Reviewerly/exHarmony huggingface dataset:
Issues of implementing the requirements of educational standards of a new generation

The development of requirements for the content of education is due to the dynamics of the factors of the external environment, and, fi rst of all, the trends of economic development. The essence of the requirements lies in the closest approximation of the content of educational programs, regulated by federal educational standards, to the content of the practical activities of graduates, determined by professional standards. The conducted research is aimed at analyzing the methodological base for linking professional standards and federal state standards at the level of formation of competencies and indicators. A systematic approach to evaluating the model of educational activity made it possible to assess the change in the nature of the interaction between the requirements of the labor market and the ways to meet them within the framework of the content and main aspects of the quality of educational programs. The features of the implementation of relations between communities of employers in the process of forming professional standards and their translation into methods of evaluation tools, which are an integral part of professional educational programs, are presented. The article formulates the main results of the study, namely, the methodological problems of the quality of education and the direction of the scientifi c, methodological and organizational and methodological activities of educational organizations in the community with employers and regional bodies that coordinate the requirements for graduates.
Segment-tree based stereo matching algorithm of segmented regions collaborative optimization

In order to improve the matching rate of weak and repeated texture regions, caused by a wrong match in somewhere of the image, which will be propagated to adjacent pixels for the non-local aggregation algorithm based on minimum spanning tree image segmentation, a segmented regions collaborative optimization for stereo matching algorithm based on segmenttree (ST) is proposed. In the cost calculation stage, the cost calculation method of multi-feature fusion based on color and gradient is used from the perspective of probability to obtain the initial matching cost. Then, in the cost aggregation stage, the segmented regions collaborative optimization algorithm is used to filter the segmented regions based on ST, and the collaborative optimization strategy of matching cost within the region is realized by selecting the main disparity and the proportion-based penalty optimization method. Finally, the disparity map is obtained through some post-processing. The algorithm test on Middlebury’s V2 datasets. The experimental result shows that the average error matching rate of this algorithm is 5.72%, and it shows that the algorithm in this paper has a high matching accuracy.
Evolution of a haskell repository and its use of monads

Package repositories for a programming language are increasingly common. A repository can keep a register of the evolution of its packages. In the programming language Haskell, with its defining characteristic monads, we can find the Stackage repository, which is a curated repository for stable Haskell packages in the Hackage repository. Despite the widespread use of Stackage in its industrial target, we are not aware of much empirical research about how this repository has evolved, including the use of monads. This paper presents an empirical study that covers the evolution of fourteen Long-Term Support (LTS) releases (period 2014 - 2020) of available packages (12.46 gigabytes), including the use of monads from the mtl package that provides the standard monad core (e.g., state, reader, continuations). To the best of our knowledge, this is the first large-scale analysis of the evolution of the Stackage repository with regard to packages used and monads. Our findings show, for example, a growing trend of packages is depending on other packages whose versions are not available in a particular release of Stackage; opening a potential stability issue. Like previous studies, these results may evidence how developers use Haskell and give guidelines to Stackage maintainers.
Crowd density estimation and warning model in key control areas of large factories based on CNN

To meet to the requirements of the national production safety law of China, large manufacturing enterprises need to implement the sampling of dynamic number of employees in key areas, emergency evacuation drill and emergency evacuation organization. Aiming at the existing problems such as high randomness of overtime work of workshop employees, frequent worker aggregation phenomena for short meetings, fast aggregation, short time and high risk of dining employees in the canteens, uneven personnel distribution and crowded entrances and exits in the conference center, this paper analyzes the psychological and moving behavior characteristics of personnel in these typical scenarios, and introduces the convolution neural network (CNN) model in the field of machine vision, to build a multi-scenario employee number statistics and density estimation model for factory workshops, canteens and large meeting centers. Further, according to the density-risk relationship, a crowd aggregation risk early warning model is established. Finally, taking the video surveillance system (VSS) as the data acquisition source, the application cases of practical scenes such as workshop, canteen and meeting center are designed to verify the effectiveness of the density estimation model and aggregation risk early warning model proposed in this paper. Thereby this paper provides technical guarantee for the safety of employees in large manufacturing enterprises.
A makeup transfer method based on attention generative adversarial networks

The images generated by the existing makeup transfer methods have some problems, such as large loss of facial structure and inconsistent color distribution with reference images. In this paper, a makeup transfer method is proposed, which can keep facial structure information unchanged. Firstly, this paper builds an efficient generator model by using the characteristics of U-Net which combines up-sampling and down-sampling information to extract image features and SE-Net which emphasizes useful features and suppresses useless features. At the same time, a loss function is designed to constrain the facial color distribution of the generated image so that the generated image is as consistent as possible with the color distribution of the reference image. Experiments show that the makeup transfer method in this paper not only can capture the color distribution of the reference image's face, but also better preserves the facial features of target image with an average SSIM of 0.8740.
Face Anonymity Based on Facial Pose Consistency

