Reviewerly/exHarmony huggingface dataset:
Availability and Reliability of Service Function Chain: A Quantitative Evaluation View

Abstract Service function chain (SFC) based on network function virtualization (NFV) technology can handle network traffic flexibly and efficiently. The virtual network function (VNF), as the core function unit of SFC, can experience software aging, which reduces the availability and reliability of SFC and even leads to service interruption, after it runs continuously for a long time. Software rejuvenation technique can effectively combat software aging. However, its effectiveness in improving the availability and reliability of SFC needs to be evaluated. Compared with existing models, this paper proposes a semi-Markov model to capture the behaviors of each VNF in a SFC from the occurrence of software aging to recovery by software rejuvenation technique under the condition that the failure times and recovery times follow general distribution, while considering trigger intervals of software rejuvenation technique. We then derive the calculation formulas of the steady-state availability, transient availability, and reliability, which are applied to evaluate the effectiveness of software rejuvenation technique. Finally, we conduct sensitivity analysis and numerical experiments to analyze the effects of system parameters, the number of VNFs and trigger interval of software rejuvenation technique on availability and reliability of SFC, and the effects of time-varying parameters on transient availability of SFC.
Implicit Feedback Awareness for Session Based Recommendation in E-Commerce

Abstract Information overload is a challenge in e-commerce platforms. E-shoppers may have difficulty selecting the best product from the available options. Recommender systems (RS) can filter relevant products according to user’s preferences, interest or observed user behaviours while they browse products on e-commerce platforms. However, collecting users’ explicit preferences for the products on these platforms is a difficult process since buyers prefer to rate the products after they use them rather than while they are looking for products. Therefore, to generate next product recommendations in the e-commerce domain, mostly shoppers’ click behaviour is taken into consideration. Shoppers could indicate their interest in the products in different ways. Spending more time on a product could imply a different level of user interest than skipping quickly the product or adding basket behaviour could show more intense interest than just browsing. In this study, we investigate the effect of applying the generated explicit ratings on RS by implementing a framework that maps users’ implicit feedback into explicit ratings in the e-commerce domain. We conduct computational experiments on well-known RS algorithms using two datasets containing mapped explicit ratings. The results of the experimental analysis indicate that incorporating calculated explicit ratings from users’ implicit feedback can help RS models perform better. The results suggest that there is more performance gap between using implicit and explicit ratings when factorisation machine RS model is used.
Learning light field macro-GEPI representation for semantic segmentation

2D optical imaging systems cause irreparable stereo visual feature loss in the pixel-level semantic segmentation task, especially in challenging scenes. To overcome this limitation, light field imaging with microlens arrays can acquire the 4D multi angle subaperture images, providing a viable alternative to semantic segmentation. However, these images are redundant and difficult to process. Therefore, we propose a macro generalized epipolar-plane image (macro-GEPI) representation. The macro-GEPI is obtained by slicing the subaperture images, stacking the slice images in sequence, and finally aligning them. This new image representation can clearly highlight the spatial-angular interactive information of different objects in one image. Based on this, we propose a convolutional neural network (CNN) that can independently learn the light field spatial-angular information for semantic segmentation. We also present a light field dataset composed of 1840 macro-GEPIs with annotations. We have studied the proposed network and compared it to state-of-the-art (SOTA) algorithms. The experiments showed that our network outperformed SOTA algorithms on the macro-GEPI dataset and a benchmark light field dataset, especially in challenging scenes. Moreover, our algorithm achieved a high mean intersection over union of 90.83%, demonstrating the reliability of the light field macro-GEPI representation and the effectiveness of our CNN algorithm.
Balanced image captioning with task-aware decoupled learning and fusion

Image captioning aims to generate natural language descriptions for images. Word occurrences usually obey Zipf’s Law, the imbalance phenomenon makes the conventional training bias to majority data. However, this imbalance distribution has not been considered adequately in captioning works. In this paper, we match the imbalance learning methods in classification with image captioning, making the empirical study. We also propose a Task-aware Decoupled Learning and Fusion (TDLF) approach, which outperforms the former. Image captioning differs from classification in three main aspects: 1) captions are sequential labels that exist co-occurrence, 2) the generation methods usually follow the autoregressive manner, 3) the imbalance ratio is extremely large. To deal with these problems, our TDLF method introduces multi-task learning into the re-balancing approach. The model is composed of a shared autoregressor and two task classifiers, i.e., a conventional training classifier, and a balance-training classifier. The model is further equipped with a task-aware decoupling strategy, we propose the Task Perception Indication (TPI) to measure whether the conventional training is shifted. The balance-training classifier is trained by the biased data separately and the generations of two tasks are fused according to the TPI. Experiments on the MSCOCO database show that our model outperforms the state-of-the-art methods on generation accuracy and word diversity, demonstrating the effectiveness of the proposed method.
HEADSS: HiErArchical Data Splitting and Stitching software for non-distributed clustering algorithms

