Reviewerly/exHarmony huggingface dataset:
A projector-based approach to quantifying total and excess uncertainties for sketched linear regression

Abstract Linear regression is a classic method of data analysis. In recent years, sketching—a method of dimension reduction using random sampling, random projections or both—has gained popularity as an effective computational approximation when the number of observations greatly exceeds the number of variables. In this paper, we address the following question: how does sketching affect the statistical properties of the solution and key quantities derived from it? To answer this question, we present a projector-based approach to sketched linear regression that is exact and that requires minimal assumptions on the sketching matrix. Therefore, downstream analyses hold exactly and generally for all sketching schemes. Additionally, a projector-based approach enables derivation of key quantities from classic linear regression that account for the combined model- and algorithm-induced uncertainties. We demonstrate the usefulness of a projector-based approach in quantifying and enabling insight on excess uncertainties and bias-variance decompositions for sketched linear regression. Finally, we demonstrate how the insights from our projector-based analyses can be used to produce practical sketching diagnostics to aid the design of judicious sketching schemes.
Research on Acoustic Modeling of Low Resource Uyghur Language Based on Transfer Learning

Due to the shortage of Uyghur corpus resources, the performance of Uyghur acoustic models has been poor. Therefore, this paper proposes a Uyghur acoustic modeling method based on transfer learning, which uses the weight transfer method to transfer DNN hidden layers of large-scale English and Chinese acoustic models. In order to compare experiments, this paper builds a baseline system based on DNN-HMM. The training criteria are xEnt and MPE respectively, and the WER of the baseline system is 26.39% and 25.62% respectively. In this paper, 32 experiments were designed to find the best acoustic model. Finally, the optimal acoustic model WER was reduced to 18.75%. Through many experiments in this paper, it can be shown that the weight transfer method can effectively improve the performance of Uyghur acoustic model.
Exploring Modal Locking in Window Manipulation

Window manipulation plays a vital role in multi-tool user interaction, especially for programmers exploring software artifacts, gathering information for better understanding. However, today’s window managers offer only limited means to organize screen contents, which increases cognitive efforts for both tool builders and users. Builders must account for live integration of composite views; users might have to work around disruptive mode errors when actual tasks conflict with a tool’s design. We follow a pattern-finding approach and present four new verbs for direct window manipulation, which we consolidated from existing tools and systems. If window managers would offer to stash, duplicate, split, and link views, we believe that programmers could better maintain flow during exploration activities.
Nested Recurrent Residual Unet (NRRU) on GAN (NRRG) for Cardiac CT Images Segmentation Task

With the development of technology, medical imaging technology plays a more and more important role in the processing of diagnosing. As a popular method of digital image processing, convolutional neural network especially U-Net take lots of medical image segmentation tasks. Thus, kinds of models or methods about U-Net are proposed in these years. In this paper, we proposed two new network architectures. One is a new member of U-Net family and created by hybridizing two networks, R2U-Net and Unet++, then the new “U-Net” was named Nested Recurrent Residual U-Net (NRRU). The other network was named Nested Recurrent Residual Unet-GAN (NRRG). Its architecture is based on Pixel-to-Pixel, where NRRU acts as the generative network. After that, I test the performance of NRRG on the dataset from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge (MM-WHS 2017), the results show that the new architecture performance good.
Key technologies of intelligent interpretation cloud platform for coal mine logging

In order to meet the needs of intelligent mining of coal mine for detecting data and realize intelligent interpretation of coal mine logging, an online intelligent interpretation cloud platform for single well of coal mine logging based on web is designed. Based on Browser-Server (B/S) architecture, man-machine interactive display is realized on the front-end browser, data processing and interpretation are achieved on the back-end cloud server; data management is carried out by using SQL database to facilitate data interaction with other platforms; coal and rock identification and interpretation of intelligent logging is realized by using python; data encryption is designed by using block chain technology to realize data life cycle. The data transmission protocol adopts the general protocol technology, the communication between the front end and the back end adopts the Https protocol, and the data transmission adopts the JSON format, which facilitates the data docking with other platforms. The platform is not restricted by terminal form. As long as there is a browser in the network, it can be operated, used by users. The platform has no version restrictions and is convenient for developers to maintain. The platform construction, front-end design, back-end design, database design and data encryption design method formed in this paper provide new ideas and solutions for the development of intelligent logging interpretation.
Data-Driven Performance Prediction in a Geometry Game Environment

