Reviewerly/exHarmony huggingface dataset:
Enhancing Matrix Factorization-based Recommender Systems via Graph Neural Networks

Due to the serious information overload problem caused by the rapid development of the Internet, recommender system (RS) has been one of the most concerned technologies in the past decade. Accompanied with the prevalence of social networks, social information is usually introduced into RS to pursue higher recommendation efficiency, yielding the research of social recommendations (SoR). Almost all of existing researches of SoR just consider the influence of social relationships, yet ignoring the fact that correlations exist among item attributes and will certainly influence social choices. Therefore, this work introduces the graph neural networks to enhance matrix factorization-based recommender systems. And the proposal in this work is named GNN-MF for short. The user subspace and item subspace in matrix factorization are represented with the use of deep neural networks, in which parameters are learned by back propagation. The experiments well prove efficiency of the GNN-MF.
A New Method of Identifying Graphite Based on Neural Network

The unique physical properties of graphite enable it to be applied in various fields of the national economy and people’s livelihood, which has very important industrial value. Many countries have listed graphite as a key mineral. To promote the transformation of the mining industry to informatization and intelligence, the realization of the intelligent recognition of graphite is particularly critical. Aiming at the problems of long time and low efficiency in manually identifying graphite, an improved AlexNet convolution neural network is proposed for graphite image recognition. First, we perform image preprocessing on the data set by means of random cropping, horizontal flipping according to probability, and normalization processing to achieve the purpose of data enhancement. Then we use the activation function ReLU6 to compress the dynamic range to make the algorithm more robust, using the batch standardization algorithm for normalization to speed up the convergence speed, modifying the size of the convolution kernel to enhance the generalization ability, and adding dropout regularization to the fully connected layer to further prevent overfitting. Finally, in the simulation experiment, compared with the existing method, the given method reduces the loss value and improves the average accuracy of identifying graphite.
Shape optimization of underwater robots based on resistance reduction

If the robot wants to improve its work efficiency, it needs to minimize its resistance. A good shell shape can reduce its resistance at a certain extent. For the under water robot designed in this paper, under the condition that the water flow velocity is 0.5m/s and the elevation angle is 0°, the shape of the robot is constructed by three kinds of curves. And three kinds of robot shapes are obtained. These three kinds of curves are ordinary spline, Bezier curve and B-spline curve. The resistances of the three kinds of robots are calculated separately. The result shows that the shape of robot is constructed by B-spline curve design is the resistance least. To further study the B-spline curve, the robot model is constructed with the 3rd B-spline curve, the 4th B-spline curve and the 5th B-spline curve, separately. After calculation and comparison, it is found that the robot constructed by the 3rd B-spline curve is the resistance least, and finally the robot shape is constructed by the 3rd B-spline curve.
AORN Position Statement on the Value of the Perioperative Nurse Educator

AORN JournalVolume 114, Issue 4 p. 378-379 From AORN AORN Position Statement on the Value of the Perioperative Nurse Educator First published: 29 September 2021 https://doi.org/10.1002/aorn.13532Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume114, Issue4Special Focus Issue: Quality ImprovementOctober 2021Pages 378-379 RelatedInformation
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort

Large amounts of annotated data have become more important than ever, especially since the rise of deep learning techniques. However, manual annotations are costly. We propose a tool that enables researchers to create large, high-quality, annotated datasets with only a few manual annotations, thus strongly reducing annotation cost and effort. For this purpose, we combine an active learning (AL) approach with a pre-trained language model to semi-automatically identify annotation categories in the given text documents. To highlight our research direction's potential, we evaluate the approach on the task of identifying frames in news articles. Our preliminary results show that employing AL strongly reduces the number of annotations for correct classification of even these complex and subtle frames. On the framing dataset, the AL approach needs only 16.3% of the annotations to reach the same performance as a model trained on the full dataset.
Research on software service risk uncertainty calculation method based on information entropy

Software is the soul of computer system. The risk faced by software service directly determines the trustworthiness of software system. Taking the information entropy theory as the main method, this paper proposes a software service risk uncertainty calculation method based on information entropy. By calculating the entropy weight coefficient of each risk component factor, the uncertainty of software service risk is calculated. The research findings of this paper will provide theoretical and methodological support for the comprehensive evaluation of software service risk and the measurement of software service trustworthiness.
Finite Generation and Presentation Problems for Lambda Calculus and Combinatory Logic

