Reviewerly/exHarmony huggingface dataset:
Possible Application of Service-Oriented Architecture in Domain of Land Administration System

Service-oriented architecture refers to a software development paradigm in which individual components of the system are treated as services. This paper aimed to investigate the usage of service-oriented architecture in the land administration system. The possible advantages of this approach would be the ability to handle the system complexity by using multiple smaller components and the ability to use the appropriate programming languages, frameworks, and paradigms for each part of the system. In this paper, it is presented how a system, such as Land Administration System, can be (re)built based on service-oriented architecture, with concrete examples of its parts (layers). The goal of this paper is to represent the way how different systems, interacting with each other, can perform their tasks, such as adding a new parcel, all with the aim of failure-free functioning of these systems, with following analysis of the advantages and disadvantages of this approach. It is concluded that migrating Land Administration System to this type of architecture could provide a better performance of the whole system, since applying service-oriented architecture increases interoperability of land administration system and other systems with which it interacts.
Progressive and Efficient Verification for Digital Signatures

Digital signatures are widely deployed to authenticate the source of incoming information, or to certify data integrity. Common signature verification procedures return a decision (accept/reject) only at the very end of the execution. If interrupted prematurely, however, the verification process cannot infer any meaningful information about the validity of the given signature. We notice that this limitation is due to the algorithm design solely, and it is not inherent to signature verification. In this work, we provide a formal framework to handle interruptions during signature verification. In addition, we propose a generic way to devise alternative verification procedures that progressively build confidence on the final decision. Our transformation builds on a simple but powerful intuition and applies to a wide range of existing schemes considered to be post-quantum secure including the NIST finalist Rainbow. While the primary motivation of progressive verification is to mitigate unexpected interruptions, we show that verifiers can leverage it in two innovative ways. First, progressive verification can be used to intentionally adjust the soundness of the verification process. Second, progressive verifications output by our transformation can be split into a computationally intensive offline set-up (run once) and an efficient online verification that is progressive.
Multi-carrier Steganography Algorithm Based on Executable Program

Executable file steganography makes use of the redundancy of executable programs to hide secret information and realize the secure transmission of secret information, which is one of the research hotspots in the field of information security. Single carrier steganography has some problems, such as low security and insufficient steganography capacity. In order to further improve the concealment and solve the problem of too centralized hiding capacity, a technology of information decentralized hiding is introduced by using the working principle of threshold secret sharing. The encrypted secret information is divided into several blocks according to the agreed algorithm, and then is hidden into multiple executable files under the windows system folder. When users need secret information, the system will automatically extract and assemble this scattered and hidden information to form a complete secret information. In this way, even if a malicious attacker can get one or more executable files containing secret information, it cannot effectively extract the hidden information.
The Corpus of Emotional Valences for 33,669 Chinese Words Based on Big Data

Emotion theories are mainly classified as categorical or dimensional approaches. Given the importance of emotional words in emotion research, researchers have constructed a co-occurrence corpus of 7 types of emotion words through word co-occurrence and big data corpora. However, in addition to the categorical approach, the dimensional approach plays an important role in natural language processing. In particular, valence has an important influence on the study of emotion and language. In this study, the co-occurrence corpus of 7 types of emotion words constructed by Chen et al. [1] was expanded to create a corpus of emotional valences. Then, stepwise multiple regression analysis was performed with the predicted criterion variables and 15 predictor variables. The criterion variables were the emotional valences of 553 frequently occurring stimulus words included in the Chinese Word Association Norms [2]. The predictor variables included the emotion co-occurrences scores for 2 clusters (a cluster of literal emotion words and a cluster of metaphorical emotion words) and 7 types of emotions (happiness, love, surprise, sadness, anger, disgust, and fear) [the emotional words were common words from both the co-occurrence corpus of 7 types of emotion words constructed by Chen et al. [1] and the Chinese Word Association Norms established by Hu et al. [2]] and the virtue word co-occurrences score. The results showed that the scores for literal happiness word co-occurrences, metaphorical happiness word co-occurrences, literal disgust word co-occurrences, literal fear word co-occurrences, and virtue word co-occurrences could predict the valence values of emotion words, with the multiple correlation coefficients of multiple regression analyses reaching .729. Subsequently, the valence values of 33,669 words were established using the formula obtained from the multiple regression analysis of the 553 words. Next, the correlation between the actual valence values and the predicted valence values was analyzed to test the cross-validity of the established valences using the common words in the norm established by Lee and Lee [3] for the emotionality ratings and free associations of 267 common Chinese words. The results showed that the correlation between the 2 was .755, indicating that the predicted values generated by the big data corpora and word co-occurrence had a degree of similarity with the manually determined values. Based on theories and tests, this study used the co-occurrence data of 7 emotions and virtue to construct the corpus of emotional valences for 33,669 Chinese words. The results showed that the combined use of big data corpora and word co-occurrence can effectively expand existing corpora that were established based on emotional categories, improve the efficiency of manual construction of corpora, and establish a larger corpus of emotional words.
The Case for Cross-Entity Delta Encoding in Web Compression