The rapid technological evolution of the last years motivated students to develop competencies and capabilities that will prepare them for an unknown future of the 21st century. In this context, teachers intend to optimise the process of learning and make it more dynamic and exciting by introducing gamification. Thus, this paper focuses on a data-driven assessment of geometry competencies, which are essential for developing problem-solving and higher-order thinking skills. We explored them in the domain of knowledge inference, whose primary goal is to predict or measure the students' knowledge over questions as they interact with a learning platform at a specific time. Hence, the main goal of the current paper is to compare several well-known algorithms applied to the data of a geometry game named Shadowspect in order to predict students' performance in terms of classifier metrics such as Area Under Curve (AUC), accuracy, and F1 score. We found Elo to be the algorithm with the best prediction power. However, the rest of the algorithms also showed decent results, and, therefore, we can conclude that all the algorithms hold the potential to measure and estimate the actual knowledge of students. In turn, this means that they can be applied in formal education to improve teaching, learning, organisational efficiency and, as a consequence, this can serve as a basement for a change in the system.
Research on the Characteristics of English Text Based on Computer and Its Translation

Abstract With the continuous progress and development of the society, the computer level is constantly improving and deepening. Along with the progress of computer, many related industries have made great progress successively. For example, some of the characteristics of English text and related translation, more and more attentionly. English plays an important role in our daily life. English is taught as a required course from primary school to college graduation. Even for people working in foreign companies and college students studying abroad, English exists as a second mother tongue. Now, people have also made great efforts in English text research and translation, believe inthe near future. It is an inevitable trend that people will pay more attention to the characteristics of English texts and their translation studies. Therefore, the research in this field is getting deeper and deeper.
Minimum Adversarial Distribution Discrepancy for Domain Adaptation

Domain adaptation (DA) refers to generalize a learning technique across the source domain and target domain under different distributions. Therefore, the essential problem in DA is how to reduce the distribution discrepancy between the source and target domains. Typical methods are to embed the adversarial learning technique into deep networks to learn transferable feature representations. However, existing adversarial related DA methods may not sufficiently minimize the distribution discrepancy. In this article, a DA method minimum adversarial distribution discrepancy (MADD) is proposed by combining feature distribution with adversarial learning. Specifically, we design a novel divergence metric loss, named maximum mean discrepancy based on conditional entropy (MMD-CE), and embed it in the adversarial DA network. The proposed MMD-CE loss can address two problems: 1) the misalignment from different class distributions between domains and 2) the equilibrium challenge issue in adversarial DA. Comparative experiments on Office-31, ImageCLEF-DA, and Office-Home data sets with state-of-the-art methods show that our method has some advantageous performances.
Automating the Functional Correspondence Between Higher-Order Evaluators and Abstract Machines

The functional correspondence is a manual derivation technique transforming higher-order evaluators into the semantically equivalent abstract machines. The transformation consists of two well-known program transformations: translation to continuation-passing style that uncovers the control flow of the evaluator and Reynolds’s defunctionalization that generates a first-order transition function. Ever since the transformation was first described by Danvy et al. it has found numerous applications in connecting known evaluators and abstract machines, but also in discovering new abstract machines for a variety of $$\lambda $$ -calculi as well as for logic-programming, imperative and object-oriented languages. We present an algorithm that automates the functional correspondence. The algorithm accepts an evaluator written in a dedicated minimal functional meta-language and it first transforms it to administrative normal form, which facilitates program analysis, before performing selective translation to continuation-passing style, and selective defunctionalization. The two selective transformations are driven by a control-flow analysis that is computed by an abstract interpreter obtained using the abstracting abstract machines methodology, which makes it possible to transform only the desired parts of the evaluator. The article is accompanied by an implementation of the algorithm in the form of a command-line tool that allows for automatic transformation of an evaluator embedded in a Racket source file and gives fine-grained control over the resulting machine.
Mining social interactionsin connection traces of a campus wi-fi network

Wi-Fi technologies have become one of the most popular means for Internet access. As a result, the use of mobile devices has become ubiquitous and instrumental for society. A device can be identified through its MAC address within an autonomous system. Although some devices attempt to anonymize MAC addresses via randomization, these techniques are not used once the device is associated to the network [7]. As a result, device identification poses a privacy problem in large-scale (e.g., campus-wide) Wi-Fi deployments [5]: if the mobile device can be located, the user who carries that device can also be located. In turn, location information leads to the possibility to extract private knowledge from Wi-Fi users, like social interactions, movement habits, and so forth.
Development of a hospital virtual tour with virtual reality-based panorama

