Reviewerly/exHarmony huggingface dataset:
Randomized Neighbour Grey Wolf Optimizer

At present time, swarm-intelligence-related algorithm covers a wide area of optimization. Grey wolf optimizer (GWO) algorithm is one of them. Grey wolves work in a group and they pantomimist the communal style of the wolf. In GWO a solution is upgraded by consolidating the instruction from the three best solutions in the explore area [1]. This article introduced an altered divergent of GWO for improving the exploration capacity of wolves. The proposed procedure is named as randomized neighbour grey wolf optimizer (RNGWO). To justify the performance of RNGWO, 12 benchmark functions are studied. Further, the gained results are compared with the GWO, fully informed grey wolf optimizer, power low-based local search in spider monkey optimization, and artificial bee colony algorithm. The procured outcomes justify the prospective RNGWO method.
Research on colorful spaced drawing scheme based on OpenCV

In theoretical research, object recognition has been a hot topic in the field of computer vision for the last decade or so. There are many researches related to this topic in the top conferences and journals every year. In some fine-grained application scenarios, such as face recognition and fingerprint recognition, there have been relatively mature applications. In this paper, we study the theories related to object recognition and design and implement a colorful spaced drawing scheme based on OpenCV. Based on this, we extract and match features for color recognition algorithms, and finally form a color feature-based object recognition scheme. The aim is to understand the feature detection algorithm and solve some color path recognition problems in depth, in order to select the appropriate algorithm and optimize it for specific scenarios in the practical application process.
Research on multi-sensor high dynamic range imaging technology and application

In dynamic scene imaging, multi-sensor high dynamic range (HDR) fusion technology used to solve the ghosting problem of multi-exposure fusion based on camera aperture, and achieve high-quality HDR video imaging. Therefore, it has made significant progress in image capture and synthesis of HDR optical imaging. First, we introduce the development status of multi-sensor based on time bracketing and space bracketing. Then introduce the different method of multi-exposure image fusion, including aligning different exposures, rejecting misalignment information and patch-based optimization. Finally, discuss the key technical issues and development trends of this technology in HDR imaging technology.
Video reconstruction of generative adversarial network based on global perception

Abstract Aiming at the problem that the current video reconstruction can not take into account the high perception quality and high temporal correlation, a video reconstruction of generative adversarial network based on global perception is proposed. The generator adopts a recurrent and iterative network architecture. In order to make up for the lack of extraction of global information by the frame alignment network, the alignment network combines the global information extracted by the global information perception to perform frame alignment. Through the static temporal loss and the temporal statistics loss, combined with the relative discriminator of sequence frames input, the temporal correlation of the generated images sequence is improved. Experimental comparisons were performed on the Vid4 and REDS test sets, and the best image perception quality index (LPIPS/NIQE) were obtained, as well as better temporal correlation (tLPIPS), reaching 0.192/3.417/0.328 and 0.138/3.223/0.217. Experimental results show that the proposed method can effectively improve the perceptual quality of reconstructed video frames and has good temporal correlation.
A study on the error analysis in the Mongolian-Korean translation and educational implications: Focusing on Korean undergraduate students

The present study attempts to analyze and classify errors in translations of Korean Mongolian language learners and to suggest educational implications based on the error analysis. Korea and Mongolia have had an established relationship since 1990 and held exchanges in various fields for a short period of time, and as a result the demand for professional translation is increasing. To meet this demand, undergraduate schools also require educational programs for professional translators, yet it is difficult to find any research or studies about the issue. In this study, Mongolian texts translated by Korean Mongolian learners were collected and analyzed, and the errors were classified into 1) grammar related errors, 2) translation skill related errors, and 3) meaning related errors. Based on the analysis, the following implications can be presented. First, it is necessary for students to recognize the differences between the use of punctuation marks in the two languages and to learn how those them appropriately in the target language. Second, there is the issue of transcribing Mongolian into Korean. Although precise common rules for transcribing have not yet been established, teachers should suggest a consistent transcription guide. Third, since many errors related to the source language appear, it is necessary to avoid literal translations and complete the translation suitable for the target language. Finally, students need to be exposed to various types of texts and practice the style of writing that fits to the text.
Research Through Co-Design (RTC) and Ergonomics

