Reviewerly/exHarmony huggingface dataset:
A Modified Long Short-Term Memory Cell

Machine Learning (ML), among other things, facilitates Text Classification, the task of assigning classes to textual items. Classification performance in ML has been significantly improved due to recent developments, including the rise of Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Transformer Models. Internal memory states with dynamic temporal behavior can be found in these kinds of cells. This temporal behavior in the LSTM cell is stored in two different states: “Current” and “Hidden”. In this work, we define a modification layer within the LSTM cell which allows us to perform additional state adjustments for either state, or even simultaneously alter both. We perform 17 state alterations. Out of these 17 single-state alteration experiments, 12 involve the Current state whereas five involve the Hidden one. These alterations are evaluated using seven datasets related to sentiment analysis, document classification, hate speech detection, and human-to-robot interaction. Our results showed that the highest performing alteration for Current and Hidden state can achieve an average F1 improvement of 0.5% and 0.3%, respectively. We also compare our modified cell performance to two Transformer models, where our modified LSTM cell is outperformed in classification metrics in 4/6 datasets, but improves upon the simple Transformer model and clearly has a better cost efficiency than both Transformer models.
Pengembangan Model Desain Pembelajaran Daring Berbasis Teknologi Informasi dan Komunikasi di Era Budaya Digital Sekolah Dasar

The research conducted is a type of technology development research for learning in elementary school children. First conduct a survey on the use of online learning based on information and communication technology in the era of digital culture, the use of online applications by learners. In the development stage, the development of learning tools is carried out which includes making blogs or webs and also online applications, developing learning modules. conduct an assessment or survey of online applications controlled by teachers. The application was agreed by the teachers as a whole. From the results that have been obtained that the application used is more familiar with the Google classroom site which is linked to the teacher's web or blog and the learning model formed blended learning with problem-based learning strategies, or also with applications as a forum for communication using Whats App, sms, messenger, telephone, and e-mail and zoom or meet. the application of online learning, the focus of technology can be in the form of training for teachers, in addition to students or parents of students, both in the use of learning management systems monitoring and conducting online evaluations, virtual work. that the online learning model in the era of digital culture, the use of online applications as a means of collaborating and communicating which in this case is used online applications can be concluded that the model is an exciting learning model for students and students.
A syntactic method for robot imitation learning of complex sequence task

Purpose Aiming at the problems of weak generalization of robot imitation learning methods and higher accuracy requirements of low-level detectors, this study aims to propose an imitation learning method based on structural grammar. Design/methodology/approach The paper proposes a hybrid training model based on artificial immune algorithm and the Baum–Welch algorithm to extract the action information of the demonstration activity to form the {action-object} sequence and extract the symbol description of the scene to form the symbol primitives sequence. Then, probabilistic context-free grammar is used to characterize and manipulate these sequences to form a grammar space. Minimum description length criteria are used to evaluate the quality of the grammar in the grammar space, and the improved beam search algorithm is used to find the optimal grammar. Findings It is found that the obtained general structure can parse the symbol primitive sequence containing noise and obtain the correct sequence, thereby guiding the robot to perform more complex and higher-order demonstration tasks. Practical implications Using this strategy, the robot completes the fourth-order Hanoi tower task has been verified. Originality/value An imitation learning method for robots based on structural grammar is first proposed. The experimental results show that the method has strong generalization ability and good anti-interference performance.
Multiple Strategies Boosted Orca Predation Algorithm for Engineering Optimization Problems

Abstract This paper proposes an enhanced orca predation algorithm (OPA) called the Lévy flight orca predation algorithm (LFOPA). LFOPA improves OPA by integrating the Lévy flight (LF) strategy into the chasing phase of OPA and employing the greedy selection (GS) strategy at the end of each optimization iteration. This enhancement is made to avoid the entrapment of local optima and to improve the quality of acquired solutions. OPA is a novel, efficient population-based optimizer that surpasses other reliable optimizers. However, owing to the low diversity of orcas, OPA is prone to stalling at local optima in some scenarios. In this paper, LFOPA is proposed for addressing global and real-world optimization challenges. To investigate the validity of the proposed LFOPA, it is compared with seven robust optimizers, including the improved multi-operator differential evolution algorithm (IMODE), covariance matrix adaptation evolution strategy (CMA-ES), gravitational search algorithm (GSA), grey wolf optimizer (GWO), moth-flame optimization algorithm (MFO), Harris hawks optimization (HHO), and the original OPA on 10 unconstrained test functions linked to 2020 IEEE Congress on Evolutionary Computation (CEC’20). Furthermore, four different design engineering issues, including the welded beam, the tension/compression spring, the pressure vessel, and the speed reducer, are solved using the proposed LFOPA, to test its applicability. It was also employed to address node localization challenges in wireless sensor networks (WSNs) as an example of real-world applications. Results and tests of significance show that the proposed LFOPA performs much better than OPA and other competitors. LFOPA simulation results on node localization challenges are much superior to other competitors in terms of minimizing squared errors and localization errors.
How Should I Start Research in Cyber Security? Suggestions for Researchers According to Bibliometric Analysis Data

