Reviewerly/exHarmony huggingface dataset:
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control

While Deep Reinforcement Learning (DRL) has emerged as a promising approach to many complex tasks, it remains challenging to train a single DRL agent that is capable of undertaking multiple different continuous control tasks. In this paper, we present a Knowledge Transfer based Multi-task Deep Reinforcement Learning framework (KTM-DRL) for continuous control, which enables a single DRL agent to achieve expert-level performance in multiple different tasks by learning from task-specific teachers. In KTM-DRL, the multi-task agent first leverages an offline knowledge transfer algorithm designed particularly for the actor-critic architecture to quickly learn a control policy from the experience of task-specific teachers, and then it employs an online learning algorithm to further improve itself by learning from new online transition samples under the guidance of those teachers. We perform a comprehensive empirical study with two commonly-used benchmarks in the MuJoCo continuous control task suite. The experimental results well justify the effectiveness of KTM-DRL and its knowledge transfer and online learning algorithms, as well as its superiority over the state-of-the-art by a large margin.
Enabling Web-Scale Knowledge Graphs Querying

Knowledge Graphs (KGs) have become an asset for integrating and consuming data from heterogeneous sources. KGs have an influence on several domains such as health-care, manufacturing, transportation and energy. Over the years, the Web of Data has grown significantly. Today, answering complex queries on open KGs is practically impossible due to the SPARQL endpoints availability problem caused by well-known scalability and load balancing issues when hosting Web-size data for concurrent clients. To maintain reliable and responsive open Knowledge Graph query services, several solutions have been proposed: while SPARQL endpoints enforce restrictions on server usage such as imposing limited query execution time or providing partial query results, alternative solutions such as Triple Pattern Fragments (TPF) attempts to tackle the problem of availability by pushing query processing workload to the client-side but suffer from the unnecessary transfer of irrelevant data on complex queries as a result of the large intermediate results. The aim of our research is to develop a new generation of smart clients and servers to balance the load between servers and clients, with the best possible query execution performance, and at the same time reducing data transfer volume, by combining SPARQL endpoints, TPF and shipping compressed KG partitions. The proposed solution shall, on the server-side, offer a suitable query execution service according to the current status of the server workload. On the client-side, we plan research on novel client-side caching mechanisms on the basis of compressed and queryable KG partitions that can be distributed in a modular fashion. In addition, we plan to leverage query logs to optimize the number and the distribution of partitions as well as distributing the query load across a network of collaborative clients.
User Personalized Location k Anonymity Privacy Protection Scheme with Controllable Service Quality

The existing location privacy protection methods only provide the privacy protection parameter k for users to choose to achieve their personalized privacy protection requirements, but users can not control the quality of service. To this end, this thesis proposes a user personalized location k anonymity privacy protection scheme with controllable service quality. The quality of service is quantified according to service similarity, and both it and the degree of privacy protection are used as user-controllable parameters, which meets the user’s more personalized privacy protection needs. At the same time, the background knowledge of historical query probability is quantified. The greedy strategy combined with the position entropy measurement mechanism is used to generate a k-anonymity set that can resist the attacker’s background knowledge inference attack, and a perturbation location is selected to complete the anonymity. The performance comparison experiment results show that this method can obtain very good privacy protection performance and efficiency.
Research on Classification Model of Government Big Data Based on Deep Learning

Based on analyzing the textual features of Government Big Data, this paper proposes the Attention-BiLSTM-CNN hybrid classification model. Firstly, the model uses the BiLSTM model to extract the sentence vector of the text, and then introduces an attention mechanism to automatically learn the weight distribution of keywords, and finally uses the CNN model to calculate the probability of the category. And design experiments to verify the effect of the new model. The experimental results show that the classification model proposed in this paper has a good classification effect in the government big data corpus, which can improve the efficiency of government big data classification.
Business Operations and Service Management within Blockchain in the Internet of Things

Emerging technologies like blockchain can bring new and innovative solutions to business problems and provide alternative solutions to business management and ICT Operations and Services. With the role of blockchain in the provision of security and trust for the Internet of Things (IoT) being developed and matured, blockchain will face similar challenges as that of other technologies around business integration and the transformation of ICT Operations and Services required to develop, install, and manage the service. The issues of vendor management, risk management, security, compliance, audit, security, safety, and independence still exist for blockchain use and adoption. In addition, the take up of blockchain within the IoT is also tied to the business opportunities, market changes, cost savings, and regulatory regimes, as well as overcoming incumbency and vested interests. Even with the use of automation and artificial intelligence alongside the blockchain, the need for standards, processes, policies, procedures, and governance remain at the network level, device level, information level, operational level, service level, business level, and organization integration level. This chapter discusses the aspects of the wider business integration, adoption, and service management of blockchain with the IoT.
Iteratively Composing Statically Verified Traits