Information technology has an important role in socialising the institutional profile of hospitals as public services. Virtual reality (VR) technology, along with panoramic photography as a multime...
Mixed Method Development of Evaluation Metrics

Designers of online search and recommendation services often need to develop metrics to assess system performance. This tutorial focuses on mixed methods approaches to developing user-focused evaluation metrics. This starts with choosing how data is logged or how to interpret current logged data, with a discussion of how qualitative insights and design decisions can restrict or enable certain types of logging. When we create a metric from that logged data, there are underlying assumptions about how users interact with the system and evaluate those interactions. We will cover what these assumptions look like for some traditional system evaluation metrics and highlight quantitative and qualitative methods that investigate and adapt these assumptions to be more explicit and expressive of genuine user behavior. We discuss the role that mixed methods teams can play at each stage of metric development, starting with data collection, designing both online and offline metrics, and supervising metric selection for decision making. We describe case studies and examples of these methods applied in the context of evaluating personalized search and recommendation systems. Finally, we close with practical advice for applied quantitative researchers who may be in the early stages of planning collaborations with qualitative researchers for mixed methods metrics development.
Cyber-Crimes Issues on Social Media Usage Among Higher Learning Institutions Students in Dar ES Salaam Region, Tanzania

Social media allow people to communicate with others and this has increased online communication. There are numerous risks of attack on the use of internet and cyber criminals across the digital world. Cyber-crimes is the latest and the most sophisticated problem in the digital world. Cybercrime varies from computer fraud, unauthorized hacking, forgery, infringements of privacy, online gambling, propagation of harmful content, phishing, computer viruses, falsification of prostitution, theft, espionage, copyright infringement , financial crimes, sale of illegal articles, pornography, intellectual property crime, e-mail spoofing, cyber defamation and cyber stalking. The study was guided by the following objectives, to establish various forms of cybercrimes performed on social media, to examine the effects of cyber-crimes on the security of social media, to study the impact of cyber-crimes on social media security. The research population was used from the selected higher learning institutions and a sample size of 132 was used. Simple random sampling was used in sampling the respondents. A descriptive statistics analysis was done, comprising the generation of mean and standard deviation. SPSSv16.0 software was used to generate the mean and standard deviation. The findings show that many students share too much information through emails and social media which may pose threats to them. Most organization they have implemented multilayered security mechanisms information security may leak on the social sites. Cyber-bullying has spread widely especially are prone to the practice of cyber-bullying. Security attacks such as hacking, spoofing spamming cyber stalking Trojan, worms, viruses and exposure scams are common through social media, hence the ICT policy and government policy have address such issues. Higher learning institutions need to invest more resources in training students, awareness, research and development, IT security and social media crimes.
Digital transformation and resistance in the transition to the online world

The article describes the consequences of the pandemic for business and the behavior of some market participants and educational institutions both during the pandemic and after its expected end. Such issues as the changing nature of the movement towards universal digitalization, as well as the reasons that affect the ease of its adoption, are raised, and the situation of market participants who decide to return to the previous format of work after the end of the pandemic is analyzed. The materials of the article can be used in the preparation of students in the field of Economics.
Ordering sentences and paragraphs with pre-trained encoder-decoder transformers and pointer ensembles

Passage ordering aims to maximise discourse coherence in document generation or document modification tasks such as summarisation or storytelling. This paper extends the passage ordering task from sentences to paragraphs, i.e., passages with multiple sentences. Increasing the passage length increases the task's difficulty. To account for this, we propose the combination of a pre-trained encoder-decoder Transformer model, namely BART, with variations of pointer networks. We empirically evaluate the proposed models for sentence and paragraph ordering. Our best model outperforms previous state of the art methods by 0.057 Kendall's Tau on one of three sentence ordering benchmarks (arXiv, VIST, ROC-Story). For paragraph ordering, we construct two novel datasets from Wikipedia and CNN-DailyMail on which we achieve 0.67 and 0.47 Kendall's Tau. The best model variation utilises multiple pointer networks in an ensemble-like fashion. We hypothesise that the use of multiple pointers better reflects the multitude of possible orders of paragraphs in more complex texts. Our code, data, and models are publicly available1.
Comment on “Resurfaced paleodunes from the Botucatu erg amid Cretaceous Paraná volcanics”, by Hartmann and Cerva-Alves (2021)