With the development of artificial intelligence, there are more and more applications related to face images. The recording of face information causes potential cyber security risks and personal privacy disclosure risks to the public. To solve this problem, we hope to protect face privacy through face anonymity. This paper designs a conditional autoencoder that uses the data preprocessing method of image inpainting. Based on the realistic generation ability of StyleGAN, our autoencoder model introduces facial pose information as conditional information. The input image only contains pre-processed face-removed images. Our method can generate high-resolution images and maintain the posture of the original face. It can be used for identity-independent computer vision tasks. Experiments further proves the effectiveness of our anonymization framework.
Query-oriented entity spatial-temporal summarization in fuzzy knowledge graph

Knowledge Graph (KG) is a relatively new concept that has garnered a lot of attention. Furthermore, the information in KG is frequently ambiguous and imprecise, necessitating the creation of a Fuzzy Knowledge Graph (FKG). FKG describes the imprecise information of the entity by employing the fuzzy value of predicates or objects. Entity summarization can extract the most concise and important information from lengthy descriptions of an entity. Existing work, however, focuses solely on entity summarization in KG while ignoring the fuzziness of entity relationships in FKG. Thus, this paper proposed an FFCA-based approach for query-oriented entity spatial-temporal summarization. Fuzzy Formal Concept Analysis (FFCA) is used to turn the FKG into the regular KG initially. The summarized RDF triples can then be obtained by combining the time-centric and location-centric tri-adic concepts from diverse FKGs. Finally, various template-based queries are designed for evaluating the performance of the proposed approach.
Applying spectral tools in a mixed siliciclastic-carbonate environment: An integrative sedimentological case study from the Estiva Formation, Pernambuco Basin, Northeast Brazil

The aim of this work is to present a case study of the application of reflectance and imaging spectroscopy, integrated with petrographic data from core samples of the late Albian-Turonian carbonate environment from the Pernambuco Basin, northeast Brazil. We discuss the main application of this technique and their respective limitations in sedimentological and stratigraphical descriptions. Four lithofacies were described, namely: dolostones, shales, marls, and sandstones, as well as late calcite veins, filling the dolostone lithofacies. The point spectral characterization (ASD-FieldSpec), in the SWIR range, allowed the individualization of some lithologies based on the main absorption features, such as dolostones (∼2318–2320 nm), shales (∼2210 nm), marls (absorption features at ∼2210 and 2318–2336 nm) and the calcite vein (2340 nm). The sandstone lithofacies show an absorption feature at 2319 nm due to dolomite cement and at 2209–2220 nm (terrigenous input). The hyperspectral imaging (HySpex data – SWIR-384 camera) corroborated the textural and mineral classification of core samples, including the recognition of late clay filling and microbial mats and the compositional variation between dolomite and marl. The geological, petrographic, and spectral data integration made it possible to determine with a better accuracy a high-resolution description of the studied core samples. The innovative use of reflectance and imaging spectroscopy thus represents a useful tool for sedimentology studies and carbonate mineral prospecting.
Improved ALS Recommendation Algorithm Based on Interest-Value Model

Abstract In the era of big data, all kinds of information around, how to effectively get the information others want and achieve recommendation is the original purpose of recommendation algorithm, traditional recommendation has collaborative filtering, content-based, model-based recommendation algorithm. Matrix factorization recommendation algorithm solves the problems of data sparsity and low space utilization brought by dimensional explosion. ALS(Alternating Least Squares) algorithm is one of the matrix factorization and the only one recommendation algorithm implemented in Spark, which is in line with the computation method of big data distributed computing. However, in the traditional ALS, there are still some shortcomings: the determination of the number of Implicit features does not give a clear definition, the information factor is lost after matrix factorization, which leads to the poor interpretability of the decomposed matrix, and the performance parameters of the ALS model are not optimal in terms of performance. Based on the above shortcomings, the IVM-ALS(ALS Based on Interest-Value Model) based on interest-value is proposed, an interest-value model is introduced, a solution to solve the Implicit feature number is introduced, and the feasibility of the feature matrix of the subdivided item is enhanced, so as to meet the actual requirements., And improve the algorithm performance than traditional ALS.
Visual context embeddings for zero-shot recognition

