communication characteristics of the SW26010 to reduce the waiting time for synchronization, maximizes the regularity of access to improve memory access bandwidth, and achieves overlap between memory access and computation simultaneously. Moreover, the idea of the algorithm can be extended to incomplete LU factorization (ILU factorization) because of consistent dependencies. The experimental results on a core group(8 * 8 network composed of 64 cores) of SW26010 show that swStructTRSV can achieve an average speedup of over 30 in the sequential version. swStructTRSV on SW26010 achieves solving speedups of 2.2 and 6.3 over the fast STRSV (fSpTRSV) previously implemented on SW26010 and MKL on Intel Xeon Gold 6132, respectively. swStructTRSV significantly outperforms cuSparse on NVIDIA TITAN RTX in terms of overall execution time. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC), as the extension of the mobile cloud, has been widely investigated. However, in most existing VFC works, the scenario with point-of-interests (PoIs) is rarely considered, which can lead to underutilization of vehicle computing resources and overloading of the computing load for roadside units (RSUs). Therefore, this paper focuses on improving the quality of services for PoI-based VFC by utilizing the computation capabilities of vehicles to assist the RSU. An NP-hard problem is formulated to maximize the number of tasks completed within the duration of delay constraints. To solve the problem, a delay-aware task offloading algorithm, named Delay-Aware Task Offloading (DTO), is proposed. DTO preferentially operates on tasks with tighter delay constraints. To minimize the completion delay for each offloaded task, DTO selects the set of service vehicles to perform tasks. Moreover, a vehicle-resource-aware task offloading algorithm, named Vehicle-Resources-Aware Task Offloading (VRTO), is proposed to maximize the number of tasks completed within the duration of delay constraints by efficiently utilizing the available resource of the vehicle. Specifically, VRTO allocates the data to each service vehicle according to the service configuration of vehicles and the delay constraints of tasks. Finally, extensive experiments are carried out to evaluate the effectiveness of the proposed algorithms. The performance is successfully improved by more than 30% compared with the baselines in terms of the number of completed tasks. [ABSTRACT FROM AUTHOR]

Identifying the location of the sensor nodes is a challenging aspect of Wireless Sensor Networks (WSNs) in a distributed environment. It is vital to understand where data and events come from when sensor nodes collect data and report on them. This paper uses a nature-inspired Jaya algorithm to estimate the location coordinates of target nodes in a distributed architecture of WSNs. The Jaya algorithm handles the nonlinear optimization problem of node localization in a randomly distributed sensor node configuration. The range-based, Received Signal Strength Indicator method is used for the distance estimation. MATLAB software is used to assess the proposed work, and a comparison is made with the Particle Swarm Optimization, Krill Herd Optimization, and Salp Swarm Algorithm based node localization algorithms in terms of localization error and computation time. The localization error analysis is done for the different numbers of anchor nodes and different values of degree of irregularity to verify the effectiveness of the proposed work. [ABSTRACT FROM AUTHOR]

The traditional variational mode decomposition (VMD) algorithm causes under-decomposition and over-decomposition problems when performing signal decomposition. Many studies have addressed various optimization algorithms. When the optimization parameters are set too small, the best decomposition cannot be found. On the other hand, setting too large parameters leads to a waste of computational resources. Therefore, an improved VMD algorithm is proposed in this paper, which effectively solves the above problems by establishing an adaptive method for choosing the values of decomposition parameters and its iteration termination condition. In order to meet the monitoring data transmission requirement, a hybrid compression scheme based on the improved VMD is proposed. The monitoring signal is effectively decomposed by the improved VMD algorithm, and then thresholded by the lifting wavelet transform (LWT) method, and further compressed by combining quantization as well as entropy encoding methods. It is experimentally verified that its compression scheme can effectively improve compression performance and has a high signal-to-noise ratio, providing an effective solution for data transmission in equipment monitoring. [ABSTRACT FROM AUTHOR]

The scarcity of defect samples and the imbalance of defect types lead to the fact that achieving defect detection in color-patterned fabrics remains a challenge in the textile industry. Defect detection methods based on traditional auto-encoder are difficult to solve the problem of defect detection in complex color-patterned fabrics. In order to solve the problem of weak feature representation of traditional auto-encoders, this paper proposes an unsupervised method based on triplet attention multi-scale U-shape denoising convolutional auto-encoder (TA_MSUDCAE). The method further enhances the feature representation capability of the auto-encoder by introducing a triplet attention mechanism based on utilizing the multi-scale information of the image. Firstly, the defect-free samples that are added with Gaussian noise are used as inputs to the model in the training stage. The model is trained to reconstruct and repair the defective regions. Secondly, the test image is input to the trained model to obtain a normal reconstructed image, and the residual image is obtained by calculating the difference between the input image and the reconstructed image. Finally, the defect detection and localization results are obtained by threshold segmentation and mathematical morphology processing of the residual image. A large number of experiments have been carried out on a variety of representative color-patterned fabrics, and the results prove the effectiveness of the proposed method in fabric defect detection. [ABSTRACT FROM AUTHOR]

Existing weakly supervised named entity recognition (NER) research only deals with flat entities and ignores nested entities. This paper proposes a multi-stage nested entity recognition method (MNR) that utilizes weakly labeled data to recognize nested entities. However, weak labels generated through external knowledge bases have two problems: incompleteness and labeling bias. To address this challenge, the MNR comprises two models. First, we propose a neural transition-based attention model (NTAM) to solve the problem of weak-label incompleteness by learning the correlation between words. Simultaneously, the NTAM obtains candidate entities, including nested entities. Second, we propose a multi-marker fusion attention judgment model (MAJM) for selecting candidate entities through context semantics, candidate entities' meanings, and their boundary information, thereby solving the labeling bias problem. The boundary information of candidate entities is enhanced by fusing their type markers. To our knowledge, we are the first to recognize nested entities under weak supervision by alleviating the noise of weakly labeled data. Experiments on three public nested NER datasets prove the effectiveness of our proposed method under weak supervision and demonstrate that the method outperforms previous state-of-the-art models under supervision. [ABSTRACT FROM AUTHOR]

