Reviewerly/exHarmony huggingface dataset:
Hypergraph based semi-supervised symmetric nonnegative matrix factorization for image clustering

Semi-supervised symmetric nonnegative matrix factorization (SNMF) has been shown to be a significant method for both linear and nonlinear data clustering applications. Nevertheless, existing SNMF-based methods only adopt a simple graph to construct the similarity matrix, and cannot fully use the limited supervised information for the construction of the similarity matrix. To overcome the drawbacks of previous SNMF-based methods, a new semi-supervised SNMF-based method called hypergraph based semi-supervised SNMF (HSSNMF), is proposed in this paper for image clustering. Specifically, HSSNMF adopts a predefined hypergraph to build a similarity matrix for capturing the high-order relationships of samples. By exploiting a new hypergraph based pairwise constraints propagation (HPCP) algorithm, HSSNMF propagates the pairwise constraints of the limited data points to the entire data points, which can make full use of the limited supervised information and construct a more informative similarity matrix. Using the multiplicative updating algorithm, a discriminative assignment matrix can then be obtained by solving the optimization problem of HSSNMF. Moreover, analyses of the convergence, supervisory information, and computational complexity of HSSNMF are presented. Finally, extensive clustering experiments have been conducted on six real-world image datasets, and the experimental results have demonstrated the superiority of HSSNMF while compared with several state-of-the-art methods.
Tools of the Google Colaboratory cloud environment for programming training and the preparation for the unified state exam in informatics

One of the cloud technologies that can be used to teach programming based on the Python programming language is the Google Colaboratory cloud service. This service can be used by teachers both when teaching certain themes of the section "Algorithms and programming" as part of a school informatics course, and when preparing students for the tasks in programming of the Unified State Exam in informatics. The article discusses the possibilities of the Google Colaboratory cloud service for creating interactive notebooks using the example of an interactive notebook and practical work on the theme "A precondition loop", as well as interactive notebooks "Task No. 24 of the Unified State Exam in informatics" for students and teachers. The applicability of these notebooks for programming training based on the Python language as part of a school informatics course and for preparing students for tasks in programming of the Unified State Exam in informatics is substantiated.
A multimodal time-series method for gifting prediction in live streaming platforms

Viewer gifting is an important business mode in live streaming industry, which closely relates to the income of the platforms and streamers. Previous studies on gifting prediction are often limited to cross-section data and consider the problem from the macro perspective of the whole live streaming. However, the multimodal information and the time accumulation effect of live streaming content on viewer gifting behavior are ignored. In this paper, we put forward a multimodal time-series method (MTM) for predicting real-time gifting. The core module of the method is the multimodal time-series analysis (MTA), which targets at effectively fusing multimodal information. Specifically, the proposed orthogonal projection (OP) model can promote cross-modal information interaction without introducing additional parameters. To achieve the interaction of multi-modal information at the same level, we also design a stackable joint representation layer, which makes each target modality's representation (visual, acoustic and textual modality) can benefit from all the other modalities. The residual connections are introduced as well to ensure the integration of low-level and high-level information. On our dataset, our model shows improved performance compared to other advanced models by at least 8% on F1. Meanwhile, the MTA is able to meet the real-time requirements of the live streaming setting, and has demonstrated its robustness and transferability in other tasks. Our research may offer some insights about how to efficiently fuse multimodal information, and contribute to the research on viewer gifting behavior prediction in the live streaming context.
An Integrated Framework to Support Bachelor Design Students in 3-D Modeling

Design and computer graphics curricula for tertiary education include 3-D modeling skills. Students must learn to represent (complex) 3-D objects by means of parametric surfaces or polygonal meshes. Three-dimensional modeling may be a complex task, and students must be able to accrue a certain deal of experience in the field before passing the exam. The main difficulties the students must face mainly concern the comprehension of 3-D shapes and the choice of the most appropriate modeling techniques. This article proposes a framework to support bachelor's degree students in modeling 3-D objects by means of parametric surfaces. The proposed framework provides two augmented reality (AR) apps for smartphones and a web portal. The mobile AR apps allow students to deeply visualize 3-D object shapes, thus performing a set of guided exercises to practice basic modeling techniques. On the other hand, the web portal allows students to share their models with teachers and classmates to obtain feedback and comments. The preliminary results show the effectiveness of the proposed solution, as all volunteers involved in the experimental phase achieved better results after using the tools. Moreover, the students' opinions about the proposed framework are positive.
The magical realistic world of the virtual recreation