The aim of this study is to discover the research trends in the field of cyber security with performance analysis and to reveal the intellectual structure of the field of cyber security with scientific mapping. For this purpose, articles published in the field of cyber security between 1998-2021 in the WoS database were examined. The research was carried out in accordance with the bibliometric analysis guide. In the data collection phase, 1,631 articles were included in the study by taking into account the criteria determined among 15,781 studies using the PRISMA procedure. R program was used in bibliometric analysis. According to the findings of the study, there has been a significant increase in article productivity in the field of cyber security after 2020. Although IEEE Access is the journal with the highest number of publications in the field, IEEE Transactions on Smart Grid ranks first according to h-index and g-index values. Considering the topics studied according to the years, it is seen that in the first years, issues related to the law such as cybercrime and cyber terrorism were examined, and recently, in addition to these, current technological issues have been included. It is observed that the most effective publication is 'The Internet of Things for Health Care: a Comprehensive Survey' by Islam et al. which examines the security of the Internet of Things in health care, which is also a current issue.
Correctness of Flat Classification

Classifications are widely used in semantic networks and decision support systems based on formal knowledge and are part of computer ontologies. Classifications and computer ontologies built on them are the result of the work of one or more experts. As a result, such classifications reflect the subjective view of the author or authors on the world and the relationship between the classes (concepts) of the created classification. In the work, the authors propose an approach that will allow assessing how correctly the classification is constructed.
Classification and Synthesis of the Main Dropshipping Disàdvantages to Eliminate them using Software Agents

Dropshipping is one of the most used models of modern e-commerce. However, some of its disadvantages can have a negative impact on business. The main disadvantages of dropshipping are identified and those that are expected to be eliminated at the pricing stage by implementing special software using software agents are distinguished. As a result of the work carried out, a set of the most significant disadvantages has synthesized based on the most important criteria for buyers and a hypothesis was put forward regarding the minimization of their negative impact. The resulting software may be adapted for use by dropshipping retail companies.
Interpolated Implicit Pixel-based Novel Hybrid Approach Towards Image Steganography

Background: Steganography is the approach of camouflaging the covert object within another cover object. This manuscript suggested a novel steganography approach to conceal the covert data presence. The basic idea behind this is to generate an information-hiding approach that increases the payload capacity and good PSNR value without sacrificing much distortion of the image. Objectives: To develop a novel data-hiding approach that increases imperceptibility, robustness, and payload capacity. Methods: The Neighbour Mean Interpolation technique is used to scale up the original image to generate Interpolated pixels of the given image. An even-odd scheme on the interpolated stego pixel is used to camouflage the obscure code. MATLAB is used for the implementation of the new approach and results calculation. Results: The Experimental analysis reveals that our suggested approach has a finer embedding capacity for camouflaging the secret data as the original image of size (MxN) is scaled up to size (2M- 1 x2N-1) and also manages the good visuality of the cover or graven image. The proposed method is compared with Jung and Yoo, and Selvrani's method. The result of this comparison shows that the proposed method has finer imperceptibility than these two previously existing techniques. Conclusion: A novel approach towards image steganography using neighbor mean interpolation has been proposed and implemented. A new steganography method is used for camouflaging the confidential code into the cover object using NMI without producing any major differences in the input image. The new approach provides better imperceptibility, robustness, and payload capacity.
Vibration analysis of a bowed string involving dynamics of a soundbox and neck and its application to a Chinese traditional bowed string instrument "Erhu"

In this paper, the author extends existing analysis methods for bowed string vibration to that involving the dynamics of a soundbox and neck of a bowed string instrument. Almost all the components of the instrument such as strings, nut, tuning pegs, neck, soundbox, and bridge are physically modeled and numerically analyzed by the finite element method, thereby yielding an equation of motion of the total multi-degree-of-freedom (MDOF) system. Then, the MDOF system is introduced into an existing equivalent circuit representation of the vibrations of a bowed string. The developed method is applied to the physical modeling sound synthesis of the erhu, a Chinese traditional bowed string instrument. Trial numerical calculations demonstrate that the developed method is effective in the sound synthesis of the erhu and also has the potential to analyze the three-dimensional spatial vibration of a bowed string and the effect of the interaction between the string and soundbox vibrations.
Vibration analysis of piano strings involving dynamics of hammer shanks