Flexible flat cable (FFC) detection is the premise of robot 3C assembly and is challenging because FFCs are often non-axis aligned with arbitrary orientations having cluttered surroundings. However, to date, the traditional robotic object detection methods mainly regress the object horizontal bounding box, in which the size and aspect ratios do not reflect the actual shape of the target object and hardly separate the FFCs in dense. In this paper, rotated object detection was introduced into FFC detection, and a YOLO-based arbitrary-oriented FFC detection method named YOLOOD was proposed. Firstly, oriented bounding boxes are used to reflect the object's physical size and angle information and better separate the FFCs from the dense background. Secondly, the circular smooth label angular classification algorithm is adopted to obtain the angle information of FFCs. Finally, the head point regression branch is introduced to distinguish between the head and the tail of the FFC and expand the range of FFC detection angle to 0 ∘ , 360 ∘ . The proposed YOLOOD can reach the detection performance with an average precision of 90.82% and a detection speed of 112 FPS on an FFC dataset. Meanwhile, an actual FFC grasping experiment demonstrated the proposed YOLOOD's effectiveness and feasibility in practical assembly scenarios. In conclusion, this paper innovatively introduces rotating object detection into robot object detection, and the proposed YOLOOD solves the problem of detecting and locating non-axis aligned FFCs and has particular significance for robot 3C assembly. [ABSTRACT FROM AUTHOR]

The problem of continuous community search in temporal graphs aims to identify dense subgraphs that exist continuously over time, which is widely used in sociology, biology, communication, and other fields. Existing methods can find communities that satisfy both temporal and structural constraints, but did not consider constraints on vertex attributes. In this paper, we investigate the continuous community search problem with attribute constraints in temporal graphs. Specifically, we aim to identify graphs that meet temporal, structural, and attribute constraints. To solve the problem efficiently, we first propose a pruning enumeration algorithm (PRUNE_ENUM), which uses the properties of the lower bound of vertices of k-core, the upper bound of attribute intersection, and subgraph continuity to prune useless search space during computation. We then propose an optimized enumeration algorithm (FILTER_ENUM) that first generates much smaller subgraphs using subsets of the given query attributes, then takes these smaller subgraphs as input for enumeration. During the enumeration, we propose two pruning rules to reduce search space based on attribute similarity and subgraph maximization to improve the efficiency. We conduct comparative experiments on four real-world temporal networks, and the experimental results demonstrate the high efficiency of our algorithms. [ABSTRACT FROM AUTHOR]

Unsupervised opinion summarization is the technique of automatically generates summaries without gold reference, and the summaries that reflects aspects of information about the entity. Although there are more mature studies on unsupervised opinion summarazaiton, but these studies focus more on unsupervised training methods and ignore the extraction of information by the model. In this paper, we propose JointSum, an unsupervised opinion summarization method based on variational autoencoder model. JointSum first extracts aspect and sentiment information in reviews by joint attention and dictionary learning, respectively. Joint attention consists of text attention and auxiliary attention, which can extract key information in the input text from different fine-grained levels. Then we calculate the variance and mean of the Gaussian distribution in variational autoencoder model using aspect and sentiment information. In addition, we added the review score prediction subtask to increase the robustness of the model. Finally, in generation phase, we adopt pointer-generator network because it includes copy and coverage mechanism that can solve problems in text generation. Experiments on Amazon and Yelp datasets, the results show that the model has good performance in both automatic and human evaluation, the ROUGE-L value on the Yelp dataset gets 20.83. [ABSTRACT FROM AUTHOR]

Inverse problems occur in many scientific fields. Albeit grid search, where points of a regular grid are tested as possible solutions, is a straightforward and robust method to numerically solve inverse problems, it is computationally intensive and becomes prohibitive when the problem has a high dimensionality. Heterogeneous clusters are a viable and cost-effective solution to exploit the combined computational power of multiple available computers. In this paper, we present a computing framework that supports efficient grid search for inverse problems on heterogeneous clusters. Scheduling the workload on such systems might be challenging, especially when nodes are comprised of CPUs and GPUs with different computational speeds. The framework dynamically schedules computations on the processing elements of the cluster according to a selected performance index, which is determined at run-time. The framework is extensible, as it allows easy integration of additional inverse problems. [ABSTRACT FROM AUTHOR]

In this paper, we present a new key exchange protocol in which Alice and Bob have secret keys given by quadric surfaces embedded in a large ambient space by means of the Veronese embedding and public keys given by hyperplanes containing the embedded quadrics. Both of them reconstruct the isomorphism class of the intersection which is a curve of genus 1, and is uniquely determined by the j-invariant. An eavesdropper, to find this j-invariant, has to solve problems which are conjecturally quantum-resistant. [ABSTRACT FROM AUTHOR]

In the application scenario of a smart factory, the computing tasks of the smart devices are intensive, and these tasks are sensitive to delay. The smart devices need to offload their tasks to an edge server for execution. However, the computing resources of edge servers are limited, and a workload imbalance will tend to arise among multiple edge servers. To solve these problems, we propose a joint optimization scheme of task caching and offloading based on task scenario awareness, which decouples the joint optimization problem into two subproblems: task caching and task offloading. These two subproblems are solved using a task-scenario-aware bilateral auction algorithm, which enables an edge server to adapt its task caching strategy to the task scenario of the smart devices. Experimental results show that this paper's scheme reduces the task execution time by up to 21.5% compared to existing schemes and has better load balancing effects. [ABSTRACT FROM AUTHOR]

Mobile edge computing can meet the needs of users in real time by pushing cloud resources, such as computing, network, and storage, to the edge of a mobile network. Although mobile edge technology has brought many conveniences to mobile services, many security problems, such as mutual authentication between mobile users and servers, and the impersonation of mobile users or servers, have not been properly solved. In this paper, we propose a lightweight and secure authentication protocol to solve such security problems. We conducted a formal security analysis of our protocol using ProVerif and the Real-or-Random model to prove its correctness and security, and even if the registration center is attacked, our protocol is still secure. At the end of this paper, by comparing the communication and computing costs with other existing protocols, we prove the security and efficiency of our protocol. [ABSTRACT FROM AUTHOR]

