Reviewerly/exHarmony huggingface dataset:
An Analytical Study on a Benchmark Corpus Constructed for Related Work Generation

Automatic related work generation aims at producing a related work section for a given scientific paper. Demand for this task replacing a labor-intensive process has substantially increased in recent years. Considering the lack of an open and large-scale dataset for related work generation, we introduce NudtRwG ( https://github.com/NudtRwG/NudtRwG-Dataset/ ), a collection of 2,084 document sets, each with a target paper, a ground truth related work, and the corresponding reference papers. To our knowledge, NudtRwG is the first open, large-scale and high-quality dataset for related work generation. The contribution of this work apart from the dataset is two-fold: firstly, we present a detailed description of the data collection procedure along with an analysis on the characteristics of the dataset; secondly, we conduct an analytical study, investigating the effects of summative sections (abstract, introduction and conclusion) and other sections of reference papers on related work generation. Experiments reveal that the two parts are equally important and other sections should not be ignored. When generating a related work section, researchers should consider not only summative sections, but also other sections of reference papers.
Reference-oriented Loss for Person Re-identification

Deep metric learning methods are quite effective in exploring discriminative feature embeddings, among which triplet loss and its variants are widely utilized. However, in existing methods, the tightness information for intra-class samples is ignored, leading to large intra-class divergence and severe inter-class overlapping problem. To address this issue, a novel loss function called reference-oriented triplet loss is proposed in this paper. The proposed method introduces several reference images to guide training. More specifically, distances between the reference image and images of the same identity are required to be as similar as possible. By introducing reference images, images from the same class become much closer with each other and the inter-class overlapping problem is alleviated. Comparing to baseline batch hard triplet loss, the mAP accuracy increases by 3.75%/5.69% on person re-ID datasets Market1501 and DukeMTMC-Reid. Comparison results with state-of-the-art algorithms also demonstrate effectiveness of the proposed algorithm.
Probabilistic inference on uncertain semantic link network and its application in event identification

The Probabilistic Semantic Link Network (P-SLN) is a model for enhancing the ability of Semantic Link Network in representing uncertainty. Probabilistic inference over uncertain semantic links can process the likelihood and consistency of uncertain semantic links. This work develops the P-SLN model by incorporating probabilistic inference rules and consistency constraints. Two probabilistic inference mechanisms are incorporated into the model. The application of probabilistic inference on SLN of events for joint event identification verifies the effectiveness of the proposed model.
LRC-256, an Efficient and Secure LFSR Based Stream Cipher

In this work we introduce LRC-256, a new hardware-based stream cipher that is designed based on an irregular clock-controlled combination generator with a unique mechanism of altering steps. Our proposed algorithm uses a 256-bit main key and a 32-bit message key. The most important measures we consider in designing LRC-256 are maximum period, high linear complexity, resistance to standard attacks, and secure statistical properties. The base component of our algorithm is a clock-controlled combination generator with a 1-bit memory. In order to demonstrate the usefulness and performance of our proposed stream cipher, we implemented LRC-256 on a standard FPGA simulator, and we compared it with 12 standard and well-known stream and block ciphers, such as RC4, RC6, AES, 3DES, W7, Snow 2.0 LILI-II, and Helix. The result of our comparison proves that LRC-256 has higher efficiency and better security. Hence, LRC-256 is a proper option for resource-constrained devices where efficiency and safety are the most important metrics.
A Multiple Kernel Machine with Incremental Learning using Sparse Representation

In this paper, a new multiple kernel machine with in-situ learning capability is introduced for pattern classification using sparse representation framework. A vector-valued score function for a given pattern is generated as a linear combination of multiple kernel functions, each of which measures the similarity between the pattern to be classified and the representative samples from every class. The representative samples for different classes are selected through the sparse approximations, by forcing the representation of the vector-valued score function to be sparse in a dictionary of kernel functions with respect to all data samples from different classes. After the sparse weight matrix is found, the vector-valued score function for a new pattern with unknown class label can be computed. An incremental learning process is developed which allows for incremental updating of the system without impacting the previously learnt patterns. The proposed multiple kernel machine was then implemented and benchmarked against other multi-kernel methods for classifying underwater objects using low frequency sonar data.
Supersensitive Optical Phase Measurement Using Deterministically Generated Multiphoton Entangled State

We use a multiphoton Greenberger-Horne-Zeilinger state to demonstrate optical phase measurement with sensitivity that beats the standard quantum limit. The state is produced deterministically at a rate of 76 MHz from a single point source.
Boolean Models Guide Intentionally Continuous Information and Computation Inside the Brain

In 1943 Machculloch and Pitts create the formal neuron where many input signals are linearly composed with different weights on the neuron soma. When the soma electrical signal goes over a specific threshold an output is produced. The main topic in this model is that the response is the same response as in a Boolean function used a lot for the digital computer. Logic functions can be simplified with the formal neuron. But there is the big problem for which not all logic functions, as XOR , cannot be designed in the formal neuron. After a long time the back propagation and many other neural models overcame the big problem in some cases but not in all cases creating a lot of uncertainty. The model proposed does not consider the formal neuron but the natural network controlled by a set of differential equations for neural channels that model the current and voltage on the neuron surface. The steady state of the probabilities is the activation state continuous function whose maximum and minimum are the values of the Boolean function associated with the activation time of spikes of the neuron. With this method the activation function can be designed when the Boolean functions are known. Moreover the neuron differential equation can be designed in order to realize the wanted Boolean function in the neuron itself. The activation function theory permits to compute the neural parameters in agreement with the intention.
The use of COANDA EFFECT in air circulation for acquiring lifting force of an UAV model

Based on the COANDA EFFECT, the model of an unmanned aerial vehicle (UAV) can be built that has the ability to rise from the ground as a result of the air flow that follows the fuselage design. The virtual three-dimensional model of the flight device is built and analysed from the point of view of external airflow stream so that high or low pressure zones can be set, as well as velocity values that have an influence on the ground lifting conditions. The possibilities of airflow optimization are analysed so that the device can perform easy take off from the ground. The numerical flow analysis is performed on the virtual model using the ANSYS CFX program. The results obtained in terms of flow velocity, static and total pressure, as well as turbulence values in the fluid area are presented.
P‐16.7: Optical human‐machine interaction based on sub‐wavelength gratings

Optical signals input from a light source are used as media for human‐machine interaction via the coupling of sub‐wavelength gratings covered on the display screen. The system includes a laser pen, a transparent sub‐wavelength grating optical film which couples the incident light into waveguide, and photodetectors set on the sides of the film to sense and decide the position of incident light. Furthermore, we can load the modulated signal on the incident light so that it can transfer data to the display device. The design provides a novel remote ‘touch’ interaction which is of great practical meaning in the large screen displays.
An Efficient Privacy-Preserving Search Technique for Encrypted Cloud Data

In the present scenario of data management and computing, private data has been widely outsourced into cloud storage for its ability to provide low-cost access and maintenance services offered by public clouds such as Windows Azure, Google cloud platform, Amazon S3, IBM Bluemix, and so on for flexibility and economic benefits as well. Therefore, sensitive data such as Email, medical records, and other critical data assets must be kept in an encrypted form in order to preserve privacy and integrity of data. The major challenge in keeping encrypted data in cloud is that the searching process would be more complex. The data owner normally does not want the cloud provider to understand and analyze what has been done by the data owner and data user on the encrypted data in cloud storage. However, data owner and data user want to perform some computations on encrypted data such as sorting of records, splitting, searching, and ranking. This paper proposes a technique to perform search over encrypted data. When comes to performing, searching, and ranking on encrypted data, an algorithm which is mathematically strong is essential and it must be performed by the cloud provider without privacy leak. As a huge number of data users and data owners are maintaining large collection of documents in the cloud, it is essential to support multiple keyword searches in encrypted cloud data in order to present relevant documents with maximum accuracy and privacy preservation. The proposed technique for searching over encrypted data domain allows data users to search and rank according to the input query with less complexity in computation.
Research on Construction of the Software Technology Profession in Vocational Institutes Based on the External Vision

Professional construction of vocational education is the key to determine the quality of talent cultivation and an important symbol to determine the level of institute running. The target and vision of professional construction shall focus on external social market, enterprises, and labor contributions, instead of being limited inside the vocational institutes. Only in this way can the professional construction resources be fully utilized and can the professional construction be turned from the extensive development pattern to economical development pattern. In this paper, through taking the professional construction of software technology as an example, the detailed implementation method and process of professional construction in vocational institutes based on the external vision are illustrated from four aspects such as professional construction thoughts, teaching team construction, on-campus and off-campus practice base construction, and the construction of social service ability.
PECULIARITIES OF PROJECT ACTIVITIES OF FUTURE PRIMARY SCHOOL TEACHERS

The peculiarities of project activity of future primary school teachers during professional training in higher education institutions are considered in the article. One of the activities that helps to shape the professionalism of future primary school teachers is project activity, which is an integral part of the work of future professionals during the educational process. The article focuses on ICT as a tool used in project activities. With the help of ICT, future professionals will be able to help create a standalone product project. Therefore, when using ICT in primary school, future primary school teachers should learn to give students the freedom to choose and equip them with the necessary tools. Future primary school teachers should recognize as a core value students’ personalities, their personal and subjective qualities as the basis for organizing the educational process. At the heart of modern education reforms there is not only gaining knowledge at the lessons in primary school , but also students' independent search and project activity. In the course of the research we found that the use of ICT in project activities can be divided into the following blocks: the use of ICT in the creation of projects (information search, typing and description of information found); discussion of projects (network communication); use of ICT in presenting results. Analyzing the processes that contribute to the formation of project competence of future primary school teachers in the process of vocational training, we have identified the following stages of work during the project work in the project activity process: motivational; design; activity-related; reflexive evaluation. The stages of project work proposed by us during the project activity of future elementary school teachers help to shape the project competence of future specialists during vocational training.
Radar Signal Unintentional Modulation Feature and Clustering Sorting Methods

With the complexity of the radar system, radar signal sorting is facing an increasingly severe form. Therefore, the research on radar emitter signal sorting model and algorithm based on intentional modulation feature and clustering in the current stage has important theoretical and practical significance. This paper studies the meaning, research methods of intentional modulation features and clustering sorting method.
Temporal Learning of Dynamics in Complex Neuron Models using Backpropagation

One of the major challenges of computational cognitive neuroscience is to apply models of neural information processing to complex tasks. Hierarchical learning architectures like deep convolutional networks can be trained to solve tasks efficiently, but utilize simple mechanisms of activity integration and output generation. On the other hand, biologically plausible models of activation dynamics incorporate detailed mechanisms of changing membrane potentials and axonal firing properties. Making such elaborate models trainable requires learning of internal model parameters. Here, we propose to apply supervised learning and train a model of canonical cortical circuits via backpropagation through time. We train the model to settle to target equilibrium values, to generate oscillations, and to solve a contour completion task.
MovIPrint

MovIPrint is a user-friendly, interactive installation that uses software and a depth-sensing camera to capture human body movement. After inputting digital data such as images or video into the software, MovIPrint offers people innovative and user-friendly ways to explore that data by manipulating it with their body movement. We use media content and/or wireframe design to enable people to then fabricate their own moving images and 3D digital models.
Research on Heterogeneous Data Exchange Technology Based on Shadow Table

In the process of continuous development of enterprise informatization, with the expansion of old applications and the increasing of new applications, users will face the problem of data exchange between different hardware platforms, different network environments and different databases. Due to the coexistence of multiple application modes, the problems of data exchange between multiple systems are not standardized, network data sharing is not easy, data synchronization is not guaranteed, and data security is not guaranteed, which results in the phenomenon of “information island” and brings great inconvenience to users. The functions provided by traditional system software or tool software can not solve the problem of heterogeneous data exchange well. The research and implementation of a flexible, efficient, concise and transparent heterogeneous data exchange system is very necessary. This paper plans to study and implement a user configurable heterogeneous data exchange system which can shield complex interfaces between various data systems and has high security and reliability.
The Main Limitations of Applying Blockchain Technology in the Field of Education

The article identifies the main features of the application of blockchain technology, which caused a lot of positive feedback in scientific circles and professional communities. After success in the financial market, where the blockchain contains blocks that are iced in the chain, which describes absolutely all transactions with virtual coins (such as Bitcoin, Litecoin, Ethereum and others), there were prospects for evaluating the possibility of its successful application in other areas. Considering that the sphere of education in Russia is going through a period of reforms, special attention is paid to the opportunities and problems of application in this sphere. The article discusses the effectiveness of the application of blockchain technology for the distributed registry and for mass application in the field of education. And also an estimation of the probability of the possibility of improving the educational process. The analysis of the advantages of the technology that allowed it to successfully spread its crypto currency in the market of anonymous transactions and their application in the subject field of education, in comparison with classical technologies, with the central node-regulator, for example, the client-server.
Behavioural cybersecurity: applications of personality psychology and computer science

"Behavioural cybersecurity: applications of personality psychology and computer science." Ergonomics, 63(1), pp. 119–120
Optimal Bandwidth Allocation for Data Transmission in Virtual Data Centers

In this paper, we propose to use the transmission time of data blocks as the metric to measure the network utilization, and study the bandwidth allocation for multi-path data transmission in virtual data centers. We start with the case that data transmission requires no minimum bandwidth, and formulate the bandwidth allocation problem as two convex optimization problems. In the first problem, the maximum transmission time of all data blocks is minimized under the given data block sizes and link capacities, which is named as the maximum transmission time minimization (MTTM) problem. The second convex optimization problem minimizes the transmission time summation of all data blocks, and the problem is named as the transmission time summation minimization (TTSM) problem. These two problems are then generalized to the case that data transmission has minimum bandwidth requirement, and the corresponding problems are named as general MTTM (G-MTTM) and general TTSM (G-TTSM) problems. By exploring the properties of the G-MTTM problem, we design a fast algorithm for it, named as TTM-based algorithm. Extensive simulation results demonstrate that the G-MTTM and G-TTSM problems can be efficiently solved, and to use transmission time of data blocks as the metric for network utilization measurement and optimization is practical.
Impact of Supply Chain Digitalisation on a Connected Global Market

This chapter focuses on the impact of supply chain digitalisation on a connected global market. The first section focuses on the dynamic consumer requirements and preferences. The second section appraised the segmentation and mapping of digital technologies. The third section examines the contemporary application of digital technologies including: big data, blockchains, artificial intelligence, machine learning, and data analytics. The final section analysises the rules and regulations the form the basis of a contemporary framework for the governance of digital technologies.
Empirical Study on the Distribution of Object-Oriented Metrics in Software Systems

We attempt to model the probability distribution of object-oriented software metrics. We employ 5 distribution models to find out the distributions the metrics follow. We use AIC, BIC and RMSE as goodness-of-fit measures. Though the past studies have shown that the software projects frequently follow power law, having a Pareto distribution, we seek to study more number of software systems and distribution models to infer more generalizable results, since they occasionally seem to follow Log-normal or Gamma distribution as well. Apart from these three models we have also considered Weibull distribution and Generalized Pareto Distribution (GPD). In this study, we have made an attempt to answer the hypothesis that the object-oriented software metrics follow a particular distribution by comparing various distributions applied over a large number of projects using a recognized statistical framework.
Resampling of Data with Rothstein's Code for the Slope of a Line

A method to resample and scale data to arbitrary length with built-in interpolation was adapted from the scalar image transformation methods based on the Rothstein binary code for the slope of a line. Unlike other transformation methods, binary code transformations have built in interpolation. The theoretical validity of the method is based on human vision reception. An application to resample simulated signal to data lengths of powers of 2 for a Fast Fourier Transform (FFT) was presented as an example. A demonstration of validity was that FFT points from data of arbitrary N scaled to a power of two using the method matched the FFT from the original data series limited to powers of 2. A simple comparison to the methods of zero-padding and an averaging interpolation was also demonstrated. The computational advantage of the method may enhance or supplement standard signal processing techniques.
Big Data Analysis on Learning of Freshmen Based on Open Teaching

The open teaching platform is a teaching platform designed by Huazhong Agricultural University independently for the study of university computer basic courses. The platform has been used since 2012 and has accumulated a large amount of data. Based on big data technology, this paper analyzes the learning behavior of information course of freshmen, aiming to help the universities to explore new teaching mode based on artificial intelligence, reconstruct the teaching process, and establish a multi-dimensional comprehensive intelligent evaluation system based on big data.
Automated Thematic and Emotional Modern Chinese Poetry Composition

Topic and emotion are two essential elements in poetry creation, and also have critical impact on the quality of poetry. Inspired by this motivation, we propose a novel model to inject rich topics and emotions into modern Chinese poetry generation simultaneously in this paper. For this purpose, our model leverages three novel mechanisms including (1) learning specific emotion embeddings and incorporate them into decoding process; (2) mining latent topics and encode them via a joint attention mechanism; and (3) enhancing content diversity by encouraging coverage scores in beam search process. Experimental results show that our proposed model can not only generate poems with rich topics and emotions, but can also improve the poeticness of generated poems significantly.