Reviewerly/exHarmony huggingface dataset:
Monitoring analysis of the learning Chinese craze based on foreign newspaper data

With the role of Chinese in the international prominence, more and more foreigners begin to study Chinese as a second language. Therefore, this paper conducted a monitoring analysis of the learning Chinese craze through the foreign mainstream newspaper's views to study the relationship between the learning Chinese craze and the growth rate of overseas students coming to China, and predicted the number of prospective overseas students coming to China by establishing forecast model. In the data acquisition stage, this paper used the TF-IDF method to determine the four keywords about Chinese learning. Then, this paper established the corresponding newspaper resource library and to monitor the "Chinese Learning Craze" based on the resource library. In addition, in order to predict the growth rate of overseas students coming to China more accurately, this paper also added newspaper data based on the Google retrieval data of Chinese learning keywords. Then, a multivariate linear regression equation was established by studying the relationship between the learning Chinese craze and the growth rate of overseas students coming to China. Finally, the prediction model of the number of overseas students coming to China was established.
A Fast and Secure Key-Value Service Using Hardware Enclaves

Trusted execution as offered by Intel Software Guard Extensions (SGX) enables confidentiality and integrity for cloud-hosted services. While in principle all kinds of workloads can be secured using trusted execution, key-value stores have gained special attention as these services are an essential building block of most complex cloud deployments. So far the main design challenge has been to address current performance limitations of SGX. In addition, we identify the integration of SGX-guarded workloads with recent network technology, especially Remote Direct Memory Access (RDMA) as an upcoming requirement. RDMA allows fast direct access to remote memory at high bandwidth. However, SGX protected memory cannot be directly accessed over the network. Furthermore, due to the more powerful network, secured services might likely be CPU-bound as a result of the necessary cryptographic operations.
Automatic Detection of Word Substitutions within a Language over Periods of Time

As natural languages change over time, automatically identifying this change is an area of interest. In this work, we develop a system that detects phrase substitution, new appearing phrases, and extinguishing phrases within a language. We also measure the overall language change. We achieve that using unsupervised learning, i.e., without time-based parallel corpora. Our proposed model employs an adversarial training procedure to learn how to align between time-based word em-beddings spaces and time-independent global word embed dings space, within the same language. We also propose a simple and effective dictionary-based validation method. We apply our approach to the Turkish language. As a dataset, we use the Turkish parliamentary minutes from 1920 to 2014. We accomplish promising results; %54.76 accuracy measured by our validation method on words with at least %0.01 probability.
Prediction-based Resource Provision and Virtual Machines Placement in Cloud Data Center

Abstract With the rapid development of virtualization, cloud servers provide the power and flexibility that single servers struggle to provide. However, low utilization of physical machines and high energy consumption are the main concerns for cloud service providers. In this paper, we propose a predict-and-place framework to decrease the number of active physical machines in cloud centers. We analyze the historical VM (virtual machine) request records to predict the VM demands in the next several days and then use an offline VM placement strategy to keep the number of active servers as less as possible while satisfying the SLA (Service-Level Agreement) requirements. In the prediction process, we enhanced the classical Holt’s linear prediction method on account of inevitable outliers to increase the prediction accuracy. We conduct experiments to compare the prediction accuracy of Perceptron, classical Holt-Winters, and our refined, robust Holt-Winters method. The experiment based on real data shows the simple perceptron has an inferior result. Our improved prediction method reduces the MAPE (Mean Absolute Percentage Error) by about 50% compared with the classical one, and the predict-and-place framework decreases the average number of active physical machines effectively in the cloud data center.
Thoreau’s Unenthusiastic Study of Dante

With the exception of Longfellow, Henry David Thoreau was the best prepared of any American author of the nineteenth century for the appreciation of Dante’s Commedia. As an undergraduate at Harvard...
Image Restoration of Agricultural History Based on Neural Patch Synthesis

In recent years, the study of agricultural history has been constantly promoted. In the process of developing agricultural modernization, more and more attention has been paid to the origin of agriculture. With the development of digital image restoration technology and neural network, this study proposes to repair agricultural propaganda paintings of new China by means of neural patch synthesis. First, use the structure of the encoder and decoder to repair the overall structure of the image, and use the idea of the discriminator to make the overall image realistic. Secondly, the texture information of the globally restored image is further optimized by using the feature layer of VGG network, resulting in better results of both global and local restoration. Finally, the actual discriminator is used for realistic processing. The experimental results show that the restored images produced by this method are lifelike, without obvious artificial boundary, and the restored parts are not blurred, which is suitable for the restoration of agricultural history images.
Graphical User Interface for Multimedia Retrieval System Based on Impression

