Reviewerly/exHarmony huggingface dataset:
Evaluation System of Mobile English Learning Platform by Using Deep Learning Algorithm

At present, China’s economic development continues to progress and foreign exchanges are also increasingly frequent. Learning to master the world’s universal pronunciation, English, has become a more important link. However, people’s living habits make it difficult to carry heavy desktop devices for a long time, so mobile English learning platform meets the development needs of people’s English teaching. Based on the existing English mobile learning platform, this paper puts forward the concept of integrating artificial intelligence and deep learning technology into it. Through deep learning, the learning status and learning situation of students in the process of English learning are extracted, so as to analyze the needs and learning interests of each student in English learning and then push corresponding English materials to each student, which improves the efficiency of English learning. In addition, deep learning can also model the data of students’ behaviors and build a language vector feature extraction mechanism and translation quality evaluation model, so as to carry out certain intelligent auxiliary correction and correction on students’ English grammatical expressions and spoken English. The research in this paper has achieved good results through practice. The practice results show that the integration of deep learning into the existing mobile English teaching platform can optimize the functions of the existing platform, provide more ideas for the development of online English learning, and has good theoretical and practical value.
Construction of Knowledge Graph for Flag State Control (FSC) Inspection for Ships: A Case Study from China

The flag state control (FSC) inspection is an important measure to ensure maritime safety. However, it is difficult to improve ship safety management efficiency using data mining due to the scattered and multi-source ship inspection knowledge. In this paper, the emerging knowledge graph technology is used to integrate multi-source knowledge for the FSC inspection. Firstly, an ontology model is built to systematically describe the knowledge and guide the construction of the data layer of the knowledge graph. Then, the BERT-BiGRU-CRF model is used to extract entities from the unstructured data of the FSC inspection. The extracted results are associated with structured and semi-structured data and stored in the graph database Neo4j to construct the knowledge graph. In addition, a case study of the FSC inspection knowledge graph of Dafeng Port in Yancheng, China, is conducted to verify the strength of the proposed method. The results show that the knowledge graph can correlate trivial knowledge and benefit the efficiency of the FSC inspection. Moreover, the knowledge graph can reflect the deficiency characteristics of ships and support the safety management of water transportation.
A Book Recommendation System Considering Contents and Emotions of User Interests

Although the benefits of reading are widely recognized, many people seldom read even though they often claim to have interest in reading. Since conventional book recommendation systems require keywords or a browsing history related to books that reflect user interests, users who rarely read struggle to obtain satisfactory results. In this study, we propose a book recommendation system that enables both users who read habitually and those who rarely read to easily get results that reflect their interests with their own content of interest as queries. Our proposed method identifies recommended books based on the similarity of the vectors of contents and emotions, contained in tweets about the content of user interests and book reviews. In this study’s experiments, we confirmed the effectiveness of our proposed method.
Impoliteness Strategies Found in Deddy Corbuzier's Podcast on YouTube

This study aimed at determining impoliteness strategies in a talk show entitled Deddy Corbuzier podcast on YouTube. In this research, the researchers used a theory from Culpeper (1996). The researcher used qualitative method to analyse the impoliteness strategies in this study. This research used impoliteness strategies. The data source of the research was the talk show Deddy Corbuzier podcast on YouTube. This research contained utterances from the conversational dialogues in the talk show. Each utterance had several types of impoliteness strategies. In collecting data, this research used the non-participatory technique and observation method by Sudaryanto (2015). Next, the data analyzed with the pragmatic identification method. This research found 15 data examined from the type of impoliteness strategy and the results were divided into bald on record impoliteness (5 data), positive impoliteness (4 data), negative impoliteness (3 data), mock politeness or sarcasm (2 data), withhold politeness (1 data). The most common strategy used by Deddy Corbuzier podcast on YouTube was bald on record impoliteness.
Active vs. Passive: A Comparison of Automata Learning Paradigms for Network Protocols

Active automata learning became a popular tool for the behavioral analysis of communication protocols.The main advantage is that no manual modeling effort is required since a behavioral model is automatically inferred from a black-box system.However, several real-world applications of this technique show that the overhead for the establishment of an active interface might hamper the practical applicability.Our recent work on the active learning of Bluetooth Low Energy (BLE) protocol found that the active interaction creates a bottleneck during learning.Considering the automata learning toolset, passive learning techniques appear as a promising solution since they do not require an active interface to the system under learning.Instead, models are learned based on a given data set.In this paper, we evaluate passive learning for two network protocols: BLE and Message Queuing Telemetry Transport (MQTT).Our results show that passive techniques can correctly learn with less data than required by active learning.However, a general random data generation for passive learning is more expensive compared to the costs of active learning.
Penyengat Island Riau Island: Towards A World Heritage

