Reviewerly/exHarmony huggingface dataset:
GAIPS: Accelerating Maximum Inner Product Search with GPU

In this paper, we propose the GAIPS framework for efficient maximum inner product search (MIPS) on GPU. We observe that a query can usually find a good lower bound of its maximum inner product in some large norm items that take up only a small portion of the dataset and utilize this fact to facilitate pruning. In addition, we design norm-based, residue-based and hash-based pruning techniques to avoid computation for items that are unlikely to be the MIPS results. Experiment results show that compared with FAISS, the state-of-the-art GPU-based similarity search framework, GAIPS has significantly shorter query processing time at the same recall.
Modelling Rajasthan Assembly Election: Sentimental Analysis of Rajasthan Political Twitter Data

With emergence of social media, data is growing daily exponentially. Various stakeholders processing this data and creating application to cater the need of different sections of the society. From last two years world has witness how fate of two countries changed by social media by changing votersâ€™ choices. Social analyst are now using social media and work for different political parties to influence local voters by sharing their ideologies, work and manifestos. This paper works on data of 2018 election and confined to Rajasthan state where data of tweeter was collected belonging to party chief person, political pages and analyses them using python tweepy api. Paper also use KNN algorithm to trace out the type of text or hashtags trending during elections and try to decode the ideology behind these texts and find out how tweets sentiments work and influence people.
Performance Enhancement and Reduce Energy Consumption with Load Balancing Strategy in Green Cloud Computing

Cloud computing is an innovative technique which provides on-demand access of computing resources like processing power, memory, storage, network, etc. Cloud providers always suffering from the proper allocation of the resources for the execution of requested tasks. Because of this conflict or improper matching of tasks with the computing resources, they lead with QoS and performance degradation, and at end result, it may have bigger issue with greater energy consumption. Hence, in cloud environment, load balancing is an important approach to allocate resources to execute requested tasks with utmost efficiency and with fully utilization of computing resources. With effective load balancing mechanism, we can utilize the cloud resources, enhance performance, achieve QoS, and can reduce significant amount of energy consumption. Proposed load balancing algorithm focuses on response time and processing time for the execution of requested tasks to better justify the results in Green Cloud Environment. By reducing response time and processing time of proposed system, we can enhance system performance and achieve better Quality of Service (QoS) compared with any existing load balancing policies like Round Robin, active monitoring, LFU, and Throttled in Cloud system. By cutting down processing time and response time would result in better energy efficient mechanism for Green cloud computing. By using proposed load balancing algorithm, we can tentatively reduce response time by 25% and can reduced data center processing time by nearly 20% with compared to Round Robin algorithm. With provided solution of effective load balancing strategy, there is a significant reduction in energy consumption by cloud environment and helps to manage Green Cloud Environment more robust.
HTTP BOT: How to Generate Large http Requests Logs

It is hard to imagine the world without Big Data now. More and more developers are involved in creating distributed computing applications. This type of applications requires complex tests. As the result, the question often arises - where to get a dataset that is suitable in terms of content and size. We propose an easy way to get any large logs of http requests directly on your local machine.
The historical and pedagogical analysis of formation of professional readiness to the division management in the protection of public order

The article is dedicated to a historical and pedagogical analysis of the development of the formation of professional readiness of cadets to manage units during the protection of public order. The problematic aspects of the development of the officer training system are highlighted and substantiated. The concept of readiness to manage a unit and the specifics of performing tasks in the protection of public order are revealed. The education features with a significant impact in the process of formation of professional readiness in the protection of public order are underlined.&#x0D; The authors’ attention is focused on the pedagogical approaches of A.V. Lunacharsky, M. N. Pokrovsky, S. T. Shatsky, P. P. Blonsky, and later A. S. Makarenko and V. A. Sukhomlinsky who were the advocates of proximity of training and practice. They publicized the value of the use of labor, and, as a result, practical methods in the study of various academic disciplines, the formation of students’ skills related to the development of creative and activity capabilities of a person.&#x0D; Special attention is paid to the current state of implementation of educational standards for the successful solution of problems of officers’ training.&#x0D; The paper reveals the sequence of development of formation of professional readiness of students to manage protection units for public order. The research analyses pedagogical conditions of increasing efficiency of the process under consideration (at the initial stage, at the stage of formation and intensive development, at the present stage).
Digital Platforms in the Age of Mobility: A Contribution Towards Language Teaching and Learning