Static verification relying on an automated theorem prover can be very slow and brittle: since static verification is undecidable, correct code may not pass a particular static verifier.In this work we use metaprogramming to generate code that is correct by construction.A theorem prover is used only to verify initial "traits": units of code that can be used to compose bigger programs.In our work, meta-programming is done by trait composition, which starting from correct code, is guaranteed to produce correct code.We do this by extending conventional traits with pre-and postconditions for the methods; we also extend the traditional trait composition (+) operator to check the compatibility of contracts.In this way, there is no need to re-verify the produced code.We show how our approach can be applied to the standard "power" function example, where metaprogramming generates optimised, and correct, versions when the exponent is known in advance.
Time and Incentive-Aware Neural Networks for Life Insurance Premium Prediction

Life insurance premium analysis is significant because it can assess the market conditions and formulate development strategies for the companies. However, this task is less explored, because it is too difficult to capture the intricacy of life insurance products and it is highly influenced by both regulatory policies and social welfare. By thoroughly examining the life insurance premium gained by all insurance companies in China from 2001 to 2019, we find that the premium is greatly affected by two factors, time and the incentive mechanism that yields the “Good-Start” phenomenon. We, therefore, propose a novel Time and Incentive-aware Long-Short-Term-Memory (LSTM-TI) network to exploit the effectiveness of LSTM in modeling highly non-linear sequential data while incorporating the time effect and the incentive signals essentially. Empirical evaluation on the dataset shows that our LSTM-TI model attains significantly better performance than the strong baseline methods.
Algebra and Hilbert space structures induced by quantum probes

In the general setting of quantum controls, it is unrealistic to control all of the degrees of freedom of a quantum system. We consider a scenario where our direct access is restricted to a small subsystem S that is constantly interacting with the rest of the system E. What we investigate here is the fundamental structure of the Hilbert space that is caused solely by the restrictedness of the direct control. We clarify the intrinsic space structure of the entire system and that of the operations which could be activated through S. The structures hereby revealed would help us make quantum control problems more transparent and provide a guide for understanding what we can implement. They can be deduced by considering an algebraic structure, which is the Jordan algebra formed from Hermitian operators, naturally induced by the setting of limited access. From a few very simple assumptions about direct operations, we elucidate rich structures of the operator algebras and Hilbert spaces that manifest themselves in quantum control scenarios.
Research on Progress and Inspiration of Entity Relation Extraction in English Open Domain

In the era of big data, how to extract unrestricted type of entity relations from open domain text is a challenging topic. In order to further understand related deep issues, this paper summarized the latest progress in the field of English entity relation extraction, ranging from binary to n-ary entity relation extraction; furthermore, some milestone systems are introduced in detail. This paper makes a preliminary exploration on the extraction of entity relations in the Chinese open domain. In particular, the inspiration of English to Chinese has also promoted the development of Chinese entity relation extraction.
CaReNet: CapsNet Based Regression Network

We propose a capsule based regression network (CaReNet), a framework that is based on capsule networks (CapsNet), rather than on the conventional convolutional neural networks (CNNs) to determine estimates of continuous variables. The core principles of CaReNet remain that of routing-by-agreement and translation equivariance proposed in the CapsNet architecture for classification problems. However, unlike the CapsNet architecture, the final layer capsules in CaReNet architecture capture the number of values to regress in their dimensionality. An output vector returned by each of these capsules contains all the regressed values while the corresponding activity vector, determined by squashing an output vector, captures the likelihood of the regressed values being present in the corresponding capsule. We show that our novel CaReNet architecture achieves state-of-the-art performance on regressing facial keypoints from images, an important problem that plays a significant role in face recognition systems. The performance of CaReNet has been evaluated on the dataset from Kaggle Facial Keypoints Detection competition. Further, the flexibility in CaReNet architecture allows it to be easily extendable to any other regression problem such as 3D reconstruction of facial models from 2D images.
Urgent Problems of References Arrangement in Research Documents