Malay history and culture cannot be separated from the existence of Penyengat Island. This island once had a strategic role. First, Penyengat Island was the fortress of Raja Haji during the war against the Dutch. Second, as the administrative center of the Riau-Lingga Kingdom. Third, to become the center of Islamic Malay studies which is well-known in the Malay world. Various disciplines have existed on this island. Studies from various disciplines have been carried out. The results mostly recommend that Penyengat Island provide lessons for the life of the Malay community now and in the future. This study uses the historical method through four stages of work, namely heuristics (collection of sources), source criticism (external to the material and internal to the content), interpretation and historiography. The primary sources are Malay manuscripts that were born on Penyengat Island in the late 19th and early 20th centuries. The secondary sources are scientific journals and government policies that discuss Penyengat Island. These primary and secondary sources are then analyzed and interpreted. The final stage is historiography to strengthen the narrative and support data for the submission of Penyengat Island as a world heritage. Penyengat Island has a tangible and intangible cultural heritage. The forerunner of the Indonesian language is recognized as being born from there. Penyengat Island deserves to be a world heritage.
Deep Neural Network Concept for a Blind Enhancement of Document-Images in the Presence of Multiple Distortions

In this paper, we propose a new convolutional neural network (CNN) architecture for improving document-image quality through decreasing the impact of distortions (i.e., blur, shadows, contrast issues, and noise) contained therein. Indeed, for many document-image processing systems such as OCR (optical character recognition) and document-image classification, the real-world image distortions can significantly degrade the performance of such systems in a way such that they become merely unusable. Therefore, a robust document-image enhancement model is required to preprocess the involved document images. The preprocessor system developed in this paper places “deblurring” and “noise removal and contrast enhancement” in two separate and sequential submodules. In the architecture of those two submodules, three new parts are introduced: (a) the patch-based approach, (b) preprocessing layer involving Gabor and Blur filters, and (c) the approach using residual blocks. Using these last-listed innovations results in a very promising performance when compared to the related works. Indeed, it is demonstrated that even extremely strongly degraded document images that were not previously recognizable by an OCR system can now become well-recognized with a 91.51% character recognition accuracy after the image enhancement preprocessing through our new CNN model.
Case Technology in Teaching Professional Foreign Communication to Law Students: Comparative Analysis of Distance and Face-to-Face Learning

Foreign language speaking skills need much practice in order to be improved, which is why it is essential to use different teaching techniques to instruct students effectively. The multiplicity and multitasking of the foreign language teaching process requires the introduction of technology that ensures consistently effective results of professional foreign language learning, i.e., case technology in the algorithmized educational process. The conducted research contributes to the issue of case technology implementation—the algorithm of professional task solutions, aimed at improving students’ English-speaking skills—in teaching professional legal English. The aim of the study is to investigate the effectiveness of case technology in the improvement of ESP speaking skills in the course of distance and face-to-face learning. Comparative analysis of the distance and face-to-face format of case technology implementation in teaching professional foreign communication to law students was carried out in the course of the two-semester study conducted at SPbPU (N 120) in the period from January 2020 to July 2021. Mixed qualitative and quantitative methods were applied to collect and analyse data for the study. The results of the study showed that, for such assessment criteria as task completion, discourse competence and meaningfulness, the use of case technology in a face-to-face format was more effective than the one carried out on-line. As it can have some pedagogical implications on the improvement of English-speaking skills while teaching professional legal English at university, additional effort should be taken to ensure the effectiveness of case technology in the course of distance learning.
On the Complexity of Determining Defeat Relations Consistent with Abstract Argumentation Semantics

Typically in abstract argumentation, one starts with arguments and a defeat relation, and applies some semantics in order to determine the acceptability status of the arguments. We consider the converse case where we have knowledge of the acceptability status of arguments and want to identify a defeat relation that is consistent with the known acceptability data – the σ-consistency problem. Focusing on complete semantics as underpinning the majority of the major semantic types, we show that the complexity of determining a defeat relation that is consistent with some set of acceptability data is highly dependent on how the data is labelled. The extension-based 2-valued σ-consistency problem for complete semantics is revealed as NP-complete, whereas the labelling-based 3-valued σ-consistency problem is solvable within polynomial time. We then present an informal discussion on application to grounded, stable, and preferred semantics.
Abstract Argumentation with Conditional Preferences

