Reviewerly/exHarmony huggingface dataset:
Step-width theoretics and numerics of four-point general DTZN model for future minimization using Jury stability criterion

Future minimization, or say, discrete-time time-varying minimization, can be successfully solved by discrete-time Zhang neuronet (DTZN) models. A DTZN model is obtained through the discretization of a continuous-time Zhang neuronet (CTZN) model. However, most of the existing numerical differentiation formulas cannot be applied to discretizing a CTZN model for obtaining an effective DTZN model. Zhang et al discretization (ZeaD) formulas are the ones that can successfully address the problem about discretization. Recently, the four-point general ZeaD formula has been proposed (Hu et al., 2018), which can be applied to obtaining the four-point general DTZN model for future minimization. This paper focuses on researching the step-width-related stability of the four-point general DTZN model for future minimization. The step-width is a crucial parameter in the DTZN model. If the step-width is out of its corresponding interval, DTZN model does not converge in terms of residual error and fails in solving future minimization problem. By using Jury stability criterion, the analytic solution of step-width interval is proposed corresponding to the four-point general DTZN model. In addition, the optimal value of step-width is investigated, and the analytic solution of the optimal value is proposed as well. When step-width is equal to the optimal value, DTZN model has the fastest convergence rate to steady state with respect to residual error, and is farthest from instable state, thus having the best stability. The theoretical analyses are consistent with previous special-case investigations, and their correctness is confirmed by numerical experiments and results (i.e., numerics).
Discovering Patterns for Fact Checking in Knowledge Graphs

This article presents a new framework that incorporates graph patterns to support fact checking in knowledge graphs. Our method discovers discriminant graph patterns to construct classifiers for fact prediction. First, we propose a class of graph fact checking rules (GFCs). A GFC incorporates graph patterns that best distinguish true and false facts of generalized fact statements. We provide statistical measures to characterize useful patterns that are both discriminant and diversified. Second, we show that it is feasible to discover GFCs in large graphs with optimality guarantees. We develop an algorithm that performs localized search to generate a stream of graph patterns, and dynamically assemble the best GFCs from multiple GFC sets, where each set ensures quality scores within certain ranges. The algorithm guarantees a (1/2−ϵ) approximation when it (early) terminates. We also develop a space-efficient alternative that dynamically spawns prioritized patterns with best marginal gains to the verified GFCs. It guarantees a (1−1/ e ) approximation. Both strategies guarantee a bounded time cost independent of the size of the underlying graph. Third, to support fact checking, we develop two classifiers, which make use of top-ranked GFCs as predictive rules or instance-level features of the pattern matches induced by GFCs, respectively. Using real-world data, we experimentally verify the efficiency and the effectiveness of GFC-based techniques for fact checking in knowledge graphs and verify its application in knowledge exploration and news prediction.
A Simple Calibration Procedure for a 2D LiDAR With Respect to a Camera

This paper proposes a simple calibration procedure for a planar scanning laser rangefinder or a 2D LiDAR with respect to a regular monocular camera, in which a point-like target is utilized to obtain point-to-point correspondences, which can make the calibration problem simple. In the proposed method, a small spherical object is attached to a rod, which is hand-held, and the correspondences are simply and effectively acquired by setting the target in front of the sensors and repeatedly moving the target downward by hand, until the LiDAR captures the target. We also describe possible noise present in the proposed data acquisition process, followed by simulation and real experiment. The simulation study examines the performance of the proposed calibration in the presence of noise, in which the LiDAR parameter estimation is successful unless the noise is excessive. In the real experiment, the proposed calibration is conducted by constructing a real calibration system, where we evaluate the reprojection error of the proposed calibration. In both simulation and experiment, a conventional calibration procedure with a v-shaped board target is also conducted for evaluating the proposed calibration procedure. It is confirmed that the proposed method is more effective in point cloud acquisition and calibration performance.
Neural Binary Representation Learning for Large-Scale Collaborative Filtering

