Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Sentence-Level using Dependency Embedding and Pre-trained BERT Model

Sentiment analysis is a valuable field of research in NLP with many applications. Dependency tree is one of the language features that can be utilized in this field. Dependency embedding, as one of the semantic representations of a sentence, has shown to provide more significant results compared to other embeddings, which makes it a potential way to improve the performance of sentiment analysis tasks. This study aimed to investigate the effect of dependency embedding on sentence-level sentiment analysis through experimental research. The study replaced the Vocabulary Graph embedding in the VGCN-BERT sentiment classification system architecture with several dependency embedding representations, including word vector, context vector, average of word and context vectors, weighting on word and context vectors, and merging of word and context vectors. The experiments were conducted on two datasets, SST-2 and CoLA, with more than 19 thousand labeled sentiment sentences. The results indicated that dependency embedding can enhance the performance of sentiment analysis at the sentence level.
Blind and Robust Watermarking Algorithm for Remote Sensing Images Resistant to Geometric Attacks

To address the problem of weak robustness against geometric attacks of remote sensing images' digital watermarking, a robust watermark- ing algorithm based on template watermarking is proposed in this paper, which improves the robustness of digital watermarking against geometric attacks by constructing stable geometric attack invari- ant features. In this paper, the Discrete Fourier Transform domain template watermark is used as the invariant feature against geometric attacks, and the embedding of the cyclic watermark is used to improve the watermark robustness for recovering the watermark synchroniza- tion relationship. To achieve blind extraction of the watermark, a parameter extraction method based on noise extraction is designed. The experimental results demonstrate that the proposed method can effectively improve the robustness of digital watermarking of remote sensing images against geometric attacks. Meanwhile, it can also resist common image processing attacks and compound attacks.
Activation Function Effects and Simplified Implementation for Hopfield Neural Network

The activation function is crucial in the Hopfield neural network (HNN) to restrict the input–output relation of each neuron. The physical realizability and simplicity of the hardware circuit of activation function are beneficial to promote the practical engineering application of the HNN. However, the HNN commonly used hyperbolic tangent activation function involves a complex hardware circuit implementation. This paper discusses a piecewise-linear activation function (PWL-AF) with simplified circuit implementation and a tri-neuron small-world HNN is built as a paradigm. The hardware implementation circuit of the HNN is greatly simplified, benefited from the PWL-AF with a simple analog circuit. Meanwhile, the dynamics related to the PWL-AF and initial conditions are numerically explored. The numerical results demonstrate that the PWL-AF-based HNN can produce dynamical behaviors like the HNN based on the hyperbolic tangent activation function. Nevertheless, the multistability with up to six kinds of coexisting multiple attractors emerged because of the PWL-AF breakpoint. This can give more flexible and potential aspects in multistability-based engineering applications. Especially, the PWL-AF breakpoint value simultaneously acts as the offset booster and amplitude controller in regulating the offset boosting and amplitude rescaling of neuron states. Afterwards, an analog circuit with three straightforward operational amplifiers (op-amp)-based circuit modules is designed for the PWL-AF, and a PCB-based analog circuit is thereby implemented for the tri-neuron small-world HNN. The hardware experiments agree with the numerical simulations, implying the feasibility of the PWL-AF simplification for the HNN.
DRC-SG 2.0: Efficient Design Rule Checking Script Generation via Key Information Extraction

Design Rule Checking (DRC) is a critical step in integrated circuit design. DRC requires formatted scripts as the input to design rule checkers. However, these scripts are manually generated in the foundry, which is tedious and error prone for generation of thousands of rules in advanced technology nodes. To mitigate this issue, we propose the first DRC script generation framework, leveraging a deep learning-based key information extractor to automatically identify essential arguments from rules and a script translator to organize the extracted arguments into executable DRC scripts. We further enhance the performance of the extractor with three specific design rule generation techniques and a multi-task learning-based rule classification module. Experimental results demonstrate that the framework can generate a single rule script in 5.46 ms on average, with the extractor achieving 91.1% precision and 91.8% recall on the key information extraction. Compared with the manual generation, our framework can significantly reduce the turnaround time and speed up process design closure.
Exploration of Bitflip’s Effect on DNN Accuracy in Plaintext and Ciphertext

