Reviewerly/exHarmony huggingface dataset:
Multi-level Cascading Extreme Learning Machine and Its Application to CSI Based Device-Free Localization

Extreme Learning Machine (ELM) is greatly fast in its learning speed and has been widely applied to a variety of applications. However, ELM is usually considered as a shallow-structured model, which has merely one hidden layer, with its performance restricted in some complicated applications. To enhance the performance of ELM, in this paper, we propose a novel Multi-Level Cascading ELM (MLC-ELM) which is composed of multiple ELMs, with each in a level. Furthermore, there is an information flow between adjacent levels, which is the transformation of the previous level’s output, performed through a normalization operation and a scaling operation. The information flow is considered to be part of the input of the level following it. The output of the proposed MLC-ELM algorithm is the output of the final level. We conduct experiments in a Channel State Information (CSI) based Device-Free Localization (DFL) scenario to demonstrate the validity of MLC-ELM, with the results showing its effectiveness.
DIGITAL LEARNING ENVIRONMENT AS MULTI-AGENT SYSTEM: STUDY OF THE EDUCATIONAL AGENTS EVALUATIVE POSITIONS IN RUSSIAN UNIVERSITIES

Purpose of the study: Sociological analysis of the university education digitalization; identifying the evaluative positions for the educational process agents; study of motivational factors for applying to online courses; comparative analysis of evaluative positions in terms of information exchange effectiveness in the digital learning environment and education quality.&#x0D; Methodology: We utilize sociological survey and comparative analysis to identify significant motivations of agents, to evaluate the quality of education by criteria of freedom of choice, preference for the learning format, individualization of the educational route. The empirical base consists of sociological data obtained by online and offline questioning.&#x0D; Principal Findings: The revealed trends of diversity and target conjugation in the university educational environment agent’s positions reflect the institutional shifts in the educational system. These bring about changes in the social role repertoire, the revision of norms and values in the context of the rapid growth of digital practices.&#x0D; Applications of this study: The study results can serve as a basis for improving the university's educational policy and technologies for information support of the educational process. Identifying the problem points of agency and their target conjugation seems to be a tool of social technology to achieve the effectiveness of digital education.&#x0D; Novelty of this study: We give accent to multi-agent approach in the study of distance learning, a comprehensive assessment of information exchange and motivational factors of interacting agents in the university educational environment, education quality evaluation by the criterion of meeting the expectations of students, teachers and administration in the context of learning environment digitalization.
On the Implementation of a Secure Email System with ID-based Encryption

Email continues to be the major business correspondence tool as one of the most regularly used means of communication and collaboration today. The unsecured nature of email, however, can cause unauthorized disclosure of sensitive information. Currently, many email systems are based on schemes such as S/MIME,PGP and PKI to secure communication. However, authentication methods utilized in these systems can become costly with the growing burden of certificate management and pre-enrolment as the number of users increases. In this paper, a secure email framework while taking into account Identity-Based Encryption without pairing is considered. The proposed system provides secure email environment that eliminates PKI hassles with effortless key creation and management.
A Stable Modulation Classification Method in Dynamic Environments Using Improved Genetic Algorithm

With the development of software radio and cognitive radio technology, much work so far has focused on automatic classification of communication signals, but it still cannot meet the requirements in dynamic environments. Starting from the robustness of feature set, a communication signal modulation classification method with dynamic signal noise ratio(SNR) adaptability is proposed. Firstly, the four categories of features of the signal, such as the instantaneous features of the signal, the high-order cumulant features, the fractal theory features and the entropy features, are extracted to form the original feature set. Besides, the anti-noise evaluation function is constructed by analyzing the variation law of each feature under different SNR. Then, we get a robust feature set through this evaluation function. But there are still a lot of problems such as poor classification ability and information redundancy, thus, the second reduction based on our improved genetic algorithm(GA) is proposed. The experimental results show that the new algorithm improves the classification performance of feature subsets at the expense of time cost.
Suffix rank