Integrating hashing into collaborative filtering (CF) has been a promising solution to address the efficiency problem of large-scale recommender systems, i.e., hashing users and items into binary codes and then making recommendations in Hamming space. However, most of the existing hashing methods for CF solely focus on modeling the user-item similarity (a.k.a. preference) but omit the user-user and item-item similarities, which cannot well preserve the original geometry in the vector space. Another tough issue is how to effectively tackle the encoding loss from the continuous vector space to the discrete Hamming space. In this paper, we propose a neural binary representation (NBR) learning approach by combining hashing with a neural network for the large-scale CF tasks. Our NBR problem is formulated as a Hamming similarity loss plus two anchor smoothing terms, which jointly preserve the intrinsic user-item, user-user, and item-item similarities. To compensate the encoding loss introduced by hashing, the anchors for users and items are pre-learned using both user-item interactions and the side-information of users and items through an AutoEncoder model. In particular, to solve the NBR problem, we develop a computationally efficient algorithm, which learns the binary codes in a fast bit-by-bit way and achieves less quantization deviation than the conventional CF hashing schemes. The extensive experiments on three benchmarks validate the superiority of our NBR approach in comparison to the state-of-the-art CF hashing methods.
A Two-Stage Ensemble of Differential Evolution Variants for Numerical Optimization

Differential evolution (DE) is a popular paradigm of evolutionary algorithms, which has been widely applied to solve diverse optimization problems and has gained much success in a series of academic benchmark competitions.Recently, ensemble methods received an increasing attention in designing highquality DE algorithm.Motivated by this consideration, a novel two-stage ensemble of DE variants, called TSEDE, has been proposed in this paper.In TSEDE, based on the number of fitness evaluations, the whole evolutionary process is divided into two stages.In the former stage, TSEDE using a multi-population-based framework focuses on improving the searchability, which employs three popular and efficient DE variants, namely SHADE, JADE, and ''DE/current-to-rand/1.''In the latter stage, LSHADE is used to emphasize the convergence.Moreover, an elite strategy is used to ensure that the current best solution is assigned to each constituent variant at each generation.TSEDE is tested on the CEC2005 benchmark suit and compared with nine typical algorithms.The results confirm that the proposed method is very competitive.
Diversification across mining pools: optimal mining strategies under PoW

Abstract Mining is a central operation of all proof-of-work (PoW)-based cryptocurrencies. The vast majority of miners today participate in “mining pools” instead of “solo mining” in order to lower risk and achieve a more steady income. However, this rise of participation in mining pools negatively affects the decentralization levels of most cryptocurrencies. In this work, we look into mining pools from the point of view of a miner: We present an analytical model and implement a computational tool that allows miners to optimally distribute their computational power over multiple pools and PoW cryptocurrencies (i.e. build a mining portfolio), taking into account their risk aversion levels. Our tool allows miners to maximize their risk-adjusted earnings by diversifying across multiple mining pools. Our underlying techniques are drawn from both the areas of financial economy and computer science since we use computer science-based approaches (i.e. optimization techniques) to experimentally prove how parties (and in particular miners) interact with cryptocurrencies in a way of increasing their Sharpe ratio. To showcase our model, we run an experiment in Bitcoin historical data and demonstrate that a miner diversifying over multiple pools, as instructed by our model/tool, receives a higher overall Sharpe ratio (i.e. average excess reward over its standard deviation/volatility).
The 10th anniversary of the cryptocurrency market: Its current state and prospects

The article introduces a classification of research programs related to the cryptocurrency market. Each of them is surveyed, with the emphasis placed on the most recent studies. Despite the presence of market frictions, its informational efficiency tends to increase, thereby making it more comparable with the conventional financial markets. In particular, cryptocurrencies can be used for portfolio diversification. Yet, they can hardly compete with fiat money. Currently, central banks are only interested in examining and adopting some of the cryptocurrency features to create their own wholesale digital currencies.
Automatic PTZ Camera Control Based on Deep-Q Network in Video Surveillance System

Recently, Pan/Tilt/Zoom (PTZ) camera has been widely used in video surveillance systems. However, it is difficult to automatically control PTZ cameras according to moving objects in the surveillance area. This paper proposes an automatic camera control method based on a Deep-Q Network (DQN) for improving the recognition accuracy of anomaly actions in the video surveillance system. To generate PTZ camera control values, the proposed method uses the position and size information of the object which received from the video analysis system. Through implementation results, the proposed method can automatically control the PTZ camera according to moving objects.
Recommending heterogeneous resources for science gateway applications based on custom templates composition