Delta encoding and shared dictionary compression (SDC) for accelerating Web content have been studied extensively in research over the last two decades, but have only found limited adoption in the industry so far: Compression approaches that use a custom-tailored dictionary per website have all failed in practice due to lacking browser support and high overall complexity. General-purpose SDC approaches such as Brotli reduce complexity by shipping the same dictionary for all use cases, while most delta encoding approaches just consider similarities between versions of the same entity (but not between different entities). In this study, we investigate how much of the potential benefits of SDC and delta encoding are left on the table by these two simplifications. As our first contribution, we describe the idea of cross-entity delta encoding that uses cached assets from the immediate browser history for content encoding instead of a precompiled shared dictionary: This avoids the need to create a custom dictionary, but enables highly customized and efficient compression. Second, we present an experimental evaluation of compression efficiency to hold cross-entity delta encoding against state-of-the-art Web compression algorithms. We consciously compare algorithms some of which are not yet available in browsers to understand their potential value before investing resources to build them. Our results indicate that cross-entity delta encoding is over 50% more efficient for text-based resources than compression industry standards. We hope our findings motivate further research and development on this topic.
Multi-spin-flip engineering in an Ising machine

A merge process is proposed to engineer a multi-spin flip in an Ising machine. The merge process deforms the Hamiltonian (energy function) of the Ising model. We prove a theorem for the merge process and show that a single-spin flip in the deformed Hamiltonian is equivalent to a multi-spin flip in the original Hamiltonian. A merge process induces a transition within the subspace of feasible solutions. We propose a hybrid simulated annealing (SA) algorithm with the merge process. The hybrid algorithm outperforms the conventional SA algorithm, genetic algorithm, and tabu search in the binary quadratic knapsack problems (QKP) and the quadratic assignment problems (QAP). Finally, the hybrid merge process is used in a real Ising machine. The performance is improved in QKP and QAP instances. The merge process is generally applicable to existing Ising machine hardware because the deformed Hamiltonian keeps the format of the Ising model.
Image Despeckling Using Hybrid Method

The Synthetic Aperture Radar (SAR) is a satellite imaging which is severely degraded by noise and appears as granular pattern which affects the overall quality of the image and causes difficulty in interpretation and further processing like segmentation, detection and recognition of the target in the image for computer vision systems and also for manual interpreters. Speckle noise is multiplicative nature and it is complicated to filter it. In this paper a hybrid methods consisting of Frost filter and bayes thresholding are implemented for the removal of Speckle noise and qualitative measures like PSNR and MSE are evaluated.
A New Human Factor Study in Developing Practical Vision-Based Applications with the Transformer-Based Deep Learning Model

The convolutional neural network is a deep learning architecture that has dominated most computer vision tasks for several years. But starting from 2020, Transformer architecture has turned to be a new challenger that has been expected to replace convolutional neural networks in the near future. Unlike researchers that prefer observing any new possibility in order to look for chances of improvement, achieving a new state-of-the-art model is not a goal for most practitioners. This paper observes in detail how the two types of architectures allow practitioners to easily use them in actual applications. Major models regarding each architecture in each computer vision task are described and summarized according to their task variety, availability, outputted performances, and computational resources. In conclusion, this paper discovers that the younger Transformer-based models are not inferior in terms of task variety, outputted performance, and computational resources. But it is the problem of availability that makes Transformer-based models more difficult to use at this moment.
Building a “Corpus of 7 Types Emotion Co-occurrences Words” of Chinese Emotional Words with Big Data Corpus