Graph embedding maps a guest graph into a host graph, thus enabling structural simulation, processor allocation, and algorithm porting. It is used to design the physical layout of Network-on-Chip (NoC) and to study the simulation capabilities of a parallel architecture. Wirelength is one of the indicators to measure the quality of graph embedding. Minimum wirelength in NoC design means a smaller wiring area and less wiring cost. In parallel computing, it means shorter communication time and delay. In this paper, the guest graph is the hierarchical folded cube with good communication and fault tolerance capabilities. The host graphs are the linear array and the complete binary tree, both of which are widely used in graph embeddings. We solve the embedding problems in linear time for hierarchical folded cubes into linear arrays and complete binary trees with minimum wirelength, respectively. [ABSTRACT FROM AUTHOR]

The results of a parallel implementation of a randomized vector algorithm for solving systems of linear equations are presented in the paper. The solution is represented in the form of a Neumann series. The stochastic method computes this series by sampling only random columns, avoiding multiplication of matrix by matrix and matrix by vector. We consider the case when the matrix is too large to fit in random-access memory (RAM). We use two approaches to solve this problem. In the first approach, the matrix is divided into parts that are distributed among MPI processes and stored in the available RAM of the cluster nodes. In the second approach, the entire matrix is stored on each node's hard drive, loaded into RAM, and processed in parts. Independent Monte Carlo experiments for random column indices are distributed among MPI processes or OpenMP threads for both approaches to matrix storage. The efficiency of parallel implementations is analyzed. Results are given for a system governed by dense matrices of size 10 4 and 10 5 . [ABSTRACT FROM AUTHOR]

Fault diagnosis of interconnection networks is vital to ensuring the reliability and maintenance of multiprocessor systems. Based on graph theory, diagnostic algorithms for solving the problem of fault diagnosis in interconnection networks have been widely studied. As the number of processors in multiprocessor systems has increased in recent years, fault diagnosis algorithms based on graph theory cannot meet the current diagnosis requirements of some interconnection networks, such as t-diagnosable systems. In this paper, we use the evolution diagnosis approach to study fault diagnosis in t-diagnosable systems under the PMC model. First, for a t-diagnosable system G with a syndrome σ , we use a simple centralized algorithm to generate its simplified diagnosis graph G f , which contains all the faulty nodes in G. Based on the graph, we prove that the faulty node set in G is the minimum cover set of G f . Next, we prove that the problem of computing the minimum cover set for G f is equivalent to the problem of computing the optimal solution of a zero-one integer program. Using this result, we propose a novel genetic algorithm to solve the problem of the zero-one integer program. The simulation results show that the diagnostic accuracy of our proposed algorithm is equal to or greater than 96% and that the proposed algorithm outperforms its competitors in terms of diagnostic accuracy, number of iterations and running time. [ABSTRACT FROM AUTHOR]

Iterative stencil computations are widely used in numerical simulations. They present a high degree of parallelism, high locality and mostly-coalesced memory access patterns. Therefore, GPUs are good candidates to speed up their computation. However, the development of stencil programs that can work with huge grids in distributed systems with multiple GPUs is not straightforward, since it requires solving problems related to the partition of the grid across nodes and devices, and the synchronization and data movement across remote GPUs. In this work, we present EPSILOD, a high-productivity parallel programming skeleton for iterative stencil computations on distributed multi-GPUs, of the same or different vendors that supports any type of n-dimensional geometric stencils of any order. It uses an abstract specification of the stencil pattern (neighbors and weights) to internally derive the data partition, synchronizations and communications. Computation is split to better overlap with communications. This paper describes the underlying architecture of EPSILOD, its main components, and presents an experimental evaluation to show the benefits of our approach, including a comparison with another state-of-the-art solution. The experimental results show that EPSILOD is faster and shows good strong and weak scalability for platforms with both homogeneous and heterogeneous types of GPU. [ABSTRACT FROM AUTHOR]

The Sunway TaihuLight is the first supercomputer built entirely with domestic processors in China. On Sunway Taihulight, the local data memory (LDM) of the slave core is limited, so data transmission with the main memory is frequent during calculation, and the memory access efficiency is low. On the other hand, for many scientific computing programs, how to solve the storage problem of irregular access data is the key to program optimization. Software cache (SWC) is one of the effective means to solve these problems. Based on the characteristics of Sunway TaihuLight structure and irregular access, this paper designs and implements a new software cache structure by using part of the space in LDM to simulate the cache function, which uses new cache address mapping and conflicts solution to solve high data access overhead and storage overhead in a traditional cache. At the same time, the SWC uses the register communication between the slave cores to share on the different slave core LDMs, increasing the capacity of the software cache and improving the hit rate. In addition, we adopt a double buffer strategy to access regular data in batches, which hides the communication overhead between the slave core and the main memory. The test results on the Sunway TaihuLight platform show that the software cache structure in this paper can effectively reduce the program running time, improve the software cache hit rate, and achieve a better optimization effect. [ABSTRACT FROM AUTHOR]

