Reviewerly/exHarmony huggingface dataset:
Local Clustering with Mean Teacher for Semi-supervised learning

The Mean Teacher (MT) model of Tarvainen and Valpola has shown good performance on several semi-supervised benchmark datasets. MT maintains a teacher model's weights as the exponential moving average of a student model's weights and minimizes the divergence between their probability predictions under diverse perturbations of the inputs. However, MT is known to suffer from confirmation bias, that is, reinforcing incorrect teacher model predictions. In this work, we propose a simple yet effective method called Local Clustering (LC) to mitigate the effect of confirmation bias. In MT, each data point is considered independent of other points during training; however, data points are likely to be close to each other in feature space if they share similar features. Motivated by this, we cluster data points locally by minimizing the pairwise distance between neighboring data points in feature space. Combined with a standard classification cross-entropy objective on labeled data points, the misclassified unlabeled data points are pulled towards high-density regions of their correct class with the help of their neighbors, thus improving model performance. We demonstrate on semi-supervised benchmark datasets SVHN and CIFAR-10 that adding our LC loss to MT yields significant improvements compared to MT and performance comparable to the state of the art in semi-supervised learning <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> The code is available at: https://github.com/jay1204/local_clustering_with_mt_for_ssl.
Research on Mass News Classification Algorithm Based on Spark

In recent years, with the explosion of the number of Internet news, people pay more and more attention to how to classify the mass of news. Therefore, this paper studies the mass news classification algorithm based on Spark, aiming at the problem of how to classify mass news data quickly and efficiently. In this paper, a large amount of news text is segmented based on Jieba segmentation tool, and several versions of stop words list are combined to remove stop words. Secondly, on the basis of traditional convolutional neural network, this paper proposes a news classification algorithm based on the combination of pre-trained Word2vec and improved CNN. In addition, the classification algorithm proposed in this paper is parallelized based on Spark, which improves the speed of mass news classification. In this paper, the standard data sets are used to compare and experiment the proposed news classification algorithm. The experimental results show that compared with the traditional algorithm, the news classification optimization algorithm designed in this paper has obvious improvement in multiple evaluation indexes such as accuracy, recall and F1. In addition, after parallel design of the algorithm proposed in this paper based on Spark, compared with the serial algorithm, the speed improvement effect is also more significant.
The Real Estate Big Data Analysis System Based on Cloud Computing

In order to further improve the mobile Internet application technology and provide powerful computing power for the real estate intermediary system, this paper studies the basic theory of big data and the framework theory of system development, and a new generation of information technology platform based on cloud computing technology, Web2.0 technology and Hadoop technology is used to make corresponding decisions for real estate development. Then the system development framework theory, based on research and analysis based B/S architecture, ASP.NET Web forms and Java based SSM, the design of SSH password free login, Hadoop cluster operation, Hadoop development environment and the function realization of key modules of server and client are put forward. At the same time, the data transmission between Hadoop big data platform and relational database is realized, and the running results of the system are tested and analyzed. The results show that the system runs smoothly, which greatly improves the informatization and intelligence level of the real estate company.
GEOLOGICAL AND GEOMORPHOLOGICAL ANALYSIS AND HYDROGEOCHEMICAL ENVIRONMENT OF AQUIFERS OF THE SOUTHERN WEST COAST OF KARNATAKA, INDIA

Dakshina kannada district is situated in peninsular region. The peninsula is composed of geologically ancient rocks of diverse original and most of them have undergone metamorphism. The early Precambrian tonalitic gneisses invaded by granites, granulites and dolerite dykes. Granulites are mostly restricted to areas south of Mangalore. High grade alumina rich (corundum bearing) metamorphic schists have been encountered and younger alkaline intrusive rocks like Aegerine syenites have been reported. There are ve rivers and estuaries. Number of lineaments cut across each other and some lineaments are parallel to each other. The Arabian sea class is the largest among other land cover features in the study area. The river/tidal creek land cover appear as long irregular and sinous in outline. Mulki river, Netravati river, Gurupur river at southern terrain. The qualities of groundwater at sandy aquifer are good, lateritic/weathered gneissic rocks it is sweet.
Object-level Scene Context Prediction