This paper proposes a method of using a paint-based graphical user interface (GUI) in the multimedia retrieval system based on impression. The multimedia retrieval system based on impression could retrieve multimedia data by specifying impression words and their weights. This paper enables us to use a GUI based on the impression evaluation method by space (IEMS). The IEMS uses a plane containing impression words as the impression space. The impression of an object is specified by circling the areas matching the impression. The degree of the impression is expressed by painting color. In order to use the GUI of the IEMS, the proposed method calculates the weights of impression words based on the color painted. The feasibility of the proposed method is demonstrated by showing a running example of the prototype system.
Computer-based Techniques for Predicting the Failure of Student Studies Using the Decision Tree method

Abstract The purpose of this study is to predict students who have the potential to drop out of college so that the selection process for prospective students is more effective. Based on the problems that have been raised. The research method used was the forecasting method proposed to predict prospective students who drop out before entering college using the Decision Tree C4.5 method and Forward Selection. The tool used in this study used rapidminer 9.2. Based on the research results obtained, using 90% training data and 10% testing data resulted in an accuracy of 82.52% and obtained attribute models that affect the classification of student graduation, namely the Study Program and Age attributes.
Fostering mathematical logical intelligence in primary school students

Abstract Education must form thinking people, who develop all their potentialities, among them, those referring to mathematical logical intelligence. The objective is to elaborate a didactic proposal that fosters mathematical logical intelligence in students. For this, the research was framed in a quantitative approach with fieldwork design and at a descriptive level. The survey was applied as a technique and as instruments a questionnaire for teachers and a test for students. The population was made up of all teachers and students in basic primary education. An intentional non-probability sample was applied, made up of 22 teachers who attend primary school programs and 30 students of the same educational level. The results identify the lack of strategies related to the processes of classification, serialization, and number of primary school teachers during their pedagogical work. Next, we describe the shortcomings that students have in applying mathematical logic processes in a series of activities proposed in the evaluative test. Finally, didactic strategies are defined according to the conditions of the learning environment analyzed so that from the work of the classroom it is possible to promote logical mathematical intelligence, since cognitive stimulation in the student by means of learning experiences according to the level of development allows to reach levels of thought of greater complexity.
Fast Dynamic Graph Algorithms

We show that dynamic graph algorithms are amenable to parallelism on graphics processing units (GPUs). Evolving graphs such as social networks undergo structural updates, and analyzing such graphs with the existing static graph algorithms is inefficient. To deal with such dynamic graphs, we present techniques to (i) represent evolving graphs, (ii) amortize the processing cost over multiple updates, and (iii) optimize graph analytic algorithms for GPUs. We illustrate the effectiveness of our proposed mechanisms with three dynamic graph algorithms: dynamic breadth-first search, dynamic shortest paths computation and dynamic minimum spanning tree maintenance. In particular, we show that the dynamic processing is beneficial up to a certain percentage of updates beyond which a static algorithm is more efficient.
Consideration of the Essential Topics for Role Estimation for AIWolf

In recent years, research on using artificial intelligence (AI) to play the Werewolf game has been actively conducted. The Werewolf game is a multiplayer communication game, where the players are divided into two teams who each aim to eliminate the opposing team through discussion. In the Werewolf game, a different role (possibly repeated) is assigned to each player. The role is referred to as the "Role." The Role of a player is known only to himself/herself. Good strategies for the Werewolf game are predicting and inferring other players' Roles. The AI player that plays the Werewolf game is called "AIWolf." In studies on AIWolf, the research on estimating the Role using machine learning has attracted a great deal of attention. However, most of the research on estimating the Role has not sufficiently discussed the required information for Role estimation, although it has described the efficiency of Role estimation. In this paper, we focus on this problem and aim to clarify the type of utterance, which is a feature that is sufficient for estimating the Role, by analyzing log files. The type of utterance is referred to as the "Topic." First, we select the required features from the Topic based on the results of the analysis of log files and propose the features required for the estimation of the Role. Second, we design and implement a prediction model with proposed features to evaluate their effectiveness. Additionally, we also implement prediction models from related research and compare them with our model. Finally, we evaluate the effectiveness of the proposed features for estimating the Role by comparing the Role estimation accuracy for the prediction models. From the experimental results, we confirmed that the accuracy of the prediction model in this paper is higher than that of one of the prediction models in the related research, and is statistically sufficient for estimating the Role. Therefore, we demonstrated that the features selected by analyzing the log files are effective in estimating the Role of AIWolf.
Designing Authorization Procedures for Multi-channel and Public Participation-Based System Architecture for Civil Registration and Population Data

