Matrix Multiplication Method Research Articles

Barriers to Implementing Succession Planning in Government Organizations with a Structural Interpretive Approach

Objective: Nowadays, an effective management system in government organizations leads to vibrant life and the growth and development of the country. To benefit from such a system, competent and suitable managers must exist within the organization's lifecycle, and in their absence, the organization should have access to alternative human resources. The purpose of this study is to investigate the barriers to implementing succession planning in government organizations using a structural interpretive approach. Methodology: This research is practical and falls within the category of descriptive surveys. Data analysis is performed using a structural interpretive approach, specifically the cross-impact matrix multiplication method, with the help of MICMAC software. Findings: The findings include 84 indicators or variables that have been identified and categorized into 14 categories across seven levels as barriers to implementing succession planning. The research also shows that legal barriers are the most significant, while individual barriers are the most impacted by other influential factors in implementing succession planning. Conclusion: Based on the research findings regarding human resources barriers, it is recommended that managers focus on identifying and nurturing internal talents, paying sufficient attention to talent management, and understanding the importance of planning for optimal talent management.

Improved privacy-preserving PCA using optimized homomorphic matrix multiplication

Principal Component Analysis (PCA) is a pivotal technique widely utilized in the realms of machine learning and data analysis. It aims to reduce the dimensionality of a dataset while minimizing the loss of information. In recent years, there have been endeavors to utilize homomorphic encryption in privacy-preserving PCA algorithms for secure cloud computing. These approaches commonly employ a PCA routine known as PowerMethod, which takes the covariance matrix as input and generates an approximate eigenvector corresponding to the primary component of the dataset. However, their performance is constrained by the absence of an efficient homomorphic covariance matrix computation circuit and an accurate homomorphic vector normalization strategy in the PowerMethod algorithm. In this study, we propose a novel approach to privacy-preserving PCA that addresses these limitations, resulting in superior efficiency, accuracy, and scalability compared to previous approaches.We attain such efficiency and precision through the following contributions: (i) We implement space and speed optimization techniques for a homomorphic matrix multiplication method, specifically tailored for parallel computing scenarios. (ii) Leveraging the benefits of this optimized matrix multiplication, we devise an efficient homomorphic circuit for computing the covariance matrix homomorphically. (iii) Utilizing the covariance matrix, we develop a novel and efficient homomorphic circuit for the PowerMethod that incorporates a universal homomorphic vector normalization strategy to enhance both its accuracy and practicality.Our privacy-preserving PCA scheme, implemented using our innovative homomorphic PowerMethod circuit, surpasses the state-of-the-art approach with an average speedup of 1.9 times on datasets with size 200×256. Notably, our scheme demonstrates an even more remarkable estimated speedup of 25 times when applied to larger datasets of size 60000×256, along with an R2 accuracy improvement of up to 0.285, showcasing efficiency and accuracy that has not been reported by previous approaches.

ISM-MICMAC-based study on key enablers in the adoption of solar renewable energy products in India

The demand for energy in India has been increasing steadily due to the country's rapid demographic and economic expansion. This has led to the realization of the significant potential of solar energy in the Indian energy sector. This study aims to identify and evaluate the factors driving the adoption of solar renewable energy products in India. After examining 40 ABDC-indexed articles and deployment of a nominal group approach, we have compiled a list of 19 factors that impact the adoption of solar renewable energy products. To better understand the relationship between these factors, we utilized the cross-impact matrix multiplication (MICMAC) method to create a driving force-dependency quadrant graph. Additionally, we identified each factor's hierarchical structure using the ISM soft technique. The results show that loyalty, willingness to use, and purchase intentions were identified as dependent drivers, while market imperfections, absorptive capacity, product availability, cognitive resonance, and eco-labelling awareness are independent drivers that require attention. This information can be beneficial for decision-makers in developing proactive strategies, end-users, research organizations, and academic institutions alike.

An improved method for quantum matrix multiplication

An improved method for quantum matrix multiplication

Project Management of Assembled Buildings Based on the I-M Model (Construction) Risk Path Identification and Management Study

Prefabricated building is the development trend of the construction industry in the future. The construction of prefabricated building project is different from the traditional cast-in-place project. It has the characteristics of high construction standard, difficult management and strict requirement of component accuracy. The construction and management process of prefabricated building project may be affected by various uncertain risk factors. In this paper, various uncertain risk factors in the construction of prefabricated building projects are taken as the research object, and the hierarchical structure diagram of the main influencing factors is constructed by using the interpretative structural model ( ISM ) model and the cross-influence matrix multiplication ( MICMAC ) method, and the dependence and driving force of the main influencing factors are classified to reveal the mechanism of various uncertain risk factors. On the basis of clarifying the mechanism of various risk factors, a detailed summary is made. This study is conducive to accurately identifying various uncertain risk factors and straightening out the mechanism of action between influencing factors, and providing a certain methodology for reducing risks in construction.

