Reviewerly/exHarmony huggingface dataset:
Hybrid session-aware recommendation with feature-based models

Abstract Session-based recommender systems model the interests of users based on their browsing behavior with the goal of making suitable item suggestions in an ongoing usage session. Most existing work in this growing research area make only use of the most recent observed interactions for each user, and they typically solely rely on user–item interaction data (e.g., click events) for interest modeling. Thus, they do not leverage important forms of other information which are commonly available in practical settings. In this work, we therefore propose a hybrid approach for personalized session-based ( “session-aware” ) recommendation, which (i) is able to take into account various types of side information as model features and which (ii) can be combined with existing session-based (or session-aware) recommendation models. Technically, our approach is based on stacking several session-based modeling approaches with efficient machine learning methods for tabular data, in our case using Gradient Boosting Machines (GBMs). We successfully evaluated our approach (named HySAR ) on two public e-commerce datasets. Specifically, we also demonstrate the effectiveness of a number of novel model features that we engineered in the course of this research. These features, which were mostly unexplored in previous works, relate to various types of information related to the users, their actions, the items, as well as contextual session characteristics. Different existing recommendation approaches and further problem specific features can be easily added in our generic method to improve recommendations.
An AI-based English education platform during the COVID-19 pandemic

Abstract This study examines whether the use of AI-Pengtalk, an AI-based Conversational English programme, provided by a broadcasting company (EBS) that specializes in public education can significantly improve conversational English skills and bridge the English language proficiency gap associated with parental socioeconomic status. Over the course of four weeks from April 27 to May 22 in 2020, 108 fourth-grade classes in 54 elementary schools voluntarily participated in this experiment. Two classes in each school were designated as a treatment group and a control group. For the treatment group, a tablet installed with a pilot version of AI-Pengtalk was provided and students were encouraged to make use of the programme. Two sets of surveys and English tests were placed pre and post hoc. After 4 weeks, test scores, log files, and survey responses of participants were analysed. A series of DID analyses demonstrate that the use of AI-Pengtalk improves the treatment group’s self-evaluation of their English abilities, confidence in using English, preference on English itself, and amount of time spent on studying English during the pilot experimental period compared to the control group. When other variables were controlled, the use of AI-Pengtalk also helped the treatment group achieve higher test scores. This study implicates that the use of smart English education like AI-Pengtalk may especially be able to better compensate for academic setbacks caused by low parental SES or, in the case of English learning, the reluctance to converse in English with other students.
Could You Evaluate Sounds in a Virtual Environment? Evaluation Components of Auditory Experience in a Metaverse Environment

The study aims to develop an auditory experience evaluation questionnaire to improve metaverse environments’ presence and derive evaluation components considering the acoustic and auditory user experience (AUX) through a survey. After conducting a survey with a total of 232 participants, five evaluation components were extracted from auditory presence and AUX evaluation factors through principal component analysis (PCA) and reliability analysis (RA): ‘realistic auditory background’, ‘acoustic aesthetics’, ‘consideration of acoustic control and accessibility’, ‘auditory utility and minimalist design’, and ‘auditory consistency’. In particular, although AUX evaluation factors such as ‘ease of access to sound control’ have limitations in improving the sense of presence, negative factors of presence such as ‘distraction due to sound’ can be improved by utilizing AUX evaluation factors, so it is judged that the sense of presence in the metaverse environments can be improved by enhancing the auditory sense of presence and AUX evaluation factors according to the composition of the five evaluation components derived in the study. The study can be used as a basis for developing an auditory experience evaluation questionnaire for the metaverse platform, creating sound design guidelines, and identifying sound development priorities.
Dynamic Depth Learning in Stacked AutoEncoders

The effectiveness of deep learning models depends on their architecture and topology. Thus, it is essential to determine the optimal depth of the network. In this paper, we propose a novel approach to learn the optimal depth of a stacked AutoEncoder, called Dynamic Depth for Stacked AutoEncoders (DDSAE). DDSAE learns in an unsupervised manner the depth of a stacked AutoEncoder while training the network model. Specifically, we propose a novel objective function, aside from the AutoEncoder’s loss function to optimize the network depth: The optimization of the objective function determines the layers’ relevance weights. Additionally, we propose an algorithm that iteratively prunes the irrelevant layers based on the learned relevance weights. The performance of DDSAE was assessed using benchmark and real datasets.
CMKG: Construction Method of Knowledge Graph for Image Recognition