Existing word-embeddings have performed well in various downstream tasks, but there may be a bias towards the text domain because they are learned from a text corpus. When word-embeddings are used in the Zero-Shot Recognition(ZSR) task, the task becomes a mapping problem between two completely different heterogeneous domains, a low-level visual feature domain, and a word embedding domain, and due to the bias of word-embeddings, it was not easy to learn this mapping function. However, if the context of the visual domain can be learned and embedded, the mapping function of ZSR will be much easier to converge because it only needs to learn the mapping between domains that are more correlated to each other. Therefore, in this paper, we propose a new methodology for embedding the context contained in the visual domain using the annotation information collected from the image dataset. In addition, to utilize the annotations collected from the image dataset for embedding, we proposed a new distance formula to measure the contextual distance between the bounding boxes of objects. Finally, it was verified through various experiments on two datasets that the embeddings learned by our new methodology performed well when applied to ZSR.
Application of multiple automated guided vehicle path planning algorithm in IoT smart warehouse system

In order to avoid the occurrence of Automated Guided Vehicle (AGV) collisions in the intelligent warehouse system and improve the cooperative efficiency of multiple AGVs in the intelligent warehouse system, an improved A* path planning algorithm is proposed considering the possible occurrence of collisions in the intersection of multiple AGVs. This article improves the traditional A* algorithm, dynamic mechanism and conflict resolution strategy are introduced, and the path and mode of AGV are optimized to improve operation efficiency. The experimental results show that the improved A* path planning algorithm can effectively resolve conflicts and shorten operation time.
Impact of Open Access on Library Collections and Collection Development Services

The growth of open access (OA) journals has been rapid and substantial. While still not the predominant form of journal publishing, OA journals of varying types have impacted the scholarly communication ecosystem in a profound way. Libraries and librarians have been at the front lines of this effort from the beginning, working with researchers, funders, and institutional administrators to bring out substantive change to the unsustainable models of costly dissemination of research. After over 20 years of progress in both the transition from print to online, as well as opening access to read, how have OA resources fit in with academic libraries' collections? Are OA resources currently considered part of a library's collection? If not, will they ever be? More broadly, what has been the impact of the open access movement and OA resources on library collections, the concept of library collections, and the practice of collection development? How has the impact of OA on collections differed between libraries in the Global North vs. the Global South?
A Method of Constructing Power System Dispatcher Personas Based on Convolutional Neural Network

Abstract The recommendation algorithm has been applied in many fields and achieved pretty results in recent years. The personas for a large number of dispatchers in the power system are an indispensable step in the process of intelligent recommendation. In order to recommend dispatchers of different levels according to different operation scenarios, the aim of dispatcher personas is the classification of business level. In this paper, we focused on constructing dispatcher personas of Chinese power system. Unlike of the previous research work, we used convolutional neural network(CNN) to create dispatcher personas. The contributions of this paper are reflected in the following aspects: 1. dataset were built automatically based on internal data; 2. a CNN text classifier were trained to construct the dispatcher personas; 3. the effectiveness of the model is proved by experiments on real data. The experimental results show that the method proposed in the paper is superior to the baseline method and the average accuracy is 96%.
Could jamstack be the future of web applications architecture?

The ubiquity of internet connections has made web applications one of the most widespread means of contents delivery. Indeed, very often they are preferred to applications run locally due to their flexibility, portability, maintainability, and so forth. However, the growth of web contents, and correspondingly of users, has raised critical issues that can be reduced to the required communications between clients and servers and to the consequent overload problems.
Vision for a secure Elixir ecosystem

Since its inception in 2011, Elixir has emerged as a popular programming language. Currently, Elixir is used in a diverse set of domains, such as instant messaging, smart farming, and e-commerce. Usage of Elixir in above-mentioned domains necessitates gaining an understanding of the state of vulnerabilities that are reported for Elixir programs. An empirical analysis of vulnerability-related commits, i.e., commits that indicate action taken to mitigate vulnerabilities, can help us understand how frequently vulnerabilities appear in Elixir programs. Such understanding can also be a starting point to integrate secure software development practices into the Elixir ecosystem. We conduct an empirical study where we mine 4,446 commits from 25 open source Elixir repositories from GitHub. Our findings show that (i) 2.0% of the 4,446 commits are vulnerability-related, (ii) 18.0% of the 1,769 Elixir programs in our dataset are modified in vulnerability-related commits, and (iii) the proportion of vulnerability-related commits is highest in 2020. Despite Elixir being perceived as a 'safe' language, our empirical study shows programs written in Elixir to contain vulnerabilities. Based on our findings, we recommend researchers to investigate the root causes of introducing vulnerabilities in Elixir programs.
Neural network quantization algorithms based on weights and activation values

