Miracle-dz/UniFAR-FTUs huggingface dataset:
LBBGEMM: A Load-balanced Batch GEMM Framework on ARM CPU s

The trend in modern high performance computing is to decompose a large linear algebraic problem into many small problems that can be solved independently. Although there are many studies to obtain near-peak performance for large-scale dense matrix operations, it is not sufficient for batch operations with small matrices. The study of small GEMM kernel optimization and load balanced scheduling of batch operations on ARM processors is not enough. In this paper, we present LBBGEMM, a load-balanced batch GEMM framework for optimizing large groups of variable-size small GEMM to boost near-optimal performance based on ARMv8 architecture. The LBBGEMM is divided into the install-time stage and the run-time stage. At the install-time stage, we analyze the characteristics of each transposition mode to design the high-performance small GEMM kernel without data packing. This strategy greatly reduces the memory access overhead. In addition, we optimize instruction scheduling and instruction selection carefully to achieve optimal performance. The run-time stage provides a comprehensive auto-tuning process for batch GEMM by using a tiling designer and a pre-grouped dynamic scheduling algorithm. The tiling designer generates high-performance execution plans for each group of matrices with different sizes. Then we divide the large group of GEMM operations into small task groups. These task groups are assigned to threads for execution in the form of command queues through our proposed dynamic mapping between threads and tasks. This pre-grouped multi-thread task scheduling algorithm greatly improves the speedup of multi-thread. The experiments show that LBBGEMM could achieve significant performance improvements in batch GEMM compared with other mainstream BLAS libraries.
A New Continuous-Time Algorithm for Calculating Algebraic Connectivity of Multi-Agent Networks

—How to maintain the connectivity is an important issue in mobile agent networks. In this paper, we propose a new continuous-time algorithm for calculating the algebraic connectivity of the graph representing the interaction between agents. This is simpler than the conventional algorithm in the sense that less multiplications are needed. We study the dynamical behavior of the proposed algorithm and prove that it can ﬁnd the algebraic connectivity of the graph for almost all initial conditions.
Analysis and prediction of operator churn based on neural network and machine learning

With the continuous development and application of big data mining technology, mobile operators hope to use data mining technology to identify which users may churn and when will churn occur. By establishing a churn prediction model, analyzing users' historical and current data, extracting key data to assist decision-making, discovering hidden relationships and patterns, and predicting possible future behaviors, mobile operators can help mobile operators achieve these requirements.
An Approach to Detect Overlapping Diseases in Tomato Leaf Using CNN

India is the world’s largest agricultural country and the world’s it is on second position in the list of tomato producers. There is a need for lots of advancement in technology. Tomato yields differ based on how they are produced. The most important factor that affects the crop production quality and quality is the Leaf disease in these tomato plants. As a result, it’s vital to correctly diagnose and characterise these diseases. Tomato production is influenced by a variety of diseases. Early detection of the tomato plant diseases would help us to minimize the impact of the disease on tomato plants and maximize the production of crop. Various novel methods of diagnosing certain diseases have been widely used. The existing research has shown that the Convolution Neural Network-based approach gives us impressive results compared to traditional approaches. This work will address the same problem of disease detection. In the proposed methodology, CNN will be used along with image segmentation for detecting multiple or overlapping diseases in the same leaf. The proposed model will first use a image segmentation module for generating multiple instances of the same image, then we will use the some of trained CNN model to find the affected disease in the given leafs. By using the above results, we will suggest the disease control methods to the end-user.
Fuzzy Reliability Evaluation Using Pythagorean Fuzzy Set Approach Emphasizing Score Function and Accuracy Function

The present study proposes a new technique to determine the reliability of different complex systems using Pythagorean fuzzy set theory. The fuzzy reliability set theory has been applied in many fields such as management, engineering, etc. In this paper we evaluate system reliability in which the reliability of the components of a system are represented by Pythagorean fuzzy element for modeling uncertainty and vagueness in real life situations.The proposed method can model and analyze the fuzzy system reliability in a more flexible and more intelligent manner. This new method also uses the score function and accuracy function for better comparison of different complex systems.
BS2Vec: A Spatial Representation Learning Model for Large Scale Base Stations