We investigate the problem of building a suffix array substring index for inputs significantly larger than main memory. This problem is especially important in the context of biological sequence analysis, where biological polymers can be thought of as very large contiguous strings. The objective is to index every substring of these long strings to facilitate efficient queries. We propose a new simple, scalable, and inherently parallelizable algorithm for building a suffix array for out-of-core strings. Our new algorithm, Suffix Rank , scales to arbitrarily large inputs, using disk as a memory extension. It solves the problem in just O (log n ) scans over the disk-resident data. We evaluate the practical performance of our new algorithm, and show that for inputs significantly larger than the available amount of RAM, it scales better than other state-of-the-art solutions, such as eSAIS, SAscan , and eGSA.
Networked Governance

The interactive “hands-on” workshop session on Networked Governance addressed a real-life use case from TRIPLE, a European funded project in the field of Digital Humanities. “The TRIPLE project aims to facilitate more efficient and effective Social Sciences and Humanities research for societies at large by involving civil society, public institutions and companies into scientific projects, thus strengthening the links between different types of stakeholders” [1]. Workshop participants discussed the need for multistakeholder cooperation and networked governance in the age of global interdependence. The output of the discussion aimed to ground the purpose and core principles of the multistakeholder "Trust Building System" developed within the TRIPLE platform.
Computational support for facilitating parental reflective functioning in everyday parent-child interaction

A parent's capacity to understand the mental states of both him/herself and the child is regarded as important in the parent-child relationship. We propose Dyadic Mirror, a wearable smart mirror that is designed to foster this parental capacity in everyday parent-child interactions. Its key feature is to provide a parent with a second-person live-view from the child, i.e., the parent's own face as seen by the child, during their face-to-face interaction. Dyadic Mirror serves as an intuitive cue that helps the parent be aware of (1) his/her emotional state, and (2) the way he/she would be now being seen by the child, thereby facilitating to infer the child's mental state.
Resolution Effect of Training Sets in Several Super-resolution Algorithms

In this paper, we mainly focus on the effect of training data set on the image reconstruction result of super-resolution algorithm based on deep learning. The main work is divided into two parts. In the first part, we compare three representative algorithms of super resolution image reconstruction based on interpolation with three representative algorithms of super resolution image reconstruction based on deep learning under the same evaluation index. In the second part, we obtained four sets of training data sets with different resolutions by sampling the same data set with different multipliers, and used the three super-resolution algorithms based on deep learning to train the four sets of training data. The final experimental results not only show that the super-resolution algorithm based on deep learning is significantly better than the traditional super-resolution algorithm based on interpolation in the quality of image reconstruction, but also that without considering the influence of objective factors, the greater the image resolution of the training data set, the better the final image reconstruction effect.
Worldwide Ground Target State Propagation

When using long-range sensors to track targets located on the Earth's surface, such as ships at sea or cars on land, the literature often discusses the use of a local 2D tangent-plane coordinate system. This paper derives and compares a number of methods of propagating target motion over short and long distances on an ellipsoidal Earth under particle filter or tangent-plane Gaussian approximations.
Linguistic Documentation of Software History

Open Source Software (OSS) projects start with an initial vocabulary, often determined by the first generation of developers. This vocabulary, embedded in code identifier names and internal code comments, goes through multiple rounds of change, influenced by the interrelated patterns of human (e.g., developers joining and departing) and system (e.g., maintenance activities) interactions. Capturing the dynamics of this change is crucial for understanding and synthesizing code changes over time. However, existing code evolution analysis tools, available in modern version control systems such as GitHub and SourceForge, often overlook the linguistic aspects of code evolution. To bridge this gap, in this paper, we propose to study code evolution in OSS projects through the lens of developers' language, also known as code lexicon. Our analysis is conducted using 32 OSS projects sampled from a broad range of application domains. Our results show that different maintenance activities impact code lexicon differently. These insights lay out a preliminary foundation for modeling the linguistic history of OSS projects. In the long run, this foundation will be utilized to provide support for basic program comprehension tasks and help researchers gain new insights into the complex interplay between linguistic change and various system and human aspects of OSS development.
Exploring Relevant Parts Between Legal Documents Using Substructure Matching

Legal documents are typically hierarchically structured. This paper focuses on ordinances and rules (OR documents for short) in the local governments, which are designed for social lives under the governments. OR documents are composed of provisions for social lives in various aspects such as healthy development of youths and landscape preservation. OR documents in different local governments share common provisions but also include different provisions depending on their social situations. There is a large demand on helping governmental officers draft OR documents, especially searching “relevant parts” of OR documents. To help drafting OR documents, this paper designs the relevancy of OR documents with two basic measurements; matching ratio and provision commonality. Based on the relevancy, this paper develops a structured document search algorithm for OR documents. Experimental evaluation on real OR documents in Japan demonstrates that the proposed algorithm successfully discovers relevant parts of OR documents.
Learning Control for Robotic Manipulator with Free Energy