This paper aims to propose authorization scenarios for updating population data systems that involve public participation through several different telecommunications channels, called Multi-Channel and Public Participation (MPP)-Based Population Administration System. This paper reveals a number of important technical aspects that arise and must be considered in the authorization scenario. This paper also discusses broadly the authorization techniques that have been used separately in many previous cases. Conformity methods between behaviour of important technical aspects of MPP-Based Population Administration System and authentication methods that are commonly used in system implementations is the base for supporting the implementation of authorization scenarios. The proposed authorization scenario is to utilize a combination of Claim-Based and Multi-Factor authentication techniques because it has a match between important aspects and technical authentication. The combination of authentication techniques in the authorization procedure is able to verify and validate and determine the role of those who wish to collaborate in the service process.
Location-Sensitive Resource optimization for Profit Maximization in Distributed Data Centers

Infrastructure in distributed cloud computing systems (DC2Ss) is concurrently shared by multiple different applications to flexibly and cost-effectively provide various services. It becomes a challenge to maximize the profit of DC2Ss due to the fact that Internet service provider (ISP) bandwidth price, availability of green energy, electricity price, and revenue brought by tasks differ from multiple sites. To solve it, this work designs a Location-Sensitive Resource optimization approach by considering spatial variations in DC2Ss to achieve profit maximization for DC2Ss by optimizing ISP and server resources. This work formulates the profit maximization problem as convex optimization and solved with the interior point method. Realistic data-based simulations prove that higher profit and throughput are achieved than two widely used approaches.
Weighted DeepFM: Modeling Multiple Features Interaction for Recommendation System

Recommendation system is an efficient way to filter information, and it has been widely used to predict users' potential interests. To build a powerful recommendation system, we need to use as much information as possible to profile users and items precisely, so it can make more accurate and personalized recommendation for users. The recommender which use merely identifications of user and item as features can't fully utilize the information provided by the data, but also decrease the generalization of the model which have poor performance in both academia and industry. So, using multiple features and learning the intrinsic complex relationship among them are critical for a good recommendation system. In this work, we propose an end-to-end, deep learning based recommendation model without feature engineering. The model consists of two parts, which are used to capture the low-order linear relationship and high-order nonlinear relationship of the features called Weighted Interactive Model and Deep Model. In the Weighted Interactive Model, we use pointwise multiplication to capture low-order relationship of two features. To make the model more expressive, each interaction and dimension of the interactions are assigned by different weights. In addition, we use the user-item rating matrix to build expressions of user and item, which improves generalization of the model. We have conducted many experiments on the open data set for different parameters, and the results outperform than other state-of-the-art methods which show the effectiveness of our proposed model.
Computer technique for the suitability of digital evidence in attacking an information system: Forensic analysis for the digital custody chain

Abstract This article presents results of a research project that attempted to create a model of forensics in an information system; since forensic analysis allows to establish the causes of security commitment information system. The aim of the study was to determine the software tools to be used in the management of security incidents in an information system; required for the design of computer art for extracting digital evidence to anchor the chain of custody items. The research methodology was descriptive and applied document type, by analyzing the different tools technique that allows maintenance of digital evidence was designed, preserving the integrity of this as a test mechanism in a judicial process. In this way, the procedures to be performed to store information were established without being altered chain of custody solving the above questions: What is the chronological order in which the events of alteration, transmission or access occurred? What evidence have to believe you are a victim?, what are the damages incurred?, who do responsible for the incident?, what people are suspicious of the event?, who has investigated the incident and which actions has taken to preserve, identify, collect and analyze the data involved?; ensuring this way through technical steps and tools to be used for the preservation of the chain of custody, as an evidence in a court case.
Finding Moving Objects in Astronomical Image Datasets