As the continuous development of digital technologies continues to have a profound impact on educational contexts (including teacher education settings), it has become paramount to not only understand whether and how teacher education and training is keeping pace with the ubiquitous nature of digital technology, but also to contribute to the continuous development of competences and initiatives within this scope.Focusing on language teaching and learning, this paper aims to support teachers and teacher educators working in technology-laden environments by putting together a broad ranging annotated list of digital platforms and tools that can be used to augment and support online activities and collaboration and promote articulated, technology-enhanced teaching and learning activities. Emphasizing the role played by Mobile Assisted Language Learning (MALL) and drawing from recent trends and an extensive review resulting from a collaboration that involved teacher educators, researchers and practitioners, these tools are described taking into account current approaches to language learning and assessment, as well as their perceived applicability, effectiveness and shortcomings. Highlighting the importance of collaboration, this work is effective in providing practical examples and best practices, whose potential can be harnessed and ultimately replicated by other practitioners and teachers in training, thus contributing the ongoing momentum of practice-driven innovation, that could ultimately inform language learning and teaching policies.
Categorizing 2019-N-CoV Twitter Hashtag Data by Clustering

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Research on the Application of Internet of Things in English Teaching

Under the background of global internationalization, English talents are paid more and more attention, so English teaching, as an English talent training platform, has a greater responsibility. At this time, people analyze the situation of modern English teaching, and the results show that there are many problems in modern English teaching, all of which have an impact on the quality of English talents. Therefore, it is necessary to focus on the problems improve. At this time, the relevant fields put forward a kind of English teaching mode based on Internet of things technology. This mode gives full play to the teaching function of Internet of things technology, and can solve the problems in the past, so as to improve the quality of English teaching. However, the application of this model is not very extensive, and English teaching related personnel do not know much about it. Therefore, this paper will carry out relevant research and analyze the application of Internet of things in English teaching.
Extractive Summarization of Text Using Weighted Average of Feature Scores

In the era of information overload, need for applications to comb through huge number of documents to extract important information is increasing. This information is helpful in assessing whether or not a document is relevant. Automatic text summarization is one of the solutions to the problem of extracting useful information from huge collection of textual data. A summarizer converts a lengthy document into a short summary by extracting important sentences from it without losing the crucial information. A summarizer can be either abstractive or extractive. An extractive summarizer relies on the statistical features of the input text to create a summary by merely copying the important sentences, whereas an abstractive summarizer tries to understand the context of the document and generates a summary which may contain new sentences not part of the original document. This paper focuses on extractive summarization technique. An approach for generating short and precise summary from a single document using weighted average of feature scores has been proposed. Sentences are ranked based on their scores, and top 40% sentences are selected to form the summary. Experiments were carried out on 250 documents from BBC News summary dataset. The results were compared with existing online summarizers and the proposed summarizer gave better average recall, precision and F-measure values.
Handwritten Signature Verification Using Complex Wavelet Transform and Mutual Information Based Kernel

Signature Verification is an essential and challenging task in forensic document analysis. Signature Verification is a process where the authenticity of a given signature is validated; i.e. it is confirmed, after analysis, whether the given signature is forged or not. The objective of the work proposed here is to create a new algorithm that can be used for verification of signatures used in cheques and various legal documents for customer authentication in banking systems. The proposed algorithm is designed using Daubechies complex wavelet transform (DCxWT) which has superior approximate shift invariant and edge representation properties over real valued wavelet transform. This is what motivates the use of DCxWT in the Signature Verification task. For classification of valid and forged signatures we have used Support Vector Machine (SVM) classifier. We have used Mutual Information kernel that is an alternative kernel of the KL divergence kernel in SVM classifier. Proposed method is compared with KL-divergence based kernel and some other standard kernels like Poly, RBF, and Tanh kernel of SVM Classifier. The method we proposed is juxtaposed with different state-of-the-art methods and effectiveness of the algorithm is also measured using different quantitative performance measures.
Research on Disambiguation Attributes Based on Semantic Hierarchical Model