The increase in data volume is challenging the suitability of non-distributed and non-scalable algorithms, despite advancements in hardware. An example of this challenge is clustering. Considering that optimal clustering algorithms scale poorly with increased data volume or are intrinsically non-distributed, accurate clustering of large datasets is increasingly resource-heavy, relying on substantial and expensive compute nodes. This scenario forces users to choose between accuracy and scalability. In this work, we introduce HiErArchical Data Splitting and Stitching (HEADSS), a Python package designed to facilitate clustering at scale. By automating the splitting and stitching, it allows repeatable handling, and removal, of edge effects. We implement HEADSS in conjunction with HDBSCAN, where we achieve orders of magnitude reduction in single node memory requirements for both non-distributed and distributed implementations, with the latter offering similar order of magnitude reductions in total run times while recovering analogous accuracy. Furthermore, our method establishes a hierarchy of features by using a subset of clustering features to split the data.1
A project-based learning model in interpreting course

This research aims to design a project-based learning model to be utilized in interpreting courses. It employs the research and development methods with the Dick and Carey model. The stages of the research comprised instructional analysis, establishing assessment instruments, determining learning strategy, and constructing the learning materials. The research results revealed that the established project-based learning model was confirmed well-founded for teaching online interpreting courses regarding appraisals from the English language teaching assessment experts, materials experts, and future users. Accordingly, the established project-based learning model can be exploited to flourish the advancement of future lecturers to teach interpreting courses in an online environment
Mixture lightweight transformer for scene understanding

In adapting Transformer from language to computer vision, the major obstacles are the high computational complexity and large model size of Transformer blocks, derived from the great quantity of visual tokens and high resolutions of input entities. To address these challenges, this paper presents a mixture lightweight Transformer (MLT) backbone for image understanding, where each Transformer block, called SH-Transformer, adopts Single-Head Self-Attention (SHSA) and Convolutional Inception Module (CIM). Unlike previous Transformers that compute Multi-Head Self-Attention (MHSA), SHSA limits the representation of input token into a single head, resulting in a low-dimensional embedding that greatly reduces computational complexity. In spite of adding a small number of model parameters, SHSA greatly minimizes the number of input tokens. As complementary of SHSA that only investigates global interactions, CIM is designed to explore multi-scale local information using lightweight convolutions in a multi-path parallel manner. Experimental results reveal that MLT yields competitive or state-of-the-art results with respect to recent transformers while keeping smaller model size and lower computational costs for different visual tasks, including image classification, semantic segmentation and object detection. Particularly, the proposed method has 4.2% improvement to the tiny version of Pyramid Vision Transformer on image classification of top-1 accuracy on ImageNet-1K.
Self-positioning of the book publishing leaders in Russia

Book publishing is an individual sector of economy, which needs development of successful strategies, also communicational, in the conditions of the ongoing transformation. The aim of the article is to study self-positioning as an element of corporate communication of the leading Russian book publishing groups and publishers. The principal method is content-analysis of the relevant information blocks from the official Internetpages of 15 leaders of the domestic book publishing. The results outline self-positioning with seven components, among which the most frequent are “Production”, “History”, and “Leadership”. Notably, some book publishers emphasize on solution of super-tasks. The companies use chiefly different sets of components, which fact implies individuality of their approaches to self-positioning. The frequency of some components is linked unexpectedly to the monopolization of the domestic book market. Generally, the use of the considered element of corporate communication by the leaders of book publishing in Russia can be judged rather mature, although successful self-positioning may require some additional components.
Image Segmentation Based on Fuzzy Method

Image is an important means for human to cognize the world, and image processing technology is also a key research direction in machine learning. In image processing technology, image segmentation is a very critical part of the current academic research hotspot. At present, the fuzzy C-means clustering (FCM) algorithm of image segmentation algorithm uses iterative method to classify samples, which needs less storage space and time. However, FCM algorithm also has many shortcomings, how to use clustering algorithm for real-time, automatic, high-quality image segmentation, has been a problem to be solved. In order to solve the massive data of color image, this paper uses the SLIC method to calculate the super-pixel image over-segmentation. Direct processing of the huge amount of information contained in a color image will degrade the performance of the algorithm. Therefore, image preprocessing is very important.
A RF Fingerprint Clustering Method Based on Automatic Feature Extractor