We aim for the mass detection of the moving objects hidden within the collections of images that belong to a particular region in the sky. To achieve this, we compare the mosaics created from the images of the night sky that fall within the range of a spatial query enclosed region, fusing them together through image co-addition using the highest pixel value, instead of their accumulation. The mosaic comparison process adapts to the different values available at the pixels through the use of our proposed tool called the threshold curve to improve faint moving object detection.
The structural organization of the manifestations of professional personality deformation in teachers with different levels of situational reflection

The study of the structural organization of the manifestations of the professional deformation of the personality of teachers with low and high level of situational reflection. Using structural psychological analysis, it was established that the structure of the manifestations of professional deformation of the personality of teachers with a low level of situational reflection is characterized by a low degree of integration of its components. For teachers with a high level of situational reflection, on the contrary, the structure of the manifestations of professional personality deformation is more consistent, the basic components that ensure the structural integration of the system conservatism, dogmatism.
The Research on User Interest Mining Based on Deep Learning

Abstract This paper, user behavior depth technology is studied, so that user information and search engine can interact intelligently. The system can get information that is conducive to mining users’potential search interests and preferences, so as to use this information to guide the search process and results filtering, and achieve efficient and accurate retrieval. The research process uses the mining of user logs of large data search engines, the establishment of user query classification system, and the deep mining of the behavior of user groups with different characteristics. User social network can be used to create a retrieval system in which users cooperate with each other because of their similar interests. This paper studies the calculation and method of user interest similarity to discover user groups with similar interest. User relationship network and system mining user relationship network are compared to promote the integration of the two and provide a basis for the application of recommendation system.
Singularity for Machine Learning Applications - Analysis of Performance Impact

Software deployments in general, and deep learning applications in particular, suffer from difficulty in reproducible results. The use of containers to mitigate these issues is becoming a common practice. Singularity is a container technology which targets the unique issues present in High Performance Computing (HPC) Centers. This paper characterizes the impact of using Singularity for both Training and Inference on deep learning applications.
Instance Ranking Using Data Complexity Measures for Training Set Selection

A classifier's performance is dependent on the training set provided for the training. Hence training set selection holds an important place in the classification task. This training set selection plays an important role in improving the performance of the classifier and reducing the time taken for training. This can be done using various methods like algorithms, data-handling techniques, cost-sensitive methods, ensembles and so on. In this work, one of the data complexity measures, Maximum Fisher's discriminant ratio (F1), has been used to determine the good training instances. This measure discriminates any two classes using a specific feature by comparing the class means and variances. This measure in particular provides the overlap between the classes. In the first phase, F1 of the whole data set is calculated. After that, F1 using leave-one-out method is computed to rank each of the instances. Finally, the instances that lower the F1 value are all removed as a batch from the data set. According to F1, a small value represents a strong overlap between the classes. Therefore if those instances that cause more overlap are removed then overlap will reduce further. Empirically demonstrated in this work, the efficacy of the proposed reduction algorithm (DRF1) using 4 different classifiers (Random Forest, Decision Tree-C5.0, SVM and kNN) and 6 data sets (Pima, Musk, Sonar, Winequality(R and W) and Wisconsin). The results confirm that the DRF1 leads to a promising improvement in kappa statistics and classification accuracy with the training set selection using data complexity measure. Approximately 18–50% reduction is achieved. There is a huge reduction of training time also.
The SDBC Approach

We make a clear distinction between issues that concern the enterprise-engineering aspects of Enterprise Information Systems (EIS) and issues that concern the software-engineering aspects of such systems—see Chap. . At the same time, we need to bring together enterprise modeling (driven by social theories—see Chap. ) and software specification (driven by computing paradigms—see Chap. ) for the sake of bridging the enterprise-software gap (as discussed in Chap. ). We have put conceptual foundations for this in Chap. (by considering systemics) and we have explicitly addressed the environmental and user perspectives with regard to EIS (see Chap. ). What is nevertheless missing so far is the operationalization perspective—we need an approach and methodological guidelines on what to do and how to do it in order to actually realize an enterprise-modeling-driven software generation; this is a challenge because most current approaches and methods are either rooted in social theories or based on computing paradigms—this claim has been justified in the previous chapters of the current book. For this reason, we consider previous works of the Author [1] who has been studying and addressing the mentioned challenge for more than 15 years already, reflecting innovative ideas in his SDBC APPROACH—“SDBC” stands for “Software Derived from Business Components.” Hence, the reasons for considering and developing further this approach are the following: