Reviewerly/exHarmony huggingface dataset:
Research on Copyright Protection Method of Big Data Based on Nash Equilibrium and Constraint Optimization

Data watermarking technology is an effective means to protect the copyright of big data. In order to embed robust and highly available data watermarks, firstly, based on the game theory, a Nash equilibrium model between watermark robustness and data quality is established to solve the optimal number of data partitioning. Then, the mapping relationship between data partitioning and watermark bit is established by using secure hash algorithm. Finally, under the constraint of data usability, the improved particle swarm optimization algorithm is used to calculate the optimal solution of data change for each data partitioning, and then the data is changed accordingly to complete the embedding of watermark bit. In order to verify the copyright ownership of big data, this paper also gives the corresponding watermark extraction method. Watermark extraction is the inverse process of watermark embedding. First, traverse all partitions and extract the possible embedded bit values in each data partitioning. Then, the actual embedded watermark bit is finally determined by majority voting strategies. The experimental results show that our proposed method can not only detect watermarks under different attack conditions, ensure the robustness of big data watermarks, but also achieve better data quality, and the comprehensive effect of data watermarks is better than the existing methods.
Conscious Knowledge Based Question Answering

A model of consciousness proposed by neuroscientists in 1989 is called the theater model, which uses theater as an analogy to describe "what is consciousness". This paper simplifies the problem of question answering and uses theater model to simulate the question answering mechanism of the human brain. We extract a small amount of knowledge from Freebase and use it as agents' knowledge base. Then we build a multi-round question answering agent based on the theater model and Deep Q-learning. We train the two agents against each other, and finally analyze the training results. The results show that the question answering mechanism and training method designed can simulate the human dialogue scene well, and agents have different behavior when setting different rewarding parameters.
Case Study of an On-premise Data Warehouse Configuration

The development of machine learning over the years has facilitated the joint upsurge of complex cognitive infocommunication systems. Machine Learning methods are vital elements of modern cognitive infocommunications systems because they can be used in various ways such as behavior modeling or sentiment analysis. Machine Learning algorithms requires a reliable infrastructure and vast amount of data. Therefore building data warehouse systems is one of the essential steps of of building reliable cognitive infocommunication systems. Finding and preprocessing data streams of different origins are the first steps during the creation of a data warehouse. Unfortunately, online data streams are most often formatted uniquely. Therefore, the obtained data sets must be transformed into a unified data model. The modelling and conversion of data sources serves as a key step during the unification of heterogeneous data. Storage should be persistent, and optimized for the analytical processing of data. These requirements raise technological challenges that are not common during the design of data sources. This paper gives an overview of current data warehouse technologies and suggests an infrastructure implementation. Hive is used for accessing, modifying, and running complex analytics on the stored data sets. Economical data can often be unique to the product, or the industry it covers. Different data sources used unique data formats which were tailored for their application area or needs. Moreover, some of these data sources may change their format in time. Therefore, a flexible data transformation step is required which can be configured easily. The ETL processes of the data sources are implemented in Python, and Hive. The data is loaded in a Hive data warehouse which stores data in the distributed Hadoop File System.
Research on the path of mathematics discipline construction based on demand orientation: a case study of Tianjin Agicultural University.

With the arrival of the era of big data, the basic research of mathematics has attracted unprecedented attention all over the country. To meet the needs of the development of the times, the construction of mathematics discipline has become one of the important tasks of higher mathematics teaching. Starting from the demand orientation, this paper deeply analyzes the problems existing in mathematics education at the present stage, and constructs the construction path of mathematics discipline based on demand orientation.
Temporal Smoothing for Joint Probabilistic People Detection in a Depth Sensor Network

Wide-area indoor people detection in a network of depth sensors is the basis for many applications, e.g. people counting or customer behavior analysis. Existing probabilistic methods use approximative stochastic inference to estimate the marginal probability distribution of people present in the scene for a single time step. In this work we investigate how the temporal context, given by a time series of multi-view depth observations, can be exploited to regularize a mean-field variational inference optimization process. We present a probabilistic grid based dynamic model and deduce the corresponding mean-field update regulations to effectively approximate the joint probability distribution of people present in the scene across space and time. Our experiments show that the proposed temporal regularization leads to a more robust estimation of the desired probability distribution and increases the detection performance.
학술적 글쓰기의 질적 개선을 위한 문법 - 대학에서의 장르 중심 문법 교육 -