Study on the identification of risk paths for financing small and medium-sized technology enterprises based on ISM-MICMAC model

To reduce the financial risk of small and medium-sized technology companies and to identify the risk surface. This paper constructs a system of 12 indicators affecting the financing risk of small and medium-sized enterprises from 3 aspects, secondly, ISM (explanatory structural model) and MICMACM (cross-influence matrix multiplication method) models are constructed respectively; the hierarchy is carried out by ISM and the MICMAC model is used to carry out the influence and dependence relationship between factors. The results show that: the influencing factors of financing risk path of small and medium-sized technology enterprises are divided into dependent factors, linkage factors and independent factors; no influencing factors exist in the quadrant of spontaneous factors; the operation of financial institutions, profitability level and internal accumulation level are dependent factors with high dependence and low driving force, which are the key factors directly affecting the financing risk of small and medium-sized technology enterprises; the legal system, construction of financial institutions, circulation of monetary funds, Independent factors include only government policies, which have a high driving force and will affect the whole body when they occur, and are the key factors to reduce the financing risks of small and medium-sized technology enterprises. They should be carefully considered when making decisions.

Secure Shapley Value for Cross-Silo Federated Learning

The Shapley value (SV) is a fair and principled metric for contribution evaluation in cross-silo federated learning (cross-silo FL), wherein organizations, i.e., clients, collaboratively train prediction models with the coordination of a parameter server. However, existing SV calculation methods for FL assume that the server can access the raw FL models and public test data. This may not be a valid assumption in practice considering the emerging privacy attacks on FL models and the fact that test data might be clients' private assets. Hence, we investigate the problem of secure SV calculation for cross-silo FL. We first propose HESV , a one-server solution based solely on homomorphic encryption (HE) for privacy protection, which has limitations in efficiency. To overcome these limitations, we propose SecSV , an efficient two-server protocol with the following novel features. First, SecSV utilizes a hybrid privacy protection scheme to avoid ciphertext-ciphertext multiplications between test data and models, which are extremely expensive under HE. Second, an efficient secure matrix multiplication method is proposed for SecSV. Third, SecSV strategically identifies and skips some test samples without significantly affecting the evaluation accuracy. Our experiments demonstrate that SecSV is 7.2--36.6× as fast as HESV, with a limited loss in the accuracy of calculated SVs.

FlexBNN: Fast Private Binary Neural Network Inference With Flexible Bit-Width

Advancements in deep learning enable neural network (NN) inference to be a service, but service providers and clients want to keep their inputs secret for privacy protection. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Private Inference</i> is the task of evaluating NN without leaking private inputs. Existing secure multiparty computation (MPC)-based solutions mainly focus on fixed bit-width methodology, such as 32 and 64 bits. Binary Neural Network (BNN) is efficient when evaluated in MPC and has achieved reasonable accuracy for commonly used datasets, but prior private BNN inference solutions, which focus on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Boolean Circuits</i> , are still costly in communication and run-time. In this paper, we introduce FLEXBNN, a fast private BNN inference framework using three-party computation (3PC) in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Arithmetic Circuits</i> against semi-honest adversaries with honest-majority. In FLEXBNN, we propose to employ flexible and small bit-width equipped with a seamless bit-width conversion method and design several specific optimizations towards the basic operations: i) We propose bit-width determination methods for Matrix Multiplication and Sign-based Activation function. ii) We integrate Batch Normalization and Max-Pooling into the Sign-based Activation function for better efficiency. iii) More importantly, we achieve seamless bit-width conversion within the Sign-based Activation function with no additional cost. Extensive experiments illustrate that FLEXBNN outperforms state-of-the-art solutions in communication, run-time, and scalability. On average, FLEXBNN is 11× faster than XONN (USENIX Security’ 19) in LAN, 46× (resp. 9.3×) faster than QUOTIENT (ACM CCS’19) in LAN (resp. WAN), 10× faster than BANNERS (ACM IH&MMSec’21) in LAN, and 1.1-2.9× (resp. 1.5-2.7×) faster than FALCON (semi-honest, PoPETs’21) in LAN (resp. WAN), and improves the respective communication by 500×, 127×, and 1.3-1.5× compared to XONN, BANNERS, and FALCON.

