Reviewerly/exHarmony huggingface dataset:
Research on College English Blended Teaching Model Based on Micro-classes under Network Information Technology

Abstract With the penetration of network technology in all walks of life, my country’s traditional college English teaching model has begun to reform. Based on the concept of micro-classes, the mixed teaching mode has been favored by many people. Blended teaching is a new teaching mode that combines some traditional teaching techniques with multiple teaching methods. It contains the advantages of traditional teaching mode and the convenience of online learning. Therefore, it is very suitable for college English teaching. This article starts from the concept of network information technology micro-course, briefly expounds the concept of micro-course, and introduces the development process and significance of micro-course and mixed teaching.
Exploration of the Factors Causing Autoimmune Diseases using Fuzzy Cognitive Maps with Concentric Neutrosophic Hypergraphic Approach

Neutrosophic sets are comprehensively used in decision-making environment. The manifestation of neutrosophic sets in concentric hypergraphs is proposed in this research work. The intention of developing a decision-making model using the combination of Fuzzy Cognitive Maps and concentric neutrosophic hypergraph is to rank the core factors of decision-making problem and find the inter-relational impacts. This proposed model is validated with the exploration of the causative factors of autoimmune diseases. The proposed model is highly compatible as it assists in determining the core factors and their inter-association. This model will certainly benefit the decision-maker at all managerial levels to design optimal decisions.
Research on the Design of Japanese Intensive Reading Course based on Computer Network Teaching

Abstract With the development of the times and the deepening of foreign language teaching reform, the traditional Japanese teaching mode can no longer meet the needs of modern teaching. Computer aided Japanese intensive reading teaching has been more and more used in Japanese Teaching in Colleges and universities in China. Computer assisted Japanese intensive reading teaching breaks through the limitations of single course learning, arouses the enthusiasm of students, improves the teaching effect in unit time, and makes up for the deficiency of traditional Japanese intensive reading teaching. Therefore, in the future Japanese teaching, we should give full play to the role of computer network technology in teaching and improve the level of teaching modernization.
PROCESSING AND QUALITATIVE VISUALIZATION IN PSEUDO-TRUE COLOURS OF LONG-TERM SERIES OF SATELLITE DATA

Free access to moderate resolution remote sensing data enable the worldwide users for their studies of many key geophysical parameters of the Earth’s system, solving various tasks on regular monitoring of natural phenomena, including tasks on ecological space monitoring. This requires multilevel processing of satellite data. The processing results are given for the Aral Sea. This endorheic salt lake is located in Central Asia on the border of Kazakhstan and Uzbekistan. Aral was chosen as an example not by chance as because before shallowing, it was the fourth-largest lake in the world. During the process of drying, the lake was divided into three parts. Currently, the eastern part of the lake has completely disappeared. To the Aral Sea is happening a real ecological disaster. A long-term series of satellite data are needed to monitor the dynamics of changes. The active operation of remote sensing satellites usually exceeds their estimated lifetime. For example, spacecrafts “Terra” and “Aqua”, launched in 1999 and 2002, respectively, have an estimated lifetime of sensor MODIS as 6 years, but they are still used in the NASA EOS program aimed at Earth exploration. With the aging sensors has been a degradation of its optics equipment which affects the quality of the data in some channels. It limits the simple creation of a color image in TRUE colors by put the bands spectral range of visible radiation to corresponding layers RGB-composite. The article describes the technology of making quality images by digital operations with MODIS channels. It eliminate such a problem as “banding” of the image and create new synthesized bands. The results of processing are demonstrated using annual Terra/MODIS data for the autumn period from 2000 to 2019. Besides, taking into account that a water body has been chosen as the object of monitoring, the article presents the options of water surface detection based on spectral indices - indices calculated in mathematical operations with different spectral ranges (channels) of remote sensing data related to certain parameters. Thematic processing in Geomatica software is shown on Landsat-8 images: the sample profile of index image is demonstrated. Taking into account that the survey area exceeds the size of the standard Landsat scene, a mosaic image was made for complete coverage of the region.
Genetic Learning Analysis of Fuzzy Rule-Based Classification Systems Considering Data Reduction

In this paper, we evaluated the Knowledge Base building of Fuzzy Rule-Based Classification Systems (FRBCS) with the purpose of find a balance between the accuracy and interpretability objectives. Regarding to build, we compared two well-known algorithms: Wang-Mendel, to generate the rule base, and NSGA-II, to learn the rules and tuning the membership functions. The Wang-Mendel algorithm was also used to introduce a seed in NSGA-II initial population, in order to increase its quality and improve convergence speed. Taking into account that the automatic building of the fuzzy systems knowledge base is challenger, because of the amount of data available in several real problems, we analysed the impact of data reduction on it. The experiments were carried out with 23 datasets divided into small and medium-large size, and the results showed that the use of genetic learning is suitable to large datasets as well as data reduction, improving the accuracy and interpretability of the FRBCS arising.
Thought and Practice on Teaching Space Planning and Design of New Engineering —Based on the Case of Reconstruction of Teaching Space at College of Human Settlements, Xi’an Eurasia University