In this paper, we study conditional preferences in abstract argumentation by introducing a new generalization of Dung-style argumentation frameworks (AFs) called Conditional Preference-based AFs (CPAFs). Each subset of arguments in a CPAF can be associated with its own preference relation. This generalizes existing approaches for preference-handling in abstract argumentation, and allows us to reason about conditional preferences in a general way. We conduct a principle-based analysis of CPAFs and compare them to related generalizations of AFs. Specifically, we highlight similarities and differences to Modgil’s Extended AFs and show that our formalism can capture Value-based AFs.
Just a Matter of Perspective

Many structured argumentation approaches proceed by constructing a Dung-style argumentation framework (AF) corresponding to a given knowledge base. While a main strength of AFs is their simplicity, instantiating a knowledge base oftentimes requires exponentially many arguments or additional functions in order to establish the connection. In this paper we make use of more expressive argumentation formalisms. We provide several novel translations by utilizing claim-augmented AFs (CAFs) and AFs with collective attacks (SETAFs). We use these frameworks to translate assumption-based argumentation (ABA) frameworks as well as logic programs (LPs) into the realm of graph-based argumentation.
Formalising an Aspect of Argument Strength: Degrees of Attackability

This paper formally studies a notion of dialectical argument strength in terms of the number of ways in which an argument can be successfully attacked in expansions of an abstract argumentation framework. The proposed model is abstract but its design is motivated by the wish to avoid overly limiting assumptions that may not hold in particular dialogue contexts or in particular structured accounts of argumentation. It is shown that most principles for gradual argument acceptability proposed in the literature fail to hold for the proposed notion of dialectical strength, which clarifies their rational foundations and highlights the importance of distinguishing between logical, dialectical and rhetorical argument strength.
<b>ParMA</b>: Parallelized Bayesian Model Averaging for Generalized Linear Models

This paper describes the gretl function package ParMA, which provides Bayesian model averaging (BMA) in generalized linear models. In order to overcome the lack of analytical specification for many of the models covered, the package features an implementation of the reversible jump Markov chain Monte Carlo technique, following the original idea by Green (1995), as a flexible tool to model several specifications. Particular attention is devoted to computational aspects such as the automatization of the model building procedure and the parallelization of the sampling scheme.
Exploiting Digital Surface Models for Inferring Super-Resolution for Remotely Sensed Images

Despite the plethora of successful Super-Resolution Reconstruction (SRR) models applied to natural images, their application to remote sensing imagery tends to produce poor results. Remote sensing imagery is often more complicated than natural images and has its peculiarities such as being of lower resolution, it contains noise, and often depicting large textured surfaces. As a result, applying non-specialized SRR models like the Enhanced Super Resolution Generative Adversarial Network (ESRGAN) on remote sensing imagery results in artifacts and poor reconstructions. To address these problems, we propose a novel strategy for enabling an SRR model to output realistic remote sensing images: instead of relying on feature-space similarities as a perceptual loss, the model considers pixel-level information inferred from the normalized Digital Surface Model (nDSM) of the image. This allows the application of better-informed updates during the training of the model which sources from a task (elevation map inference) that is closely related to remote sensing. Nonetheless, the nDSM auxiliary information is not required during production i.e., the model infers a super-resolution image without additional data. We assess our model on two remotely sensed datasets of different spatial resolutions that also contain the DSMs of the images: the DFC2018 dataset and the dataset containing the national LiDAR fly-by of Luxembourg. We compare our model with ESRGAN and we show that it achieves better performance and does not introduce any artifacts in the results. In particular, the results for the high-resolution DFC2018 dataset are realistic and almost indistinguishable from the ground truth images.
Image Dehazing Technique Based on Sky Weight Detection and Fusion Transmission