We solve several finite generation and presentation problems for lambda calculus and combinatory logic.
Improved Compression for Word Embeddings by Scaling Principal Components

Word embeddings have been adopted as a fundamental component of many natural language processing applications for their ability to capture meaningful semantic relationships. However they often present a significant computational bottleneck due to memory requirements. In this article we present a postprocessing technique for embeddings, based on modifying their principal components, that enables compression while maintaining comparable if not better performance relative to the original embedding. Specifically, our technique can reduce the overall memory footprint of popular embeddings such as GloVe and word2vec by 50% while maintaining the same performance on different metrics, including commonly used similarity and analogy tasks as well as on end-to-end tasks such as text classification. Compared to the original embeddings and previous postprocessing methods, this approach improves accuracy on these tasks and leads to better semantic vector representations particularly when using compressed versions of these vectors for memory and performance savings. While compressing the original vectors is possible, this approach outperforms these as well as other postprocessing methods across a range of compressed sizes.
TASK APPROACH IN THE SYSTEM OF METHODICAL TRAINING OF INFORMATICS TEACHERS

The classifications of pedagogical tasks are analyzed, and revealed the peculiarities of their use in the system of methodical training of future teachers. The methodical task in which the pedagogical situation in the plane of studying of a subject is modeled is allocated. The expediency of professional training of future computer science teachers with the use of educational and methodical tasks at all stages of higher education is substantiated, examples of such tasks are considered and the stages of their solution are generalized.
On intersection property of Reed – Muller like codes

A self-complementary code having parameters of the binary linear Reed – Muller code is called a Reed – Muller like code. It is denoted $LRM_{r,m}$ (briefly LRM code if we do not specify parameters). The class of such codes contains a rich family of LRM codes obtained by the Pulatov construction. The intersection problem of such codes is investigated in the paper. We establish the existence of two LRM codes of order r having length 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> with intersection of size $2k_{1}k_{2},1\leq k_{s}\leq|RM_{r-1,m-1}|,s=1,2$ for any $m, m\geq 4$.
3D reconstruction of HDR surfaces with adaptive speckle projection

Speckle projection provides rich textures for correlation, and has been widely used in three-dimensional(3D) reconstruction. However, for objects with high dynamic range(HDR) surface, conventional uniform speckle projection usually causes over-exposure and over-dark regions simultaneously in the captured images, leading to miss-match and errors in 3D result. It is difficult to perform defect elimination via adjustment of speckle intensity globally or the camera exposure time. To tackle this problem, this paper proposed a novel adaptive speckle projection method to distinguish the appropriate projection intensity of specific parts in the speckle pattern, thereby avoiding over-exposure, while the dark regions not being affected. First, uniform intensity patterns of multiple gray-levels are projected onto the surface of testing object, the appropriate projection intensity at each pixel position in the camera coordinate system is calculated, and the saturated area in the captured image is marked. Then, a set of orthogonal fringe patterns are projected onto the testing object to establish the coordinates mapping relationship between the camera and the projection system, and the adaptive speckle pattern under the projection coordinate system is generated. Finally, the generated adaptive speckle pattern is used to scan the testing object, and the spatial-temporal correlation algorithm is used for 3D shape retrieval. Experimental results demonstrate feasibility of 3D shape reconstruction of HDR surfaces with the proposed method, and obvious advantages compared with the traditional methods in terms of reconstruction completion and measurement accuracy. Keywords: adaptive speckle, high dynamic range, coordinates mapping, spatial-temporal correlation, 3D reconstruction
Online Course on Soft Skills for Global Engineers

Soft skills in engineering jobs are critical to success as 21st century engineers live in a «global village», work in international teams and communicate with colleagues from different countries. Moreover, engineers have to present their projects in a clear way to raise money and to disseminate the results of their work. The authors describe the structure and content of an online course on soft skills for future engineers and provide the results of its piloting of 2020/2021 academic year. The feedback survey of 24 Bachelor students of engineering specialties show that the students have significantly improved their speaking and writing skills, have mastered different formats of presenting information and have raised their awareness in cross-cultural communication. The findings can be useful to design and customize online courses in soft skills for future engineers studying at corporate and tertiary education universities.
The study of correlation and spectral characteristics of human brain activity while performing cognitive tasks