Abstract Feature extraction concepts and statistical classifiers began to be used in computer-aided detection and diagnosis, and many different feature learning techniques such as principal component analysis, image block clustering and dictionary methods are very popular. However, in the case of less labeled data, the application and effect of deep learning are restricted. The research content of this paper is based on items such as nodule multi-semantic and fine-grained classification and rating research of WGAN synthetic oversampling learning, multi-semantic assisted disambiguation research based on multi-level association transfer learning, and multi-label incremental learning research for small sample semantic hierarchical structure. Moreover, based on the synthesis of various semantic samples, multi-semantic association transfer, and multi-label incremental learning methods, a set of effective methods and application mechanisms for intelligent auxiliary semantic hierarchical structure disambiguation are formed by describing the problem definition, proposing research methods, and discussing the experimental effects of the proposed methods in detail.
A Novel Image Recognition Method Based on CNN Algorithm

Handwritten digital image recognition is a special problem in digital recognition, which is much more difficult due to uncertainties such as writer and writing environment. But the accuracy of handwritten digital recognition is particularly important in some critical areas such as public security and finance. In this paper, we used machine learning and deep learning technology, to identify MNIST handwritten digital data sets. And the support vector machine and convolutional neural network were used to image recognition, among them, the support vector machine compares the accuracy of the identification of the two kernel functions. Convolutional neural networks are improved based on the classic LeNet-5 model. The Experiments result shown that the identification accuracy of Gaussian kernel function model and linear kernel function model is 94.03% and 90.47% for support vector machines, respectively. In contrast, Gaussian kernel function performs better, but the overall accuracy is low. The best performance is based on the classic convolutional neural network LeNet-5 improved model, the test set recognition rate can reach 99.05%.
Application of Deep Learning in the Reform of Japanese Education in the Information Age

With the advent of the era of informationization and big data, information technology has also been widely used in all aspects of education and teaching. This article aims to study the application of deep learning in the reform of Japanese education in the information age. This article first describes and explains the concept of deep learning in the information age, and then puts forward the literature analysis method, questionnaire survey method, interview method and classroom observation method to more intuitively understand the true and accurate ideas and practical implementation of students. Finally, the Japanese language education talent training model was constructed to lay the foundation for the future training of Japanese talents. The experimental results in this article show that deep learning in the information age has a great influence on Japanese education reform. Through the results of the Japanese performance comparison experiment, it can be seen that the scores of the first three classes in the experiment are greater than 0.5, which verifies the correctness of the experiment. The student performance of the class has been significantly improved compared to the performance of the ordinary class.
Novel Approach to Minimize the Memory Requirements of Frequent Subgraph Mining Techniques

Frequent subgraph mining (FSM) is a subset of the graph mining domain that is extensively used for graph classification and clustering. Over the past decade, many efficient FSM algorithms have been developed with improvements generally focused on reducing the time complexity by changing the algorithm structure or using parallel programming techniques. FSM algorithms also require high memory consumption, which is another problem that should be solved. In this paper, we propose a new approach called Predictive dynamic sized structure packing (PDSSP) to minimize the memory needs of FSM algorithms. Our approach redesigns the internal data structures of FSM algorithms without making algorithmic modifications. PDSSP offers two contributions. The first is the Dynamic Sized Integer Type, a newly designed unsigned integer data type, and the second is a data structure packing technique to change the behavior of the compiler. We examined the effectiveness and efficiency of the PDSSP approach by experimentally embedding it into two state-of-the-art algorithms, gSpan and Gaston. We compared our implementations to the performance of the originals. Nearly all results show that our proposed implementation consumes less memory at each support level, suggesting that PDSSP extensions could save memory, with peak memory usage decreasing up to 38% depending on the dataset.
Cyclic Sieving for cyclic codes

Prompted by a question of Jim Propp, this paper examines the cyclic sieving phenomenon (CSP) in certain cyclic codes. For example, it is shown that, among dual Hamming codes over Fq, the generating function for codedwords according to the major index statistic (resp. the inversion statistic) gives rise to a CSP when q=2 or q=3 (resp. when q=2). A byproduct is a curious characterization of the irreducible polynomials in F2[x] and F3[x] that are primitive.
Chinese and Western Art History Based on Big Data