Hartmann and Cerva-Alves (2021) describe a field of silicified Cretaceous Paleodunes of the Botucatu Formation present in southernmost Brazil. In this comment, we explore pressing problems of their study and demonstrate that the authors inadequately use their data to support their conclusions. We evidence that: (1) the methodology misses basic information about the used datasets, as well as petrography and geochemistry data of the Botucatu sandstones; (2) the proposed silicification model is not supported by any petrographic or geochemical data; (3) the interpretations about dune morphology, wind pattern and direction are incorrect and differ from the available eolian record for the study region; and (4) the ‘explosive’ hydrothermal hypothesis for Cerro do Jarau site is based upon a not sustainable hydrothermal model that neglects substantial evidence for an impact origin for Cerro do Jarau and is broadly inconsistent with physical constraints.
The readability of online health information for L1 and L2 Australians: text-based and user-focused research

Abstract The readability of online health information involves several factors in communication, including textual factors in verbal messaging and demographics relating to the readership, both of which impact on access to health information for first language (L1) and second language (L2) individuals in the Australian community. This research aims to identify the issues inherent in health texts as well as different readers’ comprehension of the information in them. The paper focuses first on the readability of sample health texts, and the extent to which difficult elements can be identified by the standard readability measures (Flesch-Kincaid, SMOG), as well as psycholinguistically informed measures of reading ease developed by Co-Matrix for general (L1) and L2 readers: TERA and Coh-Metrix L2. Coh-Metrix L2 points to linguistic factors that particularly challenge L2 readers of health information. A complementary study using eye-tracking was carried out to investigate the reading behaviours of 30 L1 and L2 participants seeking information from a health website. Statistically significant differences were found between L1 and L2 participants in their reading patterns, with L2 readers working more slowly and less reliably through online information. The findings highlight the need for health communicators to embrace the greater reading challenges for L2 users of the Internet.
Comparative Study on Natural Language Processing for Tourism Suggestion System

Nowadays, due to a large amount of tourism information such as attractions or activities, it is difficult for users to decide where to go or do specific activities without getting suggestions from experts, books, websites, etc. To support users wisely in choosing tourism activities, a recommendation or suggestion system using Natural Language Processing (NLP), which is a subdomain of Artificial Intelligence (AI), is proposed. NLP plays an important part in the pre-processing task of the recommender system to enhance the performance of the suggested output. This paper explores and compares the NLP techniques that are currently applied to the existing recommendation systems. Challenges and trends using NLP for suggestion systems in tourism are discussed.
Collectiveness analysis with visual attributes

Individuals within crowd scenes tend to move unconsciously in a collective manner. The nature of this phenomenon surges motivations in collectiveness analysis to quantify and detect the collective behavior within the crowds. Due to the complexity of crowd scenes, most studies focus on the collective motion of individuals. However, it requires the extraction of temporal information, i.e., motion attributes, in consecutive video frames. Based on the approach, collectiveness analysis relies on the total number of motion attributes that could not represent the total number of individuals and limits mid-level understanding within crowd scenes. Alternatively, this study proposes a novel framework for collectiveness analysis using visual attributes. It is based on visual attributes extraction approach to facilitate individual-level understanding based on still image input. By localizing individuals and classifying individuals’ head pose, the proposed framework alleviates the need to rely on temporal information and explores topological relationship propagation among individuals to infer collectiveness analysis. Inclusive experiments on various crowd densities illustrate the aims of the proposed framework to infer high-level crowd analysis with visual attributes. Its efficacy is evaluated on real crowd scenes and compared with the state-of-the-art approaches including achieving estimation of group with Average Difference (AD) = 1.68 and Mean Square Error (MSE) = 1.71. Its potential applicability is demonstrated in the context of crowd estimation and collectiveness analysis.
Proposing Remote Video Conversation System "PARAPPA": Delivering the Gesture and Body Posture with Rotary Screen

Globalization and the effect of recent infectious disease are changing the remote conversations as a new normal in business meetings, social provision and casual chatting. Previous research show that the remote conversations have a difficulty on showing the presence or attention to the other interlocutors, especially in the situation where the majority of interlocutor share the same place and one or a few interlocutor participate from difference place. This paper proposed "Parappa", a remote video conversation system, for those unbalanced condition by utilizing both physical and virtual approaches to share the nonverbal behaviors of remotely-participating interlocutor. The proposed system is constructed with a rotatable screen which projects the life-size avatar of remote interlocutor. The rotation of the screen represents the body posture and the projected avatar show the gesture. The results of preliminary analyses of eye gaze activities and frequency of the screen rotation during conversation suggests the possibilities that proposed system enables to show the presence of remote interlocutors.