Neural Networks (NNs) are increasingly deployed to solve complex classification problems and produce accurate results on reliable systems. However, its accuracy quickly degrades in the presence of bit flips from memory errors or targeted attacks on DRAM main memory. Prior work has shown that a few bit errors significantly reduce NN accuracies, but it is unclear which bits have an outsized impact on network accuracy and why. This paper first investigates the relationship of the number representation for NN parameters with the impacts of bit flips on NN accuracy. We then explore the Bit Flip Detection (BFD) framework— four software-based error detectors that detect bit flips independent of NN topology. We discuss exciting findings and evaluate the various detectors’ efficacy, characteristics, and trade-offs.
Multi Modal 2-D Canvas Based Gallery Content Retrieval

Clicking pictures using mobile cameras is an ubiquitous part of daily human lives. However, finding the same photo, after a couple of weeks, is a herculean task. Conventional gallery search is based on a 1D query input, limited to keywords, without any smart understanding of the user’s needs. The user is needed to scroll through hundreds of images to find the one. In this work, we propose a multi-modal canvas based content retrieval system. Our system allows the users to write/draw on a 2-D canvas, instead of a single line. We use the relative position of the keywords to better understand the user’s needs and retrieve the photo he needs faster. Complex ideas of perception, nearness and object relations are more easily explained through a 2-D input, rather than complex sentences. We integrate a handwriting recognition system with a clustering based sketch recognition engine, to allow users a truly multi-modal system to interact with their devices. Our system also allows users to specify the finer features of the objects, thus providing increased ease of retrieval. We show that using the proposed system, users are able to find the required image faster with a $$33\%$$ reduction in human effort. Proposed system allows users to creatively search for images rather than monotonously scrolling through hundreds of images.
A Preliminary Study on Developing University Preparation Course Textbooks for International Business Administration Students

This study aims to suggest directions of developing textbooks in the preparatory course for international students preparing to major in business administration at the university. To this end, this study conducted a preliminary survey to evaluate international learners’ Korean language proficiency and their academic performance, as well as their perception of learning Korean for academic purposes. The survey findings revealed that learners had a limited understanding of business administration vocabulary and collocations. In addition, many learners did not write any sentence on their answer sheet, while some did not even know how to write the answer sheets primarily due to a lack of familiarity with academic writing conventions. The results of the perception survey also revealed that learners have difficulty in collating words related to business administration, recognizing the writing function as one of the most important skills for succeeding in their undergraduate studies, and feeling the necessity of a preliminary university course for international students majoring in business administration. Based on the results of the survey, this paper proposes the directions for development of textbooks in the preparatory course.
Multimodal sentiment analysis with BERT-ResNet50

Aiming at the problem that the information difference between modalities in the current multimodal sentiment analysis model and the insufficient fusion between modalities lead to the low accuracy of network prediction, this paper designs a multimodal sentiment analysis model based on BERT-ResNet50. The model uses BERT and ResNet50 to extract text and image features respectively, fuses multi-modal information through the encoder layer of Transformer, and finally uses the Softmax layer to classify multi-modal information. The dataset used in this paper is the Twitter sarcasm public dataset. Through experiments, the BERT-ResNet50 model proposed in this paper is higher than the comparison models in accuracy, recall rate and F1 value, and the accuracy reaches 74.05%. Ablation experiments show that the accuracy of the model in multi-modal sentiment analysis is higher than that in single-modal sentiment analysis.
LIMITATIONS OF POST-PANDEMIC INSTRUCTIONS AND TEACHERS’ COMPETENCIES IN RELATION TO LEARNERS’ PERFORMANCE IN ENGLISH

This study examined the limitations of post-pandemic instructions and competencies of the English teacher-respondents (n=19) in Los Baños District Public Elementary Schools in relation to the English performance of the learners (n=321).A descriptive-correlational research was done to investigate relationships of limitations of post-pandemic instructions and teachers' competencies that can predict variations of learners' performance in English.The study revealed that having larger classes contributes a decreased students' achievement in English, while larger class sizes received more discipline problems among students and prevents teachers to focus on quality education.Moreover, in giving task-based activity, learners have difficulty in composing clear and coherent sentences using appropriate grammatical structures and they have a lack of concentration in learning English since they are still recovering from the impact of the lockdown.Personal level of teachers' competencies has shown proficient in all four modes of communication, listening, speaking, reading, and writing.Likewise, professional level of teachers' competence has shown outstanding level of teaching approaches, content knowledge, and assessment as to apply knowledge within and across the curriculum, use research-based teaching-learning strategies, display proficient use of English, use effective verbal and non-verbal communication, and upgrade their knowledge based from their needs.Still, over-all academic performance among grade 5 students in English is very satisfactory in post-pandemic instructions.There is no significant relationship between the professional level of teachers' competence as to teaching approaches, content knowledge, and emotional competence and academic performance of grade 5 pupils in English.Ultimately, the findings of the study served as baseline data and as basis for in-service training program for teachers.
CVCL: Context-aware Voxel-wise Contrastive Learning for label-efficient multi-organ segmentation