The aim of the study is to seek an answer to the question of whether the concept of virtual recreation can be considered in the context of the magical realism. The study prepared for this purpose has been carried out from a conceptual point of the view. The data required for the research have been obtained by reviewing the relevant literature. The reviewing process has been carried out in the context of the concepts of “virtual recreation” and “magical realism” and the study has been limited to these concepts. The data obtained as a result of the reviewing the literature have been analyzed conceptually. The analyzes have been made by first evaluating the concepts of virtual recreation and magical realism, and then comparatively examining the findings obtained from these evaluations. After the evaluations, it has been concluded that the magical realism approach is compatible with the structure of the virtual recreation and it is possible to evaluate the virtual recreation experience in the context of magical realism approach.
Breast Cancer Grading using Machine Learning Approach Algorithms

Recently, Breast Cancer (BC) becomes a more common cancer disease in women and it considers the most important sign which leads to death among women.Therefore, it requires efficient methods for detecting it to reduce the risk of death.A positive prognosis and greater chances of survival are improved if the BC is detected early.Currently, machine learning plays an important role in diagnosing BC disease.The various techniques in artificial intelligence and machine learning persuade the researchers in exploring their classification systems in classifying and detecting the BC disease.The algorithms are the K-Nearest Neighbor (KNN), the Support Vector Machine (SVM), random forest, logistic regression, and decision tree.In this study, various algorithms of the machine are proposed in designing the classification system for detecting the BC diseases.To improve the resulting quality, the Principal Component Analysis Algorithm (PCA) is applied.The system was tested and evaluated on the Wisconsin BC dataset from the University of Wisconsin Hospitals.The results were interesting and very good.The accuracy, recall, precision, and F-score of the SVM algorithm were obtained by up to 98% compared to previous work.
Fingerprints of the seismogram’s wavelet analysis results as a tool for creating a compact signal image for the purposes of neural network recognition

One of the modern directions in solving the problem of recognizing the type of a seismic event from its seismogram recorded by a single seismic receiver is the method of obtaining and comparing signal fingerprints. This paper provides a historical overview of the experience of using this technique initially for the analysis of audio recordings, and then for seismic ones. The existing method for earthquake fingerprinting, which includes the use of a two-dimensional discrete decomposition of the signal spectrogram using the Haar wavelet, is proposed to be improved based on a more accurate creation of the initial time-frequency image of the signal using a continuous wavelet transform. On the example of seismograms of known earthquakes and explosions recorded by the stations of the Kola branch of the GS RAS, the effectiveness of this approach in relation to fixing the key frequency and time features of the signal is shown. The fingerprints properties are indicated, which make them convenient for recognizing the type of seismic event using artificial neural networks.
Mixed reality results in vocational education: a case study with HoloLens 2

Pedagogy in vocational education is challenged by the increasing availability of mixed reality (MR) technologies. Wearable technologies such as the Microsoft HoloLens are being embedded in nursing education programmes in Aotearoa New Zealand and Australia and explored in other subject areas such as construction, architecture, and engineering. Empirical research undertaken with staff and students in a nursing programme reinforces and expands on previous research findings about enhanced student motivation, learning and organisational change implications, highlighted in the 2020 special issue of Research in Learning Technology that provided a 'state of the art' review of mobile mixed reality (MMR) in education as at 2019. This new research also identifies a number of technical or design constraints that need to be overcome to optimise MR for vocational and higher education use. Importantly, it highlights the need for significant improvement in integration of MR platforms into the learner journey so that use of the technology is not confined to professional users and early adopter student cohorts. Finally, the authors note the portability that MR headsets offer with the potential to deliver training across multiple campus and work-based learning environments to a consistent standard.
Didactic Peculiarities of the Use of Learning Systems in Technical Universities