RF fingerprint technology has received extensive attention and research in recent years due to its immutable nature. RF fingerprinting technology can be used as a wireless network security mechanism alone or combined with existing security mechanisms to enhance wireless network security. The early RF fingerprint research widely used the method of artificial feature extraction, but this method relies too much on expert experience. This paper proposes a semi-supervised learning approach for RF fingerprint recognition. This work directly uses the original I/Q sequence data, designs a fingerprint extractor based on the convolutional neural network (CNN), and uses K-means and DBSCAN algorithms to cluster the fingerprints. The experimental results demonstrate that after training with a small amount of labeled data, the fingerprint extractor can effectively extract features of unknown signals, and these features can well allow unknown similar devices to be clustered together by the clustering algorithm.
A Weakly Supervised Text Classification Method Based on Vocabulary Construction

Text classification is an important research direction in natural language processing. The computer can automatically classify and label texts according to certain classification standards through text classification technology. Traditional text classification tasks require a large amount of labeled data. However, human-labeled data is not only expensive, but also susceptible to the subjective consciousness of the labelers. Therefore, unsupervised text classification using computers becomes relevant. In most cases, the label name of each category is instructive for the classification task. In this paper, we design a weakly supervised text classification method. This method only needs to provide the label names that guide the classification to complete the automated text classification. Our method was tested on several publicly available datasets and performed well.
Design and Implementation of Intelligent Truck Based on Azure Kinect

In recent years, due to the impact of COVID-19, the market prospect of non-contact handling has improved and the development potential is huge. This paper designs an intelligent truck based on Azure Kinect, which can save manpower and improve efficiency, and greatly reduce the infection risk of medical staff and community workers. The target object is visually recognized by Azure Kinect to obtain the center of mass of the target, and the GPS and Kalman filter are used to achieve accurate positioning. The 4-DOF robot arm is selected to grasp and transport the target object, so as to complete the non-contact handling work. In this paper, different shapes of objects are tested. The experiment shows that the system can accurately complete the positioning function, and the accuracy rate is 95.56%. The target object recognition is combined with the depth information to determine the distance, and the spatial coordinates of the object centroid are obtained in real time. The accuracy rate can reach 94.48%, and the target objects of different shapes can be recognized. When the target object is grasped by the robot arm, it can be grasped accurately according to the depth information, and the grasping rate reaches 92.67%.
Microfacies study of the Neogene sequences and determination of sedimentary environments in the Jambour oil field / Kirkuk

The current research deals with microfacies study of the four wells drilled in the area south-east of Kirkuk within Jambour oil field and these are wells Jambour Ja-42 ,Ja-55,Ja-62 and Ja-66. The study included the bed of a Basal anhydrite cycle (Aquitanian) and configurations Serikagni, Euphrates and Dhiban Formation from early Miocene second secondary cycle (Burdigalian) and Jeribe Formation from secondary middle Miocene cycle (Langhian).&#x0D; The petrographic study showed that the most important components of limestone is skeletal grains, non-skeletal grains , cement and matrix. The current study described sedimentary facies, which are divided into: - mudstone facies , wackestone facies , packstone facies and grainstone facies and these microfacies have been divided into submicrofacies. The facies changes within the studied formations have been identified on the depositional environment and put their environmental models. Overall, the Burdigalian cycle characterized by swallowing upward , which began transgression making several and different sedimentary environment , which lasted from pelagic environment formation(Serikagni Formation) and to shallower environment(Euphrates Formation), which ended with marine regression (evaporites of Dhiban Formation) in the basin was originally inherited from the basin of Euphrates and Serikagni Formations. The lower part of Langhian cycle represented by the Jeribe Formation also affected by another transgression.
On stability Conditions of Pareto Autoregressive model

This paper concerned with studding a stability conditions of the proposed non-linear autoregressive time series model Known as Pareto Autoregressive model, acronym is defined by Pareto . A dynamical method Known as local linearization approximation method was used to obtain the stability condition of a non-zero singular point of Pareto model. In addition, we obtain the orbital stability condition of a limit cycle in terms of model parameters when the Pareto possesses a limit cycle with period .
Application of Software Methods for Automated Processing of Sources of Personal Origin