This paper investigates interactions between Ergonomics and the Research Through Co-Design (RTC) theory. The RTC model is based on Control System Theory (CST) and Research Through Design (RTD) which takes on board the nature of co-design as a generator of knowledge according to a research question. The authors investigated the role of the co-design variable in the RTC model as a fundamental link between the RTC theory and Ergonomics. As the main result, the authors simulate a co-design process in order to understand the interaction between humans and the other parts of the system to optimise the overall system performance and the human wellbeing through a democratic, transdisciplinary and participatory design feedback. In conclusion, the RTC can be considered a model for doing research in/as/for ergonomics if the research project considers the application of the co-design process as a neural mechanism.
Optimization of Eye Control Interactive Interface Based on Genetic Algorithm

In order to solve the problem of the arrangement of user function information on the human eye control interface, this paper adopts the analytic hierarchy process (AHP) to transform the layout constraint content of the human eye control interface into a hierarchical model structure. Then the quantified experimental data are used to solve the optimal solution of eye-controlled interface layout by using genetic algorithm. After this paper takes an eye-control interactive assembly process development platform as an experimental case, uses genetic algorithm to obtain the optimal solution of eye-control interface layout. In order to verify the effectiveness of this method, Tobii eye tracker was used in this paper to conduct eye movement experiments on subjects with mechanical engineering knowledge, the purpose of this method was to collect subjective feelings and eye movement information of subjects when using the eye-control interface. The subjects show higher cognitive efficiency when using the interface generated by genetic algorithm.
MLAP: Multi-level Attentive Preference Model for Session-Based Recommendation

Session-based recommendation aims to predict which item the user will click next based on user’s sessions. In session-based recommendation, extracting the relevance between user’s current session and historical sessions is the key factor to affect the recommendation performance. However, existing methods are unaware of such influence and only consider user’s multiple sessions separately as independent sequences. To solve the above problem, in this paper, we propose a novel model named Multi-level Attentive Preference (MLAP) that captures user preferences on items by exploiting multi-level information from user’s multiple sessions. In addition, MLAP can learn unified user representations from items and sessions by regarding them as different levels of user preferences. The core of MLAP is a short-term encoder and a long-term encoder. In the short-term encoder, we extract the user short-term preferences from each session of user. We also apply item-level attention mechanism to better represent each session by neglecting noisy items amongst each session. On the long-term encoder side, we obtain the user long-term preferences from their session sequences, and apply session-level attention network to select important historical sessions for learning informative user long-term preferences. The experimental results on two real-world datasets show that MLAP significantly outperforms state-of-art session-based recommendation methods.
Graph Neural Network Recommendation Model Based on Long- and Short-Term Interests

The recommendation system [1] can quickly find the information that users are interested in from the complex information and recommend it to the user, and it has been widely used in web-based services. However, the current mainstream recommendation models [16] all have the problem of sparse data [2] and ignoring the long-term and short-term interests of users. In response to these problems, we propose a graph neural network recommendation model (LSGNR) based on long-term and short-term user interest modeling. In the experiment, we used the public data set to verify and analyze the algorithm of LSGNR model. We performed an experimental comparison with the current mainstream five recommendation models. Experimental results show that the performance of the proposed algorithm is generally better than the current mainstream recommendation methods [17], and it is improved by more than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> compared with other models under the two evaluation indicators.
New verbs and dictionaries: A method for the automatic detection of neology in Spanish verbs

Abstract The appearance of new verbs can be observed regularly, but verbs are not frequently investigated in neology, and they are difficult to detect automatically. In this study, a corpus-based method is proposed to detect Spanish verbs with a series of algorithms that analyse the morphology of regular verbs. The vocabulary was drawn from a large corpus and contrasted with a major dictionary of Spanish. Then, a series of filters were applied to distinguish between valid neologism candidates and spelling mistakes. Around 88% of the neologisms proposed by the method were correct and we estimate that the system detected 76% of the neologisms present in the corpus. This procedure can be included in the workflow of a lexicographic project as a regular part of the task, as a systematic way of collecting new verbs from the data and avoiding under-representation or bias.
Robust JPEG Steganography Using Singular Value Decomposition in DCT Domain

