Reviewerly/exHarmony huggingface dataset:
Computing zeta functions of large polynomial systems over finite fields

We improve the algorithms of Lauder-Wan [11] and Harvey [8] to compute the zeta function of a system of m polynomial equations in n variables, over the q element finite field Fq, for large m. The dependence on m in the original algorithms was exponential in m. Our main result is a reduction of the dependence on m from exponential to polynomial. As an application, we speed up a doubly exponential algorithm from a recent software verification paper [3] (on universal equivalence of programs over finite fields) to singly exponential time. One key new ingredient is an effective, finite field version of the classical Kronecker theorem which (set-theoretically) reduces the number of defining equations for a polynomial system over Fq when q is suitably large.
Managing Contradictory Tensions in Agile Distributed Development

Increasingly, agile approaches are being followed in a distributed setup to develop software. An agile approach is characterised by the need to regularly welcome change requests and update the software artefact accordingly whereas distributed teams prefer to work towards following a plan to fulfil project objectives defined upfront. This results in contradictory tensions when agile is practised with teams operating in a globally distributed format. This chapter focuses on exploring the central conflict and discuss approaches to manage the conflicting forces in an agile distributed development setup. Furthermore, it presents an industry case study to provide more clarity on conflict management in such settings.
Visual information classification technique of Chinese tourism government portals

The development of new visualisation technology application tools and platforms is driven by the growth of visualisation technology application requirements and the continuous expansion of the Internet users. Based on the conception and development of the concept of data visualisation, the authors classify and compare the technical tools applied in several kinds of data visualisation technologies and distinguish the visual expressions into traditional visual expressions and new visual representations. Then, they perform the statistical analysis of the information types of the tourism government portals of Guangdong Province, Jiangsu Province, Zhejiang Province, Shandong Province, and Sichuan Province, which ranks on the top five of total tourism revenue in 2017. Furthermore, they analyse people's preferences for information types and provide suggestions based on people's preferences for information types and the types of information derived from travel administration websites.
Control and estimation for mobile sensor-target problems with distance-dependent noise

This paper investigates the scenario where a mobile sensor must observe (i.e., estimate the state of) another system, called the target. The estimation is affected by a distance-dependent noise, and for this reason we propose to control the sensor so that the effect of the noise is minimized. We propose a novel approach in which the controller and the observer are designed in tandem, with the common objective of obtaining a better estimation. We give sufficient design conditions for a general class of nonlinear systems satisfying a Lipschitz-like condition on the nonlinearity. A numerical example illustrates the obtained results.
Improved Algorithm for Solving Trust Region Sub-problem Based on Adams Two-Step Algorithm

In large-scale trust region subproblem solving, direct Adams two-step algorithm for large-scale trust region solution will result in huge memory requirement and long computing time. To solve this problem, an adaptive Adams two-step algorithm is proposed to solve the trust region subproblem. This method utilizes the characteristic of Adams two-step algorithm to reduce the dimension and reduces the matrix size through adaptive Adams two-step algorithm until it can be solved directly by Adams two-step algorithm. In the process of adaptive Adams two-step algorithm, the matrix is decomposed into many submatrices that are small enough, and the trust region subproblem solving is transformed into the two-step iterative computation of Adams, and the large-scale problem is transformed into a series of small-scale operations. At the same time, the concurrency can be found, and the trust region subproblem can be solved and the running speed can be improved. The experimental results show that even serial processing can greatly reduce the time and space cost of solving the large-scale trust region subproblem, and the speedup ratio of solving the trust region subproblem is close to 300 under the condition of two computers. The convergence of the whole solution process is good.
Chess 4.5’s participation in the Paul Masson Chess Classic Tournament of 1976

On July 24–25, 1976, the computer program Chess 4.5 participated in the Paul Masson Chess Classic Tournament. The program achieved an impressive result. This article describes my recollection of this event and its aftermath.
Stability Analysis of Electronic Circuit based on Complex Neural Network Theory

Referring to the transmission process of information in the nervous system of biosome and the structural features of electronic circuits, this paper proposed the complex neural network model for functional circuits and construction method. Firstly, we proposed the construction of complex neural network model based on traditional neural network, and analyzed the transport processes of electronic circuit signals, and then described the analysis of stability of constructing of transfer process of electronic circuit based on complex neural network model, and proposed that the stability was independent of the excitation (or interference) signal, which was the idea of its own inherent characteristics, and used the method of Routh-Hurwitz's criteria of coefficients of algebraic equation to determine that the stability depended on the structure and parameters of the circuit. Experimental results show that the complex neural network model has good equivalent relation with the electronic circuit, and the difficulty of the stability analysis of the electronic circuit can be greatly reduced in terms of weight transformation. At the same time, the model based on the complex neural network can provide some theoretical and experimental reference for the bionic protection research of electronic system in complex electromagnetic environment.
Archaeological Illustration and Publication