With the continuous development of artificial intelligence technology and the exponential growth in the number of images, image detection and recognition technology is becoming more widely used. Image knowledge management is extremely urgent. The data source of a knowledge graph is not only the text and structured data but also the visual or auditory data such as images, video, and audio. How to use multimodal information to build an information management platform is a difficult problem. In this paper, a method is proposed to construct the result of image recognition as a knowledge graph. First of all, based on the improvement in the BlendMASK algorithm, the hollow convolution kernel is added. Secondly, the effect of image recognition and the relationships between all kinds of information are analyzed. Finally, the image knowledge graph is constructed by using the relationship between the image entities. The contributions of this paper are as follows. (1) The hollow convolution kernel is added to reduce the loss from extracting feature information from high-level feature images. (2) In this paper, a method is proposed to determine the relationship between entities by dividing the recognition results of entities in an image with a threshold, which makes it possible for the relationships between images to be interconnected. The experimental results show that this method improves the accuracy and F1 value of the image recognition algorithm. At the same time, the method achieves integrity in the construction of a multimodal knowledge graph.
Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification

In spite of recent rapid developments across various computer vision domains, numerous cutting-edge deep learning algorithms often demand a substantial volume of data to operate effectively. Within this research, a novel few-shot learning approach is presented with the objective of enhancing the accuracy of few-shot image classification. This task entails the classification of unlabeled query samples based on a limited set of labeled support examples. Specifically, the integration of the edge-conditioned graph neural network (EGNN) framework with hierarchical node residual connections is proposed. The primary aim is to enhance the performance of graph neural networks when applied to few-shot classification, a rather unconventional application of hierarchical node residual structures in few-shot image classification tasks. It is noteworthy that this work represents an innovative attempt to combine these two techniques. Extensive experimental findings on publicly available datasets demonstrate that the methodology surpasses the original EGNN algorithm, achieving a maximum improvement of 2.7%. Particularly significant is the performance gain observed on our custom-built dataset, CBAC (Car Brand Appearance Classification), which consistently outperforms the original method, reaching an impressive peak improvement of 11.14%.
An EM algorithm for estimating the parameters of the skew generalized <i>t</i> -normal distribution with application to robust finite mixture modeling

AbstractThe present article describes an EM-type algorithm for estimation of the skew generalized t-normal (SGTN) distribution. The family of SGTN distributions can provide certain types of flexibility such as heavy tails and high kurtosis. The complexity of the SGTN distribution is traced to the ratio of the t density and distribution function of a normal distribution in the likelihood equations. To cope with this problem, we develop a feasible ECME algorithm for computing maximum likelihood estimates of model parameters via a selection mechanism. The proposed approach provides a robust parameter estimation method for the finite mixture model. Standard errors for the parameter estimates can be obtained via a general information-based method. Experimental results on simulated data and one real data example demonstrate the efficacy and usefulness of the proposed methodology.Keywords: ECME algorithmFinite mixture modelGeneralized t-normal distributionTruncated normal distribution AcknowledgmentsThe authors are grateful to the Co-Editors, the Associate Editor, and the anonymous referees for their valuable comments and constructive suggestions which had improved the content of this article greatly.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingA.F. Desmond acknowledges the support of NSERC Canada under Discovery Grant no. 04537.
Adoption of Blockchain Technology in Indonesian MSME Supply Chain Management (SCM).

MSMEs are the economic driving sector, but at this time there are many fraud in this sector. The main problem of MSMEs is Supply Chain Management. Blockchain technology is believed to be a solution. The purpose of this study is to determine the adoption of blockchain in the supply chain management for Indonesian MSMEs. This research was conducted on 193 Indonesian MSMEs. Data was collected by distributing online questionnaires. The method used is Structural Equation Modeling (SEM). Data is processed using Smart PLS. The results of this study are competitive pressure significantly affect behavior in intention, complexity has no significant effect on behavioral intention, complexity has a significant effect on the cost, complexity has a significant effect on upper management support, cost has no significant effect on behavioral intention, market dynamics has no significant effect on behavioral intention, Regulatory Support does not significantly effect on behavioral intention, relative advantage has a significant effect on behavioral intention, relative advantage has a significant effect on the cost, relative advantage significantly influences upper management support, and upper management support significantly affect behavior intention. From the results of this study, the results obtained that MSMEs believe the blockchain can facilitate supply chain but there must be a clear legal support and cost to adopt the blockchain.
Scientific portal Geologyscience.ru: current state and prospects of development