Image steganography is an important technology for convert communication. When the stego images are transmitted through lossy network channel, the robustness of steganography algorithm is very crucial. Based on singular value decomposition in DCT domain, a robust JPEG steganography algorithm is proposed, which can resist image compression and correctly extract the embedded secret message from the compressed stego image. First, the robust embedding domain is constructed by exploiting the correlation of the maximum singular values got from two adjacent 8 × 8 DCT blocks. Then, the framework of the proposed JPEG steganography algorithm is given and the key steps are described in details. The experimental results show that the proposed JPEG steganography algorithm can achieve competitive robustness in contrast to the state-of-the-art robust steganography algorithms. Moreover, it can extract the secret messages correctly even if the stego image is compressed by WeChat.
Compositional Data Analysis in Physical Activity and Health Research. Looking for the Right Balance

The time spent in different types of sleep, sedentary behaviour and physical activity during a day represents compositional data. Previous use of compositional analysis of daily activity data through orthonormal log-ratio coordinates in the form of pivot coordinates of four components (sleep, sedentary behaviour, light physical activity, moderate to vigorous physical activity) has facilitated critical comparison with and the resolution of contradicting reports of the influence of daily behaviour on health which had arisen from the use of standard statistical analysis. For example, there has been a long-standing debate about whether the effects of time spent sedentary are independent of the time spent in moderate to vigorous activity. However, daily activity may be usefully broken down into more categories, and other compositional analysis approaches may be relevant. Here we demonstrate the use of log-ratio balances to evaluate the association between daily activity broken into six components (sleep, short periods of sedentary behaviour, long periods of sedentary behaviour, standing, slow walking and fast walking) from a nationally representative sample of adults with health markers in obesity, lipidemia and glycemia. The balanced approach to compositional analysis of daily behaviour patterns offers a way to focus the statistical assessment and modelling on variables, representing meaningful contrasts of interest between behaviours. This is fully consistent with the view that actual effects on health are associated with exchanges between some classes of daily behaviours.
Digital Inclusion of Farmers and Rural Hinterland: The Case of Digital India

Success in the increasingly digitized social and economic realms requires a comprehensive approach to fostering inclusion. India with world’s largest digitally excluded population, undertook a major technology-based initiative - Digital India, to ensure digital inclusion of all. This qualitative study analyzes the various digital initiatives that are focused on rural sectors especially the farmers and agriculturists from the perspective of the Digital inclusion framework. It also investigates the current implementation status of these e-services and applications and the challenges involved along with some suggestive solutions. The results reflect that these initiatives are achieving digital inclusion to some extent but it still needs much more impetus and progress. This unique research presents the holistic view of these rural initiatives which can prove to be useful for other researchers and the policymakers in understanding and chalking out an action plan for enhancing rural digital inclusion through e-services and mobile applications globally.
Binary, Multi-Valued and Quantum Board and Computer Games to Teach Synthesis of Classical and Quantum Logic Circuits

The paper presents a new didactic concept of teaching synthesis and optimization of classical, multi-valued, and quantum circuits by playing games. New types of classical and quantum, board and computer games are introduced. We were able to use the concepts from this paper to successfully teach middle and high school students about synthesis of several types of circuits. Concepts of Interpretation of Quantum Mechanics are also illustrated. The goal is not only to teach how to design circuits but also how to create new algorithms. Some concepts of Machine Learning are also taught through these games.
TSK-Streams: learning TSK fuzzy systems for regression on data streams

Abstract The problem of adaptive learning from evolving and possibly non-stationary data streams has attracted a lot of interest in machine learning in the recent past, and also stimulated research in related fields, such as computational intelligence and fuzzy systems. In particular, several rule-based methods for the incremental induction of regression models have been proposed. In this paper, we develop a method that combines the strengths of two existing approaches rooted in different learning paradigms. More concretely, our method adopts basic principles of the state-of-the-art learning algorithm AMRules and enriches them by the representational advantages of fuzzy rules. In a comprehensive experimental study, TSK-Streams is shown to be highly competitive in terms of performance.
Case Based Reasoning Approaches for Reuse and Adaptation in Community Question Answering System