This chapter presents the practices for illustrating artifacts, plans and sections in archaeology, with emphasis on illustrations’ role in interpretation and the importance of a graphical information language for depicting archaeological results. After a brief review of equipment and supplies, it summarizes procedures and standards for illustrating lithics, pottery and small finds and introduces the topic of 3D scanning of artifacts and digital illustration. It also covers representation of uncertainty in illustrations, layout of artifact plates for publication, and the format of maps, plans, and architectural elevations. It concludes with a section on the publication process.
Algorithmic Improvisation of Music Rhythm

Music has two fundamental elements like the rhythm and melody. Rhythm is a perfect arrangement of notes that is the most inseparable component of music containing the length of note structures in a musical composition. Knowledge sharing on rhythmic patterns and their applications for creating new Music is a difficult task for learners of musicology. The memetic algorithm has been shown to be very effective in finding near-optimum solutions to the modeling of rhythmic patterns. The tournament selection strategy of evolutionary algorithms has been used to construct the fruitful rhythmic patterns for music composition. The work has been introduced to identify the parent rhythms and then create the versatile offspring rhythms using memetic algorithm. The tournament selection mechanism has been initiated for parent rhythms selection process for generating offspring rhythmic structures.
Realization of Data Exchange for Groundwater Dynamic Monitoring

Data structure of the National Groundwater Monitoring Project was introduced and a data exchange schema of dynamic monitoring of groundwater was proposed. The data exchange between the original data source and the destination data source was finished using the data synchronization algorithm and the groundwater dynamic monitoring data was obtained. The result shows that the data exchange schema has good scalability and supports for the distributed data exchange of groundwater dynamic monitoring in the future.
Top-down Feature Aggregation Block Fusion Network for Salient Object Detection

The emergence of deep neural networks and full convolutional neural networks has brought great progress to salient object detection. In this paper, we propose a new type of deep full convolutional neural network structure, named top-down feature aggregation block fusion network, which aims to fuse the rich features of feature aggregation blocks at each layer. In addition to the features of this layer, the feature aggregation blocks have other layer features, that is, each layer of feature aggregation blocks has both strong semantic information of the deep network and detailed features of the shallow network. In the top-down fusion process, the residual information of each layer can be learned like ResNet. At the same time, a non-local attention mechanism is introduced to improve the relevance of the context, and multiple auxiliary supervision connections are added to the intermediate layers, so that the network can more easily optimize and accelerate convergence. We have performed experiments on six benchmark datasets, and the results of the experiments show that our model is superior to the state-of-the-art methods both quantitatively and qualitatively.
Methodological Guidelines to Build Collaborative Serious Games Based on Intelligent Agents

A serious game can be defined as an e-learning system that aims to motivate and entertain students while achieving learning objectives related to problem solving, specific topics, or skills development, always focused on improving learning. The aim of this paper is to offer methodological guidelines to build collaborative serious games based on intelligent agents along with awareness services in order to support virtual learning processes. This methodological guide offers a breakdown of the main activities needed to build this kind of systems based on three components: the knowledge representation of the domain to which the collaborative serious game is oriented, the development of the MAS, and finally the description and integration of awareness services. The preliminary validation of methodological guidelines through a prototype development shows that the integration of these kinds of technologies fosters the knowledge acquisition in a playful way by the students while learning in a collaborative way.
Development of Professional Competences of Preschool Student Teachers: Methodology Competences

The paper deals with the issue of the development and improvement of professional competencies of future preschool teachers with special emphasis on methodical competencies.The aim of this research was to determine how students in the final year of vocational studies evaluated and which level of importance they attached to the competencies considered necessary in pedagogical activities in order to perform the work of a preschool teacher.The research issue is reflected in the question: Are students of the final year of vocational studies empowered with important professional competencies?The research tasks were carried out by analysing the assessment of competencies that the respondents provided using a survey and scaling techniques, as well as instruments such as a questionnaire and a scale of attitudes.The research sample included 73 respondents, students in the third year of undergraduate studies.Students' assessments point to the importance of developing professional competencies through the implementation of professional practice.The current competence of students is the most important within specific competencies, knowledge, skills and abilities -visual, musical, speech, and drama.Based on the obtained results, in the opinion of students it is essential to focus on: greater representation of professional practice during the course of studies, relation of methods in professional practice with work in other course subjects, professionalism of mentors -preschool teachers and supervising professors, and the pedagogical climate in which activities are carried out.The obtained results show the importance of students' assessments and opinions on the role of professional practice in improving competencies directed at their quality training for the future vocation of competent preschool teachers.
Moiré Pattern Removal with a Generative Adversarial Network

Moiré patterns can be seen in camera-captured digital screen photos due to the interference between the pixel grids of the camera sensor and the pixel grids of the digital screen. It severely degrades the quality of the photos. With the rapid development of personal devices, people are using digital camera to take photos more and more often. Among them, it's very common to see camera-captured screen photos, so the work of Moiré pattern removal is very meaningful for improving user experience. In this paper, we introduce a novel method of Moiré pattern removal based on the Generative Adversarial Network (GAN). To train our model, we built a dataset of paired Ground and Moiré images, which has 16,500 images totally. Experiments showed that, given Moiré images as the input, the trained generator of our GAN nets can produce Moiré-free images of high quality.
Enabling Digital Twin through Blockchain: A Strategic Perspective

Purpose: This chapter examines how blockchain can help abolish the current data fragmentation within the Internet of things (IoT) ecosystem to truly leverage the potential of digital twins. It is projected that soon by the turn of this decade the enormous number of interconnected devices across the globe shall support millions of “digital twins” with important data streams. A digital twin is the digital replica of a physical object (processes, assets, services or products). Digital twins prove to be beneficial as they allow testing or development in a safe and cost-effective manner. For example, it is better to test and crash a simulated engine than a real one. Methodology: The study was undertaken in terms of case studies and literature review on IoT. Digital twins have aided the manufacturing industry by providing data management, data tracking and other benefits. In future digital twins shall constitute a fundamental pillar of the intelligent IoT ecosystem as well as Industry 4.0. However, the problem is that a digital twin is not a cheap digital copy. It is a complex data object that tracks a physical entity’s current configuration, state, location, operational characteristics and other attributes. Thus this twin is actually the foundation for training, monitoring, maintenance, troubleshooting, optimization and other tasks vital to keep the physical entity in a healthy working condition. Findings: After detailed study, the findings suggest that it involves a lot of data whose movement consumes power and demands security. Effective data collection, storage, organization and transfer can be quite costly if not done properly. To overcome this grave issue of effectively handling data in the IoT ecosystem, blockchains have emerged as a key enabler for this networked digital twin environment. Blockchain is the process that transfers data as connected blocks through cryptology. Hence, each block is encrypted and attached to the block before and after it. Thus to hack a block one shall somehow have to change all the blocks as none of the blocks can be altered without appropriate authorization. Thus the blockchain technology optimizes storage and eliminates human error in a decentralized manner. Further through blockchains digital twins can be realized in a cheap manner for almost every industry. Blockchains have even extended the scope of digital twins from physical assets to the human healthcare industry. Originality: This chapter aims to give the reader an insight into how the blockchains are helping realize cheap, optimized and secure twins to almost every industrial and human entity. It also highlights the current successful applications of blockchain-enabled digital twins while highlighting the potential research initiatives and challenges in the field.
Blockchain for Smart Cities: Applications for IoT and Video Surveillance Systems

The recent revolution of the Internet of Things introduces new engaging operating scenarios. The IoT paradigm enables the intertwined use of physical and software components through the interconnection of devices that exchange data with each other without direct human interaction in several fields, especially in industrial and home environments. In the framework of the Italian-Vietnamese cooperation on the topics of smart cities and Blockchain, we present two applications of the blockchain technology, which can be applied, respectively in indoor, for monitoring and controlling smart homes, and in outdoor, for visual monitoring through video surveillance systems. In both cases, we propose decentralised architectures that aim at solving common IoT problems and vulnerabilities, with a specific focus on privacy issues. The introduced flexibility, extensibility and security in indoor IoT environments and outdoor video surveillance systems permit to foresee smarter cities that assist citizens with innovative services both in indoor and outdoor environments. We propose a reinterpretation of the role of the IoT gateway, making it blockchain-aware and handling IoT transactions without a central trusted authority. As for video surveillance, we focus on two different directions. First, we provide the capability to analyse multiple video-flows from different cameras deployed around the city by heterogeneous owners, guaranteeing the integrity of the timestamps and camera settings. Second, the system ensures that the positions of cameras, their orientation and their mechanical parameters are not physically manipulated; otherwise, the platform generates a warning. We guarantee citizens’ privacy monitoring and tracking camera settings and storing video flows. The absence of digital and physical manipulations guarantees citizens against possible violations of their privacy. To face these risks, we introduce a blockchain-based video surveillance system that jointly provides validation and immutability to camera settings and surveillance videos, making them readily available to authorised users in case of events and paving the way to new distributed city-wide monitoring systems.
3D Reconstruction Based on Cyclic Multi-View Stereo Network