Harris hawks optimization (HHO) is a new meta-heuristic algorithm that builds a model by imitating the predation process of Harris hawks. In order to solve the problems of poor convergence speed caused by uniform choice position update formula in the exploration stage of basic HHO and falling into local optimization caused by insufficient population richness in the later stage of the algorithm, a Harris hawks optimization based on global cross-variation and tent mapping (CRTHHO) is proposed in this paper. Firstly, the tent mapping is introduced in the exploration stage to optimize random parameter q to speed up the convergence in the early stage. Secondly, the crossover mutation operator is introduced to cross and mutate the global optimal position in each iteration process. The greedy strategy is used to select, which prevents the algorithm from falling into local optimal because of skipping the optimal solution and improves the convergence accuracy of the algorithm. In order to investigate the performance of CRTHHO, experiments are carried out on ten benchmark functions and the CEC2017 test set. Experimental results show that the CRTHHO algorithm performs better than the HHO algorithm and is competitive with five advanced meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Nowadays, intensive streams of fuzzy input data need to be processed in real-time for different fields of science and engineering. To solve this problem, a generalized model for the parallel-streaming neural element was developed in this paper. The proposed model allows minimizing hardware costs while providing scalar product and activation function calculations in real time. In particular, an algorithm and a structure for a parallel-streaming device (PSD) were developed to calculate a scalar product with the direct formation of partial products based on the analysis of a single bit-cut of multipliers, which provides working with the shortest conveyor stage. It is based on a modified Booth's algorithm that allows reducing equipment costs for processing operands with high bit-width. Moreover, it promotes the lowest equipment costs for the operands with a low bit-width. Besides, researches demonstrate that the main way of increasing the speed of the developed algorithms and structures of PSD for scalar product calculating is a preliminary formation of partial products. Further, the estimation of the model parameters shows reducing conveyor steps, improvement of the locality of connections, and an increase of an adaptation to the coming data intensity. It is proposed to use the developed algorithms and structures as a basis for building devices for parallel-streaming calculation of the scalar product in real time with high efficiency of equipment use. The main ways of harmonizing the time of incoming data and weights with the conveyor cycle of the PSD for calculation of the scalar product are determined. A methodology proposed for building conveyor devices for parallel-streaming calculation of the scalar product in real time for a given intensity of input data ensures the implementation of devices with the required speed and with minimal hardware costs. [ABSTRACT FROM AUTHOR]

The Internet-of-things (IoT) plays a significant role in healthcare monitoring, where the IoT Cloud integration introduces many new opportunities for real-time remote monitoring of the patient. Task scheduling is one of the major challenges in cloud environment. Solving that problem reduces delay, missed tasks, and failure rate, and increases the guarantee ratio. This paper proposes a new task scheduling and allocation technique: Prioritized Sorted Task-Based Allocation (PSTBA) for healthcare monitoring implemented in IoT cloud-based architecture. The proposed technique selects the best virtual machine to execute the health task considering multiple factors such as; the wait time of the VM and the Expected processing time (EPT) of the task as well as its criticality. An extensive simulation study is conducted using the CloudSim simulator to evaluate the performance of the proposed technique. The proposed technique is compared to the Sorted Task-Based Allocation (STBA) and FCFS techniques and it reduces the delay by 13.7% and 80.2%, the failure rate by 21% and 37.5%, and increases the guarantee ratio by 2.2% and 4.5% compared to STBA and FCFS, respectively. In analyzing the critical health tasks, the proposed PSTBA has also reduced the critical health tasks missed ratio by 15.7% and 50.9% compared to STBA and FCFS, respectively. The simulation results demonstrate that PSTBA is more effective than the STBA and FCFS techniques in terms of delay, missed critical tasks, guarantee ratio, and failure rate. [ABSTRACT FROM AUTHOR]

Aiming at the problem of user's task offloading in mobile edge computing and the potential leakage of location privacy during the offloading process, a privacy-preserving computing offloading scheme based on whale optimization algorithm is proposed. Using differential privacy technology to obfuscate the user's location information, the user can make task offloading decisions according to the obfuscated distance. Considering the delay, energy consumption, and their weighted sum, the offloading problem is modeled as a convex optimization problem. Then, the whale optimization algorithm is adopted to solve this optimization problem to achieve a balance between privacy protection and resource consumption. Experiments are conducted to verify the relationship between the degree of privacy leakage, the computation-offloading cost and real distance, privacy-preserving impact factor, the respective weights of time delay and energy consumption The experimental results show that the offloading scheme proposed in this paper has good performance in terms of cost and privacy protection. [ABSTRACT FROM AUTHOR]

To apply biogeography-based optimization (BBO) to large scale optimization problems, this paper proposes a novel BBO variant based on feedback differential evolution mechanism and steepest descent method, referred to as FBBOSD. Firstly, the immigration refusal mechanism is proposed to eliminate the damage of inferior solutions to superior solutions. Secondly, the dynamic hybrid migration operator is designed to balance the exploration and exploitation, which makes BBO suitable for high-dimensional environment. Thirdly, the feedback differential evolution mechanism is designed to make FBBOSD can select mutation modes intelligently. Finally, the steepest descent method is creatively combined with BBO, which further improves the convergence accuracy. Meanwhile, a sequence convergence model is established to prove the convergence of FBBOSD. Quantitative evaluations: FBBOSD is compared with BBO, seven BBO variants and seven state-of-the-art evolutionary algorithms, respectively. The experimental results on 24 benchmark functions and CEC2017 show that FBBOSD outperforms all compared algorithms, and the dimension of solving optimization problems can reach 10,000. Then, FBBPOSD is applied to engineering design problems. The simulation results demonstrate that it is also effective on constrained optimization problems. In short, FBBOSD has excellent performance and outstanding stability, which is a new algorithm worthy of adoption and promotion. [ABSTRACT FROM AUTHOR]

Image texture features and details are extremely important in image super-resolution. Meanwhile, preserving image texture features and details with less running time is also a significant problem to be solved. This paper proposes an image super-resolution method based on polynomial exponential function and non-uniform rectangular partition to restore the image structure and the details of edges and textures. The algorithm performs least-squares fitting according to the image grayscale to divide the low-resolution image into several different rectangular regions. By substituting the coordinates into each regional polynomial exponential function, the approximated regional high-resolution sub-image can be obtained to form the final whole high-resolution image. The proposed method can better represent the image textures in complex regions and effectively preserve edge details and suppress artifacts. The experimental results demonstrate that the proposed method is significantly superior to the state-of-the-art methods in the terms of subjective vision, objective quantitative evaluation, and execution time. [ABSTRACT FROM AUTHOR]