&lt;p&gt;Computer vision techniques are widely applied to the object detection, license plate recognition, remote sensing, and outdoor monitoring system. The performance of these applications mainly relies on the high quality of outdoor image. However, an outdoor image can be led to contrast decrease, color distortion, and unclear structure by poor weather conditions and human factors such as haze, fog, and air pollution. These issues may lower down the sharpness of a photo. Despite of the single-image dehazing is used to solve these issues, it cannot achieve a satisfactory result when the method deals with the bright scene and sky area. In this article, we aim to design an adaptive dehazing technique based on fusion transmission and sky weight detection. The sky weight detection is employed to distinguish the foreground and background, while detected results are applied to the fusion strategy to calculate deep and shallow transmissions. Thus, this can get rids of the subject of over-adjustment. Experimental results have demonstrated that the new method can outperform the latest state-of-the-art methods in terms of subjective and the objective assessments.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Secure Judgment of Point and Line Relationship Against Malicious Adversaries and Its Applications

&lt;p&gt;With the rapid development of the Internet and information technology, the problem of zero-trust networks has become increasingly prominent, and secure multi-party computation has become a research hotspot to solve the problem of zero-trust networks. The secure judgment of point and line relationship is an important research branch of secure computing set geometry. However, most of resent secure computing protocols of point and line relationship are designed in the semi-honest model and cannot resist malicious attacks. Therefore, this paper analyzes the possible malicious adversary behaviors and designs a secure protocol in the malicious model. In this paper, the Paillier cryptosystem, zero- knowledge proof, and cut-choose method are used to resist malicious behavior, and the real/ideal model paradigm method is used to prove the security of the protocol. Compared with the existing solutions, the malicious model protocol is still efficient and widely used in real applications.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
An Optimized Black-Box Adversarial Simulator Attack Based on Meta-Learning

Much research on adversarial attacks has proved that deep neural networks have certain security vulnerabilities. Among potential attacks, black-box adversarial attacks are considered the most realistic based on the the natural hidden nature of deep neural networks. Such attacks have become a critical academic emphasis in the current security field. However, current black-box attack methods still have shortcomings, resulting in incomplete utilization of query information. Our research, based on the newly proposed Simulator Attack, proves the correctness and usability of feature layer information in a simulator model obtained by meta-learning for the first time. Then, we propose an optimized Simulator Attack+ based on this discovery. Our optimization methods used in Simulator Attack+ include: (1) a feature attentional boosting module that uses the feature layer information of the simulator to enhance the attack and accelerate the generation of adversarial examples; (2) a linear self-adaptive simulator-predict interval mechanism that allows the simulator model to be fully fine-tuned in the early stage of the attack and dynamically adjusts the interval for querying the black-box model; and (3) an unsupervised clustering module to provide a warm-start for targeted attacks. Results from experiments on the CIFAR-10 and CIFAR-100 datasets clearly show that Simulator Attack+ can further reduce the number of consuming queries to improve query efficiency while maintaining the attack.
Knowledge Structure and Frontier Evolution of Research on Chromitite: A Scientometric Review

Big data analysis can reveal the relevance, hidden patterns, and bursts of activity in data. Therefore, big data analysis has recently aroused great interest and curiosity among scientists in various fields. The powerful data organization and visualization capabilities of CiteSpace software is an effective way to achieve this goal. Chromitite is a strategic mineral resource of global importance with several industrial applications, including steel manufacturing. Research on chromitite has not only had high economic significance, but also has important scientific value. An understanding of chromitite can be used to obtain insight into the processes operating deep within the crust and mantle. However, no big-data analysis has been performed on chromitite-related publications; hence, the evolution of various views over time is unclear. The purpose of this study was to rapidly assess and summarize the development of research in the field, and to identify and briefly describe current research developments. The CiteSpace software was used to reveal research hotspots and predict future trends. The results of the co-occurrence network analysis indicate an active collaboration among current chromitite researchers, and the countries and institutions in which they are based. Hot research topics include a focus on podiform chromitite, the origin of chromitites, and the co-occurrence of platinum group elements (PGE). The main subject of current research is podiform chromitite containing ultrahigh-pressure minerals, which will help to elucidate the relationship between chromitite and the deep processes within the earth.
Consistent Bayesian information criterion based on a mixture prior for possibly high‐dimensional multivariate linear regression models

Abstract In the problem of selecting variables in a multivariate linear regression model, we derive new Bayesian information criteria based on a prior mixing a smooth distribution and a delta distribution. Each of them can be interpreted as a fusion of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Inheriting their asymptotic properties, our information criteria are consistent in variable selection in both the large‐sample and the high‐dimensional asymptotic frameworks. In numerical simulations, variable selection methods based on our information criteria choose the true set of variables with high probability in most cases.