The subject of this research is software methods of automated preprocessing of historical sources and the development of effective solutions to problems when working with sources of personal origin. The article analyzes the current situation in the use of modern software methods. The authors demonstrate the main range of arguments for which such historical sources from a technical point of view should be considered separately. A methodological analysis of the features of the application of optical character recognition based on preprocessed data is carried out. Special attention is paid to the advantages and key parameters of the effectiveness of the final result of work when using automated text processing, including the further use of OCR methods. The scientific novelty of the research lies in the proposal and detailed description of a software solution to the current problem based on machine learning methods. The developed program has three phases of working with digital copies of sources of personal origin. It is based on the use of the OpenCV library and solving a number of problems using the Hough transform. Based on the general analysis of the study, we can highlight the main advantages of automated preprocessing of scanned documents: reducing time, improving accuracy, combating distortion and optimizing the process. The presented results of successful testing of the developed solution allow us to judge the possible areas of its effective application.
Practice of Virtual Simulation Technology (VST) Based on Proteus in Digital Circuit (DC)

Virtual reality technology has impacted all human survival ways at an explosive speed, making people reach a new height from physical entertainment activities to brain thinking activities. At the same time, the development of virtual reality technology has also produced new technologies, such as enhanced virtual reality, which provides people with a good visual experience. At present, virtual reality technology is showing a good development momentum, with an increasingly wide range of applications, and has potential research value in various fields. In this paper, by detecting the experimental data, it can be found that the application of Proteus virtual computer simulation technology in digital circuit can improve the overall security of the system to a certain extent, reduce the aging speed, and more accurately find the potential path and strengthen the system. Through the optimized reinforcement method in this paper, the overall number of reinforced systems is also reduced by 124.92 on average. It can be shown that the overall security of applying Proteus virtual computer simulation technology to digital circuits has been greatly improved. Through the test experimental data, it can be seen that the application of Proteus virtual simulation technology in digital circuit can increase the overall reliability of the system to a certain extent, reduce the aging rate, and accurately find the potential path and strengthen it. Using the optimized reinforcement scheme in this paper, the overall number of reinforcement is reduced by 124.92 on average. It is enough to show that Proteus’s virtual simulation technology can improve the reliability of digital circuits.
Urban Road Traffic Simulation Based on Robot Motion

With the rapid growth of residents’ economic level, the number of private car users has also exploded, and the consequent problems such as traffic congestion, environmental degradation, and increased road accident rates have become factors restricting the healthy development of cities. Urban trunk roads have a unique position in the urban road system because of their highly complex functions of traffic, places, and landscape. The urban road traffic simulation technology proposed in this paper provides an effective way to solve these problems. The purpose of the research is to quantitatively evaluate a variety of arterial road traffic optimization methods, identify and summarize the key factors affecting urban road traffic optimization, and verify the conclusions through case studies. This paper creates a framework for the system simulation model from the analysis of urban road flow characteristics, including vehicle intersection description model, vehicle production model, traffic rule model, vehicle model under the influence of traffic lights, intersection vehicle address tracking model, and evaluation index model. After analyzing the advantages and disadvantages of various existing vehicles, the robot motion planning model is selected for the micro-simulation system of urban road traffic signal control. The simulation system provides an interactive user interface that allows users to input signal timing parameters and traffic flow parameters into the system. The final results of the research show that the optimal path and optimization time of SRL-CACA+ are 16.79 and 8.41 s, respectively. SRL-CACA+ algorithm is better than SRL-CACA algorithm and SRL-ACA algorithm in terms of optimal path and optimization time achieved better performance.
Data-Driven Fuzzy Clustering Approach in Logistic Regression Model

Logistic regression, as one of the special cases of generalized linear model, has important role in multi-disciplinary fields for its powerful interpretability. Although there are many similar methods such as linear discriminant analysis, decision tree, boosting and SVM, we always face a trade-off between more powerful interpretability and accurate predictability inevitably. Because the inference and prediction are the two main purposes of a model and the latter has been paid so much attention, this paper studies the logistic regression and its imperfection mending. It is found that there happens to be the non-existence of maximum likelihood estimates when there is relatively clearer partition in logistic regression model. Therefore, a data-driven fuzzy clustering method has been proposed. Then data sets from UCI machine learning repository are employed to evaluate the performance of proposed approach. The experiment results indicate that fuzzy clustering logistic regression model improves prediction accuracy in comparison with decision tree and linear discriminant analysis. At the same time, interpretability of logistic regression has been reserved in this model.