Reviewerly/exHarmony huggingface dataset:
Text Spotting in Vietnamese Documents

Text spotting in images is a common task in the scientific world and the research to apply in practice is an important problem. In this paper, we propose a system for recognizing text documents both printed text and handwriting text in Vietnamese. We use the text detection module and text recognition module for the main section. In addition, we also provide some features in preprocessing module and post-processing module to correct the error of prediction, improve accuracy especially for Vietnamese handwritten. The proposed system was trained in a huge, various type data with English, Vietnamese language and the best model get good results in many evaluation datasets. The experimental results in the recognition module show that our system achieves 2.94% of character error rate (CER), which helps improve CER of another previous approach on the VNOnDB-Word dataset of the Vietnamese online handwritten text recognition competition (HANDS-VNOnDB or VOHTR2018). These results are better than all results compare to other recognition systems in the competition.
Order-Hiding Range Query Over Encrypted Cloud Data

Range query on encrypted data has been widely studied to achieve fast query processing while keeping data and queries secret. Prior work hides even the ordering information of the data, but sacrifices accuracy (e.g., false positives) and efficiency (e.g., exponential complexity in the domain size) as a trade-off. Moreover, hiding the order alone is not sufficient to prevent the recent database reconstruction attacks on range query abusing access pattern and volume leakage. Several countermeasures prevent such information leakage but incur practically unacceptable storage or computational overhead due to fake records embedded in the database and database re-encryption procedures. In this paper, we first propose a range query scheme RevealRE, which is highly efficient, but reveals ordering information. We then extend RevealRE to construct HideRE, a range query scheme that does not leak data ordering information while accurate (i.e., no false positives/negatives) and efficient (i.e., logarithmic search time). HideRE can mitigate the database reconstruction attacks by introducing deceptive queries, which are indistinguishable from regular queries while obfuscating the queried ranges. We experimentally confirm efficiency of our construction under various data distribution models, demonstrating the practicality. Resilience to database reconstruction attacks of our order-hiding range query scheme is formally proved.
The Realization of Interpersonal Meaning in the Report Text of the Undergraduate Students of Universitas Batanghari Jambi

In interpersonal meaning, language is used to see the relationship between the speaker/writer with the listener/reader. Using interpersonal meaning to analyze written text is useful to see how the writer gives information and suggestion to the reader. This study investigated the interpersonal meaning realizing in the report text that is 354 clauses from 20 texts written by the undergraduate students. It used the framework from the Systemic Functional Linguistics by Eggins (2004) and Halliday and Matthiessen (2014). This research is qualitative case study research because it focuses on the analysis or interpretation of the written material. It analyzes mood system, modality system, speech function and studentsâ€™ mastery in writing report text. From the analysis, it found that mostly declarative mood realized in the report text, it is 334 clauses and it is categorized as statement in the speech function. In the modality system, the most type realized in the text is modalization type, it is 38 clauses. By analyzing report text using interpersonal meaning, it was found the studentsâ€™ mastery in the writing skill. The are only some students did mistakes in writing some clauses. Therefore using interpersonal meaning to analyze text help to know the mastery of the students in writing and also can improve their skill. It can be said that interpersonal meaning also realize in the report text. The realization can be seen from the types of mood, modality and speech function that were found in the report text.
What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models

Pre-training by language modeling has become a popular and successful approach to NLP tasks, but we have yet to understand exactly what linguistic capacities these pre-training processes confer upon models. In this paper we introduce a suite of diagnostics drawn from human language experiments, which allow us to ask targeted questions about the information used by language models for generating predictions in context. As a case study, we apply these diagnostics to the popular BERT model, finding that it can generally distinguish good from bad completions involving shared category or role reversal, albeit with less sensitivity than humans, and it robustly retrieves noun hypernyms, but it struggles with challenging inferences and role-based event prediction -- and in particular, it shows clear insensitivity to the contextual impacts of negation.
The Realization of Griceâ€™s Maxims in English Teacherâ€™s Interaction with Male and Female Students