In recent years, neural networks have demonstrated attractive performance in a variety of computer vision tasks. This has led to the deployment of CNNs into real-world applications. However, most existing CNNs require a large amount of storage space and computational resources, which hinders their deployment on mobile devices. In order to solve this problem, the existing research shows that quantization and pruning can greatly reduce the memory occupied by the neural network in the device without reducing the accuracy of image recognition, which plays a great role in solving the deployment problem of CNNs. Therefore, this paper classifies and analyzes the quantitative algorithm model of neural network according to the parameters that need to be quantified, summarizes the common defects of the current quantitative algorithm in terms of accuracy, and looks forward to its future development trend.
Collaborative is better than adversarial

Topic modeling is a popular technique for learning the thematic structure of large corpora composed of unlabeled documents, without human supervision. In recent years, various neural network-based algorithms have been proposed to solve this task. In particular, there is an extensive literature showing how Variational AutoEncoders (VAEs) and Generative Adversarial Networks (GANs) approaches have been successful in identifying recurrent discussion topics. In this paper we propose a new neural topic detection model called Generative Cooperative Topic Modeling (GCTM), in which a Generator and a denoising AutoEncoder, rather than learning through a competitive process, act cooperatively. We show that this cooperative model has a faster convergence and surpasses the adversarial approach, as well as other popular topic detection algorithms based on VAEs, when tested on three common public datasets and with a variety of performance indicators.
The Application of Artificial Intelligence Technology in the Asset Management of Start-Ups in the Context of Deep Learning

With the coninuous improvement and development of artificial intelligence (AI) technology, this technology has been used in the asset management of companies. To improve the asset management level of Chinese start-ups, firstly, back-propagation neural network (BPNN) has been studied in depth, and an evaluation system of the company's asset quality has been established. Secondly, the BPNN is integrated with the evaluation indicators of asset quality, and an evaluation model of asset quality based on BPNN is constructed. Next, start-up A is taken as the experimental object; the evaluation score of the asset quality of A company is input into the model, which proves that there is still a certain gap between the asset management level of start-ups and mature companies. Finally, to find out the problems of the company's asset quality, the traditional financial analysis method is used to carry out a specific microanalysis of the evaluation indicators of its asset quality. In view of the existing problems, suggestions are put forward for prudent investment, improve inventory operation efficiency, increase investment in R&D and innovation, improve the quality of sales outlets, and increase the proportion of high-quality intangible assets. The asset quality evaluation system for start-ups established here includes 19 evaluation indicators. The BPNN-based asset quality evaluation model selects 5 mature companies in the same industry as sample companies. The scores of the evaluation indicators of asset quality of the 5 sample companies in the past three years are normalized and input into the model. The model contains 19 nodes of the input layer, 39 nodes of the hidden layer, and 1 node of the output layer. The target error rate is 0.001, the learning rate is 0.1, the number of training times is 1000, and the training function is the trainlm function. This research has a certain reference for the application of AI technology in the asset management of start-ups.
An improved fingerprint matching algorithm to detect malware encrypted traffic based on weighted Bayes

Traffic security is one of the important topic in cyber security of smart city. With the widespread use of encrypted traffic, more and more malware prefers to use encrypted traffic to transmit malicious information. Since the transmission content is not visible, the traditional detection method based on deep packet inspection is not effective anymore. In this paper, by analyzing the protocol and the sessions of malicious encrypted traffic and benign traffic, a weight naive Bayes-based method for detecting malware encrypted traffic and classifying malware family is proposed. The method construct a hybrid fingerprint for each malware family traffic and benign traffic. First a hybrid fingerprint-based identification is performed to distinguish between malware families and benign applications. Second, a feature generalization method is adopted to improve the robustness of the fingerprint. Finally, for indistinguishable fingerprints, the target host information characteristic, combined weighted Bayesian is used to distinguish different benign applications and malicious families.
Power equipment indicator status detection algorithm based on the improved YOLOv4 and HSV color space

This paper proposes a power equipment indicator status detection algorithm based on the improved YOLOv4 and HSV color space. Firstly, the image of power meter is preprocessed by denoising and histogram equalization. Secondly, the PSA attention mechanism module is added to the network layer of YOLOv4 to improve its power of small target detection. The improved YOLOv4 is used to detect the indicator lights in the image, the color of the detected indicator light is determined through the HSV space. Experimental results verify that the proposed algorithm has better performance than several existed algorithms.