With the prosperity of mobile communications, Cell-ID (the unique identifier of a base station) trajectory mining has become popular. To predict locations of base stations (BS) with Cell-ID trajectories and enhance the application of machine learning on Cell-ID trajectory mining, it is essential to learn representations of BSs. Existing methods primarily concentrate on the spatial correlations, which only reflect topological structural relations of BSs, and limit the capability for modeling the BS's representation. To address this issue, we attempt to leverage an unsupervised deep graph embedding technique to learn the representation for large-scale BSs, namely BS2Vec. It incorporates spatial attributes and correlations by developing a variational graph auto encoder with the attention mechanism. The spatial attributes such as vague addresses and Location Area Codes (LAC) imply the coarse-grain location information of BSs, which can significantly enhance the model's ability. To validate BS2Vec, we design a location evaluation task to predict Cell-ID locations merely with Cell-ID trajectories. Experiments on real-world datasets demonstrate the performance gain compared to shallow graph embedding methods. Moreover, it improves the trajectory prediction accuracy from 0.76 to 0.83 compared to the state-of-the-art Cell-ID embedding method.11These authors contributed to the work equally and should be regarded as co-first authors.
Dynamic Grouping of Wind Power Clusters Based on Ultra-Short- Term Wind Power Forecasting with NWP

Research on wind power prediction control can help to solve the problem of insufficient absorptive ability of power grid, and have great significance in the power system safety and stable operation. Therefore, the paper puts forward a method of multiple time scale dynamic wind cluster partition based on BP neural network. The method takes into account numerical weather prediction to forecast the wind ultra-short-term power, according to the trend of the wind power prediction power of wind farm output power dynamic category, in order to classify wind farm output power, and establish wind farm output power cluster priorities. The validity and rationality of the proposed method are verified by simulating and analyzing cases with domestic wind power clusters. The results show that the numerical weather prediction can effectively improve the accuracy of wind power prediction, and the absolute mean error and root mean square error are improved by 5% and 4% respectively. And the dynamic clustering of wind farms lays the foundation for the regulation of wind power.
The Local Importance of Node in Complex Networks

This paper presents a new weighted graph to represent the strength of relationships between nodes, this graph can be a direct reflection of the interaction frequency of interconnected nodes, through the calculation of the weights, it can measure the local importance of the node results show that the local importance of a node is proportional to the degree of the node and the interaction frequency of the node. Under certain conditions, the local importance is inversely proportional to the degree of its adjacent nodes. Streszczenie. W artykule przedstawiono nowy diagram wazony do prezentacji sily relacji miedzy korzeniami sieci. Graf moze byc bezpośrednim odbiciem wzajemnych oddzialywac czestotliwościowych wspolpracujących korzeni. Poprzez obliczenia wag moze oceniac znaczenie korzenia lokalnego. Wyniki wskazują, ze znaczenie korzenia lokalnego jest proporcjonalne do stopnia korzenia i czestotliwości wspolpracy. W szczegolnych warunkach lokalne znaczenie jest odwrotnie proporcjonalne do stopnie korzenia przylegającego. (Lokalne znaczenie korzenia w sieci zlozonej).
Optimized Computation for Determinant of Multivariate Polynomial Matrices on GPGPU

The determinant of the matrix is a fundamental concept in linear algebra and has various applications in research and engineering. Compared to the easy-calculating numeric matrix, a symbolic matrix with multivariate polynomial entries is hard to calculate because of immediate expression expansion, which often leads to drastic exhaustion of the physical memory. In this paper, we present a novel four-stage-approach to calculate the accurate determinant of multivariate polynomial matrices on GPGPU, through using the modular method, the Stockham Fast Fourier Transformation(FFT), the inverse FFT, and the Chinese Remainder Theorem. In our approach, the computation tasks in each stage are deployed on GPGPU so that the calculation could be recovered without loss at any point in case of inevitable interruptions happened during the parallel processing. In addition, to control the parallel computation time, we propose a time prediction model for the computation of polynomial determinant according to the basic matrix attributes. The experiment results show that our approach owns substantial speedups compared to Maple, allowing memory and time usage in steady increments.
Equivalent circuit analysis of a system of coupled helical transmission lines in a complex environment

The complex boundary value problem of a system of coupled transmission lines which takes into account the effects of the dielectric and metal shell environment is solved by a simple equivalent circuit analysis. The results are compared with those for a simple system of coupled helices in free space now available in the literature. The effects of the various environmental parameters—like the thickness of the dielectric sleeve for the outer helix, the ratio of the outer helix‐to‐inner helix radii, the ratio of the outer helix‐to‐inner helix contangents of the pitch angle—are studied on the variation of the optimum length with frequency for a system of helical couplers as used in a traveling‐wave tube.
CFCT: The cell function classification method for complex tables