Griceâ€™s maxims refer to the use of the four conversational implicature and cooperative principle proposed by Paul Herbert Grice (1975). In order to make communication successful, participants have to follow such a principle. This study is aimed at investigating the realization of Griceâ€™s maxims in English teacherâ€™s interaction with male and female students at Bilingual Boarding School and the gender difference that influenced the interaction. The researcher uses descriptive qualitative in analyzing the data. The main instrument used was the researcher herself with the help of the data sheet as the secondary instrument. The object of the study was the teacherâ€™s interaction with male and female students in a different classroom. Then, the data were analyzed in four steps of interactive analysis: data collection, data reduction, data presentation, and data conclusion. To avoid bias or subjectivity, the researcher applied the triangulation of expert judgment. The results show that all the four Griceâ€™s maxims, quality, quantity, relevant, and manner are realized in the teacherâ€™s interaction with male and female students. It was realize by observing and flouting the maxims. In the interaction with male students, there are 4 observances of Griceâ€™s maxims realized they are observed quality maxim, quantity maxim, relevant maxim, and manner maxim. In non-observance of maxims, there are flouted quality, quantity, relevant and manner maxims. On the other hand, the realization of Griceâ€™s maxims in the interaction with male students was observed the quality, quantity, relevant, manner and flouted 2 maxims were quantity and relevant maxim. The results of this study can pedagogically contribute to English language studies. In the classroom interaction in discourse and pragmatic development in general, the realization of maxims (observed and flouted) can be directly included as a good example of the natural authentic of English.
Effect of Image Splicing and Copy Move Forgery on Haralick Features of a Digital Image

Nowadays, digital images have become the fastest way of transferring information, but the existence of advanced photo-editing tools makes it easy to alter digital image content which may be used as proof in a legal case, thus creating a serious problem. The most frequent techniques of image forgery include splicing and copy move. Splicing is an image forgery technique in which, the forger crops a part of the first image and places it in the second image while Copy move forgery is a type of image manipulation that involves copying and pasting at least one component of an image onto other areas of the same image for the purpose of duplication or removal of objects in the image. Finding the integrity of a digital image is critical since it can be used as a legal proof in a multitude of sectors, including investigation of a crime scene. Equally, finding the features of an image that change as a result of image manipulation such as copy move and splicing is very important as this can be used to distinguish between forged and original image. Therefore, in this paper, we looked upon the effect of image splicing and copy move forgery on haralick features of a digital image. CoMoFoD dataset and Images frames extracted from original and spliced videos were used in thisexperiment. The result of this experiment shows that splicing and copy move manipulations have no effect on haralick features of a digital image. As a result, these features cannot be used to tell if an image has been spliced or was manipulated bya copy move forgery
Integration of Specific Local Search Methods in Metaheuristic Algorithms for Optimizing the Feature Selection Process: A Survey Authors

Metaheuristic algorithms have proven to be quite effective at solving global optimization issues, particularly feature selection difficulties. This class of algorithms often uses a specialized local search technique as an inner component or as a post-processing mechanism to improve the performance of their search process. This paper presents a comprehensive survey of the use of local search methods integrated into metaheuristic algorithms for optimizing the feature selection process. Based on the manner of operation, the local search methods examined in this study were classed as one-way or two-way. In addition, practical suggestions were also discussed to point out possible future directions.
An Enhanced Neural Graph based Collaborative Filtering with Item Knowledge Graph

Recommendation system is a process of filtering information to retain buyers on e-commerce sites or applications. It is used on all e-commerce sites, social media platform and multimedia platform. This recommendation is based on their own experience or experience between users. In recent days, the graph-based filtering techniques are used for the recommendation to improve the suggestions and for easy analysing. Neural graph based collaborative filtering is also one of the techniques used for recommendation system. It is implemented on the benchmark datasets like Yelp, Gowalla and Amazon books. This technique can suggest better recommendations as compared to the existing graph based or convolutional based networks. However, it requires higher processing time for convolutional neural network for performing limited suggestions. Hence, in this paper, an improved neural graph collaborative filtering is proposed. Here, the content-based filtering is performed before the collaborative filtering process. Then, the embedding layer will process on both the recommendations to provide a higher order relation between the users and items. As the suggestion is based on hybrid recommendation, the processing time of Convolutional neural network is reduced by reducing the number of epochs. Due to this, the final recommendation is not affected by the smaller number of epochs and also able to reduce its computational time. The whole process is realized in Python 3.6 under windows 10 environment on benchmark datasets Go Walla and Amazon books. Based on the comparison of recall and NDCG metric, the proposed neural graph-based filtering outperforms the collaborative filtering based on graph convolution neural network.
The value of social interactions and incentives on the use of a digital contact tracing tool post COVID-19 lockdown in Singapore