In this study, we develop a physical model of a grand piano string involving a high-fidelity expression of hammer shank dynamics. A hammer shank is modeled as a general beam, and its governing equation is analyzed using the finite element method (FEM). In addition, the rotational motion of the hammer shank is explicitly expressed and incorporated into the formulation of the FEM. The developed method also involves a more general physical model of piano strings, which is numerically analyzed using the finite difference method (FDM). The proposed method is validated by comparing numerical results with previously reported measurement results.
Effectively Utilizing the Category Labels for Image Captioning

As a further investigation of the image captioning task, some works extended the vision-text dataset for specific subtasks, such as the stylized caption generating. The corpus in such dataset is usually composed of obvious sentiment-bearing words. While, in some special cases, the captions are classified depending on image category. This will result in a latent problem: the generated sentences are in close semantic meaning but belong to different or even opposite categories. It is a worthy issue to explore an effective way to utilize the image category label to boost the caption difference. Therefore, we proposed an image captioning network with the label control mechanism (LCNET) in this paper. First, to further improve the caption difference, LCNET employs a semantic enhancement module to provide the decoder with global semantic vectors. Then, through the proposed label control LSTM, LCNET can dynamically modulate the caption generation depending on the image category labels. Finally, the decoder integrates the spatial image features with global semantic vectors to output the caption. Using all the standard evaluation metrics shows that our model outperforms the compared models. Caption analysis demonstrates our approach can improve the performance of semantic representation. Compared with other label control mechanisms, our model is capable of boosting the caption difference according to the labels and keeping a better consistent with image content as well.
EVOCE Robot: Developing Prototypes and Teaching Young Learners English Vocabulary

This study aimed to find out the prototype of an EVOCE robot that gave impact of students’ English vocabularies enhancement. In this study, the authors used two types of research design, namely, prototyping method and experiment research design. The mixed method is applied in this research qualitatively and quantitatively. Both vocabulary tests and observational methods of prototyping data collection were employed. The robot prototype used in the class as a tool to examine how was the effect of this media helped young learners in acquiring basic English vocabulary. The prototype has been through the test and the result showed that it suited with the needs for young learners. Nonprobability sampling was the technique used. A total of 40 students from two classes made up the research sample. The vocabulary score achievement pre and post test using EVOCE robot was compared with data analysis and t-test. The findings of value of t-stat &gt; t-table at the significant level of 5% (1,679 &gt; 1,328) meaning that the robot helped students become better acquisition in getting new vocabulary and influenced their level of vocabulary score. As the result, this research therefore has consequences for the teacher’s understanding that the usage of robots can both increase students’ vocabulary and also have an impact on their level of English proficiency.
Legality Discrimination of Japanese Web Advertisements by Complex-valued SVM using Document Features based on Discrete Fourier Transform

In Internet advertising, text information is added to increase the appeal of the ad to the viewers. However, some of the advertising documents contain inappropriate expressions. Wording or expressions that exaggerate the efficacy of a product or that recommend a product by a medical professional may violate the Pharmaceutical Affairs Law and the Act against Unjustifiable Premiums and Misleading Representations. Therefore, a system that can effectively and quickly detect problematic advertisements is required. Some advertisements cannot be properly classified based on word statistics alone. Therefore, information other than word statistics must be embedded in the document vector. The advertising documents targeted in this study have characteristics such as “biases in the word positions of specific words” and “periodic occurrence of specific words.” Frequently appearing words in problematic documents (especially in cosmetics advertisements) have strong biases in their word positions, resulting in a complex multimodal distribution of position of occurrence. Therefore, embedding word order information and word period information in document vectors is considered very effective for identifying problematic advertising documents.
Subjective Difficulty Estimation of Educational Comics Using Gaze Features