Tables are one of the most common approaches to organizing, presenting and managing data. However, these tables are often designed to be user-friendly, making it easier for people to find and compare data. It is difficult for machines to understand the contents of tables automatically. Moreover, the existing works on table cell classification focus on relational tables, which ignore the other complex table data structures, and are poorly generalized. To address these issues, we propose a cell classification method for complex tables called CFCT, which considers both the textual content, stylistic features, and numerical content of cells. Moreover, CFCT uses the auto-encoder to extract the cell embeddings and introduces the adversarial training in the auto-encoder to obtain more representative cell embedding. Then, we utilize a transformer-based model to classify the functional roles of cells in complex tables, which can mine the structural information of the table data in a more parallel way. To demonstrate the effectiveness of our approach, we evaluate the performance of the CFCT on three datasets, DeEx, SAUS, and CIUS. The experimental results show that CFCT obtained a 3.6% improvement in terms of F1 score compared with the state-of-the-art deep learning method. The comprehensive experiments demonstrate that our CFCT is effective for the functional classification of cells in complex tables. More importantly, we find that adversarial training can fuse multiple table information to produce more accurate features.
A Peformance Evaluation of YOLOv3 and CIE Lab Color Space Pixel Color Analysis in Fire Detection

Fire is one of the most common disasters that can happen at any time. In order to reduce the likelihood of damage and loss there have been several models that were developed in order to detect these fires and alert authorities so that fire does not spiral out of control. One of these methods is YOLOv3 - a convolutional neural network that can be used to detect fire. Another one of these methods is pixel color analysis which uses the CIE Lab color space in order to detect fire. The models will be tested using low resolution images, and each model is evaluated in terms of precision, accuracy, recall, and F1 measure. This study aims to evaluate the performance of both methods in order to provide a recommendation as to which model is better for detecting low quality fire images. The results of the study showed that pixel color analysis had an accuracy of 93.28%, precision of 97.43%, recall of 84.89%, and an F1-measure of 90.73%. YOLOv3 had an accuracy of 90.02%, precision of 99.77%, recall of 74.39%, and an F1-measure of 85.23%.
Learning Local Representation by Gradient-Isolated Memorizing of Spiking Neural Network

Spiking neural networks (SNNs) show promise in implementing low-power AI hardware due to their event-driven sparsity and leaky integrate-and-fire (LIF) neurons with binary spikes, rather than traditional activation functions with analog output. However, SNNs have a low capacity to process the local input due to their spike encoding paradigm. To address these problems, we propose a novel SNN architecture with a gradient-isolated memorizing mechanism, horizontal LIF, and vertical LIF neurons to learn local representation. Moreover, to take full advantage of local representations and global representations, we design a concurrent SNN architecture within the local-global joint representation. For proof-of-concept, we evaluate our proposal on MNIST, Fashion MNIST, and CIFAR-10 datasets, which achieve 98.68% and 90.37%, and 87.19% accuracy with 5 time steps, respectively, while maintaining low computational complexity.
Intrusion Detection System for AI Box Based on Machine Learning

This paper presents an integrated application of network intrusion detection. The intercepted packets are first analyzed using a machine learning algorithm, the HistGradient Boosting classifier, to detect network traffic as abnormal or normal. If the network traffic is unnatural or disruptive, the system will immediately notify the information security expert whether to disrupt the network traffic. We propose an information security application combined with hardware and software. The application system can secure IoT devices or any field that needs to use the network environment, including abnormal network traffic, system outages, betrayals, and other cyber threats. The system is scalable and can easily port to other devices or platforms. This study will introduce datasets and pre-processing methods, machine learning model building, abnormal packet handling, and operating system environment building of the AI BOX device. Two public datasets were used to train and test the model, and our model obtained 99.9% prediction accuracy. The system has been successfully tested on the Yocto Project operating system of the AI BOX device.
Comparative Study on Pairs Trading Using Machine Learning Algorithms

Hedge funds employ the trading of pairs as one of their most lucrative trading strategies. It’s especially intriguing since it eliminates the time-consuming process of evaluating assets by focusing on comparable prices. Upon the convergence of the pair’s price, a profit can be achieved by buying a discounted investment and selling an overvalued investment. However, as the data became more widely available, finding rewarding pairs turned out to be a cumbersome process. Pair Trading is a two-step mechanism. The first step is to identify the pairs of stocks that belong to the same market segment or have similar market capitalization. It is like choosing the same fruit from a fruit platter that shares similar identical features. The second step is to keep a watch on the distance between them in the subsequent trading cycle. When the distance between them abruptly rises, it is assumed that the spread between them will converge. If the pairs are mean-reverting, then the overachieved stock is short and the under-achieved stock is long. When spreads converge, the investor liquidates all positions resulting in a profit. Results shows that we achieved accuracy of 95% pairs to be successful traded. Also, compared all results which are achieved from different machine learning algorithms.
Centralized Multi-objective Framework for Smart EV Charging in Distribution System