We assessed the preferences and trade-offs for social interactions, incentives, and being traced by a digital contact tracing (DCT) tool post lockdown in Singapore by a discrete choice experiment (DCE) among 3839 visitors of a large public hospital in Singapore between July 2020 - February 2021. Respondents were sampled proportionately by gender and four age categories (21 - 80 years). The DCE questionnaire had three attributes (1. Social interactions, 2. Being traced by a DCT tool, 3. Incentives to use a DCT tool) and two levels each. Panel fixed conditional logit model was used to analyse the data. Respondents were more willing to trade being traced by a DCT tool for social interactions than incentives and unwilling to trade social interactions for incentives. The proportion of respondents preferring no incentives and could only be influenced by their family members increases with age. Among proponents of monetary incentives, the preferred median value for a month's usage of DCT tools amounted to S$10 (USD7.25) and S$50 (USD36.20) for subsidies and lucky draw. In conclusion, DCE can be used to elicit profile-specific preferences to optimize the uptake of DCT tools during a pandemic. Social interactions are highly valued by the population, who are willing to trade them for being traced by a DCT tool during the COVID-19 pandemic. Although a small amount of incentive is sufficient to increase the satisfaction of using a DCT tool, incentives alone may not increase DCT tool uptake.
Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm

In order to find a chaotic trajectory sequence with strong global optimization ability to help the genetic selection of direction after the reversal of chemotaxis, an improved genetic algorithm based on chaos optimization is proposed by combining the characteristics of chaotic motion with the improved genetic algorithm. The optimal coverage problem in sensor networks can carry out fine optimization search on local areas. The results show that the overall trend of fitness and optimization efficiency is relatively stable. The optimization efficiency will be gradually improved with the continuous progress of time and genetics, and the error analysis will be reduced. This will greatly improve the impact of various adverse factors in the optimization process. In addition, the change rate of fitness is basically kept at a high change rate, which also reflects that the basic framework of the model is very excellent, and the whole algorithm structure and data processing are improved by 54%. The improved genetic algorithm proposed in this paper is used to adjust and optimize the controller parameters. When the uncertain parameters change greatly, the control system still has good control quality and strong robustness.
Curricular Robust Reinforcement Learning via GAN-Based Perturbation Through Continuously Scheduled Task Sequence

Reinforcement learning (RL), one of three branches of machine learning, aims for autonomous learning and is now greatly driving the artificial intelligence development, especially in autonomous distributed systems, such as cooperative Boston Dynamics robots. However, robust RL has been a challenging problem of reliable aspects due to the gap between laboratory simulation and real world. Existing efforts have been made to approach this problem, such as performing random environmental perturbations in the learning process. However, one cannot guarantee to train with a positive perturbation as bad ones might bring failures to RL. In this work, we treat robust RL as a multi-task RL problem, and propose a curricular robust RL approach. We first present a generative adversarial network (GAN) based task generation model to iteratively output new tasks at the appropriate level of difficulty for the current policy. Furthermore, with these progressive tasks, we can realize curricular learning and finally obtain a robust policy. Extensive experiments in multiple environments demonstrate that our method improves the training stability and is robust to differences in training/test conditions.
Detecting and Visualizing Stops in Dance Training by Neural Network Based on Velocity and Acceleration

Various genres of dance, such as Yosakoi Soran, have contributed to the health of many people and contributed to their sense of belonging to a community. However, due to the effects of COVID-19, various face-to-face activities have been restricted and group dance practice has become difficult. Hence, there is a need to facilitate remote dance practice. In this paper, we propose a system for detecting and visualizing the very important dance motions known as stops. We measure dance movements by motion capture and calculate the features of each movement based on velocity and acceleration. Using a neural network to learn motion features, the system detects stops and visualizes them using a human-like 3D model. In an experiment using dance data, the proposed method obtained highly accurate stop detection results and demonstrated its effectiveness as an information and communication technology support for remote group dance practice.
Regularization Meets Enhanced Multi-Stage Fusion Features: Making CNN More Robust against White-Box Adversarial Attacks

Regularization has become an important method in adversarial defense. However, the existing regularization-based defense methods do not discuss which features in convolutional neural networks (CNN) are more suitable for regularization. Thus, in this paper, we propose a multi-stage feature fusion network with a feature regularization operation, which is called Enhanced Multi-Stage Feature Fusion Network (EMSF2Net). EMSF2Net mainly combines three parts: multi-stage feature enhancement (MSFE), multi-stage feature fusion (MSF2), and regularization. Specifically, MSFE aims to obtain enhanced and expressive features in each stage by multiplying the features of each channel; MSF2 aims to fuse the enhanced features of different stages to further enrich the information of the feature, and the regularization part can regularize the fused and original features during the training process. EMSF2Net has proved that if the regularization term of the enhanced multi-stage feature is added, the adversarial robustness of CNN will be significantly improved. The experimental results on extensive white-box attacks on the CIFAR-10 dataset illustrate the robustness and effectiveness of the proposed method.
Robust Self-Testing of Four-Qubit Symmetric States