Th e purpose of research. Currently , the problem of plagiarism is quite relevant and affects the scientific and educational spheres. Thus, the experts of the Russian State Library have organized special research, and they are issuing a series of articles devoted to that activity. Within the framework of fighting plagiarism and other negative phenomena, it becomes necessary to highlight mostly wide-spread problems of reference-arrangement for research papers, and to outline possible solutions for them. We will consider this issue in the first article of the series. Material s an d methods. I n order to achieve the aforementioned target the article uses the results of the plagiarism tests for research papers carried out by the Russian State Library with the help of specially designed software “Antiplagiat.RSL”. Results . Th e common defects of research papers are: inclusion of fictitious sources into the reference list, lack of homogeneity in reference arrangement, inadequate references to the groups of sources, quoting the derivative sources instead of the original one, spreading false information because of blind copying other researchers’ mistakes. Plagiarism tests carried out over scientific texts helped to identify the most frequent incorrect borrowings (overlapping wording of conclusions and generalizations, the same sentences used for describing results and methods, as well as word – for – word reproduced literature reviews). The documents revealed to become sources for such plagiaristic borrowings would often be works of research advisors and other authoritative specialists, candidate dissertation theses close in theme or structure, earlier publications of authors themselves, including those written in co-authorship. Conclusion . Numerou s flaws are made by emerging authors due to the lack of knowledge, that is why students’ and post-graduates’ efficient training in work with GOST state standards as well as the quality of educational and methodological literature in research-paper writing would be of use. The article offers ways of improvement for research publication arrangement and specialized software products destined to make reference arrangement automatic.
Digital Transformation of the Health Sector in Greece: Evaluation of the Websites of Health Organizations in the Region of Attica

The purpose of the current study is the comparison and evaluation of the websites of all the public hospitals and private clinics in the region of Attica and the identification of possible problems and content omissions that may be present in order to be amended or improved.The population of the survey includes all public hospitals and general private and quasi clinics which operate within the borders of Attica region excluding specialist outpatient private clinics.The analysis showed that the web pages of the private clinics and public hospitals in the Attica basin have a similar outline mainly in terms of their content and their design and are mostly informative concerning their structure but are less informative for users in terms of decision making and procedures on matters relating to their health.Therefore it becomes clear that these websites need to be improved and redesigned enough so as to reach a level that will meet the citizens' needs.
The Research Project «Names of Modern Professions» in Extracurricular Work with 6-th Grade Russian Students

The aim of the study was to investigate approaches to the formation of general educational skills. A survey examining the design and research process was carried out by 6th-grade Russian students over the course of an extracurricular project entitled «Names of Modern Professions». In the paper, the selection of the «Lexicology» section for such activities carried out by school pupils is substantiated and stages of work on the project are described. The applied methodology involves theoretical analysis of scientific literature, formative experimentation, analysis of products of educational activities, observation and description. It is concluded that the design of extracurricular research activities in the Russian language contributes to the formation of general educational competencies in conducting surveys and searching for information on the basis of subject skills.
Special Random Search Methods

An important subclass is formed by those methods whose mutation transition probabilities p n have Lebesgue densities. Mutation transition probability
Research on Blind Signature Based Anonymous Electronic Cash Scheme

With the development of network economy, as a new transaction method, electronic cash makes online transactions become more convenient. However, online transactions also bring the risk of user privacy disclosure, which has attracted wide attention. To solve this problem, this paper proposes a new blind signature scheme based on RSA and applies it to electronic cash system. The electronic cash scheme increases the difficulty of analyzing the relationship between electronic currency addresses by confusing the relationship between the user’s public address and the actual address, which improves the privacy protection level of the proposed electronic cash scheme. The proposed anonymous electronic cash system can be used to protect the privacy of user identity and complete user anonymous transactions and meet the security and efficiency requirements. Finally, the performance analysis shows that the communication cost of anonymous electronic cash system in this paper is low, which significantly improves the security of electronic cash anonymous transactions and has a good application prospect.
General Ontology Overview

Before we discuss the ontology for modern conflict, we need an understanding of ontologies in general. Chapter 1 included an introduction to ontologies and mentioned foundational ontologies as the upper level or most primitive of ontologies. A foundational ontology is domain-neutral. It defines a set of categories and relationships that is general in nature and is thought to represent reality at its most basic level. The thing, class, and relationship trio is a primitive foundational ontology.
Design of Purebred Dog Recommendation System Using MCDM Approach

