Reviewerly/exHarmony huggingface dataset:
Information-Theoretic Method for Assessing the Quality of Translations

In recent years, the task of translating from one language to another has attracted wide attention from researchers due to numerous practical uses, ranging from the translation of various texts and speeches, including the so-called "machine" translation, to the dubbing of films and numerous other video materials. To study this problem, we propose to use the information-theoretic method for assessing the quality of translations. We based our approach on the classification of sources of text variability proposed by A.N. Kolmogorov: information content, form, and unconscious author's style. It is clear that the unconscious "author's" style is influenced by the translator. So researchers need special methods to determine how accurately the author's style is conveyed, because it, in a sense, determines the quality of the translation. In this paper, we propose a method that allows us to estimate the quality of translation from different translators. The method is used to study translations of classical English-language works into Russian and, conversely, Russian classics into English. We successfully used this method to determine the attribution of literary texts.
Modeling Mobile Writing: Applying Sociocognitive Models of Writing to Mobile Contexts

Current cognitive and sociocognitive models of writing conceptualize writing processes as complex interactions between multidimensional mechanisms that activate a writer’s social motivations, psychomotor processes, and cognitive resources in order to engage in writing. These models have been developed through years of empirical research employing a variety of data channels, such as keystroke logging; however, research about mobile writing processes have been understudied. This paper presents a study of mobile writing processes that used keystroke-logging methods in order to expand scholarship of writing processes into the realm of mobile writing. By examining how participants ( N = 10) wrote on mobile devices at the keystroke level, as well as combining textual and keystroke analysis to examine context text-message (SMS) composition, this study argues for theoretically framing mobile writing as an embodied performance.
Two-Step Matching Method Based on Co-Occurrence Scale Space Combined with Second-Order Gaussian Steerable Filter

Multimodal images refer to images obtained by different sensors, and there are serious nonlinear radiation differences (NRDs) between multimodal images for photos of the same object. Traditional multimodal image matching methods cannot achieve satisfactory results in most cases. In order to better solve the NRD in multimodal image matching, as well as the rotation and scale problems, we propose a two-step matching method based on co-occurrence scale space combined with the second-order Gaussian steerable filter (G-CoFTM). We first use the second-order Gaussian steerable filter and co-occurrence filter to construct the image’s scale space to preserve the image’s edge and detail features. Secondly, we use the second-order gradient direction to calculate the images’ principal direction, and describe the images’ feature points through improved GLOH descriptors. Finally, after obtaining the rough matching results, the optimized 3DPC descriptors are used for template matching to complete the fine matching of the images. We validate our proposed G-CoFTM method on eight different types of multimodal datasets and compare it with five state-of-the-art methods: PSO-SIFT, CoFSM, RIFT, HAPCG, and LPSO. Experimental results show that our proposed method has obvious advantages in matching success rate (SR) and the number of correct matches (NCM). On eight different types of datasets, compared with CoFSM, RIFT, HAPCG, and LPSO, the mean SRs of G-CoFSM are 17.5%, 6.187%, 30.462%, and 32.21%, respectively, and the mean NCMs are 5.322, 11.503, 8.607, and 16.429 times those of the above four methods.
John von Neumann’s Discovery of the 2<sup>nd</sup>Incompleteness Theorem

Shortly after Kurt Gödel had announced an early version of the 1st incompleteness theorem, John von Neumann wrote a letter to inform him of a remarkable discovery, i.e. that the consistency of a formal system containing arithmetic is unprovable, now known as the 2nd incompleteness theorem. Although today von Neumann's proof of the theorem is considered lost, recent literature has explored many of the issues surrounding his discovery. Yet, one question still awaits a satisfactory answer: how did von Neumann achieve his result, knowing as little as he seemingly did about the 1st incompleteness theorem? In this article, I shall advance a conjectural argument to answer this question, after having rejected the argument widely shared in the literature and having analyzed the relevant documents surrounding his discovery. The argument I shall advance strictly links two of the three letters written by von Neumann to Gödel in the late 1930 and early 1931 (i.e. respectively that of November 20, 1930 and that of January 12, 1931) and finds the key for von Neumann's discovery in his prompt understanding of the Gödel sentence A – as the documents refer to it – as expressing consistency for a formal system that contains arithmetic.
A Convergent Algorithm for Equilibrium Problem to Predict Prospective Mathematics Teachers’ Technology Integrated Competency