Despite the significant performance improvement on multi-organ segmentation with supervised deep learning-based methods, the label-hungry nature hinders their applications in practical disease diagnosis and treatment planning. Due to the challenges in obtaining expert-level accurate, densely annotated multi-organ datasets, label-efficient segmentation, such as partially supervised segmentation trained on partially labeled datasets or semi-supervised medical image segmentation, has attracted increasing attention recently. However, most of these methods suffer from the limitation that they neglect or underestimate the challenging unlabeled regions during model training. To this end, we propose a novel Context-aware Voxel-wise Contrastive Learning method, referred as CVCL, to take full advantage of both labeled and unlabeled information in label-scarce datasets for a performance improvement on multi-organ segmentation. Experimental results demonstrate that our proposed method achieves superior performance than other state-of-the-art methods.
Important factors in measuring learners' satisfaction and continuance intention in open and distance learning (ODL) institutions

Purpose The aim of this study is to explore and establish the relationships between justice dimensions, expectation-confirmation model (ECM) and continuance intention and also to examine the mediating effect of learners’ satisfaction and perceived usefulness toward continuance intentions of the university learning management system (LMS) within open and distance learning (ODL) context. Design/methodology/approach A cross-sectional research design was used, and the data were collected from a sample of 366 respondents utilizing the online survey questionnaire approach. Hypotheses were tested using the partial least square (PLS) SEM technique. Findings The findings reveal that justice dimensions (distributive, procedural, interpersonal and informational justice) have a significant relationship with learners’ satisfaction. Performance confirmation have significant relationship with learners’ satisfaction and perceived usefulness. Perceived usefulness has a direct significant relationship toward learners’ satisfaction, and learners’ satisfaction has a direct significant relationship toward continuance intention. For mediation analysis, learners’ satisfaction was found to have a mediation effect in the relationship between the justice dimensions and continuance intention. Learners’ satisfaction was also found to have a mediation effect on the relationship between performance conformation and continuance intention. However, learners’ satisfaction was found not to have a mediation effect on the relationship between perceived usefulness and continuance intention, and perceived usefulness does not have a mediation effect in the relationship of performance confirmation and learners’ satisfaction. Originality/value This study advances knowledge and provides valuable insights to practitioners and academic scholars to have a better understanding of the effective attributes of the university LMS, and also to maximize the revenues and the sustainability of the institutions in long term, especially in the 21st century.
Impact of China’s Foreign Direct Investment on The Rwandan Economic Growth