Contextual information plays an important role in solving various image and scene understanding tasks. Prior works have focused on the extraction of contextual information from an image and use it to infer the properties of some object(s) in the image or understand the scene behind the image, e.g., context-based object detection, recognition and semantic segmentation. In this paper, we consider an inverse problem, i.e., how to hallucinate the missing contextual information from the properties of standalone objects. We refer to it as object-level scene context prediction. This problem is difficult, as it requires extensive knowledge of the complex and diverse relationships among objects in the scene. We propose a deep neural network, which takes as input the properties (i.e., category, shape, and position) of a few standalone objects to predict an object-level scene layout that compactly encodes the semantics and structure of the scene context where the given objects are. Quantitative experiments and user studies demonstrate that our model can generate more plausible scene contexts than the baselines. Our model also enables the synthesis of realistic scene images from partial scene layouts. Finally, we validate that our model internally learns useful features for scene recognition and fake scene detection.
Analysis of Risks to Data Privacy at the State Level Throughout the United States

Concern about compromised personal information on the Internet in recent years, now accelerated by fears about compromised information in the context of the global COVID-19 pandemic and other data security. Sweeney in 1987 demonstrated that in the United States, approximately 87% of persons in the United States could be identified uniquely from only three relatively easily found data points, (PC,G,B), or 5-digit postal code, gender, and birthdate including year. In this paper, we (1) update Sweeney's results to 2020, and note that her estimates persist; (2) disaggregate her approach to find results state-by-state in the United States; and (3) replace her requirement to identify three data points to two data points, with approximately the same effort required to identify not the individual, but at least one member of the nuclear family unit.
The role and significance of computer animation in school algebra of complex numbers

The article discusses computer technologies in teaching mathematics, which is relevant in the light of the digitalization of education. The role and importance of computer animation in algebra lessons in the study of complex numbers in the tenth grade in accordance with the current school textbooks are presented. Animation drawings can be used as demonstration drawings when studying the relevant topic, as a tool for testing knowledge, they provide an opportunity for experimentation, support the research style of developing learning. The use of animated drawings effectively replenishes the arsenal of teaching aids for a practicing mathematics teacher. This allows you to eliminate unnecessarily burdensome computational difficulties, to visualize mathematical concepts and statements, turning some of them into obviously correct ones, to facilitate the teacher's work in compiling tasks of the same type with different data. The management of the student's educational and research work on the manufacture of animation-geometric models of objects and phenomena contributes to the successful assimilation of knowledge by students, stimulates an experimental research approach to teaching.
Function and Influence of Database Technology in Distributed Accounting Books under Network Environment

Abstract As the main form of value embodiment of accounting subject, distributed account book is becoming more and more important in accounting management activities. However, due to its own specific economic attributes, it is often restricted by many internal and external conditions such as enterprise development. Especially in the objective situation of the continuous popularization of network application in recent years, with the continuous progress of database technology, its role in business management is increasingly obvious, and its impact on the quality of corporate accounting information is more and more obvious. The development of database technology in the network environment is conducive to the development of distributed accounting records through artificial system, and the establishment of a computer-based traditional system and modern Distributed Accounting registry. In this changing process, the development of innovation and management theory runs through all fields, but the influence of network information technology is the most basic. It directly or indirectly affects the evolution of distributed accounting system structure. The development of network database technology will contribute to the smooth connection between the Distributed Accounting registry and other management system subsystems. An enterprise ensures the data exchange within the enterprise, even between the supply and demand chains. In this process, the quality of accounting information improves the efficiency in the decision-making process. However, the research of computer accounting is usually dealing with the accounting treatment of specific business. It mainly studies the impact of database technology on the network environment in distributed accounting books.
Review on Data Base as a Cloud

Cloud computing is a service that allow and use computing as product wide service. It has set new parameters the IT sector and getting it recognition every day by expanding itself. The paradigm of computing has received a significant excitement and attends on the technological sphere. Cloud computing shares are based in different countries it data and different resources are always based on different devices to that are connected to each other through internet connection. If we go by the term, then a cloud data base management system(DBMS) is a system of database management that operates through cloud computing. Cloud computing provides a secure platform so it is expected that in future DBMS acting through cloud computing can be increased there is an internet in outsourcing DBMS tasks to third party results. Benefits of DBMS using cloud computing are discussed in this paper their limitation opportunities and other issues are reviewed here.
Double Layer Cryptography using Multiplicative Cipher and Chemical Periodic Table