The aim of this article is to provide a method of grammar education that will lead to the improvement of the quality of academic lectures by the university itself, so that it focuses on common academic writings. In this process, we proposed a grammar education method that leads to a qualitative improvement of the writing of the learners. The commonality of academic writing genres at the college is related to the macro structure of the text by ''creativity of the thesis statement'' and ''systematicity of structure''. These two commonalities can be related to the grammatical structure that is the earner looks at his previous sentence himself and is subject to meta-recognition. The relationship between the key-words that make up the thesis statement, and the process of looking at the vertical and horizontal relationships of the abstract that structurally project this can act grammatically to improve the quality of academic writing.By looking at the grammatical relations of the ''actual structure'' of the macro structure shown in the previous article of his own, the learners have discovered ''potential structure'' which is more meaningful than the one produced by the self. It will be possible to improve their accidents qualitatively by focusing on those with more significant values in the rewriting processing.
Experimental Study on Measurement Resolution of Side Channel Waveform in Correlation Power Analysis

Side-channel attack is a serious threat that can retrieve the secret key by analyzing side channel information leakage from cryptographic devices. The analysis to extract the key is affected by quantization error in the measurement. We conducted measurements on correlation power analysis (CPA) by controlling the quantization resolution and the authors showed that the key extraction is difficult in the reduced resolution, and there is a limit to the extraction efficiency.
Facilitating the Co-Evolution of Semantic Descriptions in Standards and Models

One of the main activities of the Interfaces and Architecture (IFA) working group within the Network Function Virtualisation (NFV) Industry Specification Group (ISG) at the European Telecommunications Standards Institute (ETSI) is the specification of requirements and information modelling of NFV descriptors and artefacts. The information elements are spread across 11 different NFV-IFA specifications covering the scope of functionality of the individual functional blocks and reference points.
Adaptive Multi-stage Multi-strategy Word Representation Learning in HowNet

Multi-stage and multi-strategy word representation learning (M2WRL) model with sense and sememe in HowNet outperforms most single-stage and single-strategy models in effectiveness and efficiency. However, fixed stages and using order among strategies are decided empirically, which could lead to poor generalizability of those models. Combined with attention strategy and maximum strategy, this paper proposes an adaptive multistage multi-strategy word representation learning (A-M2WRL) model with senses and sememes in HowNet. Compared with our previous M2WRL model, A-M2WRL model is more generalized, reducing human workload. The effectiveness of A-M2WRL model is evaluated on two classical tasks, word similarity and word analogy. Experiment results indicate that our A-M2WRL model overall outperforms other baselines.
Face HotPot: An Interactive Projection Device for Hot Pot

Hot pot is a popular cuisine in China. It is also one of the first meal choices that young people elect to make together. However, during the preparation period, especially with new friends, there will always be a few moments of awkward silence while waiting for it to cook. To relieve this, we designed and developed Face HotPot, an interactive device for hot pots, consisting of a personalized dipping sauce matching system and interactive game. While waiting for the meal, users can get a personalized hot pot dipping sauce based on their facial features and get food by participating in the interactive projection game. The whole process of Face HotPot can continuously provide users with new chat topics and promote mutual understanding of each other's personalities, thus optimizing their eating and social experience with hot pot. In this paper, we demonstrate Face HotPot design motivation and system implementation.
Application of Computer Program Combined with Magnetic Prospecting in Polymetallic Ore Prospecting

Abstract With the continuous progress and growth of human social and economic activities, the demand and consumption of polymetallic mineral resources are increasing. The current exploration and survey of polymetallic mineral resources has been difficult to meet the needs of human development, and there is an urgent need for technological upgrading and reform. Based on this, this paper first studies the characteristics of magnetic survey of polymetallic deposits, then analyzes the judgment and interpretation of magnetic anomalies of polymetallic ores, and finally gives the application of magnetic prospecting based on computer program in the survey of polymetallic deposits.
Invariant Operations on Generalized Neurofunctions

The research studied the spectral properties of logic algebra functions that are implemented by one generalized neural element. There are specified criteria of logic algebra functions implementation by one generalized neural element relating to a given system of characters. The research introduces the concept of characteristic vector for Boolean function relating to a given system of characters and demonstrates that Boolean functions, implemented by one generalized neural element, are uniquely defined by their characteristic vectors. The particularity mentioned for eigenvectors can be effectively applied for compression of generalized neural functions in the spectrum range of Walsh-Hadamard basis functions system, including the description either of invariant operations or compact representation over these functions.
Research on Intelligent Linkage Server Switch in Case of Power Loss in Computer Room