The purpose of this study was to investigate the exact impact in quantitative terms of China’s FDI on the Economic development of the African continent with reference to Rwanda. The target population comprised 10 staff members of National Institute of Statistics of Rwanda, Rwanda Development Board, Ministry of Trade and Industry, Ministry of Finance and Economic Planning, United Nations (UN) Statistics, and World Bank's World Development Indicators. The sample size was 10 key informants. The researcher used questionnaire, interview guide and desk review as data analysis tools. The SPSS was used to analyze data into descriptive and inferential statistics. Results discover that in Rwanda a foreign direct investment depends on the consumer prices index. The study found out that 54.3% agreed with the consumer prices index, foreign direct investment is influenced by owner-occupiers' housing costs as it was evidenced by 62.8%, it was seen the retail prices index, where 56.7% agreed. Correlation analysis between the Owner-occupiers' housing costs and economic growth show a significant correlation between owner-occupiers' housing costs and job creation (r=.206; p-value=.000). This was statistically correlated given the p value was &lt;0.005 proposing that increase in owner-occupiers' housing costs has led to a job creation in Rwanda. The study demonstrated that 64.6% agreed that the nominal effective exchange influence foreign direct investment, 45.1% strongly agreed with the real effective exchange rate. Correlation results felt that choice of price or cost felt that it is significantly correlated with a growth domestic product (r=0.123*; p value=0.034). The study sought to evaluate the effect of Balance of Trade on the economic growth of Rwanda. The study evidenced that participants agreed that the financial account statements by 55.5% while the study found that capital account is affecting foreign direct investment. It was shown by 53.0%. Results show a significant correlation between capital account and poverty reduction (r=0.105, p-value=0.071). The correlations were statistically significant given that the p value was &lt; 0.05 proposing that an increase in capital account was insignificant with the economic growth and vice versa. The study concludes that inflation rate, exchange rate and balance of trade and thus FDI, have a significance effect on the economic growth in Rwanda. The study recommends that foreign direct investment strategies could be adopted in everyday activities. It would be appropriate to consider most favored nation and trade liberalization and investment as a criteria of exporting companies for improving economic growth. Future research can be conducted using longitudinal research to identify factors which contribute to economic growth. Keywords:&nbsp;Foreign Direct Investment, Economic Growth, Rwanda
Fake Customer Review Detection System

Online purchasing is rising bit by bit since each service or product is easily accessible. Sellers are obtaining more reaction to one’s corporation factors. Several people generally frustrated kinds of persons misdirect others by sharing false comments to encourage or damage the image of any specific goods or services according to wish. Such people are known as perception spammers and the false reviews they give are considered as fake comments. Although customer reviews could be beneficial, naïve confidence in such comments is unsafe for either the buyers or sellers. Many consumers read research before making any online purchase. Moreover, the comments could be misleading for additional benefit or profit, so any buying decision relied on web comments should be taken carefully. Our work is mainly directed to SA at the document level, more specifically, on movie reviews dataset. Machine learning techniques and SA methods are expected to have a major positive effect, especially for the detection processes of fake reviews in restaurant reviews, e-commerce, social commerce environments, and other domains. In machine learning-based techniques, algorithms such as SVM, NB, and NLP are applied for the classification purposes SVM is a type of learning algorithm that represents supervised machine learning approaches, and it is an excellent successful prediction approach. The SVM is also a robust classification approach. The main goal of our study is to classify restaurant reviews as a real review or fake review using SA algorithms with supervised learning techniques. Keywords: — Supervised Machine Learning Techniques, Support Vector Machine, Natural Language Processing and Naıve Bayes.
Transformer online monitoring and intelligent diagnosis system design

Transformer is a pivotal equipment in the power system, and its safe and stable operation affects the entire power system. Therefore, the on-line monitoring and fault diagnosis of transformers are particularly important in practical applications. The traditional maintenance method is called regular maintenance. This maintenance method is to call a large number of inspection workers at regular intervals to carry out large-scale inspections, not only to collect various working data of the transformer, but also to observe its working status, and finally manually. Check whether there are hidden dangers in major machines, and judge whether the transformer can run safely and stably according to the data obtained by these large amounts of manpower and material resources. In order to improve the efficiency and accuracy of transformer fault diagnosis, and to carry out all-round real-time online monitoring and fault diagnosis of 220kV power transformers, considering the actual needs of electricity companies, this paper is based on the Qt development platform, C++, MySQL and other development environments. And developed a transformer online monitoring and intelligent diagnosis system, which mainly has the functions of providing basic information management, online monitoring, fault diagnosis, giving operation and maintenance suggestions, and providing users with visual comprehensive analysis. Tested with actual data, it is proved that the system can objectively reflect the operating state of the transformer and carry out real-time fault diagnosis. The system designed in this paper meets the needs of operators to understand the status of distribution transformers, and effectively solves the problem of untimely detection of distribution transformer faults. It can effectively improve the efficiency of detection, reduce the probability of major accidents, and reduce operation and maintenance costs., which saves financial and human resources, and is of great significance for improving economic efficiency and personal and equipment safety.
Reading picture books with infants and toddlers: learning through language

Click to increase image sizeClick to decrease image size AcknowledgmentI would like to extend our gratitude to the Indonesian Endowment Fund for Education (Lembaga Pengelola Dana Pendidikan) under the Ministry of Finance of the Republic of Indonesia for sponsoring one of the author’s studies.