Constraint Programming is a powerful paradigm to model and solve combinatorial problems. While there are many kinds of constraints, the table constraint is perhaps the most significant—being the most well-studied and has the ability to encode any other constraints defined on finite variables. However, constraints can be very voluminous and their size can grow exponentially with their arity. To reduce space and the time complexity, researchers have focused on various forms of compression. In this paper, we propose a new approach based on maximal frequent itemsets technique and area measure for enumerating the maximal frequent itemsets relevant for compressing table constraints. Our experimental results show the effectiveness and efficiency of this approach on compression and on solving compressed constraint satisfaction problem. [ABSTRACT FROM AUTHOR]

This paper is the first one that reveals two potential problems in CSMA/CN and proposes an adaptive collision notification (ACN) to solve them. The famous CSMA/CN (collision notification) protocol can detect the ongoing transmission collision early and use CN feedback to inform the sender to abort its transmission immediately, thereby improving the channel utilization. However, there are two potential problems in CN: (a) CN cross-boundary, namely the CN feedback might be later than a standard transmission, and (b) CN interference, namely the CN feedback might interfere with other links. To solve the above problems, in ACN, we design a generalized CN structure and a flexible CN feedback mechanism. The receiver will determine whether to send CN feedback by the collision occurring position for solving the problem (a) and select the transmission power and length of the CN feedback by the interference range for solving the problem (b). We also theoretically quantify the transmission opportunity and power of the CN feedback. We compared ACN and standard CSMA/CN through simulation experiments. Extensive simulations verify that ACN can solve both problems and significantly improve the system throughput, while CSMA/CN will have low throughput due to both problems. Therefore, the design of ACN is an extension of the standard CSMA/CN, which can help wireless network engineers to optimize CSMA/CN network deployment. [ABSTRACT FROM AUTHOR]

In this paper, a novel parallel algorithm is proposed to solve the problems of heavy computation and long simulation time in the field of compressible flows. In this algorithm, a third-order upwind scheme and a fourth-order central difference scheme are employed, with a third-order Runge-Kutta method for time stepping. Considering the powerful floating-point computing ability of the Graphics Processing Unit (GPU), this paper establishes the algorithm on the basis of GPU. Moreover, the direct numerical simulation method is adopted in this algorithm to improve the solution accuracy of the simulation results. To further enhance the efficiency of the algorithm, several optimization strategies are explored in the design of the algorithm as well. Both accuracy and feasibility of the algorithm are verified by a classical two-dimensional example. Compared with solving this example on the Central Processing Unit platform, the experimental results demonstrate that the maximum speedup ratio achieved by our approach is 18.03 times. [ABSTRACT FROM AUTHOR]

Due to the problem of high link load of edge cache and small storage space of edge server, a caching architecture by the collaborative of edge nodes and the cloud server is proposed. The content cache location is designed and optimized, which can be the content provider, cloud server (CS), and edge node (EN). In the proposed system, cloud servers collaborate with edge servers and the performance of content caching can be improved by coordinating caching on the cloud server or caching on the edge server. In this paper, a cloud-edge collaborative caching model based on the greedy algorithm is proposed, which includes the content caching model and collaborative caching model. Network architecture, file popularity estimation, link capacity, and other factors are considered in the model. Correspondingly, a cloud-edge collaborative cache algorithm based on a greedy algorithm is proposed. The related optimization problem is decomposed into the knapsack problem of cache layout in each layer, and then the greedy algorithm is used to solve the knapsack problem of cache placement and cooperative cache proposed in this paper. The affiliation between CS cache and EN caches in the layered architecture is improved and recognized. In the experimental results, the link load is reduced, the cache hit rate is improved by using the proposed method of edge caching, and it also has obvious advantages in the average end-to-end service delay. [ABSTRACT FROM AUTHOR]

Appearances of specific transition patterns during data transfer in bus lines of modern high-performance computing systems, such as communicating structures of accelerators for deep convolutional neural networks, commercial Network on Chips, and memories, can lead to crosstalk faults. With the shrinkage of technology size, crosstalk faults occurrence boosts and leads to degradation of reliability and performance, as well as the increasing power consumption of lines. One effective way to alleviate crosstalk faults is to avoid the appearance of these specific transition patterns by using numerical-based crosstalk avoidance codes (CACs). However, a serious problem with numerical-based CACs is their overheads in terms of required additional bus lines for representing code words. To solve this problem, in this paper we present a novel CAC that is based on the use of three symbols (three-value) to represent the code words in the bus lines, rather than classical binary CACs based on binary, i.e., 0 and 1 symbols. Our proposed CAC, named summation-based tri-value crosstalk avoidance code (ST-CAC), reduces the worst-case delay in bus lines with respect to binary CACs, and it can efficiently be applied to any arbitrary channel width of lines. The use of three symbols to represent code words in ST-CAC enables to increase the number of code words of a numerical system without increasing the number of required bus lines significantly. The experimental results show that CACs based on the use of three symbols can reduce the number of additional lines compared to binary CACs by 33%. Moreover, we show in the paper, that the delay of wires in the presence our ST-CAC can reduce by 33% with respect to state-of-the-art binary value CACs. [ABSTRACT FROM AUTHOR]

The Jacobi iterative algorithm has the characteristic of low computational load, and multiple components of the solution can be solved independently. This paper applies these characteristics to the ternary optical computer, which can be used for parallel optimization because it has a large number of data bits and reconfigurable processor bits. Therefore, a new parallel design scheme is constructed to solve the problem of slow efficiency in solving large linear equations. And the elaborate experiment is used to verify. The experimental method is to simulate the calculation on the ternary optical computer experimental platform. Then, the resource consumption is numerically calculated and summarized to measure the feasibility of the parallel design. Eventually, the results show that the parallel design has obvious advantages in computing speed. The Jacobi iterative algorithm is optimized in parallel on ternary optical processor for the first time. There are two parallel highlights of the scheme. First, the n components are calculated in full parallel. Second, the modified signed-digit (MSD) multiplier based on the minimum module and one-step MSD adder are used to calculate each component to eliminate the impact of large amount of data on calculation time. The research provides a new method for fast solution of large linear equations. [ABSTRACT FROM AUTHOR]