Although model-free reinforcement learning methods are illustrated to own abilities to learn a wide range of control and decision-making tasks, it is time-consuming and needs a huge number of samples to learn optimal policy. Therefore, model-based reinforcement learning algorithms are always treated as the most efficient ways to settle robotic control problems. In this work, a model-based reinforcement learning tracking control method with free energy is proposed. The control objective is to make sure that the proposed controller can let a robotic manipulator with parameters uncertainties and disturbance follow the desired trajectory and obtain good tracking performance. The main contribution of this work is to employ the free energy principle to optimize the problem of robotic control. The probability distribution of robotic dynamic model is learnt by a evolving neural network, a free energy based reinforcement learning control method uses the learnt model to find optimal control policy. The experiment results demonstrate that the proposed tracking control method is effective in solving robotic tracking control problems.
Research on Command Information System Architecture for Human-Computer Fusion

In the intelligent war, building a "computer brain" that can understand, think and cooperate with human is one of the development directions of the future command information system. The design concept of command information system architecture for human-computer fusion is proposed, which is the transfer of human intelligence to computer intelligence. The system composition of the core elements such as the game confrontation deduction environment is designed innovatively. The operational mechanisms before and during the war are elaborated. The command information system architecture reference model of human-computer fusion is formed. Finally, the Department of Defense Architecture Framework (DoDAF) is used to verify the top-level guidance of architecture design to the construction of human-computer fusion system through the typical application case of future operation.
Simultaneous Estimation of Filtered and Smoothed State Probability Density Functions by Multiple Distribution Estimation

This paper shows that for the multiple distribution estimation filter (MDEF)[1] [2], the one-step-behind (OSB) smoothed state probability density function (PDF) used for the estimation of the filtered state PDF is the key factor for the high filtering accuracy of the MDEF. The MDEF calculates the OSB smoothed state PDF prior to the calculation of the filtered state PDF at the current epoch and the filtered state PDF is estimated by the marginalization of the conditional state PDF with respect to this smoothed state PDF. Since the OSB smoothed state PDFs are obtained at every time step prior to the observation update, the MDEF can be regarded as providing the simultaneous estimation of the filtered and OSB smoothed state PDFs. In this paper, we numerically evaluate the estimation accuracy for the OSB smoothed state estimates by the MDEF using the benchmark filtering problems [3]-[5] and compare it with those for the particle and the Gaussian smoothers employed to the MDEF. We confirmed that the smoothing accuracy for the OSB smoothed state estimates was more accurate than that for the Gaussian smoother and almost comparable to that for the particle smoother, while the calculation cost was significantly lowered than that for the particle smoother.
Cold Start and Learning Resource Recommendation Mechanism Based on Opportunistic Network in the Context of Campus Collaborative Learning

The mainstream cold start scheme in social network mainly deals with the problems of information overload and the accuracy and efficiency of recommendation. However, the problem of information overload is quite different from the problem of information transmission delay caused by insufficient contact of nodes in the mobile Opportunistic network. And in the campus collaborative learning environment, learner nodes often have a lack of awareness of their own needs of learning resources and lack of search ability for learning resources, in order to solve the above problems, this paper for the mobile social network cold start stage definition and stage division, On this basis, the paper provides solutions to the file transfer strategies in the cold start-up stage and the community operation stage of the nodes respectively, And according to the high degree of activity nodes can often be contact more information, the higher intimacy between nodes means that the nodes are more familiar and higher transmission success rate characteristics, In this paper, a learning resource recommendation mechanism based on node activity and social intimacy is proposed, and the algorithm has been tested and verified to have high accuracy for the recommendation mechanism based on message attributes.
MODERN INFORMATION MOBILE TOOLS OF TRAINING IN HIGHER EDUCATION INSTITUTIONS