The dog is one of the first animals domesticated by human, and for thousands of years, it has been artificially bred into hundreds of types in order to provide certain traits that humans want.Nowadays, the selection of dogs by potential adopters has become a problem due to the availability of different type of breeds with their physical and mental characteristics.This study aims to design a decision support system through an analytical model that uses variety of data on the characteristics of purebred dogs obtained from different sources.Data from official sources is obtained from international purebred dog organizations that set the standards for each breed type, while data from unofficial sources is obtained from the dog lovers community, experts, and kennel owners.The result of the study provides appropriate recommendations to potential adopters in selecting a breed that suits their preferences and needs.
Quick Lists: Enriched Playlist Embeddings for Future Playlist Recommendation

Recommending playlists to users in the context of a digital music service is a difficult task because a playlist is often more than the mere sum of its parts. We present a novel method for generating playlist embeddings that are invariant to playlist length and sensitive to local and global track ordering. The embeddings also capture information about playlist sequencing, and are enriched with side information about the playlist user. We show that these embeddings are useful for generating next-best playlist recommendations, and that side information can be used for the cold start problem.
A Hybrid Distributed Frequent Itemset Mining Method with Its Application in Medical Diagnosis

The world has entered an era of globalization, which represents the explosion of information. The application and development of big data technology make the scale of medical data geometric growth. People cannot intuitively see the correlation and the implicit relationship between complex medical data, which leads to the situation of more data and less knowledge. By using Spark and a variety of data pre-processing techniques and machine learning related algorithms, we implemented a platform which could help patients recommend more accurate treatment plans, help doctors analyze the relationship between diseases, and provide more natural results through a visual interface. Besides, we proposed a distributed frequent itemset mining algorithm (DSDFIM) based on the adjacency list and Spark. After evaluating, the proposed algorithm could reduce data transportation once between main memory and secondary storage, and improved the speed of data processing through distributed computing, compared with the classic algorithm. Meanwhile, it could solve the problem of merging frequent itemset of the same item under different independent paths.
Supervised attention for speaker recognition

The recently proposed self-attentive pooling (SAP) has shown good performance in several speaker recognition systems. In SAP systems, the context vector is trained end-to-end together with the feature extractor, where the role of context vector is to select the most discriminative frames for speaker recognition. However, the SAP underperforms compared to the temporal average pooling (TAP) baseline in some settings, which implies that the attention is not learnt effectively in end-to-end training. To tackle this problem, we introduce strategies for training the attention mechanism in a supervised manner, which learns the context vector using classified samples. With our proposed methods, context vector can be boosted to select the most informative frames. We show that our method outperforms existing methods in various experimental settings including short utterance speaker recognition, and achieves competitive performance over the existing baselines on the VoxCeleb datasets.
Explicable Question Answering

Question answering over Knowledge Graphs has emerged as an intuitive way of querying structured data sources and has witnessed significant progress over the years. However, there is still plenty of space for improvement and there exist specific challenges that are still far from being effectively solved. In this research project, we aim to address some of these challenges and provide innovative solutions in the field. Our research will mainly focus on deep learning approaches such as sequence to sequence models and ranking methods. We plan to contribute to the challenges of explicability and complex queries by further researching the areas and providing resources together with more robust models by using probabilistic methods and meta-learning approaches.
Evolving Meaning for Supervised Learning in Complex Biomedical Domains Using Knowledge Graphs

Knowledge graphs represent an unparalleled opportunity for machine learning, given their ability to provide meaningful context to data through semantic representations. Knowledge graphs provide multiple perspectives over an entity, describing it using different properties or multiple portions of the graph. State-of-the-art semantic representations are static and take into consideration all semantic aspects, ignoring that some may be irrelevant to the downstream learning task. The goal of this Ph.D. project is to discover suitable semantic representations of knowledge graph entities that are adapted to specific supervised learning tasks. I will use Genetic Programming to evolve tailored semantic representations, and develop novel approaches that integrate them with different supervised learning techniques. These novel approaches will be anchored by a framework that integrates different semantic representation approaches and two representative learning approaches, Support Vector Machine and Graph Convolutional Neural Networks, and allows a comparative evaluation using benchmarks. The developed approaches will be applied to two bioinformatics tasks, prediction of protein interactions and gene-disease associations, where the impact of data size and complexity will be investigated.