Past studies used human rated as the way of establishing a corpus which costs a lot of time and money but contains insufficient words, also the Categorical Approach was seldom used for building corpus, which may also lead to study bias. Therefore, study 1 of present study has used the Spreading Activation Model as the structure, and used big data of text corpus and word co-occurrences to build a corpus that contains more categories of emotions and much more words. First, study 1 selected the words that can clearly describe the meanings or can effectively evoke the feeling of its emotion category for seven emotions, including Happiness, Surprise, Sadness, Anger, Disgust, Fear, and Love. Then study 1 calculated the averages of co-occurrences for selected words and text corpora by seven emotions categories (measure is Baroni-Urbani, unit is chunk), it computes the averages of co-occurrences by emotional categories for 33669 words, it represents the conceptual consonance of words and the emotions. Study 2 has investigated the practical use of the corpus built in study 1, and used C-LIWC dictionary which was built by human rated as a comparison, taking the posts of Happy Board, Sad Board, Hate Board of PTT Bulletin Board System into the analyses of emotions recognition, result showed that Corpus of 7 Types Emotion Co-occurrences Words" built in study 1 had higher correct rate than human rated corpus. Present study has also compared the correct rates between the Corpus of 7 Types Emotion Co-occurrences Words and CLIWC (Chinese Linguistic Inquiry and Word Count), result showed correct rates of two databases were significant different, the corpus of present study has higher correct rate. Present study has built a text corpus for the material of emotion research, and the results also supports a potential of building the corpora of emotional words with big data measures.
Comparison of Usability and Immersion Between Touch-Based and Mouse-Based Interaction: A Study of Online Exhibitions

Equipment in the next few years. As large-screen personal devices to view virtual exhibitions, tablet computer and computers have two interactive modes of touch and mouse. This study aims to investigate whether mouse and touch affect users' usability and immersion experience. During the process, the typical interactive actions of viewing online exhibitions under the two methods of tablet pc touch and computer mouse were extracted, and 2 typical tasks were set up. The operation count and operation time were recorded, and then the usability and immersion scores were obtained by subjective scale. Finally, a brief experience interview was conducted to investigate the experience differences of viewing virtual exhibitions under different interactive modes. The results showed that the usability score and immersion score of using tablet pc touch were better than using computer mouse, and the operation time was more for the table pc than for computer mouse. The results of this research can be used as a reference for the interactive design of the online virtual exhibition on tablet pc and computer.
Data Mining-Based Variant Subset Features

A subset of accessible variants data is chosen for the learning approaches during the variant selection procedure. It includes the important one with the fewest dimensions and contributes the most to learner accuracy. The benefit of variant selection would be that essential information about a particular variant isn't lost, but if just a limited number of variants are needed, and the original variants are extremely varied, there tends to be a risk of information being lost since certain variants must be ignored. Dimensional reduction, also based on variant extraction, on the other hand, allows the size of the variant space to be reduced without losing information from the original variant space. Filters, wrappers, and entrenched approaches are the three categories of variant selection procedures. Wrapper strategies outperform filter methods because the variation selection procedure is suited for the classifier to be used. Wrapper techniques, on the other hand, are too expensive to use for large variant spaces due to their high computational cost; therefore each variant set must be evaluated using the trained classifier, which slows down the variant selection process. Filter techniques have a lower computing cost and are faster than wrapper procedures, but they have worse classification reliability and are better suited to high-dimensional datasets. Hybrid techniques, which combine the benefits of both filters and wrappers approaches, are now being organized.
Formalized Chinese Sentence Pattern Structure and Its Hierarchical Analysis

Sentence Patterns (句式) have always been important in Chinese grammar teaching, but they lack the corresponding formal representation that fits with information processing. Sentence pattern analysis in Chinese grammar teaching generally adopts Sentence Component Analysis (句子成分分析法), while the dichotomy idea of the Hierarchical Analysis of Phrase Structure (层次分析法) is dominant in the construction of mainstream Chinese Treebank. This situation impedes the exchange and resource sharing between these two application areas. This paper reviews the formalized schema of Sentence Pattern Structure which is subject to the Diagrammatic Syntactic Analysis (图解法) of Sentence-based Grammar (句本位语法), and expounds its hierarchical characteristics and application value in teaching Chinese as a foreign language.
SSAG-Net: Syntactic and Semantic Attention-Guided Machine Reading Comprehension