The paper discusses the current state of the Portal "Information and analytical environment to support scientific research in geology" (http://geologyscience.ru), as well as a number of its future prospects related to the development of an intelligent digital space of geological knowledge. It is assumed that this space will help to form new processes for generating geological data and knowledge.
A multi-manifold learning based instance weighting and under-sampling for imbalanced data classification problems

Abstract Under-sampling is a technique to overcome imbalanced class problem, however, selecting the instances to be dropped and measuring their informativeness is an important concern. This paper tries to bring up a new point of view in this regard and exploit the structure of data to decide on the importance of the data points. For this purpose, a multi-manifold learning approach is proposed. Manifolds represent the underlying structures of data and can help extract the latent space for data distribution. However, there is no evidence that we can rely on a single manifold to extract the local neighborhood of the dataset. Therefore, this paper proposes an ensemble of manifold learning approaches and evaluates each manifold based on an information loss-based heuristic. Having computed the optimality score of each manifold, the centrality and marginality degrees of samples are computed on the manifolds and weighted by the corresponding score. A gradual elimination approach is proposed, which tries to balance the classes while avoiding a drop in the F measure on the validation dataset. The proposed method is evaluated on 22 imbalanced datasets from the KEEL and UCI repositories with different classification measures. The results of the experiments demonstrate that the proposed approach is more effective than other similar approaches and is far better than the previous approaches, especially when the imbalance ratio is very high.
Automated Platelet Counter with Detection Using K-Means Clustering

Platelet is a blood cell type that is stored and circulated in the human body. It acts as a blood thickening agent and prevents blood from overflowing whenever bleeding occurs. An excessive or inadequate number of platelets could lead to platelet-related diseases. The current practice of platelet counting involves the manual counting process using a haemocytometer, Wright’s Stain which uses the dyes to facilitate the differentiation of blood cell types, and a tally counter. Yet, this process can be time-consuming, demanding, and exhausting for haematologists, and likely to be prone to errors. Thus, this paper presents a study on automated platelet counter and detection using image processing techniques. The K-Means Clustering was employed to count and detect the presence of platelets in microscopic blood smear images. Several processes were performed prior to the K-means clustering, including image enhancement and YCbCr image formatting. Subsequently, image masking, as well as area thresholding were applied to eliminate every unwanted entity and highlight the visibility of the platelets before the number of platelets could be detected and counted. A comparative experiment was designed in which the K-Means Clustering platelet count and detection were compared with the actual number of platelets reported by haematologists. The platelet counts and detection were categorized into three detection categories which are Less Detection (LD), Accurate Detection (AD), and Over Detection (OD). The proposed study was evaluated to 90 testing platelet images. Out of the 90 testing images, 75 platelet images were perfectly counted and detected which returned 91.67% of accuracy. This signifies that the K-Means Clustering algorithm was discovered to be efficient and dependable for automated platelet counter and detection
Development of a Wearable Sensor Glove for Real-Time Sign Language Translation

This article describes the development of a wearable sensor glove for sign language translation and an Android-based application that can display words and produce speech of the translated gestures in real-time. The objective of this project is to enable a conversation between a deaf person and another person who does not know sign language. The glove is composed of five (5) flexible sensors and an inertial sensor. This article also elaborates the development of an Android-based application using the MIT App Inventor software that produces words and speech of the translated gestures in real-time. The sign language gestures were measured by sensors and transmitted to an Arduino Nano microcontroller to be translated into words. Then, the processed data was transmitted to the Android application via Bluetooth. The application displayed the words and produced the sound of the gesture. Furthermore, preliminary experimental results demonstrated that the glove successfully displayed words and produced the sound of thirteen (13) translated sign languages via the developed application. In the future, it is hoped that further upgrades can produce a device to assist a deaf person communicates with normal people without over-reliance on sign language interpreters.
Engineering games as a means of polytechnical education for junior schoolchildren

The article describes the possibilities of using the format of engineering games in the process of polytechnic education of junior schoolchildren. Engineering games are a team game at stations, at each of which children perform tasks aimed at developing graphomotor and drawing skills; spatial representations; ability to work with various materials and designers; ability to create structures according to a sample, technical drawing, diagram, drawing, condition. topic; the ability to supplement, transform a model according to given conditions, etc. Tasks can be performed either individually or by a group (team). The article provides examples of engineering games tasks that are used in their work by the Research Laboratory of Engineering and Polytechnic Education "CONSTRUCTORiUM" of Omsk State Pedagogical University.
Formation of functional literacy in informatics lessons when studying Programming at the level of basic general education