Quantum verification has been highlighted as a significant challenge on the road to scalable technology, especially with the rapid development of quantum computing. To verify quantum states, self-testing is proposed as a device-independent concept, which is based only on the observed statistics. Previous studies focused on bipartite states and some multipartite states, including all symmetric states, but only in the case of three qubits. In this paper, we first give a criterion for the self-testing of a four-qubit symmetric state with a special structure and the robustness analysis based on vector norm inequalities. Then we generalize the idea to a family of parameterized four-qubit symmetric states through projections onto two subsystems.
Holistic Systematic Review on Methodologies of Assessing Effectiveness Cybersecurity Awareness Program

Cyber-threat is now a global concern to all internet users, as the rate of crimes has tremendously changed from physical to virtual. Implementing awareness programs at various organization levels is required, Also there is a need to know the methodologies used in measuring cybersecurity awareness program effectiveness. The paper aims to review and explore the previous methodologies applied, target organizations, and application of Machine Learning techniques in the assessment of awareness programs. Systematic literature review techniques were used to conduct the search using pre-defined keywords and published papers from 2006 to 2019, the information was used in providing an answer to the research questions. The finding shows questionnaire was widely used as a method of evaluating cybersecurity awareness and also organizations implement more awareness programs, the gap found is the lack of using ML in assessing the effectiveness of design, and implemented cybersecurity awareness programs
Evaluating Telegram Application to Empower the Students’ Vocabulary Mastery

The biggest trigger for the students&amp;rsquo; in mastering vocabulary is learning media. The inexistence of good learning media will affect the students&amp;rsquo; vocabulary mastery. One of the learning media that is much promoted and used during the pandemic of Covid-19 is the telegram application. Therefore, this research aims to measure the use of telegram applications as learning media to enhance the students&amp;rsquo; vocabulary mastery. In this research, the researcher applied a quasi-experimental method. The population of this research was the seventh-grade students at UPTD SMP Negeri 22 Barru. The samples of the research were taken using the cluster random sampling technique, there are two classes as samples, experimental class, and control class, both classes consisted of 28 students. The data was collected using vocabulary tests (pre-test and post-test) and analyzed employing statistical calculations to test the hypothesis. The result of this research shows that the mean score for pre-tests was 45.35 and the post-test was 83.57. Besides the different scores for pre-test and post-test, the mean score of the students in post-test was 83.57 is higher than the Kriteria Ketuntasan Minimal (75) in UPTD SMP Negeri 22 Barru. The result of the t-test value in the post-test was 2.214 and the t-table value was 1.684. It means that H1 was accepted and H0 was rejected and the seventh-grade students at UPTD SMP Negeri 22 Barru who are taught by using the telegram application have better vocabulary mastery than the seventh-grade students who are taught without using the telegram application.
Towards Identifying Social Factors behind (In)Efficiency of Voting Security Measures

Abstract: In this paper, we take a look at some standard requirements set to voting, and measures to achieve them. We argue that while the measures themselves are typically technical or organizational, their (in)efficiency is often determined by social factors. As the requirements set to voting are contradictory, every society will have to make trade-offs between them. Our analysis shows that one reason why some potential vulnerabilities are perceived as acceptable residual risks in some societies may be that, there simply is no tradition of abusing these vulnerabilities in this particular society. We identify a number of societal parameters, categorize them and study their effect on the (perceived) security of the respective measures.
Deep Learning-Based Natural Language Processing Methods for Sentiment Analysis in Social Networks

This paper presents an in-depth study of the sentiment of social network communication through a deep learning-based natural language processing approach and designs a corresponding model to be applied in the actual social process. Specifically, the network can dynamically select the most important word in the current state according to the information available and achieve the accurate recognition of the dynamically changing important content in a sentence. Based on this, the semantic understanding of the whole sentence is achieved through a continuous cycle of the process. In addition, considering that the semantic representation of natural language is highly dependent on contextual information, the lack of contextual information will lead to the ambiguity and inaccuracy of semantic representation. In this paper, we study the sentiment analysis algorithms in social networks at two levels, unimodal and multimodal, and construct a text sentiment analysis model and a picture-text multimodal sentiment analysis model in social networks, respectively. By comparing the experiments with the existing models on several datasets, the accuracy of the two models exceeded the benchmark models by 4.45% and 5.2%, respectively, which verified the effectiveness of the two models. The feasibility of applying the optimized convolutional neural network recurrent optimization network to social network sentiment analysis is verified by practically applying the optimized convolutional neural network recurrent optimization network to single task and multitask and comparing other existing deep learning classifiers.