Abstract "New Engineering" corresponds to emerging industries that first refers to specialties for emerging industries such as artificial intelligence, intelligent manufacturing, robotics, cloud computing, etc., and also includes the upgrading of traditional engineering majors. Through the understanding of the new engineering education and the development of science and technology, this paper studies and designs the teaching space for the new engineering specialties and upgrading engineering ones. Based on the analysis of the case of teaching space reconstruction of the Human Settlements College of Xi’an Eurasia University, we can draw some conclusions: the first is to summarize the method of teaching space planning and design of new engineering subjects; The second is how to create a teaching space that stimulates innovation ability and normalizes learning behavior; The third is to promote a new student-centered education model. We hope it will be able to provide certain reference value for teaching space planning designers of both new engineering and engineering majors.
On Calibration of Mixup Training for Deep Neural Networks

Deep Neural Networks (DNN) represent the state of the art in many tasks. However, due to their overparameterization, their generalization capabilities are in doubt and still a field under study. Consequently, DNN can overfit and assign overconfident predictions -- effects that have been shown to affect the calibration of the confidences assigned to unseen data. Data Augmentation (DA) strategies have been proposed to regularize these models, being Mixup one of the most popular due to its ability to improve the accuracy, the uncertainty quantification and the calibration of DNN. In this work however we argue and provide empirical evidence that, due to its fundamentals, Mixup does not necessarily improve calibration. Based on our observations we propose a new loss function that improves the calibration, and also sometimes the accuracy, of DNN trained with this DA technique. Our loss is inspired by Bayes decision theory and introduces a new training framework for designing losses for probabilistic modelling. We provide state-of-the-art accuracy with consistent improvements in calibration performance. Appendix and code are provided here: https://github.com/jmaronas/calibration_MixupDNN_ARCLoss.pytorch.git
False Expenditure Prevention Using Blockchain

Blockchain is one of the latest disruptive technologies in the market. Primarily known for cryptocurrencies, it is now being used for various other applications that utilize the integrity and distributed mechanism employed by the blockchain technology. In this paper we intend to create a decentralized platform to eloquently solve the false expenditure and counterfeit problems which is prevalent in major crowd-funding applications.
Simulation of Anatomical Structure in Mammography and Breast Tomosynthesis Using Perlin Noise

As a screening modality, mammography, and its possible replacement breast tomosynthesis, operates under a different set of priorities and constraints than a clinical, diagnostic modality. Breast cancer is both the most common cancer among women in industrialized countries and among the most common forms of cancer worldwide with the incidence projected to keep rising due to the aging population. Breast tissue consists mainly of fibrous connective tissue, blood vessels, milk ducts and milk glands and fat tissue. The tissue components are varied in distribution and appearance, making it difficult to make general statements on radiographic appearance of breasts. This chapter describes an attempt to simulate breast tissue, using an approach that can generate breast tissue-like 3D texture using an implementation of so-called Perlin noise.
ControlVAE: Tuning, Analytical Properties, and Performance Analysis

This paper reviews the novel concept of controllable variational autoencoder (ControlVAE), discusses its parameter tuning to meet application needs, derives its key analytic properties, and offers useful extensions and applications. ControlVAE is a new variational autoencoder (VAE) framework that combines the automatic control theory with the basic VAE to stabilize the KL-divergence of VAE models to a specified value. It leverages a non-linear PI controller, a variant of the proportional-integral-derivative (PID) control, to dynamically tune the weight of the KL-divergence term in the evidence lower bound (ELBO) using the output KL-divergence as feedback. This allows us to precisely control the KL-divergence to a desired value (set point), which is effective in avoiding posterior collapse and learning disentangled representations. In order to improve the ELBO over the regular VAE, we provide simplified theoretical analysis to inform setting the set point of KL-divergence for ControlVAE. We observe that compared to other methods that seek to balance the two terms in VAE's objective, ControlVAE leads to better learning dynamics. In particular, it can achieve a good trade-off between reconstruction quality and KL-divergence. We evaluate the proposed method on three tasks: image generation, language modeling and disentangled representation learning. The results show that ControlVAE can achieve much better reconstruction quality than the other methods for comparable disentanglement. On the language modeling task, ControlVAE can avoid posterior collapse (KL vanishing) and improve the diversity of generated text. Moreover, our method can change the optimization trajectory, improving the ELBO and the reconstruction quality for image generation.
Implementation of hermeneutic circle in heterogeneous screen text