Depleting fossil fuel resources and climatic issues have compelled the world to promote electric vehicles (EVs) as a promising option for sustainable transportation. However, the increased integration of EVs through uncontrolled charging in distribution system introduces various issues in distribution system, such as system over-loading, increased peak demand, deteriorated voltage profile, and increased losses. In this paper, the centralized framework with multiple objectives of different stakeholders for EV charging is proposed to overcome the noxious impacts of uncontrolled EV charging on distribution network and to motivate the EV user for their participation in smart EV charging. The controlled charging in this paper is studied using electricity price signals and centralized charging control strategy, with the objectives of charging cost minimization, peak-to-average ratio reduction, and load leveling. In this work, an impact of uncontrolled EV charging on distribution network is analyzed and compared with proposed framework. IEEE 33 bus system is taken as the test case, and results showcased that the proposed framework effectively manages the EV charging demand.
Quality of Service Aware Order Allocation for Inter-Regional Online Food Delivery Systems

The allocation of orders in an online food delivery system that maximizes Quality of Service (QoS) to customers by assuring long distance delivery is a challenging problem because of the different order completion properties and service expectations. Existing works in the literature are constrained by focusing either on reducing order completion time or minimizing costs associated with order delivery within a predetermined region. In this paper, we have developed a framework for optimal order allocation of inter-regional food delivery orders as an optimization function which minimizes service time to escalate QoS of the system with required constraints. The results of performance studies depict that the proposed system achieves competitive service time as well as enhances the number of orders of the system.
A Shapley-based Lightweight Global Explainer for Network Intrusion Detection System

As deep learning models usually suffer from the black-box problem, even well-performing network intrusion detection systems (NIDSs) are not trusted by safety-critical practitioners. Thus explaining the decision-making mechanism of NIDSs model is urgently needed. Shapley-based explanation methods are promising due to their theoretical guarantees and the ability to capture the interactions between features. However, they require exponential computation and are time-consuming, especially for global explanation. This makes them insufficient for the need of NIDSs to quickly understand global threats and respond rapidly to malicious behavior. According to our in-depth analysis of NIDSs' data and mechanism of Shapley-based global explanation, we identified two culprits that lead to computationally complex and time-consuming. They are significant data redundancy and global queries in two steps during explanation, respectively. To address the above problems, we develop SLGE as a novel Shapley-based lightweight global explainer. As an innovative first step, SLGE introduces a multi-objective data reduction method based on our proposed metrics for assessing data quality. Then the high-quality sub-dataset obtained by data reduction is used to optimize the two steps that require global queries during global explaining. Our experimental results demonstrate that SLGE can accelerate global explanation for deep learning-based NIDS by up to 10 times and for random forest-based NIDS by up to 3 times while ensuring high-quality (75% fidelity) explanations.
A cache sharing mechanism based on RD MA

Caching improves storage system access efficiency and is widely used in operating systems, databases, and various Internet applications. However, the efficiency of the cache is related to the cache hit ratio and cache policy. But in the HDFS, the cached data is only accessible when the job resident in one same node. The internal cache of each node is isolated, and tasks within the node only use the cached data. As a result, disk IO, job scheduling, and data distribution significantly impact data processing efficiency. This paper presents a cache-sharing mechanism based on Remote Direct Memory Access (RDMA) for HDFS. The cache of data nodes is connected through RDMA to make them accessible from any nodes. Compared with the existing HDFS cache mechanism, the scheme in this paper improves the cache utilization, reduces the access delay, and significantly improves the read and write performance of HDFS. Experimental results show that compared with the existing HDFS cache mechanism, the read file throughput rate can be improved by 2.2 times, and the write throughput rate can be improved by 0.677 times. Furthermore, when the task runs on different nodes, reading delay STDEV standard deviation decreases from 12.22s to 0.39s. Significant decrease in the I/O fluctuations.
Key Factor Selection Transformer for Multivariate Time Series Forecasting

Multivariate time series forecasting has a wide range of applications in transportation, meteorology, finance, etc. Multivariate time series include time-varying target series, exogenous time series and static data series. With the continuous expansion of dimension and scale, multiple exogenous time series can affect the target series in different degrees, and the time dependencies within different series become more complex. However, the previous researches neglect to efficiently identify the key factors and the temporal change information while learning historical time series. In order to solve the problem, we designed Key Factor Selection Transformer. It has three notable features: (i) A two-stage key factor identification-selection mechanism based on variable selection network and Prob-sparse self-attention, emphasizing the influence of key factors on prediction results. (ii) Use GRU network and self-attention mechanism to effectively learn local information and long-term correlation of historical time series. (iii) A new objective function, used to guide the learning of temporal change information. Experimental results on 4 real datasets show that our method significantly improve the prediction performance, providing a new solution to the multivariate time series forecasting problem.