In this paper, within the framework of Memory Function Formalism, one of the analysis methods in statistical physics, we study bioelectric activity features of the brain for two groups of people. The first group consisted of 10 artists with a Master of Arts degree, the second was represented by 10 people without any art education. The subjects performed the tasks of visual perception and mental image. As a result of autocorrelation analysis, we identify regions of the cerebral cortex in the activity of which the most significant differences were manifested. We observe features of electroencephalogram spectral parameters for two groups of subjects. As a result, we show that the use of statistical analysis methods allows detecting the difference in the cognitive abilities of people when performing various cognitive tasks.
The Teacher’s Competencies Necessary for the Implementation of Modern Digital and Pedagogical Technologies in Art Education

The chapter of the collective monograph is devoted to the teacher's competencies, which allow him to use modern digital and pedagogical technologies in the educational process. The article deals with the problem of changing the way of interaction between a student and a teacher under the influence of changes in the information environment and information processing technology, as well as digital and project competencies of a teacher and their relevance at the present stage of society development. The most popular digital and project competencies of a teacher have been identified using the example of teaching students and schoolchildren, both in general and in additional art education.
DeepMark: Embedding Watermarks into Deep Neural Network Using Pruning

With the rapid development of artificial intelligence in recent years, the deep neural network model has been used in many fields such as speech and images due to its excellent performance, and has achieved remarkable results. As we all know, training a deep model requires a lot of time and resources. But these trained deep learning models are very easy to be copied and diffused. Therefore, the protection of intellectual property rights of the model has gradually attracted people’s attention. A series of algorithms or technologies came into being, and one of them is model watermarking technology. Model watermarks can function like digital watermarks. Once the model is stolen, watermarks can prove the copyright of model by verifying the watermarks, maintain its intellectual property rights, and protect the model. This paper proposes a model watermark generation method based on pruning. Where to prune is selected by the calculation result of connection sensitivity, and then the information is embedded by pruning. Compared with the four proposed model watermarking methods, our method has higher fidelity and reliability. Experiments show that our watermarking method is robust against fine-tuning and weight pruning.
HUMAN RESOURCES AVAILABILITY IN GLAM DEVELOPMENT AT INDONESIA INSTITUTE OF SCIENCES (LIPI)

The collections of galleries, libraries, archives and museums (GLAM) are still scattered in the work units at LIPI. To manage these collections, adequate human resources, both quantitative and qualitative, are needed. When this research was conducted, there was a socialization activity to collaborate the GLAM management among work units at LIPI. This study aims to determine human resources available in developing GLAM at LIPI. The research method used was qualitative research. Analysis of data in qualitative research was carried out narratively. The research results show that human resources for gallery/museum exhibition at LIPI have been managed by the Center for the Utilization and Innovation of Science and Technology (PPII)-LIPI in collaboration with third parties since 2020. There were 49 librarians (37 expert librarians and 12 skilled librarians), while the number of archivists was 141 (one hundred and forty-one) people (83 Expert Archivists and 58 Skilled Archivists). The collaboration of PPII-LIPI with third parties has carried out human resources for museum management at LIPI since 2020. To improve the quality, human resources managing GLAM at LIPI need to be continuously improved, both through formal and non-formal education. The number of librarians and archivists are sufficient, but they must improve their abilities, considering that the GLAM institution at LIPI will just be developed. Of course, this GLAM is still perceived as a new institution by librarians and archivists at LIPI. It is recommended that during the development of the GLAM, gallery and museum management can be carried out by LIPI employees who have competence in galleries and museums.
IncrAMLSI: Incremental Learning of Accurate Planning Domains from Partial and Noisy Observations

Hand-encoding PDDL domains is generally considered difficult, tedious and error-prone. Many different machine learning techniques have been proposed to deal with these issues. Recent novel approaches such as AMLSI (Action Model Learning with State Machine Interactions) achieve high level of accuracy, i.e. the learnt domains are accurate enough to solve planning problems without human proofreading. However, in most of the real world applications, training datasets are difficult and costly to acquire and become available gradually over time. To tackle this issue, we present IncrAMLSI, which is a version of AMLSI for incremental training datasets. We show that IncrAMLSI outperforms AMLSI to learn IPC benchmarks and converges with few learning iterations from partial and noisy data.
The key information extraction technology of electronic documents using deep learning and image recognition technology