Emerging interdisciplinary data-intensive science gateway applications in engineering fields (e.g., bioinformatics, cybermanufacturing) demand the use of high-performance computing resources. However, to mitigate operational costs and management efforts for these science gateway applications, there is a need to effectively deploy them on federated heterogeneous resources managed by external Cloud Service Providers (CSPs). In this paper, we present a novel methodology to deliver fast, automatic and flexible resource provisioning services for such application-owners with limited expertise in composing and deploying suitable cloud architectures. Our methodology features a Component Abstraction Model to implement intelligent resource 'abstractions' coupled with 'reusable' hardware and software configuration in the form of "custom templates" to simplify heterogeneous resource management efforts. It also features a novel middleware that provides services via a set of recommendation schemes for a context-aware requirement-collection questionnaire. Recommendations match the requirements to available resources and thus assist novice and expert users to make relevant configuration selections with CSP collaboration. To evaluate our middleware, we study the impact of user preferences in requirement collection, jobs execution and resource adaptation for a real-world manufacturing application on Amazon Web Services and the GENI cloud platforms. Our experiment results show that our scheme improves the resource recommendation accuracy in the manufacturing science gateway application by up to 21% compared to the existing schemes. We also show the impact of custom templates knowledgebase maturity at the CSP side for handling novice and expert user preferences in terms of the resource recommendation accuracy.
Stone Soup: announcement of beta release of an open-source framework for tracking and state estimation

Tracking and state estimation technologies are used in a variety of domains that include astronomy, air surveillance, maritime situational awareness, biology, and the internet. Algorithms for tracking and state estimation are becoming increasingly complex and it is difficult for researchers and skilled practitioners to implement and systematically evaluate these state-of-the-art algorithms. System designers also need to objectively assess the performance of algorithms against operational requirements, and tools to conveniently perform such systematic assessment have been lacking. Recognising this problem, an initiative was taken to create an open-source frame- work called &#83;tone Soup", which would be used for the development, demonstration, and evaluation of tracking and state estimation algorithms. Stone Soup was made openly available in April 2019 as a beta version (V0.1b1). This paper introduces the Stone Soup framework and describes how users can take advantage of this framework to develop their own algorithms, set up experiments with real-world data, and evaluate algorithms.
Multi-objective Path Planning for UAV in the Urban Environment Based on CDNSGA-II

This paper is concerned with path planning for unmanned aerial vehicles (UAVs) in urban environment. Distance is the fundamental goal of path planning. Besides, safety is indispensable for the riskless UAV flight. In this paper, we propose a multi-objective path planning (MOPP) approach based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) to find an optimal collision-free path for UAV, considered both distance and safety. To this aim, firstly, we represent the urban environment by octree, which is an efficient and hierarchical data structure for spatial subdivision in 3D. Meanwhile, safety index map (SIM) are developed to capture obstacles in the geography map. Then, a Crowd Distance NSGA-II (CDNSAG-II) method is proposed to deal with MOPP problem. The experimental results demonstrate that our approach is able to find an optimal path efficiently under urban environment based on octrees.
Transformation of University Communication Strategy in Terms of Digitalization

Technology continues to impact every aspect of our lives and the education sector is no exception. Students spend the majority of their time on their laptops, phones and tablets, so referring them to resourceful websites is an effective way of being digitally up to date. The creation of an online community where academic staff and students can be online discussing topics and ideas and implicitly keep the learning process going even after the classes are finished. The transformation of Saint Petersburg Electrotechnical University's communication strategy, as a part of the development of engineering education in the context of digitalization is reviewed in the article. The main characteristics of Society 5.0, Industry 4.0, sixth wave of innovation, University 4.0, University 20.35 are presented. The meaning and use of such words as “digitalization”, “human capital”, “emotional intelligence”, “smart class”, “lifelong learning”, “university for life” and others are given. A strategic approach for promoting engineering professions in the international market of higher education based on six key principles: customer centricity, partner centricity, data governance, research and development, value and digital culture, is revealed. Different examples and numbers connected with ETU “LETI” and Honoris United Universities by each of these key principles are given in the article. The necessity of collaboration between teaching methodology and technology, in order to enhance the interrelated quality of teaching and learning for effective digital transformation are shown. University communication strategy has a direct impact on the development of the University and on key indicators that are formulated in the system of assessing the activities of the University at the State and international level. Thus, on the basis of the developed communication strategy all communications activities of the universities are carried out quite successfully and communication tools and channels are adapted to the constantly transforming portrait of the university's target audiences.
New approach to visual servo control using terminal constraints

A large class of visual servo controllers relies on an a priori obtained reference image, captured at the desired position and orientation (i.e., pose) of a camera, to yield control signals to regulate the camera from its current pose to a desired pose. In many applications, accessibility and economics of the operation may prohibit acquisition of such a reference image. This paper introduces a new visual servo control paradigm that enables control of the camera in the absence of reference image using a set of terminal constraints. Specifically, the desired pose is encoded using the angle of obliquity of the optical axis with respect to the object plane and its direction of arrival at the plane. A constrained convex optimization problem is formulated over a conic section defined by the terminal constraints to yield an error system for the control problem. Subsequently, this work introduces continuous terminal sliding mode visual servo controllers to regulate the camera to the desired pose. Lyapunov-based stability analysis guarantees that the origin is a finite-time-stable equilibrium of the system. Numerical simulation results are provided to verify the performance of the proposed visual servo controller.
Object to NoSQL Database Mappers (ONDM): A systematic survey and comparison of frameworks