The topicality of the matter considered is conditioned by low didactic efficiency of existing technical learning software and hardware tools in technical universities. In this regard, the purpose of this article is the creation and practical application of new methods for increasing the didactic efficiency of automated components of the educational process. The methodological basis of the research is the complex application of mutually complementary approaches, the main ones of which are systemic, personality-oriented, axiological and anthropological. The work describes the performance of the designed learning system and, on its example, considers the methods for increasing the didactic efficiency of learning systems subject to psychofactors of the participants of the educational process. In the proposed learning system, the results of psychodiagnostics of students’ personal characteristics and qualities are represented which are used to form new elements of system analysis – “cognitive elements of psychofactors”, which introducing made it possible to improve the system analysis. The perfection of system analysis expanded its functionality and qualitatively changed its capabilities, which in turn made it possible to increase the degree of reasonableness of decisions made by a teacher and learning system when analyzing pedagogical situations. In the presented research, a special emphasis is put on the use of psycho-corrective educational technologies of the educational process, which use gave a new pedagogical effect - students practicing psycho-corrective methods of work with their personal qualities began to successfully complete higher complexity educational tasks, which proves an increase in their cognitive motivation and cognitive abilities.
Keeping Voices in the Room: Values Clarification in Codesign for Equitable Science and Technology Education

Abstract When engaging issues at the intersection of science and society, science centers, museums and other informal STEM learning organizations struggle to center perspectives of communities most often impacted by the unequal distribution of technologies' benefits and harms. Increasingly, participatory design is being utilized to do this, but the field must continue to refine methods for accessing the expertise of community partners and keeping it present across multiple design stages and products. We share a case study in which a multi‐institution project team, developing resources for educational programming around radio frequency technologies, worked with community design to establish a values foundation that could guide initial planning and ongoing development. We share design methods adapted from values sensitive design and equity‐centered research‐practice partnership, as well as insights relevant to enacting design practice that can build relational equity, leverage data across institutional boundaries, and span locations, platforms and levels of expertise.
Multi-perspective respondent representations for answer ranking in community question answering

Answer ranking is an important task in community question answering (CQA) systems. It aims at ranking useful answers above useless answers. Existing works learn respondents’ expertise to help estimate qualities of answers. However, in most of these works, the expertise is only learned from the history answers. As a result, structure correlations between question raisers and respondents are usually ignored. Besides, these works lack an efficient way to learn respondent expertise from extensive history answers. To address the limitations, we propose a novel multi-perspective respondent representation learning(MPRR) network. First, our model learns embeddings of raisers and respondents through a heterogeneous information network(HIN) constructed by the answering records in CQA websites. The structure correlations between raisers and respondents are preserved in the learned embeddings. Second, a freezed pre-trained language model is used to learn respondents’ expertise from history answer contents more quickly. Then the multi-perspective respondent representations are generated based on their expertise and the embeddings learned in the HIN. At last, the raisers, respondents, questions, and answers are all considered to compute the matching scores. We evaluate our model on three real-world CQA dataset. Experiment results show that MPRR outperforms all baseline models with three ranking metrics on all datasets.
Weakly supervised semantic segmentation via Graph RecalibratiOn with Scaling Weight uNit

Weakly supervised semantic segmentation (WSSS) has garnered considerable attention for its efficacy in generating pixel-level labels using weak labels. Class activation maps (CAMs) are utilized by WSSS to generate pseudo-masks from image-level labels. However, these CAMs primarily focus on the most discriminative features of an object, while less discriminative features may be ignored or unidentified. Due to co-occurring pixels, it may also be impossible to distinguish between the foreground and background. In this paper, we propose a method referred to as Graph RecalibratiOn with Scaling Weight uNit (GROWN) to address these challenges. It illustrates the relation between local and global features by utilizing graph structure. Adaptively representing the image’s semantic features is possible by scaling weights that aggregate contextual features. The proposed method successfully captures long-range dependencies and extracts contextual features to improve the pseudo-mask quality. As a result, the proposed method can predict pixel-level labels effectively. The datasets PASCAL VOC 2012 and MS COCO were utilized in the experiments. GROWN outperforms state-of-the-art WSSS methods that employ image-level labels, as demonstrated by the results.
AdaDerivative optimizer: Adapting step-sizes by the derivative term in past gradient information

AdaBelief fully utilizes "belief" to iteratively update the parameters of deep neural networks. However, the reliability of the "belief" is determined by the gradient's prediction accuracy, and the key to this prediction accuracy is the selection of the smoothing parameter β1. AdaBelief also suffers from the overshoot problem, which occurs when the value of parameters exceeds the value of the target and cannot be changed along the gradient direction. In this paper, we propose AdaDerivative to eliminate the overshoot problem of AdaBelief. The key to AdaDerivative is that the "belief" of AdaBelief is replaced by the derivative term's exponential moving average (EMA), which can be constructed as (1−β2)∑i=1tβ2t−i(gi−gi−1)2 based on the past and current gradients. We validate the performance of AdaDerivative on a variety of tasks, including image classification, language modeling, node classification, image generation, and object detection tasks. Extensive experimental results demonstrate that AdaDerivative can achieve state-of-the-art performance.
Empirical Analysis of the Impact of Homomorphic Encryption on Cloud Computing