The article is devoted to modern information tools of training as a way to optimize the process of training future specialists. Emphasis is placed on the importance of technological innovation and the theory of learning in the application of a variety of technologies to teaching and teaching. It is determined that the patterns of behavior of modern students are changing with respect to their rapidly expanding digital environment. Modern digital students think and process information differently from their predecessors – thinking in parallel and linear models and reading visual images that could be used to read text. It is emphasized that new technologies, such as those described in the article, can give students of the information society and the interaction society the opportunity to receive increased and continuous participation in learning. Active students interact with their environment and manipulate objects in that environment, observe the impact of their interventions, and build their own interpretations of the phenomena and results of manipulations, share these interpretations with others. These descriptions offer connections to previously identified features of the Information Society and the Society for Interaction and New Technologies. Different approaches of providing modern active education in higher education institutions are analyzed. In summary, mobile learning devices can provide individuality through unique profiles, this category of technology can be a proper choice of learning environment aimed at enhancing individual learning orientation. Mobile devices need to be in tune with how events are happening around the world and the web, even if those devices have been used for personal purposes only. It is emphasized that a critical aspect of such a strategy is that the mobile device is not simply integrated into the educational and higher education institutions or fell into the hands of its user, as has been done, for example, with recent initiatives on computers and laptops. On the contrary, individual profiles must be intentionally designed into the device and involved in the educational process in educational institutions in general and in higher education institutions in particular.
An Incremental Algorithm for Computing All Repairs in Inconsistent Knowledge Bases

Repair techniques are used for reasoning in presence of inconsistencies. Such techniques rely on optimisations to avoid the computation of all repairs while certain applications need the generation of all repairs. In this paper, we show that the problem of all repair computation is not trivial in practice. To account for a scalable solution, we provide an incremental approach for the computation of all repairs when the conflicts have a cardinality of at most three. We empirically study its performance on generated knowledge bases (where the knowledge base generator could be seen as a secondary contribution in itself).
BOOK ART YARD: AUTHOR'S PROJECT IN PROMOTING READING

In a modern urbanistic society, the urban environment is not only an element of improvement, but also a factor of human impact. The book art courtyard allows you to decorate the courtyard area according to the appropriate theme. Dedicated to books and reading, the art courtyard is an effective means of promoting reading
The virtual training platform for computer vision

With the development of deep learning, computer vision has made great progress. Computer vision training based on deep learning requires a large number of data sets. However, manual obtaining of relevant data sets is costly, and some special samples are not easy to obtain. Therefore, in order to solve the lack of data sets, a virtual training platform is designed. The platform is able to render three-dimensional scene simulation by using Unity 3D, and automatically generates visual training data sets through corresponding script file. Through the interactive interface of the platform, users can select and adjust the model, scene, light and other variables independently according to different needs, and quickly generate multi-angle pictures and corresponding annotation information. Compared with the previous computer vision training data sets obtained by manual obtain of image information and label, it has the advantages of fast speed, low cost and large scale. At the same time, use the object detection network to evaluate the data generated by the platform and the data obtained by the previous way. It can be observed that the virtual data set can achieve certain effects. Therefore, the data sets generated by this platform can replace the traditional ones to a certain extent.
Copyright life hacks for librarians

Librarians are continuously looking for new ways to make the training they offer accessible and engaging to both colleagues and users. One area where this is especially important is copyright – a topic many librarians identify as vital to their role, but they often find it hard to attend training. Cambridge University Libraries has introduced a range of methods to reach out to even the most reluctant copyright learner and improve the overall copyright literacy of its staff. This article showcases these methods in the form of ‘life hacks’ – simple measures which can be implemented with little or no cost and using existing resources. Methods outlined include making the best use of knowledge already present within your organisation, using visual methods to attract a new audience and creating interactive online resources. Also discussed is the importance of making copyright training accessible, both to users with disabilities and those who may have constraints on their time and technological ability. The article concludes with a reflection about the challenges faced whilst creating new resources. The techniques outlined in this case study can be adapted for use by a range of libraries no matter the target audience.
The maximum number of points on a curve of genus eight over the field of four elements

The Oesterlé bound shows that a curve of genus 8 over the finite field F4 can have at most 24 rational points, and Niederreiter and Xing used class field theory to show that there exists such a curve with 21 points. We improve both of these results: We show that a genus-8 curve over F4 can have at most 23 rational points, and we provide an example of such a curve with 22 points, namely the curve defined by the two equations y2+(x3+x+1)y=x6+x5+x4+x2 and z3=(x+1)y+x2.