A study was undertaken to fetch a set of similar questions asked through Community Question Answering system. Our aim is to propose a better generic and effective similarity method to question answering structure. We used a Case Based Reasoning system in Knowledge Reprocess as it explains fresh problems with elucidation of related challenges or cases solved in the past. The key idea in this approach is recovery of like cases. Data was gathered from Stackoverflow as well as GeeksforGeeks databases. Four computer languages i.e., C, C++, Java and Python were used for the study. Generation of graphical tree of similar keywords was done with the help of Bayesian and Semantic similarities. Multiple questions were generated based on keywords using the above computer languages. The reuse and adaptation methods are involved in this model to improve the retrieval of queries.
GVNP: Global Vectors for Node Representation

Learning low-dimensional embeddings of nodes in networks is an effective way to solve the network analytic problem, from traffic network to recommender systems. However, most existing approaches are inherently transductive, their framework is built on a single fixed graph. Inspired by node2vec, we optimize the random walk strategy and propose GVNP, an unsupervised method that can learn continuous feature representations for nodes and leverage node feature information to efficiently generate node embeddings for previously unseen data in networks. Experimental results demonstrate that GVNP performs well on the transductive and inductive task.
DB2SM: An Efficient Resampling Technique for Imbalanced Data Classification

Data classification is the main task of Machine Learning. However, it is not easy to build a good classification model because it requires well-prepared data and efficient classification techniques. One of the data issues that we inevitably have to face is the imbalanced data problem. This problem typically refers to a situation that the classes are not represented equally, e.g., one class is more occur than another class. Therefore, the classifier will be biased to the majority class and give high performance for that class. To deal with this issue, various techniques have been proposed. In this work, we introduce an improvement technique, called DB2SM. Our method employs 2 times DBSCAN clustering to find a proper area and SMOTE for oversample minority instances. For more detail, the first clustering task removes the negative instances (e.g., majority class), which are located close to the positive classes. Then the second clustering task generates clusters that have only positive or negative instances. Finally, SMOTE was applied to create new positive instances. The experimental results with 20 imbalanced datasets showed that our proposed method gives a better performance in terms of accuracy, AUC and F-measure, compared to SMOTE, DBCS and DBSM.
Photonic Perceptron at Gigabit/s Speeds with Kerr Microcombs

We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.
Toward A User-Centred Design Approach for AR Technologies in Online Higher Education

The exploration of augmented reality's (AR) potential in higher education teaching and learning demonstrates an impressive scope of critical inquiry. Online higher education (OHE) represents a transformation in learning practices and educational paradigms on a global scale, with a significant opportunity for the application of AR through e-learning. The overarching goal of the current study is to understand how the presence of AR applications in an OHE multimedia program impacts student learning. The current conceptual study aims to formulate design and development requirements for an AR application, aiming to understand how a user-centred design (UCD) approach could frame such a process. The overarching goal is to design AR resources which enhance student learning in multimedia education. Implementing a user-centred approach ensures that learners are taken into account from the beginning of the design process and throughout the iterative design lifecycle. The current paper presents the results from the first phase of a multi-stage research project. First, conceptually designed personas, scenario reviews, and user journey mappings were developed based on identified learner needs and AR system requirements previously identified. The results show evaluation techniques use within a UCD process while revising AR scenarios from the learner's perspective and exploring design requirements to ensure the feasibility of the AR application. An implication of our study is to demonstrate the value of a range of evaluation techniques presented here using a user-centred design approach, which can be used to design and develop future AR and eXtended Reality (XR) technologies in educational scenarios.
Research and Application of Ontology-Based Knowledge Model for Dangerous Goods in Port

A large amount of data resources in daily management have accumulated in the the port safety supervision department and a knowledge database containing structured and unstructured data has established. The database contains data resources for daily management, safety supervision, accident handling, etc. In the process of using these data, it is faced with the problems of complex knowledge base, difficulty in retrieving related information, and low retrieval accuracy. Therefore, an ontology-based dangerous goods knowledge model is proposed, which supports both structured and unstructured data. Using semantic analysis and data labeling technology, a port dangerous goods knowledge base and a personalized information retrieval framework are established, and the retrieval process and result ranking optimization algorithm are proposed. This method improves the information retrieval efficiency, information recognition and service capabilities of managers in port safety supervision.