According to the present communication system one of the main concerns is secured transformation of data. In this paper we be inclined to propose a two-level encryption in this paper in the first level encryption we use the multiplicative ciphers and Cesar cipher in this level the plain text letters, we shall multiply the key numbers in this level and the second layer encryption we use periodic table exploitation the properties if the quality table, and thus use it for encrypting and decrypting in the same manners. For the information of network security in the second level encryption we will differently types of periodic table properties like atomic no, mass no, IUPAC name, chemical formula, and their properties.
A Game Theoretic Approach for Addressing Domain-shift in Big-data

In this paper, a novel approach is presented to mitigate the issue of domain shift observed in big-data classification. Since little information is available about the shift, we introduce a "distortion model", and obtain additional data-samples to represent the shift. Next, a deep neural network (DNN), referred as "classifier," is used to compensate for the shift by learning through these additional samples while maintaining performance on training samples. As the exact magnitude of domain shift is uncertain, we compensate for the optimal expected shift by formulating a zero-sum game. In the proposed game, the distortion model is viewed as the maximizing player which increases the domain shift while the classifier becomes the minimizing player that reduces the impact of domain shift on learning. The Nash solution of the game, which is demonstrated mathematically, provides the domain shift and its optimal adaptation through the classifier. To solve the proposed game for the Nash solution, a direct error-driven learning scheme is introduced where a cost function is derived and solved for each layer in the DNN and the distortion model. Comprehensive mathematical and simulation study is presented to demonstrate the efficacy of the approach.
Exploring the Use of Universal Design for Learning to Support In-Service Teachers in the Design of Socially-Just Blended Teaching Practices

This chapter examines the pivot to online and bended learning which occurred during the COVID health crisis and highlights how blended learning has emerged by far as the most popular and sustainable delivery option. The COVID pivot has also demonstrated, however, that blended learning too often ignores social inequities, and as a result allows them to become exacerbated. The chapter examines ways to support K-12 teachers as they seek to support social justice objectives within blended learning environments and suggests that universal design for learning can serve as a user-friendly and hands-on framework to address learner diversity in these innovative hybrid learning environments. The chapter further explores the repercussions this reflection has in relation to pre-service teacher training, in-service professional development, and leadership culture.
A Novel Personalized Preference-based Approach for Job/Candidate Recommendation

Although fuzzy-based recommendation systems are widely used in several services, scanty efforts have been carried out to investigate the efficiency of such approaches in job recommendation applications. In fact, most of the existing fuzzy-based job recommendation systems are only considering two crisp criteria: Curriculum Vitae (CV) content and job description. Other factors like personalized users needs and the fuzzy nature of their explicit and implicit preferences are totally ignored. To fill this gap, this paper introduces a new fuzzy personalized job recommendation approach aiming at providing a more accurate and selective job/candidate matching. To this end, our contribution considers a Fuzzy NoSQL Preference Model to define the candidates profiles. Based on this modeling, an efficient Fuzzy Matching/Scoring algorithm is then applied to select the top-k personalized results. The proposed framework has been added as an extension to TeamBuilder software. Through extensive experimentations using real data sets, achieved results corroborate the efficiency of our approach in providing accurate and personalized results.
Faulty parallel structure in students’ argumentative writing

In writing, to produce well-structured and understandable sentences, students must have knowledge of one aspect of grammar, namely parallelism. However, in fact, many students do not understand the concept of parallelism so that they fail to apply it in writing. This study examines the common types of faulty parallel structure in students’ argumentative writing and investigates the factors which cause the fault. This is a qualitative study that collects the primary data from students’ argumentative writing in particular topics which demand them to present their arguments and to provide their position, reasons and evidence in their writing, and the secondary data from the interview. As a result, this study discovers that students commit the faults in different types of parallelism, and mostly take place in parallel structure with elements joined by conjunction, both coordinating and correlative conjunction. The result of the interview section shows that students make the faulty parallelism due to lack of knowledge, lack of attention, and lack of vocabulary. Thus, in order to reduce the faulty parallel structure, the specific materials about parallelism should be added in writing or grammar’s lesson plan.
Active Selection of Classification Features