This study explores significant eye-gaze features that can be used to estimate subjective difficulty while reading educational comics. Educational comics have grown rapidly as a promising way to teach difficult topics using illustrations and texts. However, comics include a variety of information on one page, so automatically detecting learners' states such as subjective difficulty is difficult with approaches such as system log-based detection, which is common in the Learning Analytics field. In order to solve this problem, this study focused on 28 eye-gaze features, including the proposal of three new features called “Variance in Gaze Convergence,” “Movement between Panels,” and “Movement between Tiles” to estimate two degrees of subjective difficulty. We then ran an experiment in a simulated environment using Virtual Reality (VR) to accurately collect gaze information. We extracted features in two unit levels, page- and panel-units, and evaluated the accuracy with each pattern in user-dependent and user-independent settings, respectively. Our proposed features achieved an average F1 classification-score of 0.721 and 0.742 in user-dependent and user-independent models at panel unit levels, respectively, trained by a Support Vector Machine (SVM).
An Improved Real-Time Object Tracking Algorithm Based on Deep Learning Features

Visual object tracking is always a challenging task in computer vision. During the tracking, the shape and appearance of the target may change greatly, and because of the lack of sufficient training samples, most of the online learning tracking algorithms will have performance bottlenecks. In this paper, an improved real-time algorithm based on deep learning features is proposed, which combines multi-feature fusion, multi-scale estimation, adaptive updating of target model and re-detection after target loss. The effectiveness and advantages of the proposed algorithm are proved by a large number of comparative experiments with other excellent algorithms on large benchmark datasets.
Prediction of Driver's Visual Attention in Critical Moment Using Optical Flow

In recent years, driver's visual attention has been actively studied for driving automation technology. However, the number of models is few to perceive an insight understanding of driver's attention in various moments. All attention models process multi-level image representations by a two-stream/multi-stream network, increasing the computational cost due to an increment of model parameters. However, multi-level image representation such as optical flow plays a vital role in tasks involving videos. Therefore, to reduce the computational cost of a two-stream network and use multi-level image representation, this work proposes a single stream driver's visual attention model for a critical situation. The experiment was conducted using a publicly available critical driving dataset named BDD-A. Qualitative results confirm the effectiveness of the proposed model. Moreover, quantitative results highlight that the proposed model outperforms state-of-the-art visual attention models according to CC and SIM. Extensive ablation studies verify the presence of optical flow in the model, the position of optical flow in the spatial network, the convolution layers to process optical flow, and the computational cost compared to a two-stream model.
Effective Language Representations for Danmaku Comment Classification in Nicovideo

Danmaku commenting has become popular for co-viewing on video-sharing platforms, such as Nicovideo. However, many irrelevant comments usually contaminate the quality of the information provided by videos. Such an information pollutant problem can be solved by a comment classifier trained with an abstention option, which detects comments whose video categories are unclear. To improve the performance of this classification task, this paper presents Nicovideo-specific language representations. Specifically, we used sentences from Nicopedia, a Japanese online encyclopedia of entities that possibly appear in Nicovideo contents, to pre-train a bidirectional encoder representations from Transformers (BERT) model. The resulting model named Nicopedia BERT is then fine-tuned such that it could determine whether a given comment falls into any of predefined categories. The experiments conducted on Nicovideo comment data demonstrated the effectiveness of Nicopedia BERT compared with existing BERT models pre-trained using Wikipedia or tweets. We also evaluated the performance of each model in an additional sentiment classification task, and the obtained results implied the applicability of Nicopedia BERT as a feature extractor of other social media text.
Parallelization on a Minimal Substring Search Algorithm for Regular Expressions

Let us consider a regular expression r of length m and a text string T of length n over an alphabet Σ. Then, the RE minimal substring search problem is to find all minimal substrings of T matching r. Yamamoto proposed O(mn) time and O(m) space algorithm using a Thompson automaton. In this paper, we improve Yamamoto's algorithm by introducing parallelism. The proposed algorithm runs in O(mn) time in the worst case and in O(mn/p) time in the best case, where p denotes the number of processors. Besides, we show a parameter related to the parallel time of the proposed algorithm. We evaluate the algorithm experimentally.
Hyperspectral Image Restoration via a New Symmetric ADMM Approach

For the hyperspectral image (HSI) denoising problem, a symmetric proximal alternating direction method multiplier (spADMM) is proposed to solve the sparse optimization problem which cannot be solved accurately by traditional ADMM. The proposed method finds a high-quality recovery method using the traditional low-rank Tucker decomposition method, which can fully take into account the overall spatial and spectral correlation between HSI bands by using the Tucker decomposition. By choosing appropriate steps to update the Lagrange multipliers twice, it makes the selection and use of variables more flexible and better for solving sparsity problems. To maintain stability, we also add appropriate proximity terms to solve the problem during the computation. Experiments have shown that the spADMM has better results than the traditional ADMM. The final experimental results on the dataset demonstrate the effectiveness of the method.