Currently, the multi-view Stereo algorithm performs well in the neighborhood reconstruction, but the MVSNet method still has a huge memory consumption problem in the reconstruction of large scenes. This paper presents a recursive neural network based on GRU (gated recurrent unit) to reduce memory consumption. Firstly, the feature map of the reference image and the neighborhood image is extracted, and then the feature map of the neighborhood image is mapped to the cone of the reference image through the homography transformation. Then we use GRU to regularize the feature graphs at different depths, and finally filter and refine the depth features to further improve the quality of 3d point clouds. Compared with Convolutional Neural Networks, the Convolutional GRU regularization greatly reduces the memory consumption and improves the scalability. Experimental results show that the proposed method can reduce memory consumption and achieve high quality modeling under high resolution scenarios.
Measurement-Device-Independent QKD Based on Orbital Angular Momentum with Dual Detectors

Orbital angular momentum (OAM) has the characteristics of high dimension and rotational symmetry. Quantum key distribution (QKD) schemes using OAM can carry more quantum information, improve the transmission capacity of information and reduce the errors caused by reference frame calibration errors. In this paper, we propose a measurement-device-independent QKD scheme based on OAM with dual detectors. On the basis of the measurement-device-independent QKD protocol, we use OAMs to encode the key information and then send them to a third-party measurement device. In the process of measurement, we adopt the dual-detector theory to improve key generation rate and transmission distance. Simulation analyses show that the secure transmission distance of our scheme can reach 140 km, which is about 30km more than the scheme using single detector.
On Achieving Reliable and Efficient Precondition Execution Enforcement in Internet-of-Things

In IoT it is common that before a command can execute on a smart object, certain preconditions (on possibly other objects) should be met first to ensure safety or efficiency. Existing work has realized automatic precondition execution: when a user issues a command, her device automatically finds out all the precondition commands, and executes them in the correct order. However, security issues have not been considered: it assumes that a user device honestly follows the order it has been told to send commands to objects, and objects trust users thus do not check whether the preconditions are indeed met. In this paper we propose two strategies to enforce precondition execution order: 1) Snowball relying on signed declarations from precondition objects; 2) Onion using disposable access tokens encrypted by a trustworthy server. Our extensive analysis and experiments on a 20-node testbed show that both strategies are secure and reliable. Snowball has higher availability while Onion is more efficient and responsive: Onion uses 1.6/2.1 s to access 20 one-hop/multi-hop objects, 62%/54% of Snowball's time.
Application of Data Mining Algorithms in Determination of Voting Tendencies in Turkey

Political elections are influenced by a number of factors such as political tendencies, voters' perceptions, and preferences. The results of a political election could also be based on specific attributes of candidates: age, gender, occupancy, education, etc. Although it is very difficult to understand all the factors which could have influenced the outcome of the election, many of the attributes mentioned above could be included in a data set, and by using current data mining techniques, undiscovered patterns can be revealed. Despite unpredictability of human behaviors and/or choices involved, data mining techniques still could help in predicting the election outcomes. In this study, the results of the survey prepared by KONDA Research and Consultancy Company before 2011 elections in Turkey were used as raw data. This study may help in understanding how data mining methods and techniques could be used in political sciences research. The study may also reveal whether voting tendencies in elections could be a factor for the outcome of the election.
Game Theoretic Potential Field for Autonomous Water Surface Vehicle Navigation Using Weather Forecasts

Imperfect weather forecasts complicate robot planning. A conservative motion planning algorithm is developed to address uncertainty in autonomous boat missions. Dynamic Programming generates an optimal action for every location and game theory strategically handles uncertainty. Experimental results using Massachusetts Bay forecasts show the robot is able minimize the cost of worst-case weather.