Cloud computing has been of greater convenience in recent years owing to its flexibility and on-demand service availability. Cloud computing services allow multiple users to access the available resources concurrently. So, trust is a key parameter from every client’s point of view. While accessing the resources in the cloud security becomes the primary concern for every participant and cryptography provides a secure room for every client. The cryptographic solution can be achieved in two different ways such as the conventional mechanism and the homomorphic encryption (HE) mechanism. The main objective of these two solutions is to maintain the confidentiality, integrity, and availability (CIA) of the information and resources stored in the cloud storage. The conventional mechanism provides a secure means to encrypt and decrypt the information at the sender and receiver sides respectively. But for any kind of operation, the information needs to be decrypted which increases the computational overhead which shows the path to the HE. In HE, the user can directly perform any kind of operation on the encrypted data which can help to reduce the computational time and cost to a much more significant value. In this current research work, the main focus is on the HE. Several HE mechanisms such as the RSA, Elgamal, Paillier, and DGHV have been implemented and the performance has been analyzed in terms of computational time. The empirical analysis demonstrates that the RSA homomorphic algorithm shows an average encryption and decryption time of 138.5 and 206.25 s which is lower as compared to others. Considering average CPU utilization, RSA shows around 27% with an average of 30 MB memory utilization which is found lower as compared to other algorithms.
Artificial Intelligence and Machine Learning

With the introduction to the Arduino IDE (Chap. 1), the Nano 33 BLE Sense (Chap. 2), and interface techniques (Chap. 3) complete; we concentrate on Artificial Intelligence (AI) and Machine Learning (ML) concepts and applications for microcontroller–based systems for the remainder of the book.
Interactions of fast‐moving consumer goods in cooking: Insights from a quantitative ethnographic study

Fast‐moving consumer goods (FMCGs) are increasingly equipped with enhanced packaging that incorporates novel functionalities. Providing FMCGs with this technology is challenging due to their unique characteristics, such as their low cost and short lifespan. Knowledge derived from a comprehension of their interactions in practice can help develop FMCGs that better cater to consumer needs and are well‐integrated into real‐world contexts. To help develop a method for the formation of such practical insights, 20 households were visited where participants were then observed as they cooked a meal. The sessions were captured on video, and a detailed record of the interactions between individuals, FMCGs and other items was made. A quantitative ethnographic approach was applied to analyse and build an understanding of different aspects of these interactions including their frequential, sequential and correlational features. The findings are discussed through the lens of how an appreciation of the interactions of FMCGs can serve as a valuable guidance for the design and development of their enhanced counterparts. The discovery that FMCGs are linked to the use of other items, for instance, is proposed as an opportunity to make use of the unique properties of the other items that a given FMCG commonly interacts with as a resource to create functionalities. As an exploratory reflection of how FMCGs are utilised in practice, the methods and knowledge presented in this study can be valuable in creating enhanced FMCGs by advocating for a product development process in which decisions are firmly grounded in empirical insights.
Energy‐efficient computing for machine learning based target detection

Summary The main objective of this study is to develop real‐time, energy‐efficient embedded computing for machine learning based target detection. Convolutional neural network (CNN) model based detection, a machine learning technique, can provide higher detection accuracy than constant false alarm rate (CFAR) detection techniques even if it results in higher processing costs. In this study, we achieve three significant improvements for real‐time radar target detection by considering computational cost. The first improvement is to reduce the computational cost of the CNN model. The second achievement is the design of heterogeneous computing optimizations. The third of them is to support energy‐efficient computing solutions for mobile sensors. Compared to the initial CNN model, layer improvements decreased the number of operations by 8.5x. Real‐time operations are satisfied by hardware‐specific improvements like vectorization and parallelization. The embedded NVIDIA Jetson GPU and Intel MYRIAD VPU, which have power consumption of 15 Watts and 1 Watt, respectively, have been used to execute the energy‐efficient target detection. The most energy‐efficient solution is achieved by using Jetson AGX Xavier GPU with 32‐bit single precision and 15 Watts of power consumption.