Context: Software applications frequently interact with database systems to persist and retrieve objects. Object mapping frameworks address (i) the bi-directional conversion of data between object and target database and (ii) provide a programmatic interface for querying and storing data. The rise of NoSQL databases poses challenges beyond object-relational mapping (ORM) frameworks to abstract from various data models and non-standardized API’s, but also take into account the different database capabilities (e.g. unsupported query operators, data ordering). Objective: A systematic survey study of existing Object-NoSQL data mapping (ONDM) frameworks. Specific focus is given to the level of abstraction of data and operations to multiple database technologies, as a means to limit vendor and technology lock-in and an enabler for multi-store and polyglot architectures. Additional attention is paid to mapping strategies that are specific to NoSQL databases (e.g. object embedding, schema flexibility). Method: A systematic search methodology identifies all relevant object mapping frameworks (in total 341 frameworks). Subsequently, a subset of ONDM frameworks is selected and systematically compared in terms of criteria of: database support, interface and query functionality, architecture and software coupling. Secondly, we provide an in-depth comparison of object-oriented mapping strategies for classes, inheritance, relationships, and attribute types to NoSQL data models. Results: ONDM frameworks are most prevalent in Java, Node.JS, Python, and overall 54 frameworks support multiple (NoSQL) databases. Interfaces are frequently standardized and commonly feature a uniform query language and even native DB query mapping. However, database portability may be hindered due to non-uniform abstractions. As for mapping strategies, current frameworks do not fully exploit NoSQL’s modeling potential, such as (i) the embedding of relationship data within referring objects’ records, (ii) mapping at the individual object-level vs. class-level, and (iii) lacking collection normalization despite being supported for associations or when using relational databases. Conclusion: The study consolidates knowledge on available ONDM frameworks, and applied object-document, object-graph, and object-column mapping patterns. The study can guide practitioners in framework selection, and pinpoints areas of future development and research in this domain, most notably towards improved support for flexible, NoSQL-aware mapping strategies.
IIoT in the Hospital Scenario: Hospital 4.0, Blockchain and Robust Data Management

The Industrial Internet of Things (IIoT) consists of the pervasive application of the IoT paradigm in conjunction with analytics and artificial intelligence (AI) in industrial scenarios. Industry 4.0 (I4.0) extends further the IIoT model with the inclusion of robotics and automation, whereas Hospital 4.0 (H4.0) is the application of the I4.0 paradigm to the healthcare sector. H4.0 relies on cyber-physical systems managing several devices and software components. and the exchange of a huge amount of sensible data that includes medical records. Medical records can be much more valuable to criminals than financial data, indeed the control of medical data allows criminals to plan and realize different frauds, that the victims may identify only too late. Furthermore, the complexity of a typical H4.0 cyber-physical system makes healthcare records particularly vulnerable. Blockchain is today an emerging technology for the management of data that may avoid or mitigate the impact of threats related to data storage and management, in general, and to the administration, in particular, of healthcare records. The blockchain technology relies on cryptography and distributed consensus to guarantee data integrity, accountability and security. The exploitation of such technology is considered in this chapter, showing the advantages when used in a H4.0 scenario.
ProjFE: Prediction of fuzzy entity and relation for knowledge graph completion

A growing number of tasks about knowledge graph completion have been studied and improved recently, but most of them use translation matrices or reflect known entity to other space, always focusing on improving the method of translating known entities and relations. Differing from current works, our paper employs a combination operator instead of the translation matrix to avoid massive calculations, and takes fuzzy membership degree into consideration in the predicting process to enhance accuracy of projection. Hence, we propose a method called ProjFE to predict the missing parts of triplets for knowledge graph completion. This model uses fuzzy combination operators to combine the fuzzy known entities and relations. Score function is employed to access to a descending order of the correct candidates after combination, where the target entity is the top one. What is more, we use sigmoid and ReLU activation functions for evaluations, which could alleviate some undesirable gradient problems in the training process. It is worth noting that our method ProjFE tends to have a relatively smaller parameter size than some existing models. Besides, our model is proved to perform better in terms of Mean Rank.