The computer room of the information center is a centralized place for university data. Once a power failure occurs, it will have a great impact on the entire school. In this paper, an intelligent management scheme is designed for the situation of power loss and call in the computer room. Firstly, the IP address and data type correspondence of each server in the university are calculated to provide conditions for the intelligent opening of the server. Then, according to the importance of the server, it is divided into three categories. When the computer room is powered off, the power supply is provided to different types of servers according to the remaining power of the UPS power supply. When the computer room comes in, the main server controls the automatic startup of the other three types of servers, The theory has proved that the scheme has a certain feasibility and can effectively avoid the loss of important data in the first time when the power is cut off.
A Fast and Robust, Forehead-Augmented 3D Face Reconstruction from Multiple Images using Geometrical Methods

3D Face Reconstruction is a complex problem in Computer Vision. Until recently the existing methods were based on multiple image captures and solving complex dense correspondences between different face poses. Recent methods are based on volumetric CNNs and try to reconstruct the 3D face model from a single image. Accurate 3D face reconstructions are used nowadays for avatar modelling, in eyewear and hairstyle recommendation systems. All these require accurate face shape determination, which is subject to at least a fully frontalized 2D projection of the face, or even better to an accurate 3D volumetric reconstruction of the face. Most of the existing methods for 3D reconstruction stop somewhere in the middle of the forehead, limiting thus the obtained 3D model. We propose a mostly geometric method for facial reconstruction, based on structure from motion techniques on uncalibrated cameras, augmented with forehead surface modelling for added realism. We present a full section for proving that we are on par with state of the art deep learning techniques.
A Class of Optimal Cyclic Symbol-pair Codes from Combinatorial Designs

Abstract Motivated by the application of high-density data storage technologies, symbol-pair codes were defined by Cassuto and Blaum recently. In this coding model, every two consecutive symbols are read together to address channels with high write resolution but low read resolution, so that individual symbols cannot be read off due to physical limitations. Since such kind of code was introduced, a lot of work has been carried out by many researchers, including upper bounds, constructions, decoding et.al. In this work, we continue the research on the construction of symbol-pair codes. We will present one special construction for cyclic symbol-pair codes from the perspective of combinatorial designs.
Form Feature on Web Based Operation Support System (OSS) VSAT Modem for 3T Regions

Due to the high usage of VSAT System in 3T Regions and the lack of required supporting software, we propose the usage of specifically built OSS (Operation Support System) as a supporting software for VSAT Modem in 3T Regions. OSS will be built in the form of a web application that is easily accessible. Currently the problem that often occurs in the VSAT System is a network configuration problem caused by human error, this configuration error results in the VSAT system unable to connect to the internet. OSS willl be able to help reduce the occurrence of human errors when configuring Modem and will provide the required digital storage. OSS will also help simplify maintenance requests that will be requested by the users.
Robust Moving Targets Detection Based on Multiple Features

Moving targets detection is the most basic part of intelligent video analysis, and its detection effect is directly related to the accuracy of subsequent processing. Aim to cope with the challenges of multi-modal background in complex video environment, the paper proposes a new detection method that combines pixel-based feature and region-based feature. It proposes a new region textural statistic by fuzzy clustering to statistical texture and then fuses it with intensity of pixel. Feature vectors which consisted of rich information are used in background model. Optimal threshold segmentation method is used to obtain adaptive threshold for foreground detection. The experiments indicate that the method can achieve expected results and obviously outperform performance in scenes including multi-modal background.
Smart Courses Learning for Network Security using Computational Intelligence

This research develops a smart course to study network security. This smart courser uses computational intelligence (CI) to classify the user's capabilities in the network security learning module. The use of other computational intelligence is used in this smart course to provide suggestions on network security modules that students can work on based on previously acquired abilities. The algorithm computational intelligence used in this study is k-Nearest Neighbor and Bayesian Network (BN). The k-NN algorithm to classify the user's capabilities based on the pre-test of each module on the smart course. The Bayesian Network algorithm is used to provide further modules to the user by the wishes and abilities of the user. The k-NN and Bayesian Network test results on this smart course have an average accuracy of 85%.
Co-Attention Based Few-Shot Relation Classification Model with Dynamic Routing