Machine reading comprehension (MRC) is a task in natural language comprehension. It assesses machine reading comprehension based on text reading and answering questions. Traditional attention methods typically focus on one of syntax or semantics, or integrate syntax and semantics through a manual method, leaving the model unable to fully utilize syntax and semantics for MRC tasks. In order to better understand syntactic and semantic information and improve machine reading comprehension, our study uses syntactic and semantic attention to conduct text modeling for tasks. Based on the BERT model of Transformer encoder, we separate a text into two branches: syntax part and semantics part. In syntactic component, an attention model with explicit syntactic constraints is linked with a self-attention model of context. In semantics component, after the framework semantic parsing, the lexical unit attention model is utilized to process the text in the semantic part. Finally, the vectors of the two branches converge into a new vector. And it can make answer predictions based on different types of data. Thus, a syntactic and semantic attention-guided machine reading comprehension (SSAG-Net) is formed. To test the model’s validity, we ran it through two MRC tasks on SQuAD 2.0 and MCTest, and the SSAG-Net model outperformed the baseline model in both.
Optimization Focused on Parallel Fuzzy Deep Belief Neural Network for Opinion Mining

In this work, we propose a new parallel fuzzy deep belief neural network for sentiment analysis. We have applied several preprocessing tasks to enhance data quality and remove noisy data. Then, we have applied a semi-automatic data labeling over the dataset by combining two techniques: Vader lexicon and Mamdani's fuzzy system. In addition, we have used four extraction techniques, which are: TFIDF (Unigram), TFIDF (Bigram), TFIDF (Trigram) and GloVe in order to represent each tweet by numerical vector. Further, we have implemented three feature selection techniques which are: The mutual information approach, the chi-square method and the ANOVA technique. Finally, we have applied the deep belief network as classifier in order to classify each tweet into a neutral, negative or positive and our hybrid parallel deep-fuzzy belief neural network is deployed in a parallel design employing the Hadoop framework to overcome the issue of long runtime of huge data sets. Also, a comparisons of the proposed model's effectiveness with other existing models in the literature is carried out and the experimental results shown that our suggested parallel fuzzy model surpasses the baseline models by a considerable margin in terms of recall, runtime, F1 score, accuracy, error rate and precision.
An End-to-End System for Text Extraction in Indian Identity Cards

The process of customer verification has become a mandatory procedure to follow for a large number of sales and financial organizations. Traditionally, it has required collecting the required data from the uploaded images of identity cards and then manually entering them into a system which makes it a resource-intensive and error-prone task. Automation of this entire process is extremely beneficial for these organizations. The paper proposes an end-to-end system for preprocessing, text detection, and text recognition of identity cards by identifying the suitable methods and model architectures for each of the components in the system. The end-to-end system presented in the paper gives an accuracy of 93%. This paper is useful for researchers who want to work on improving the automation of text extraction from images, optical character recognition, and other similar use-cases
Strategies for Working Remotely: Responding to Pandemic-Driven Change with Cross-Organizational Community Dialog

In response to the COVID-19 pandemic, the Exascale Computing Project’s (ECP) Interoperable Design of Extreme-scale Application Software (IDEAS) productivity team launched the panel series Strategies for Working Remotely to facilitate informal, cross-organizational dialog in the absence of face-to-face meetings. In a time of pandemic, organizations increasingly need to reach across perceived boundaries to learn from each other, so that we can move beyond stand-alone silos to more connected multidisciplinary and multi-organizational configurations. The present paper argues that the unplanned transition to remote work, overuse of electronic communication, and need to unlearn habits associated with an overreliance on face-to-face, created unique opportunities to learn from the situation and accelerate cross-institutional cooperation and collaboration through online community dialog facilitated by informal panel discussions. Recommendations for facilitating online panel discussions to foster cross-organizational dialog are provided by applying the Simulation Experience Design Method.
Multilingual Speech Recognition for Indian Languages

Automatic speech recognition systems are of two types, such as monolingual and multilingual. Due to its ability to use transfer learning techniques and create better SR models for resource-scarce languages, multilingual speech recognition has recently become more prevalent. Generally, multilingual speech recognition models use specific parameters and modules which activate depending on the language given to the models. These models lack efficiency if language identity is not specified and also lacks the ability to recognize code-switch speech. In this work, we propose a multilingual model for English and Indian languages that can convert speech to text without specifying the language identity and recognize code-switch speech through transliterated and English text as input transcription. The transliterated text can help the model to learn and map sound to its appropriate English character in the case of multilingual and code-switched speech. This multilingual model uses the DeepSpeech architecture by Baidu. The lowest word error rate (WER) and character error rate (CER) for the best model were 30.5% and 11.69%, respectively.