With the rapid development of Internet information technology, the application of electronic bidding has provided convenience for enterprises, and at the same time has higher requirements for the diversity and timeliness of the channels for enterprises to obtain bidding information. This paper uses web crawler technology to obtain Internet public bidding data, obtains samples through manual annotation, builds a multi-task learning model based on the pre-training model ALBERT to complete Fine-tuning, and achieves project classification and key information extraction at the same time. Experiments show that the program has a certain improvement in various evaluation indicators, and has achieved good experimental results.
Artificial Face Sketch Drawing Categorization

Roy, Hiranmoy Dhar, Soumyadip Hazra, Reema Dey, Rija Kirtania, Riya Sen, SumedhaThis paper proposes a novel face sketch drawing categorization system. Usually, judges or experts are required to categorize the face sketch drawings. Here first time, we have tried to give an artificial measure for the categorization of face sketch drawings. We have measured the different facial landmark points to get some ratio measures which are very important for judging the quality of the face sketch drawn by an artist. Then, a weighted average measure is calculated to give emphasize some ratios. Finally, an error range is proposed to measure the final quality (good or bad) of the face sketch drawing. We have tested our results on CUFS and CUFSF sketch-photo databases to justify our proposed technique.
Proactive, Correlation Based Anomaly Detection at the Edge

Data management at the edge of the network is a significant research subject. Devices being active at the Internet of Things (IoT) can collect data and transfer them to a set of edge nodes for further processing. There, various activities can be realized. Among them, of great importance it is the detection of anomalies in the incoming data and their preparation to be the subject of advanced processing tasks. In this paper, we propose an ensemble scheme for data anomalies detection and elaborate on the use of an extended sliding window approach. We differentiate from the state of the art solutions and argue on the concept of potential anomalies confirming their presence by incorporating more data into our decision mechanism. The performance of the proposed scheme is evaluated by a set of experimental scenarios being also exposed by numerical results.
The review of color balance method for UAV image By COMPUTER technology

With the rapid development of unmanned aerial vehicles (UAV) measurement technology, Practitioners have used it to survey large-scale areas with complex terrain. However, the images obtained by UAV will be produced color difference duo to the shooting environment, produced glare duo to the light shines directly on the lens, etc. This paper on the one hand reviews and compares the classic color balance methods of brightness equalization currently; On the other hands gives a detailed explanation and effect of the application of each method..
Combining Self-supervised Learning and Active Learning for Disfluency Detection

Spoken language is fundamentally different from the written language in that it contains frequent disfluencies or parts of an utterance that are corrected by the speaker. Disfluency detection (removing these disfluencies) is desirable to clean the input for use in downstream NLP tasks. Most existing approaches to disfluency detection heavily rely on human-annotated data, which is scarce and expensive to obtain in practice. To tackle the training data bottleneck, in this work, we investigate methods for combining self-supervised learning and active learning for disfluency detection. First, we construct large-scale pseudo training data by randomly adding or deleting words from unlabeled data and propose two self-supervised pre-training tasks: (i) a tagging task to detect the added noisy words and (ii) sentence classification to distinguish original sentences from grammatically incorrect sentences. We then combine these two tasks to jointly pre-train a neural network. The pre-trained neural network is then fine-tuned using human-annotated disfluency detection training data. The self-supervised learning method can capture task-special knowledge for disfluency detection and achieve better performance when fine-tuning on a small annotated dataset compared to other supervised methods. However, limited in that the pseudo training data are generated based on simple heuristics and cannot fully cover all the disfluency patterns, there is still a performance gap compared to the supervised models trained on the full training dataset. We further explore how to bridge the performance gap by integrating active learning during the fine-tuning process. Active learning strives to reduce annotation costs by choosing the most critical examples to label and can address the weakness of self-supervised learning with a small annotated dataset. We show that by combining self-supervised learning with active learning, our model is able to match state-of-the-art performance with just about 10% of the original training data on both the commonly used English Switchboard test set and a set of in-house annotated Chinese data.
Project-Based Learning as an Integrated Component of the Educational Process at Technical University

The authors of the paper describe three stages of the technological process of implementing project-based learning into the educational process at technical universities. The main problems arising at the stage of integration and which make it difficult to create mobile groups for the introduction of a new teaching method into the educational process from structural units at different hierarchical levels of subordination are thoroughly examined. Some software products which are necessary for the implementation of project-based learning are considered in the paper. It is emphasized that without mastering the universal tools used for storing and processing data, students and university teachers will not be able to implement training projects.