The object of this research is the hermeneutic circle of screen text. The subject of this research is the peculiarities of its implementation in screen text through the plot in terms of dramaturgical representation of the reality. Particular attention is given to the analysis of playing form of screen narrative &amp;ndash; such heterogeneous screen texts as film-, tele- and video texts on the Internet. The course of the research is demonstrated on the example of the movie &amp;ldquo;Green book&amp;rdquo;, the news broadcast of 05.03.2020 &amp;ldquo;Weekly events in 60 minutes&amp;rdquo;, TikTok videotext by the user @darimbs of 03.08.2020. The author meticulously describes and analyzes the plotline of these texts, through which is implemented the hermeneutic circle. The novelty of this work consists in viewing the heterogeneous screen texts from the perspective of their implementation in linear explication of multi-component structure of hermeneutic circle on the screen. The author's special contribution to examination of heterogeneous screen texts lies in inclusion into the corpus of research materials of Interne text, besides the film texts and teletexts, which allows to comprehensively study the peculiarities of dramaturgical representation of the reality in all forms of screen narrative. It is discovered that the narrative is incorporated into the screen text &amp;ndash; in terms of its creation, the author selects an event line of the objectively true reality, which places the constitutive elements of hermeneutic circle on the screen in the sequence of frames, and the meaning is conveyed indirectly through the plot. The conducted analysis of screen text from the standpoint of implementation of hermeneutic circle, allows identifying the latent meanings, as well as reconstructing the broad idea of the text.
Structured Models for Vision-and-Language Reasoning

Author(s): Hu, Ronghang | Advisor(s): Darrell, Trevor | Abstract: Vision-and-language tasks (such as answering a question about an image, grounding a referring expression, or following a natural language instruction to navigate through a visual environment) require jointly modeling and reasoning over the two modalities of image and text. We have witnessed significant progress in joint visual and linguistic reasoning, often through neural approaches trained with the help of larger datasets and more computation resources. However, is solving these vision-and-language tasks as simple as building models with more parameters and training them on more data? If not, how can we build better reasoning models that are data-efficient and generalize well?This thesis provides an answer to the above question with structured models for vision-and-language reasoning – models with architectures that take into account the patterns and regularities in human language, visual scenes, and agents’ skills. We begin with the task of referring expression grounding, where we show that significantly better accuracy and generalization can be achieved by taking into account the compositional structures in these expressions with our proposed Compositional Modular Networks (CMNs) in Chapter 2. We further address the visual question answering task in Chapter 3 with the End-to-End Module Networks (N2NMNs) based on dynamic compositional modules that align with the reasoning steps in the questions. In Chapter 4, we extend our work on modular reasoning and propose the Stack Neural Module Networks (SNMNs) that automatically induce a proper module layout with interpretable reasoning steps. Beyond modular reasoning, we also propose to construct context-aware representations of the visual scene with Language-Conditioned Graph Networks (LCGNs) in Chapter 5 for relational reasoning, and address the problem of reading text in images for question answering with iterative pointer-augmented multimodal transformers in Chapter 6. Finally, we show that embodied tasks also require structured models, and propose the Speaker-Follower models for the navigational instruction following task in Chapter 7 with the pair of a speaker model and a follower model that complement each other. In all these scenarios, we show that by taking into account the structures in the tasks and the input modalities, our models perform and generalize significantly better than their unstructured counterparts.
An Adjustable Algorithm for Power Spike Smoothing

Rapid switching of the power switches inside a grid-connected power converter causes a huge number of high-frequency spikes, which could degrade the power converters' functioning. The power spike smoothing algorithm is a digital solution to removing these switching spikes from the sensor measurement. It uses the wavelet decomposition to reveal the high-frequency components so that specific suppression policy could apply. Reconstruction from the original low-frequency components and reduced high-frequency components result in the smoothing effect in the output waveform, and the degree of the smoothing effect is adjustable depending on the variance of each power converter through the parameter shrinkage degree.
the Connection between Cryptography and Differential Privacy: a Survey.