Some data analysis applications comprise datasets, where explanatory variables are expensive or tedious to acquire, but auxiliary data are readily available and might help to construct an insightful training set. An example is neuroimaging research on mental disorders, specifically learning a diagnosis/prognosis model based on variables derived from expensive Magnetic Resonance Imaging (MRI) scans, which often requires large sample sizes. Auxiliary data, such as demographics, might help in selecting a smaller sample that comprises the individuals with the most informative MRI scans. In active learning literature, this problem has not yet been studied, despite promising results in related problem settings that concern the selection of instances or instance-feature pairs.Therefore, we formulate this complementary problem of Active Selection of Classification Features (ASCF): Given a primary task, which requires to learn a model \(f:{{\,\mathrm{x}\,}}\rightarrow y\) to explain/predict the relationship between an expensive-to-acquire set of variables \({{\,\mathrm{x}\,}}\) and a class label y. Then, the ASCF-task is to use a set of readily available selection variables \({{\,\mathrm{z}\,}}\) to select these instances, that will improve the primary task’s performance most when acquiring their expensive features \({{\,\mathrm{x}\,}}\) and including them to the primary training set.We propose two utility-based approaches for this problem, and evaluate their performance on three public real-world benchmark datasets. In addition, we illustrate the use of these approaches to efficiently acquire MRI scans in the context of neuroimaging research on mental disorders, based on a simulated study design with real MRI data.
Incremental Learning Based on Angle Constraints

Abstract With the rapid growth of the Internet, it has become easy to obtain new data for many application domains. However, when adding new data to the current system of artificial neural networks (ANNs) to learn, it can cause the network to completely forget what it has learned before, which is called catastrophic forgetting. The main reason for these problems is the inability of ANNs to balance new classes with old ones. Therefore, to address the challenge of learning new knowledge while not suffering from catastrophic forgetting, some incremental learning algorithms have been proposed to alleviate. This paper proposes features that balance new classes with old classes by using angular distillation. And some exemplars from the old classes are retained to improve the performance on the old data. The effectiveness of our algorithm is demonstrated on CIFAR-100 dataset.
Optimization of Thermal Comfort in Rifain Digital Printing: Vertical Garden with Water Wall System Concept

Abstract Thermal comfort is a major factor for a building’s design, and is associated to room temperature conditions. Thermal comfort depends on climatic variables and several individual / subjective factors such as clothing, acclimatization, age, gender, level of health, and others. Measurements of thermal comfort made via Meteoblue and SunHour software at the Rifain Digital Printing location provide data including temperature, humidity, sun exposure and wind speed. The data is then synthesized using CBE Thermal Comfort, a software that detects conformity to thermal comfort standards in a room. The results of measurement and data synthesis indicated that the building requires a cooling temperature of 5.2 ° C. From these results, it can be said that the thermal comfort of Rifain Digital Printing is not optimal. Improvements are needed to the existing thermal conditions. One of the efforts to optimize thermal comfort is the use of vegetation and water (Talarosha, 2005). Based on synthesis result obtained through research and data processing through literature studies, we came up with a solution, namely a vertical garden with a water wall system design as an effort to overcome the problem of thermal comfort in the Rifain Digital Printing building.
ParallelNAS: A Parallel and Distributed System for Neural Architecture Search

Although deep learning takes researchers out of complicated feature engineering, designing practical deep learning models is still a complicated process. Neural Architecture Search (NAS) is famous for automating the design of deep learning models. While the fundamental problem of NAS methods is time-consuming, parallel computing is an encouraging way to relieve this problem. We designed a two-level hierarchical parallel system called ParallelNAS, comprising a parallel explorer and parallel evaluators. For the parallel explorer, a general-purposed distributed search framework is built on virtualized, massively-parallel, asynchronous infrastructure. For parallel evaluators, we integrate a segment-balanced sparsity scheme into a mainstream data-parallel architecture. The overall experiment results demonstrate that our distributed and parallel system can reach near-linear speedups on a cluster of 64 GPUs.
ROC Bot: Towards Designing Virtual Command Centre for Energy Management

Domains such as energy management rely heavily on dashboards and other related interfaces to manage the infrastructure and resources. The users of this domain use dashboards to manage the data and extensively perform periodic analysis to save energy and cost. Creating multiple dashboards for visualization of data is not user-friendly from a design perspective. This motivates the need of a single interface through which users can do data exploration, visualization and summarizing. Combining this with features such as anomaly detection can identify various issues and assist in day to day monitoring of an energy management center.In this paper, we present ROC (Resource Optimization Center) Bot, a novel data exploration tool with a natural language interface. ROC Bot leverages recent advances in deep models to make query understanding more robust in the following ways: First, ROC Bot uses a deep model to translate natural language statements to SQL, making the translation process more robust to paraphrasing and other linguistic variations. Second, to support the users in automatically summarizing data, ROC Bot provides a machine learning model that helps in writing natural looking summaries in any given tabular data.