Accurate prediction of the enemy's combat missions in combat deductions is helpful to improve the quality of command decision. Intelligent combat mission prediction method is a technical means to adapt to the fast-paced complex modern war, and is an important part of intelligent command decision technology. In this paper, based on the solution of the problems of combat mission prediction, it is modeled as a multi-instance learning (MIL) problem according to the characteristics of the problem. To effectively integrate expert knowledge and combat deduction data, a MIL model called multi-instance genetic fuzzy system (MIGFS) is designed and implemented based on the genetic fuzzy systems (GFSs). The model is composed of multiple genetic sub-fuzzy systems (sub-FSs). By means of multi-tasking genetic fuzzy systems (MTGFSs) algorithm, multiple sub-FSs have been trained separately to solve the problem of excessive time consuming and high cost because of synchronously training too many sub-FSs. Furthermore, to complete the integration of instance prediction to bag prediction, the weighted average is used instead of the traditional max function, and the mutation problem of taking the max function is solved by learning the weight parameters of the sub-FSs. A more continuous and smooth result integration method is implemented, and the prediction accuracy is improved. Finally, a demonstrative experimental case of wargaming has been taken to illustrate that MIGFS can successfully apply MIL to combat deductions with a small amount of data, and effectively solve the problems of combat mission prediction, which demonstrates the feasibility and practicability of the proposed MIGFS model. This method can well model the problems of combat mission prediction into MIL problems, and solve the problems well, which is a good start. [ABSTRACT FROM AUTHOR]

It is an enormous challenge for imbalanced data learning in the field of machine learning. To construct balanced datasets, oversampling techniques have been studied extensively. However, many oversampling methods suffer from introducing noisy samples and blurring classification boundaries, leading to overfitting. To solve this problem, this paper proposes a new oversampling method, namely CS-SMOTE, for synthesizing minority class samples by three-point interpolation. CS-SMOTE is mainly based on the center offset factor and a synthesis strategy. First, the CS-SMOTE method removes noise samples, calculates the center offset factor, and selects sparsely distributed minority class samples by using the K-distance graph technique. Next, new samples are generated based on sparse minority samples, random minority samples, and sub-cluster centers located in the same sub-cluster samples. Finally, multiple comparative experiments on 18 well-known datasets demonstrate the effectiveness and general applicability of the proposed CS-SMOTE method for the imbalanced data classification. The experiments show that CS-SMOTE outperforms other competitors in terms of classification accuracy, while avoiding the issue of overfitting. [ABSTRACT FROM AUTHOR]

Spanning trees are widely used in interconnection networks for routing, broadcasting, fault-tolerance, and securely delivering messages, as well as parallel and distributed computing. The problem of efficiently finding a maximal set of independent spanning trees (ISTs) is still open for arbitrary (general) interconnection topologies (graphs). The first contribution of this paper is presenting two efficient methods that solve the problem in Eisenstein–Jacobi (EJ) networks. The first constructs three Edge-Disjoint Node-Independent Spanning Trees (EDNISTs), whereas the second constructs six ISTs but not edge disjoint. Both constructions have time complexity O(n), where n is the number of nodes. The second contribution is providing a distributed fault-tolerant routing algorithm based on the constructed trees with time complexity O(1) and communication complexity O(|P|), where P is the routing path. The experimental results measure the maximum and the average of all maximum number of communication steps between the root node and all other nodes among all ISTs with the consideration of all possible faulty nodes. In addition, the comparison shows that the proposed method outperforms the state-of-the-art method in broadcasting in terms of node coverage in the presence of failures. [ABSTRACT FROM AUTHOR]

Due to the characteristics of distribution and virtualization, cloud storage is providing almost limitless storage services. Many users choose to upload data to the cloud to reduce storage burden, but the confidentiality of data is also at risk. When the cloud data must be deleted, the assured deletion of the data becomes a crucial problem. Assured deletion can preserve the confidentiality of the users data, and it is also an essential component of cloud storage services. In this paper, a secure and effective assured deletion scheme with orderly overwriting (SEAD-OO) is proposed. The scheme uses ciphertext-policy attribute-based encryption to achieve fine-grained access control and data sharing. To solve the problem of ciphertext left in the cloud after deleting the keys, we introduce physical deletion and logical deletion. Blockchain is introduced to ensure the validity and traceability of deletion. By comparing with related schemes, the proposed SEAD-OO is proved to be versatile. Theoretical analysis and experiments show that the scheme is safe and effective, and it provides a practical method for the assured deletion of cloud data. [ABSTRACT FROM AUTHOR]

The use of independent spanning trees (ISTs) has scientific applications in fault-tolerant requirement in network protocols and secure message distributions. Most of the designs of ISTs are for those interconnection networks with vertex symmetric property, implying that one can find ISTs rooted on a designated vertex, and, by the vertex symmetry property of the given network, hence have solved the ISTs problem on any arbitrary vertex. The existence of asymmetry makes the ISTs problem even harder than its symmetric counterpart. In this paper, we propose two linear time algorithms that solved ISTs rooted at an arbitrary vertex of WK-recursive networks and WK-recursive pyramids separately. The performance of the proposed algorithms is evaluated both, analytically and experimentally, in terms of tree heights and average distances. [ABSTRACT FROM AUTHOR]