With the development of natural neural networks, supervised methods are usually confronted with the problem of lacking labeled data. Few-shot learning methods are now a mainstream research method that allows models to classify relation base on a small amount of data. Relation classification is a basic task in natural language processing and it is the most critical step in the construction of a knowledge graph. This paper focus on few-shot relation classification and we propose a co-attention based few-shot relation classification model with dynamic routing. This model is divided into three parts: encoder layer, aggregation layer and matching layer. The encoder layer is used to extract the important features from support set and query set and convert it into vectors. Aggregation layer is to aggregate the vectors of instances in the same class. The matching layer is to compute the score between the query instances which is extracted form encoder layer and the class vector which is output by aggregation layer. We apply this model on FewRel dataset and the experiment result shows that our method is better than other methods.
Research progress of ship IRST system

Infrared Search and Track – IRST system as a passive panoramic surveillance device based on infrared features can detect and track air or surface targets. It has many advantages, such as high resolution, strong resistance to electronic interference, good concealment, and strong anti-stealth and so on. Because IRST system and the radar can be complemented by multi-sensor data fusion technology. this paper summarizes the research progress and key technology breakthrough of IRST system in domestic and abroad. Then by analyzing the outstanding advantages of IRST technology for one hand the research direction and application prospect of this technology in industrial and military fields are put forward, for the other hand the conceptual design and engineering research of this technology are summarized and prospected.
Financial Development-Poverty Reduction Nexus In Brics: A Panel Data Analysis Approach

This paper’s objectives were two, namely: (1) investigating the impact of financial development on poverty alleviation (proxied by life expectancy) and (2) exploring whether the complementarity between financial development and foreign direct investment (FDI) enhanced poverty reduction in BRICS (Brazil, Russia, India, China, South Africa) countries. The study used panel data analysis estimation techniques, namely pooled ordinary least squares (OLS), fixed effects and fully modified ordinary least squares (FMOLS) with data ranging from 1994 to 2013. The existing theoretical literature shows that financial development can have either a positive or negative impact on poverty alleviation. On the other hand, existing empirical literature produced results which are quite divergent, conflicting and diverse, namely: (1) financial development has a positive effect on poverty reduction, (2) financial development has a negative influence on poverty alleviation, (3) financial development and poverty reduction affected each other and (4) there is a negligible influence of financial development on poverty reduction. Although results show that the impact of financial development and FDI on poverty reduction is mixed, it is quite evident in majority of cases that the complementarity between financial development and FDI enhanced poverty reduction. The policy implication is that BRICS countries should implement policies designed at improving both financial development and FDI inflows in order to be able to reduce poverty.
New quantum algorithms to solve the linear equations

Abstract Based on the infinite dimensional variable quantum system, we propose two quantum algorithms to solve the linear equations. Both algorithms can be considered as infinite-dimensional versions of the HHL algorithm. From this we can see that the infinite dimensional quantum variable system as a physical resource can be widely popularized in quantum computing.
Youth of Color Living and Learning in the Age of Racial Paranoia

We are faced with the undeniable pivotal modern history of resurgent White supremacist ideology and practices fueling fear of “others.” As such, as educators, we ought to take concrete actions toward greater social justice education while engaging in critical dialogue toward healing. The current culture and racial war in schools and society at large and its effects on youth of color particularly indicates the need for a new anti-racist education, and implicates the key spaces of schools and education as sites of struggle in battling for a multicultural future free of all forms of bigotry. As an increasing number of Whites in the United States self-identifying as racially blind claim to have broken from the United States’ long history of racism, young people from all backgrounds are taking concrete action against all forms of bigotry, including capitalist ideology that blames the poor for their poverty and xenophobic tendencies that aim to dehumanize the LGBTQ community. Indeed, more and more young people, of all backgrounds, are realizing that claiming to be colorblind, classblind, or genderblind as an individual does nothing to confront systemic racism, classism, sexism, and other forms of oppression occurring in schools and society at large.
Functionally weighted neural networks: frugal models with high accuracy

In this article, we introduce the “functionally weighted neural network,” a new addition to the rich collection of artificial neural networks. Instead of a finite number of discrete nodes, we consider an infinite number of continuously distributed nodes. The weights assume a functional form, and the sum over the nodes becomes an integral. The gain is a significant reduction in the number of adjustable parameters, accompanied by an enhanced generalization performance. To quantitatively assess the quality of this new network, we have performed numerical experiments on a number of benchmark datasets. Comparison with state-of-the-art techniques reveals the advantages of the proposed method and emphasizes its modeling potential.