Educational data classification has become an effective tool for exploring the hidden pattern or relationship in educational data and predicting students’ performance or teachers’ competency. This study proposes a new method based on machine learning algorithms to predict the technology-integrated competency of pre-service mathematics teachers. In this paper, we modified the inertial subgradient extragradient algorithm for pseudomonotone equilibrium and proved the weak convergence theorem under some suitable conditions in Hilbert spaces. We then applied to solve data classification by extreme learning machine using the dataset comprised of the technology-integrated competency of 954 pre-service mathematics teachers in a university in northern Thailand, longitudinally collected for five years. The flexibility of our algorithm was shown by comparisons of the choice of different parameters. The performance was calculated and compared with the existing algorithms to be implemented for prediction. The results show that the proposed method achieved a classification accuracy of 81.06%. The predictions were implemented using ten attributes, including demographic information, skills, and knowledge relating to technology developed throughout the teacher education program. Such data driven studies are significant for establishing a prospective teacher competency analysis framework in teacher education and contributing to decision-making for policy design.
Reverse auction protocol based on quantum teleportation

Abstract Reverse auction is a revolutionary procurement technology, widely used in enterprises and governments, which can increase purchasing transparency and reduce purchasing costs. In this paper, we combine reverse auction with quantum theory and propose a novel protocol that guarantees security by adopting quantum teleportation, quantum key distribution and so on. This protocol also has the properties of anonymity of supplier, verifiability, disavowal impossibility and traceability, properties that ensure the fairness of transactions. The analysis results show that the protocol is simple, high performing and reliable. We hope that it will be applied in real life.
Secure Web Application Technologies Implementation through Hardening Security Headers Using Automated Threat Modelling Techniques

This paper investigates whether security headers are enforced to mitigate cyber-attacks in web-based systems in cyberspace.The security headers examined include X-Content-Type-Options, X-Frame-Options, Strict-Transport-Security, Referrer-Policy, Content-Security-Policy, and Permissions-Policy.The study employed a controlled experiment using a security header analysis tool.The web-based applications (websites) were analyzed to determine whether security headers have been correctly implemented.The experiment was iterated for 100 universities in Africa which are ranked high.The purposive sampling technique was employed to understand the status quo of the security headers implementations.The results revealed that 70% of the web-based applications in Africa have not enforced security headers in web-based applications.The study proposes a secure system architecture design for addressing web-based applications' misconfiguration and insecure design.It presents security techniques for securing web-based applications through hardening security headers using automated threat modelling techniques.Furthermore, it recommends adopting the security headers in web-based applications using the proposed secure system architecture design.
Multi-Class, Multi-Label and Multi-Target in Wineinformatics

“Can wine grade, price and region being predicted altogether with higher accuracy?” In previous chapters, bi-class classification and regression were discussed in grade class prediction, evaluation of wine reviewers and price prediction independently; that is, each classifier was given one task at a time. This chapter introduces an advanced computer science topic, multi-label and multi-target techniques, to Wineinformatics. Independent single-label problems, including wine grade, price and origin predictions, are merged together so that the trustworthy labels can also become attributes to provide more information for other labels.
Noise Removal from Images by Applying Deep Neural Networks

This article addresses the problem of removing noise from images using neural networks. The type of noise that is removed in this article is salt and pepper noise. This noise represents jumps in intensity in the image, usually caused by data transmission errors. To quantify the success of the noise removal model, PSNR metrics are used. The paper provides a theoretical basis for the task in the form of a description of the neural network and its components, as well as its operation. It also describes deep learning and the differences and advantages of each type. The neural network used in this paper is based on the U-Net architecture. Using the Python programming language and the Keras application user interface, a model was developed to remove added white Gaussian noise and salt and pepper noise of varying intensity from images in the CIFAR-10 and MNIST image databases. The model is applied to the author's image that is not included in the tested databases. The analysis is presented in the PSNR presented for different noise intensities and examples of images with and without noise.
Meta-Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis

The paper proposes a meta-learning-based Mandarin-Tibetan cross-lingual text-to-speech (TTS) to realize both Mandarin and Tibetan speech synthesis under a unique framework. First, we build two kinds of Tacotron2-based Mandarin-Tibetan cross-lingual baseline TTS. One is a shared encoder Mandarin-Tibetan cross-lingual TTS, and another is a separate encoder Mandarin-Tibetan cross-lingual TTS. Both baseline TTS use the speaker classifier with a gradient reversal layer to disentangle speaker-specific information from the text encoder. At the same time, we design a prosody generator to extract prosodic information from sentences to explore syntactic and semantic information adequately. To further improve the synthesized speech quality of the Tacotron2-based Mandarin-Tibetan cross-lingual TTS, we propose a meta-learning-based Mandarin-Tibetan cross-lingual TTS. Based on the separate encoder Mandarin-Tibetan cross-lingual TTS, we use an additional dynamic network to predict the parameters of the language-dependent text encoder that could realize better cross-lingual knowledge sharing in the sequence-to-sequence TTS. Lastly, we synthesize Mandarin or Tibetan speech through the unique acoustic model. The baseline experimental results show that the separate encoder Mandarin-Tibetan cross-lingual TTS could handle the input of different languages better than the shared encoder Mandarin-Tibetan cross-lingual TTS. The experimental results further show that the proposed meta-learning-based Mandarin-Tibetan cross-lingual speech synthesis method could effectively improve the voice quality of synthesized speech in terms of naturalness and speaker similarity.
Research on Equipment and Algorithm of a Multimodal Perception Gameplay Virtual and Real Fusion Intelligent Experiment

Chemistry experiments are an important part of chemistry learning, and the development and application of virtual experiments have greatly enriched experimental teaching. However, in the existing virtual experiments, there are problems such as low human–computer interaction efficiency, poor user sense of reality and operation, and a boring experimental process. Therefore, this paper designs a multimodal perception gameplay virtual and real fusion intelligence laboratory (GVRFL). GVRFL uses virtual and real fusion methods to interactively complete chemical experiments, which greatly improves the user’s sense of reality and operation. This method proposes a multimodal intention active understanding algorithm to improve the efficiency of human–computer interaction and user experience and proposes a novel game-based virtual–real fusion intelligent experimental mode that adds gameplay to the process of virtual–real fusion experiments. The experimental results show that this method improves the efficiency of human–computer interaction and reduces the user’s operating load. At the same time, the interaction between the real experimental equipment and the virtual experimental scene greatly improves the user’s sense of reality and operation. The introduction of game elements into the process of virtual and real fusion experiments stimulates students’ interest in and enthusiasm for learning.
Factor analysis of the virtual reality Stroop task

Virtual reality (VR) offers neuropsychologists high dimensional (3D) platforms for administering cognitive tasks that balance experimental control with simulations of naturalistic activities. A virtual reality version of the Stroop task, the Virtual Reality Stroop Task (VRST), was developed that leverages technological advances to enhance the ecological validity of neuropsychological assessments. The high mobility multipurpose wheeled vehicle (HMMWV) version of the VRST includes high arousal (ambush) and low arousal (safe) zones was employed in this study. This version of the VRST contains both cognitive (Stroop) and affective (arousal) components. While the VRST has been shown to have good construct validity, the factor structure has yet to be explored. This study aimed to examine the factor structure of the VRST and compare the results with a lower dimensional (2D) computer-automated Stroop task (i.e., the Automated Neuropsychological Assessment Metrics - ANAM).Data was drawn from college-aged students who completed the VRST and ANAM Stroop tasks (N = 115). Factor analyses utilized principal axis factoring (PAF), and output variables included percent of correct responses and response times the VRST and ANAM Stroop tasks.Results indicated that both Stroop tasks had two-factor solutions. Factor one measured response times and factor two measured accuracy. While factors respective of speed and accuracy factors were correlated across assessment modalities, within assessment factor correlations were low.The factors possibly indicated participants response styles in that they either focus on responding accurately or responding quickly to stimuli. Therefore, including throughput in future research examining either the ANAM Stroop task or VRST may provide useful insight into participant performance. Finally, because similar factor structures were observed for both the VRST and ANAM Stroop task this study provided additional support for the construct validity of a higher dimensional Stroop task, the VRST.
An Effective Framework for design of Dataset Using Twitter

The rapid expansion of internet usage and related services like social media and blogs has increased people's level of expressiveness in day-to-day life. Social media platforms like Twitter and Facebook facilitate people to interact and exchange opinions about people, products, and services. As a result, a vast amount of data is available online in the form of views, tweets, messages, audio, and videos. An interface is needed to collect knowledge and insights from the various tweets, ideas, and comments. Thus we have proposed the Twitter API-based Interface, able to perform Hashtag searches and extract tweets from Twitter along with the ample number of fields related to the Twitter object. Using the interface, the 55 properties of each tweet are collected and used for further investigations. The python-based library called Tweepy is used to interact with the Twitter API. Due to the availability of real-worlddata, various issues related to text analysis can be addressed. The problems such as Sentiment Analysis, Opinion Mining, Implicit and Explicit detection, genuineness of views, and Opinion Spam detection can be addressed using the dataset availability.
A Model for Rainfall Forecasting using Distinct Machine Learning Algorithm