In the past ten years, solid-state drive (SSD) has become one of the mainstream storage devices due to its performance advantages. However, how to reduce the impact of garbage collection operations on host-side IO is still a challenging issue in SSD firmware design. To solve this problem, this paper proposes a uniform scheduling of interruptible garbage collection and host IO (USIGC). The main contributions of USIGC include: First, USIGC sets up an interruptible garbage collection sub-request queue, and then uniformly schedules this sub-request queue with the host IO queue. In this way, the idle time of each channel is fully utilized to complete valid page migration and erase operation of interrupt garbage collection. Second, USIGC predicts the probability that the erase operation can be completed in the current idle time based on the historical idle interval and then makes the erase operation decision to reduce the probability of blocking the host IO. Third, by converting the amount of data that can be written in future to the present and taking the number of invalid pages of the current block as the selection basis of the victim block, the garbage collection and wear-leveling can be unified. Experimental results show that, compared with existing works, USIGC can reduce the average response time and max wait time and achieve the best wear-leveling performance. [ABSTRACT FROM AUTHOR]

Game-based learning (GBL) is familiar in several respects, as it can embed problem-solving challenges in an interactive virtual environment to improve students' motivation. Designing a stealth assessment is a tedious task that requires experienced professionals to manually derive evidence and competency approaches to precisely deduce student knowledge and skills. This necessitates that stealth assessment designers perform manual feature engineering and develop probabilistic graphical models. Since the manual feature engineering process is complex, this paper develops Optimal Deep Learning with GBL (ODL-GBL) for stealth assessment. The proposed ODL-GBL model involves parameter-tuned bi-directional long short-term memory (Bi-LSTM) with an oppositional salp swarm algorithm. The optimal Bi-LSTM model extracts hierarchical features from low-level data in an automated way (e.g., a series of student behaviours in a GBL platform) to infer evidence for competencies. The proposed ODL-GBL model alleviates the need for the costlier and more tedious process of developing evidence models. A detailed experimental analysis is used to validate the effective predictive performance of the proposed method. Among all the compared methods, the Bi-LSTM model shows superior performance, with accuracies of 60.56% and 61.34% under 70 and 40 hidden units, respectively. [ABSTRACT FROM AUTHOR]

To solve the problems of premature convergence and easily falling into local optimum, a whale optimization algorithm based on dynamic pinhole imaging and adaptive strategy is proposed in this paper. In the exploitation phase, the dynamic pinhole imaging strategy allows the whale population to approach the optimal solution faster, thereby accelerating the convergence speed of the algorithm. In the exploration phase, adaptive inertial weights based on dynamic boundaries and dimensions can enrich the diversity of the population and balance the algorithm's exploitation and exploration capabilities. The local mutation mechanism can adjust the search range of the algorithm dynamically. The improved algorithm has been extensively tested in 20 well-known benchmark functions and four complex constrained engineering optimization problems, and compared with the ones of other improved algorithms presented in literatures. The test results show that the improved algorithm has faster convergence speed and higher convergence accuracy and can effectively jump out of the local optimum. [ABSTRACT FROM AUTHOR]

The partitioning-based k-means clustering is one of the most important clustering algorithms. However, in big data environment, it faces the problems of random selection of initial cluster centers randomly, expensive communication overhead among MapReduce nodes and data skewing in data partitions, and others. To solve these problems, this paper proposes a parallel clustering algorithm based on grid density and local sensitive hash function (MR-PGDLSH) which takes into account the advantages of MapReduce and LSH (locality sensitive hash function). In the MR-PGDLSH, firstly the GDS (grid density strategy) is designed to obtain the relatively reasonable initial cluster centers. Then, a DP-LSH (data partition based on locality sensitive hash function) is proposed to divide the data set into multiple segments. The relevant data objects are mapped to the same sub-data set. The similarity function is designed to generate clusters, thereby reducing frequent communication overhead between nodes. Next, the AGS (adaptive grouping strategy) is applied to distribute the amount of data on each node evenly, which solves the problem of data skew on the node. Finally, the MR-PGDLSH is applied to mine the cluster centers in parallel, which obtains the final clustering results. Both theoretical analysis and experimental results have shown that the MR-PGDLSH is superior to the existing clustering algorithms. [ABSTRACT FROM AUTHOR]

Top-k dominating (TKD) query is one of the methods to find the interesting objects by returning the k objects that dominate other objects in a given dataset. Incomplete datasets have missing values in uncertain dimensions, so it is difficult to obtain useful information with traditional data mining methods on complete data. BitMap Index Guided Algorithm (BIG) is a good choice for solving this problem. However, it is even harder to find top-k dominance objects on incomplete big data. When the dataset is too large, the requirements for the feasibility and performance of the algorithm will become very high. In this paper, we proposed an algorithm to apply MapReduce on the whole process with a pruning strategy, called Efficient Hadoop BitMap Index Guided Algorithm (EHBIG). This algorithm can realize TKD query on incomplete datasets through BitMap Index and use MapReduce architecture to make TKD query possible on large datasets. By using the pruning strategy, the runtime and memory usage are greatly reduced. What's more, we also proposed an improved version of EHBIG (denoted as IEHBIG) which optimizes the whole algorithm flow. Our in-depth work in this article culminates with some experimental results that clearly show that our proposed algorithm can perform well on TKD query in an incomplete large dataset and shows great performance in a Hadoop computing cluster. [ABSTRACT FROM AUTHOR]

The metaheuristic optimization algorithms are relatively new optimization algorithms introduced to solve optimization problems in recent years. For example, the firefly algorithm (FA) is one of the metaheuristic algorithms inspired by the fireflies' flashing behavior. However, its weakness in terms of exploration and early convergence has been pointed out. In this paper, two approaches were proposed to improve the FA. In the first proposed approach, a new improved opposition-based learning FA (IOFA) method was presented to accelerate the convergence and improve the FA's exploration capability. In the second proposed approach, a symbiotic organisms search (SOS) algorithm improved the exploration and exploitation of the first approach; two new parameters set these two goals, and the second approach was named IOFASOS. The purpose of the second method is that in the process of the SOS algorithm, the whole population is effective in the IOFA method to find solutions in the early stages of implementation, and with each iteration, fewer solutions are affected in the population. The experiments on 24 standard benchmark functions were conducted, and the first proposed approach showed a better performance in the small and medium dimensions and exhibited a relatively moderate performance in the higher dimensions. In contrast, the second proposed approach was better in increasing dimensions. In general, the empirical results showed that the two new approaches outperform other algorithms in most mathematical benchmarking functions. Thus, The IOFASOS model has more efficient solutions. [ABSTRACT FROM AUTHOR]