Due to the successful application of data analysis technology in many fields, various institutions have accumulated a large amount of data to improve their services. As the speed of data collection has increased dramatically over the last few years, an increasing number of users are growing concerned about their personal information. Therefore, privacy preservation has become an urgent problem to be solved. The differential privacy as a strong privacy preservation tool has attracted significant attention. In this survey, we focus on the connection between differential privacy mechanisms and cryptography methods in terms of utility improvement. In specific, we firstly analyse the utility amplification effect of anonymous communication. Then, we give the hardness results about what is impossible to achieve for differential privacy mechanisms' utility from the view of cryptography. We also investigate how to improve utility by combining differential privacy mechanisms with homomorphic encryption schemes. The differential privacy develops from cryptography and still benefits from the progress of cryptography. In order to summarize the state-of-the-art and to benefit future research, we are motivated to provide this survey.
Enhancing retail business and customer experience using blockchain approach

The retailers of supermarkets today quite often use the transactional marketing strategy to entice their customers by issuing discount coupons. The proposed system aims at enhancing this reward-based system and automating customer shopping experience by building a 'pay as you choose the item' technology. Employing such a system would help in providing a faster, safer and more convenient means of transaction alongside improving the overall efficiency of the supermarkets achieving higher revenue with lesser manpower. The authors utilise the concept of blockchain to perform transactions using a cryptocurrency called value-coins. Such a digital system would be commonly available to all the outlets of the retailer.
A frame of user interface design requirements for usability and improved consumer behaviors in social commerce

User interface usability in social commerce is an important theme today because many businesses are evolving with use of social media in order to easily reach larger markets.  The study was based o ...
Essential Aspects of Blended Learning

My paper will focus on several basic features concerning blended learning, and it will build on the properties of digital technologies. In our contemporary world, students in general, and working students in particular, feel the need to use their digital skills to be successful in both the study and work environment. This means that our courses should be upgraded by resorting to the new technologies in the field of teaching and assessment. By doing so, the advantages would be multiple, starting with accessibility, affordability, flexibility and ending with inclusivity, selfempowerment and resource management. An example of such a â€˜fortunateâ€™ mixture between on-site classroom interaction and on-line activities is the case of flipped classrooms, where, as it is going to be discussed, student-time is increased and so is the quality of the interaction between the teacher and the learners.
How does a prototype synthetic mammography version compare to current synthetic mammography and to full field digital mammography

Poster: ECR 2020 / C-07603 / How does a prototype synthetic mammography version compare to current synthetic mammography and to full field digital mammography?  by:  C. Van Ongeval 1, L. Cockmartin2, S. Postema2, R. Prevos2, M. Keupers2, T. Thywissen2, G. Zhang2, H. Bosmans2; 1Leuven/BE , 2Leuven/BE
APLIKASI TELEGRAM UNTUK SISTEM MONITORING PADA SMART FARMING : TELEGRAM APPLICATION MONITORING SYSTEM FOR SMART FARMING

Industry 4.0, in agriculture, push developing technology which combining automation system and update data exchange via internet technolgoy. The technological developments, in agriculture, in Indonesian have not developed significantly, therefore it is needed a technological development toward industri 4.0. One of the developments in industry 4.0 in agriculture is monitoring system. The goal of this research is to design a monitoring system which conected to internet and using telegram application for accessing data in smart farming. In this research, data, taken, is temperature and moisture soil since both of data are very influencing in plant growth process. Thi design is just a prototype that is used for simulating monitoring process on agriculture land then it is needed temperature and moisture soil sensor for sensing, STM 32 as microcontroller, LCD for displaying monitoring data, bluetooth as connecting to gateway, PC as gateway to connect to internet and android cell phone for installing telegram application. The simulation result show data at IoT device matched to data on telegram application. With telegram application that could access monitoring data from agriculture land will increase agriculture quality result so as implementation of this technology could be one of supporting agriculture development toward industry 4.0
Where fashion, jewellery and wearable technology meet

In this article we relate the fields of fashion, jewellery and wearable technology in terms of the emphasis on respectively social, personal and instrumental values. Moreover, we describe how the subfields of soft wearables, digital jewellery and fashion jewellery emerge on the intersections of these fields. The main contribution of the article is twofold. Firstly, we identify and explore the potential of the area at the cross-section of all three fields, which is so far hardly explored by academia. We discuss what it takes from design-researchers in the field of digital jewellery to explore the newly identified design space: broadening their frame of reference and changing their approach of wearable technology. We suggest to broaden the frame of references from Art Jewellery to jewellery in general. And, we advocate a shift in the perspective on wearable technology, from criticizing what it lacks to appreciating its merits. Within wearable technology, we find technological-sturdy artefacts, which can be used in a cultural probe in order to explore emergent behaviour, interactions and appreciation. Secondly, we argue that this overview should not be misunderstood as static and over-simplified. On the one hand, we need to be aware of the heterogeneity of each of the fields. On the other hand, we like to stress the relativity of the differences between the fields. Therefore, we emphasize the importance of looking at the overview on different levels of detail, cherishing and challenging both similarities and differences of fashion, jewellery, wearable technology, in order to explore the full potential of these three fields and all possible intersections.