The article describes aspects of the methodology for teaching programming through the compilation of algorithms and programs for information processes that are in demand in the modern world of information technology. For example, programming the process of user authorization in the system using login and password, creating a group chat, a text bot that supports conversation — all these tasks are perceived by students as familiar. As a result, students' motivation and interest in learning programming increases and they are willing to solve these problems, mastering the basic algorithmic constructs of the Python programming language. This approach helps to better understand the work of a programmer in creating and developing programs, contributes to the development of functional literacy of students, as well as effective interaction between teachers and the younger generation in modern conditions of informatization and digitalization.
Refraction error analysis in stereo vision for system parameters optimization

The traditional stereo vision system with an air-based imaging model is unsuitable for multi-layer imaging conditions, where refraction effects can introduce significant errors in the measurement outcomes. Although numerous theoretical studies have developed refraction correction algorithms, it may be more beneficial and desirable to reduce the error by optimizing the system parameters in some cases. To address this dilemma, in this study, we first build a three-layer stereo vision model by forward projection and backward reconstruction. Then, the impact of various factors on the errors of reconstructed coordinates in the conditions of air-glass-air and air-glass-water is demonstrated through simulations. Real experiments are further carried out to verify the validity of the simulation. Finally, a practical guide for conducting experiments and tests is presented to assist in the design of more effective system parameters.
Intra-school control: system for evaluating subject outcomes in informatics (level of basic general education)

The article presents changed approaches to control and assessment activities in accordance with the requirements of the updated Federal State Educational Standard of Basic General Education. The structure of the system for assessing the educational achievements of students is presented, the main components of which are internal and external assessment procedures that cooperate with each other. A comparison was made of the subject results of informatics training as objects of assessment at the basic and advanced levels of basic general education. The existing approaches to assessment and the possibility of their use at various stages of educational activities in intra-school control are considered. The types and forms of assessment of subject results at different stages of educational activity are presented. Using specific examples of tasks, criteria, generalized plans for final control works, the difference in assessment materials for basic and advanced levels of informatics study is demonstrated.
Application of DJI robotics kit in general and additional education

In recent years, robotics has become increasingly popular in education. Learning the basics of robot control not only increases students' interest in informatics, but also allows them to form programming skills, develop logical thinking and digital literacy. Within the framework of the federal project "Modern School" of the national project "Education", the sites of pedagogical technoparks "Quantorium" are being formed in pedagogical universities. The material and technical base of the pedagogical "Quantorium" allows not only to prepare future teachers to include elements of robotics in the educational practice of the school, but also to organize classes for schoolchildren. The article describes the experience of conducting a class by students of the Yaroslavl State Pedagogical University named after K. D. Ushinsky dedicated to introducing schoolchildren of Yaroslavl schools to the DJI RoboMaster robot. DJI RoboMaster robot control allows students to realize the essence of the concepts of "formal performer", "algorithm", "program", "command system" in one lesson.
Enhanced quantum channel uncertainty relations by skew information

By revisiting the mathematical foundation of the uncertainty relation, skew information-based uncertainty sequences are developed for any two quantum channels. A reinforced version of the Cauchy-Schwarz inequality is adopted to improve the uncertainty relation, and a sampling technique of observables' coordinates is used to offset randomness in the inequality. It is shown that the lower bounds of the uncertainty relations are tighter than some previous studies.
Radar Emitter Structure Inversion Method Based on Metric and Deep Learning

With the rapid development of modern military countermeasure technology, deep distinguish hostile radar is essential in electronic warfare. However, traditional radio frequency (RF) feature extraction methods can easily be interfered by signal information and fail due to the lack of research on RF feature extraction techniques for complex situations. Therefore, in this paper, first, the generation mechanism of RF structure information is discussed, and the influence of different signal information introduced by different operating parameters on RF structure feature extraction is analyzed. Then, an autoencoder (AE) network and an autoencoder metric (AEM) network are designed, introducing metric learning ideas, so that the extracted deep RF structure features have good stability and divisibility. Finally, radar emitter structure (RES) inversion is realized using the centroid-matching method. The experimental results demonstrate that this method exhibits good inversion performance under variable operating parameters (modulation type, frequency, bandwidth, input power). RES inversion including unknown operating parameters is realized for the first time, and it is shown that metric learning has the advantage of separability of RF feature extraction, which can provide an idea in emitter and RF feature extraction.