Analysis of the transmission path of factors influencing employee slackness in companies based on ISM-MICMAC

Employee slackness is a prevalent behavior among current corporate employees, and this behavior harms both employee personal development and organizational effectiveness. In this study, factors are extracted from four levels: employee, leader, company, and job, and the relationships of factors influencing employee slackness are sorted out using the explanatory structural model (ISM) and the cross-influence matrix multiplication method (MICMAC), and corresponding conclusions are drawn based on the relevant mechanistic models. The results of the study show that a strong sense of overqualification and less meaningful work as independent group factors have the deepest influence on corporate employees to produce slacking behavior. The findings of this study help enterprises to understand the influencing factors of employees’ slacking behavior at a deeper level; they also provide important management insights for enterprise managers to pay attention to and alleviate employees’ slacking emotions and negative work behaviors.

Research on the identification and prevention of supply chain finance risks based on ISM-MICMAC model

Supply chain finance is an important means to address the shortage of funds for small and medium-sized enterprises (SMEs). In recent years, the global outbreak of the Covid-19 epidemic has exacerbated the financing challenges of SMEs, and the importance of supply chain finance to the real economy has become increasingly evident, but at the same time, supply chain finance has also exposed many risks. This paper identifies 21 risk factors in supply chain finance, determines the hierarchical relationship of the risk factors by constructing an explanatory structural model (ISM), and then applies the cross-influence matrix multiplication method (MICMAC) to calculate the dependency and driving force magnitude of the 21 risk factors. The conclusions are as follows: natural environmental risks ( ), the hidden risk of core enterprise management ( ) are deep-rooted supply chain finance risks and have strong driving forces. Inadequate corporate solvency ( ), inadequate corporate profitability ( Inadequate corporate operating capacity ( Inadequate growth capability of enterprises ( ), inadequate credit guarantee capacity of core enterprises ( ), low level of creditworthiness of SMEs ( ) is a direct influence on the risk of supply chain finance, which is more influenced by intermediate factors and has a strong dependency. This result will provide scientific decision-making basis for supply chain financial risk management, saving more time and cost.

Research on the Cause of Marketing Risk of New Tea Beverage Enterprises in China Based on the ISM-MICMAC Model

In order to provide an effective reference for the operation, development and investment decisions of new tea beverage enterprises, this paper investigates the influencing factors of marketing risk in the new tea beverage industry, determines 14 indicators affecting marketing risk in the new tea beverage industry by reviewing relevant literature and expert research. Then, an explanatory structural model (ISM) is constructed to reveal the logical relationship between the factors, and the cross-influence matrix multiplication method (MICMAC) is used to analyze dependency and driving force analysis. The results show five levels of the recursive structural model of marketing risk factors in China's new tea beverage industry. Based on the MICMAC driving force analysis, we believe that the more dependent influences need to be controlled by strengthening other risk factors that can have an impact on them, and that sufficient attention should be paid to the influences with higher driving forces, so as to facilitate the normal corporate marketing activities of new tea beverage companies.

The Key Success Factors for the Operation of SME Cluster Business Ecosystem

With the emphasis on cross-industry cooperation and the increasing complexity of the consumer market, an open and innovative business ecosystem has become a trend, and companies are no longer competing on their own, but rather between systems and systems. This study explores the development of SME cluster operations from a business ecosystem perspective. It summarizes the influencing dimensions and relevant factors of SME cluster ecosystem operation through literature research and expert interviews. We utilize the Fuzzy DEMATEL method and Interpretive Structural Model and classified by classification of matrix multiplication method. At the same time, this study figures out the factors’ correlation and characteristics and further develops their management implications to provide a reference for enterprises and subsequent research. The results reveal that robustness and niche creation with significant influence and low dependence are considered critical factors to influence other factors. Therefore, decision-makers should consider prioritizing control over factors in this category.

A Load Forecasting Algorithm for Power Internet of Things Using Training Data Dimension Expansion and Ensemble Learning Technique

With the development of the power internet of things (IOT), load forecasting will play an important role the power system. It can optimize the power generation planning and improve the economical operation of power IOT. In this paper, a new loading forecasting algorithm for power IOT is proposed using training data dimension expansion and ensemble learning. In the offline phase, the obtained meteorological data and time information is normalized to remove the unit effect at first. Then, the Hampel filter is used to cope with the outliers of the meteorological data from sensors. Through the preprocessing, the fingerprint of the training data is constructed. Next, the matrix multiplication method is proposed to expand the dimension of training data fingerprint information. Finally, the ensemble learning combining multiple long short-term memory (LSTM) networks are proposed to obtain multiple power load forecasting models and corresponding weight coefficients. In the online phase, the obtained meteorological data and time information are preprocessed to form the input of and power load forecasting models. The final power load forecasting is obtained by linear weighted sum method with intermediate forecasting result. In the proposed algorithm, more features of training data can be obtained by the data dimension expansion. Moreover, the ensemble learning using LSTM can make fully use of the timing sequence of training and improve the generalization performance of offline training. Experiment results illustrate that the proposed algorithm has better forecasting performance than existing methods.