Abstract After entering the era of big data, artificial intelligence has developed rapidly, and the contradiction between technology and humanities has become increasingly prominent. World culture, technology, and art are all facing new conflicts and challenges. This article mainly studies the development and experience of Chinese and Western art history based on big data. This experiment uses an ordinary computer to build a Spark cluster environment by setting up 6 virtual hosts. Chinese artists are always more inclined to subjective creation, while the traditional Western realistic painting in the Renaissance period paid more attention to the three-dimensional painting color while achieving the harmony of the picture. Then sample the fingerprint at the reference point of the sparse density distribution to cluster the indoor area into a specific number of sub-regions, and use the sampling data of each sub-region to predict the fingerprints at other unmeasured reference points to reconstruct the complete Fingerprint database. The data shows that in the experimental cluster environment, when the processing data size is 50MB or far greater than 50MB, choosing parallel algorithms can save data processing time and improve data processing efficiency. The results show that both Chinese painting and Western modernist painting pursue the spontaneity, randomness and contingency of painting in terms of aesthetic effects. This is the common ground of the pursuit of expressiveness in Chinese and Western paintings.
Multi-facet Universal Schema

Universal schema (USchema) assumes that two sentence patterns that share the same entity pairs are similar to each other.This assumption is widely adopted for solving various types of relation extraction (RE) tasks.Nevertheless, each sentence pattern could contain multiple facets, and not every facet is similar to all the facets of another sentence pattern cooccurring with the same entity pair.To address the violation of the USchema assumption, we propose multi-facet universal schema that uses a neural model to represent each sentence pattern as multiple facet embeddings and encourage one of these facet embeddings to be close to that of another sentence pattern if they cooccur with the same entity pair.In our experiments, we demonstrate that multi-facet embeddings significantly outperform their singlefacet embedding counterpart, compositional universal schema (CUSchema) (Verga et al., 2016), in distantly supervised relation extraction tasks.Moreover, we can also use multiple embeddings to detect the entailment relation between two sentence patterns when no manual label is available.
Parameter Selection for Ant Colony Optimization for Solving the Travelling Salesman Problem Based on the Problem Size

This paper describes the parameter setting for the Ant Colony Optimization (ACO) algorithm to find optimal solutions for the Travelling Salesman Problem (TSP). The TSP is a classical combinatorial optimization problem classified as NP-hard. The ACO algorithm, with the correct parameter setting, is known for good performance on NP-hard problems. According to the number of cities in the TSP the parameters of the ACO algorithms must be changed and the size of the ant colony adjusted. This paper analyzes parameter tuning in ACO and suggests specific parameter settings. The results are compared with already published recommendations. It is shown that the existing results can be significantly improved.
Cognitive Function of Human Memory Using Machine Learning

In this paper, it has been tried to analyze number of student details for the accuracy of result and for human brain development process. This work is based on human intelligence, which may be characterized to different attributes like sharpness, alertness, cleverness, reasoning capability, critical thinking, problem solving, judgment, etc. Machine learning being a classification of artificial intelligence (AI) builds mathematical model for the sample data. A data of approximately 200 students have been taken into consideration for manual calculation and simulation using WEKA software toward result and analysis. This paper summarizes to focus on how to increase the learning capability. If the past learning is good, then given sufficient time for studies where interest is at an outstanding level of a person, eventually student’s learning capability is good. This work is also based on linear regression equation. The accuracy of results could be performed by analyzing three different classification processes of BayesNet, NaïveBayes, and ZeroR.
Application of Knowledge-oriented Convolutional Neural Network For Causal Relation Extraction In South China Sea Conflict Issues

Online news articles are an important source of information for decisions makers to understand the causal relation of events that happened. However, understanding the causality of an event or between events by traditional machine learning-based techniques from natural language text is a challenging task due to the complexity of the language to be comprehended by the machines. In this study, the Knowledge-oriented convolutional neural network (K-CNN) technique is used to extract the causal relation from online news articles related to the South China Sea (SCS) dispute. The proposed K-CNN model contains a Knowledge-oriented channel that can capture the causal phrases of causal relationships. A Data-oriented channel that captures the position information was added to the K-CNN model in this phase. The online news articles were collected from the national news agency and then the sentences which contain relation such as causal, message-topic, and product-producer were extracted. Then, the extracted sentences were annotated and converted into lower form and base form followed by transformed into the vector by looking up the word embedding table. A word filter that contains causal keywords was generated and a K-CNN model was developed, trained, and tested using the collected data. Finally, different architectures of the K-CNN model were compared to find out the most suitable architecture for this study. From the study, it was found out that the most suitable architecture was the K-CNN model with a Knowledge-oriented channel and a Data-oriented channel with average pooling. This shows that the linguistic clues and the position features can improve the performance in extracting the causal relation from the SCS online news articles. Keywords-component; Convolutional Neural Network, Causal Relation Extraction, South China Sea.