As Agriculture is the pivotal point of survival, rainfall is the important source of its cultivation. Rainfall prophecy has always been a major problem as a prophecy of downfall gives awareness to people and to know in advance about rain to take necessary precautions to cover their crops from rain. A particular dataset is taken from the Kaggle community and this design predicts whether it will rain henceforth or not by using the rainfall in the dataset. Cat Boost model is executed in this design as it’s an open-sourced machine knowledge algorithm, and features great quality without parameter tuning, categorical point support, bettered delicacy, and fast prophecy. Cat Boost model is a Grade boosting toolkit and two critical algorithms classical and innovative are introduced to produce a fight in prophecy shift present in presently being prosecutions of grade boosting algorithms. Cat Boostperformed truly well giving an AUC (Area under wind) score0.8 and a ROC (Receiver operating characteristic wind) score of 89. ROC is called an assessing wind whereas AUC presents a degree or measure of separability as the model is professed enough to distinguish between classes. An Exploratory data analysis is done to examine data distribution, and outliers and provides tools for imaging and understanding the data through graphical representation.
Finding what fits: Explorative self-experimentation for health behaviour change

Changing a specific health behaviour can be highly complex and is often influenced by many personal, social, and environmental factors. Therefore, interventions that aim at behaviour change cannot be one-size-fits-all solutions, and no behaviour change technique is effective for everyone. One potential solution could be to support individuals in finding interventions through self-experimentation. This research explored the requirements for an explorative self-experimentation intervention and developed tools that support users in the process, complementing developments in quantitative self-experimentation. Based on a research through design approach, we developed three different prototypes for supporting a change in health-related behaviour, which were used and evaluated by fourteen participants over a four-week period. A thematic analysis of interviews with participants led to seven themes, which can be used as a starting point when designing for explorative self-experimentation.
European colonization and the transmission of the Ewondo language in Cameroon and Abroad: Historical and transnational perspectives

European colonization has deeply marked the linguistic practices in different African territories until today. Cameroon, which was colonized by Germany from 1884 to 1916 and by France and Great Britain from 1916 to 1960, is formally a bilingual country with French and English being its two official languages. However, the linguistic reality of the country goes way beyond these two languages. Today, more than 250 African languages are spoken in Cameroon. This article focuses on the experiences of Alexis-Bienvenu Belibi, who was born in Yaoundé in 1950 and who is today Professor Emeritus of African languages at the École normale supérieure of Yaoundé. The article evokes Belibi’s family history, his schooling, university studies and his career as a high school teacher and as a university professor. It thereby examines the different school policies implemented in Cameroon toward African languages over time as well as the impact of university actors on ongoing political changes in the educational field. It also addresses demands from the Cameroonian diaspora in Europe and the world concerning the learning of Ewondo. The article is written in the form of a conversation between Alexis-Bienvenu Belibi and Elise Pape, a Franco-German sociologist specialized in the fields of migration studies and of postcolonial memories.
A Framework for Integrating “R” Programming into the Accounting Curriculum

In recent years professional accountancy bodies (e.g., CPA), accreditation institutions (e.g., AACSB) and employers have steadily raised, and continue to raise expectations regarding the need for accounting graduates to demonstrate skills in data analytics. One of the obstacles accounting instructors face in seeking to implement data analytics, however, is that they need access to ample teaching materials. Unfortunately, there are few such resources available for advanced programming languages such as R. While skills in commonly used applications such as Excel are no doubt needed, employers often take these for granted and incremental value is only added if graduates can demonstrate knowledge in using more advanced data analytics tools for decision-making such as coding in programming languages. This, together with the current dearth of resources available to accounting instructors to teach advanced programming languages is what drives motivation for this chapter. Specifically, we develop an intuitive, two-dimensional framework for incorporating R (a widely used open-source analytics tool with a powerful embedded programming language) into the accounting curriculum. Our model uses complexity as an integrating theme. We incorporate complexity into this framework at the dataset level (simple and complex datasets) and at the analytics task level (simple and complex tasks). We demonstrate two-dimensional framework by drawing on authentic simple and complex datasets as well as simple and complex tasks that could readily be incorporated into the accounting curriculum and ultimately add value to businesses. R script programming code are provided for all our illustrations.