Atrous Pyramid GAN Segmentation Network for Fish Images with High Performance

With the development of computer science technology, theory and method of image segmentation are widely used in fish discrimination, which plays an important role in improving the efficiency of fisheries sorting and biodiversity studying. However, the existing methods of fish images segmentation are less accurate and inefficient, which is worthy of in-depth exploration. Therefore, this paper proposes an atrous pyramid GAN segmentation network aimed at increasing accuracy and efficiency. This paper introduces an atrous pyramid structure, and the GAN module is added before the CNN backbone in order to augment the dataset. The Atrous pyramid structure first fuses the input and output of the dilated convolutional layer with a small sampling rate and then feeds the fused features into the subsequent dilated convolutional layer with a large sampling rate to obtain dense multiscale contextual information. Thus, by capturing richer contextual information, this structure improves the accuracy of segmentation results. In addition to the aforementioned innovation, various data enhancement methods, such as MixUp, Mosaic, CutMix, and CutOut, are used in this paper to enhance the model’s robustness. This paper also improves the loss function and uses the label smoothing method to prevent model overfitting. The improvement is also tested by extensive ablation experiments. As a result, our model’s F1-score, GA, and MIoU were tested on the validation dataset, reaching 0.961, 0.981, and 0.973, respectively. This experimental result demonstrates that the proposed model outperforms all the other contrast models. Moreover, in order to accelerate the deployment of the encapsulated model on hardware, this paper optimizes the execution time of the matrix multiplication method on Hbird E203 based on Strassen’s algorithm to ensure the efficient operation of the model on this hardware platform.

Development of pipelined polynomial multiplier modulo irreducible polynomials for cryptosystems

In this paper, we consider a schematic solution of the pipeline multiplier modulo, where multiplication begins with the analysis of the lowest order of the polynomial multiplier, which can serve as an operating unit for high-speed encryption and decryption of data by hardware implementation of cryptosystems based on a non-positional polynomial notation. The functional diagram of the pipeline and the structure of its logical blocks, as well as an example of performing the operation of multiplying polynomials modulo, are given. The correct functioning of the developed circuit was checked by modeling in the Vivado Design Suite computer-aided design for the implementation of the multiplication device on the development/evaluation kit Artix-7 based on the Spartan 6 field-programmable gate array series by Xilinx. The effectiveness of the proposed hardware pipeline multiplier in modulo is confirmed by the Verilog Testbench timing diagram implemented for the evaluation kit Artix-7 field-programmable gate array. In addition, the developed pipelined modulo multiplier takes no more than 0.02 % of the resources of the used field-programmable gate array for a given length of input data. Compared to the matrix multiplication method, a pipelined modulo multiplier can handle a large data stream without waiting for the result of the previous multiplication step. The modulo pipelined multiplier depth depends on the bit width of the input data. The developed pipeline device can be used in digital computing devices operating in a polynomial system of residue classes, as well as for high-speed data encryption in blocks of cipher processors operating on the basis of a non-positional polynomial number system.

Real-time and automatic map stitching through aerial images from UAV

Real-time aerial map stitching through aerial images had been done through many different methods. One of the popular methods was a features-based algorithm to detect features and to match the features of two and more images to produce a map. There are several feature-based methods such as ORB, SIFT, SURF, KAZE, AKAZE and BRISK. These methods detect features and compute homography matrix from matched features to stitch images. The aim for this project is to further optimize the existing image stitching algorithm such that it will be possible to run in real-time as the UAV capture images while airborne. First, we propose to use a matrix multiplication method to replace a singular value decomposition method in the RANSAC algorithm. Next, we propose to change the workflow to detect the image features to increase the map stitching rate. The proposed algorithm was implemented and tested with an online aerial image dataset which contain 100 images with the resolution of 640 × 480. We have successfully achieved the result of 1.45 Hz update rate compared to original image stitching algorithm that runs at 0.69 Hz. The improvement shown in our proposed improved algorithm are more than two folds in terms of computational resources. The method introduced in this paper was successful speed up the process time for the program to process map stitching.