The introduction of cloud computing has brought about significant developments in information technology. Users can benefit from the multitude of cloud technology services only by connecting to the internet. In cloud computing, load balancing is the fundamental issue that has challenged experts in this research area. Load balancing helps increase user satisfaction and enhance systems' productivity through efficient and fair work assignments between computing resources. Besides, maintaining a load balancing among resources would be difficult because the resources are usually distributed in a heterogeneous way. Many load-balancing methods try to solve this problem by the metaheuristics algorithm, and each of them attempted to enhance the operation and efficiency of systems. In this paper, Grey wolf optimization (GWO) algorithm has been used based on the resource reliability capability to maintain proper load balancing. In this method, first, the GWO algorithm tries to find the unemployed or busy nodes and, after discovering this node, try to calculate each node's threshold and fitness function. The results of simulation in CloudSim showed that the costs and response time in the proposed method are less than the other methods, and the obtained solutions are ideal. [ABSTRACT FROM AUTHOR]

Software-defined networks have many benefits such as more control over the control plane and reduced operating costs through separating the control plane from the data plane in network equipment. One of the most critical problems in software-defined networks is a controller placement problem, which significantly influences its overall performance. The purpose of this problem is to determine the number of controllers required and how to assign switches. This paper attempts to solve this problem, aiming to reduce the network's operational cost and to improve their survivability and load balancing. Hence, we have tried to divide the network into several subdomains using segmentation. Then, we used multi-criteria decision-making methods to solve the controller placement in each subdomain. For this purpose, we considered criteria such as reliability rate, cost, delay, and processing capacity. To assign switches to controllers, we used the proposed mathematical model to minimize the objective function while observing the defined constraints. Furthermore, according to the proposed architecture, we used distance to the central controller, adjacent neighborhood, and controller memory to select the cluster head controller for each subdomain to communicate with the central controller. Finally, we performed experiments on topologies Uran, SwitchL3, and Sinet of the Internet Topology Zoo to evaluate the proposed method. In these experiments, we compared the results of the proposed method with our work-related methods, including Controller Placement Analytic Hierarchy Process (CPAHP) and Controller Placement Modified Density Peak (CPMDP), in terms of criteria such as cost, survivability, number of controllers, connection failure probability, average delay, and controller load-balancing rate. The results show that the proposed method outperforms CPMDP and CPAHP. Thus, the proposed method has a 24.97% and 19.76% improvement in reducing network implementation costs than CPMDP and CPAHP, respectively. [ABSTRACT FROM AUTHOR]

With the rapid development of computer hardware in the past three decades, various classic algorithms such as neural computing and bionic optimization computing have been widely used in practical problems. This paper extended the new bionic algorithm-flock algorithm proposed in 2014 and obtained a multi-objective flock algorithm to solve the multi-objective problem. This study used aggregate functions to define social ranks, and simulated the foraging behavior of chickens in the process of searching for food in the objective space and found the balance between diversity and convergence when looking for the best Pareto solution. The algorithm took five types of bi-objective functions and four types of three-objective functions as objects and compared it with four more widely used algorithms in multi-objective problems. The results demonstrate that the MOCSO (multi-objective chicken swarm optimization) algorithm shows better results in the optimization of multi-objective problems. [ABSTRACT FROM AUTHOR]

This paper presents HiperView, a visual analytics framework monitoring and characterizing the health status of high-performance computing systems through a RESTful interface in real time. The primary objectives of this visual analytical system are: (1) to provide a graphical interface for tracking the health status of a large number of data center hosts in real-time statistics, (2) to help users visually analyze unusual behavior of a series of events that may have temporal and spatial correlation, and (3) to assist in performing preliminary troubleshooting and maintenance with a visual layout that reflects the actual physical locations. Two use cases were analyzed in detail to assess the effectiveness of the HiperView on a medium-scale, Redfish-enabled production high-performance computing system with a total of 10 racks and 467 hosts. The visualization apparatus has been proven to offer the necessary support for system automation and control. Our framework's visual components and interfaces are designed to potentially handle a larger-scale data center of thousands of hosts with hundreds of various health services per host. [ABSTRACT FROM AUTHOR]

A class imbalance problem occurs when a dataset is decomposed into one majority class and one minority class. This problem is critical in the machine learning domains because it induces bias in training machine learning models. One popular method to solve this problem is using a sampling technique to balance the class distribution by either under-sampling the majority class or over-sampling the minority class. So far, diverse over-sampling techniques have suffered from overfitting and noisy data generation problems. In this paper, we propose an over-sampling scheme based on the borderline class and conditional generative adversarial network (CGAN). More specifically, we define a borderline class based on the minority class data near the majority class. Then, we generate data for the borderline class using the CGAN for data balancing. To demonstrate the performance of the proposed scheme, we conducted various experiments on diverse imbalanced datasets. We report some of the results. [ABSTRACT FROM AUTHOR]

Numerical simulation of multi-physical processes requires a lot of processor time, especially when solving ill-conditional linear systems arising in fluid dynamics problems. This paper is devoted to the development of efficient parallel methods for such systems for FireStar3D wildfire modeling code. Two alternative approaches are discussed and analyzed, based on the MILU-preconditioned conjugate gradient method and on the algebraic multigrid, respectively. The main difficulties of parallelizing these methods are considered and solutions are presented: in the first case, nested twisted factorization with a staircase pipelining, and in the second, a multicolor technique for a new smoother for strongly anisotropic grids. A novel quasi-geometric interpolation technique is presented for solving the problem of positive off-diagonal matrix entries in the multigrid. The limits of applicability of the methods are determined depending on their flexibility, robustness and parallelization capabilities. The performance comparison demonstrates the superiority of the new methods over the widely used variants of the traditional conjugate gradient method. [ABSTRACT FROM AUTHOR]