An appraisal on enablers for enhancement of waste cooking oil-based biodiesel production facilities using the interpretative structural modeling approach

With the continuous depletion of energy sources globally and serious concern regarding environmental degradation by the use of fossil fuel, biodiesel may play a key transponder. Biodiesel blended with diesel fuel achieves a decreased environmental footprint without losing the reliability of output and consumption. Biodiesel is produced from a variety of sources. Biodiesel generation from waste cooking oil (WCO) is effective for both the atmosphere and human health. Many research studies reported WCO biodiesel as a potential alternative fuel for internal combustion engine. The present study aims to provide key promoting and implementing agents for WCO utilization and WCO-based biodiesel production. A systematic literature review has been performed to identify enablers and the contextual relationship between various enablers was developed using interpretative structural modeling (ISM) and expert views. Using the method of ISM and cross-impact matrix multiplication applied to classification (MICMAC) methodology, the impact of enablers is studied. The findings revealed that all established enablers play an important role and are equally important promoters for the development of biodiesel based on WCO. The findings further suggest that human health issues, biodiesel processing plants, biodiesel support vehicles, and biodiesel production technology play a key role in the manufacture of biodiesel dependent on WCO. The most important leaders in the development of WCO biodiesel are government policy and funding, confidence in environmental issues, and financial assistance to biodiesel manufacturers.

The Multi-Risk Analysis of Various Disasters in Karanganyar Regency

Karanganyar Regency is one of the districts in Central Java which prone of the natural disasters. Based on data Posdes 2018, it is known that the types of disasters that often occurred in Karanganyar Regency during the last three years, from 2015 to 2017 were landslides, floods, and drought. Therefore, it is important to know the multi-risk level of the three types of disasters. The method used in this research is scoring and weighting, as well as overlay between constituent parameters. Based on the results of research using the VCA matrix multiplication method, it is known that the high risk level covers 3,589.67 Ha or 4.46% areas that spread in 15 districts (except Jatipuro and Jumapolo); moderate risk level covers 34,624.94 Ha or 43.04% areas that spread in 16 districts (except Tasikmadu); The low risk level covers 42,254.98 Ha or 52.53% areas that spread in 7 districts (Colomadu, Gondangrejo, Tasikmadu, Matesih, Tawangmangu, Jumapolo, Jatiyoso).

On the combined use of laser-cut panel light redirecting systems and horizontal blinds for daylighting and solar heat control, a focus on visual comfort objectives

Aiming to explore its performance as energy efficient system, in this study, the window is subdivided according to functions, in which the upper part would act as a daylight optimization unit. Two variant positions of lasercut panel, known as ‘louvre-style’ and ‘venetian-style’ are placed in the upper window unit, in order to take advantage of their angle selective redirecting capabilities. Their effectiveness to provide a satisfactory visual comfort is evaluated by assessing their potential for solar control and daylight redirection indoors. The first results indicate that the use of lasercut panel as a sole system, its insufficient to provide an adequate visual environment, with a reduced risk of glare for the occupants. Aiming to improve its performance, horizontal blinds are added as supplementary element to the interior-upper part of the window, blended in an arrangement called ‘secondary system’. As a result of a comprehensive evaluation, in which, the upper and middle window units are considered, two lasercut panel styles are identified as those providing sufficient daylight provision indoors, and a reduced risk of discomfort glare. The properties of the fenestration systems tested here, were described using their corresponding BSDF data, while the evaluation was performed using the RADIANCE matrix multiplication methods.

Extended use of error-free transformation for real matrix multiplication to complex matrix multiplication

The presence of rounding errors is frequently inevitable when performing arithmetic operations in computers due to the use of floating-point number system whose elements have finite precision. Consequently, error-free transformation algorithms are proposed as solutions, and a particular instance of these is the error-free transformation algorithm for real matrix multiplication. Based on the previously mentioned algorithm, this study proposes two error-error free transformation algorithms for complex matrix multiplication. By conducting the numerical experiment, one finds that the combination of both proposed algorithms with an accurate summation algorithm generates the most accurate results, but it also suffers from a notable increase in the computing time ratio. On the other hand, the second proposed algorithm, which is simply another form of the first one with the change of order in summation, combined with the pure floating-point addition produces results that are nearly as accurate as those generated by the former combination. Since these results are achieved with significantly smaller computing time ratio, then the study concludes that the last combination is the best method for complex matrix multiplication in terms of accuracy and computing time efficiency.