Reviewerly/exHarmony huggingface dataset:
Design of Urban Landscape Visual Simulation System based on Three-dimensional Simulation Technology

Aiming at the problems of inaccurate visual modelling of urban landscape and high cost of the system, a visual simulation system of urban landscape based on three-dimensional simulation technology is proposed and designed. For the hardware part, the overall framework of the system is designed, and the static landscape modelling and dynamic landscape driving are analysed, respectively. For the software part, the development process of the visual simulation system for urban landscape is described, the urban landscape plan is acquired, and the urban landscape model library is constructed. The experimental results show that the accuracy of the visual modelling system for urban landscape designed by the proposed method is over 80%, and the accuracy is good. The highest cost coefficient used in the visual simulation system for urban landscape is 0.7, the cost is low, and the users are satisfied with the system.
WFRFT Secure Communication Method Based on Chaotic Parameter Pool

In this study, to solve the hidden limitation of conventional weighted fractional Fourier transform (WFRFT), a random modulation order parameter pool is established by applying chaos technology. Further, a WFRFT secure communication method based on the chaotic parameter pool (CPP) is proposed. Based on the effective characteristics of tent mapping and the sequence output range, the parameter pool constructor is established by parameter transformation. Furthermore, for each parameter selection period, the information can be processed by WFRFT using different modulation orders. The modeling and simulation demonstrate that this method can significantly increase the bit error rate and processing time of unauthorized receivers. This indicates that it can greatly increase the scanning difficulty of unauthorized users and improve the concealment and security of the original information transmission.
Sensor Training Data Reduction for Autonomous Vehicles

Ensuring safety and reliability of autonomous vehicles requires good learning models which, in turn, require a large amount of real-world training data. Data produced by in-vehicle sensors (e.g., cameras, LIDARs, IMUs, etc.) can be used for training; however, both local storage and transmission of this sensor data to the cloud for subsequent use in training can be prohibitively expensive due to the staggering volume of data produced by these sensors, especially the cameras. In this paper, we perform the first exploration of techniques for reducing video frames in a way that the quality of training for autonomous vehicles is minimally affected. We particularly focus on utility aware data reduction schemes where the potential contribution of a video frame to enhancing the quality of learning (or utility) is explicitly considered during data reduction. Since actual utility of a video frame cannot be computed online, we use surrogate utility metrics to decide what video frames to keep for training and which ones to discard. Our results show that utility-aware data reduction schemes can reduce the amount of camera data required for training by as much as $16\times$ compared to random sampling for the same quality of learning (in terms of IoU).
DCS: A Policy Framework for the Detection of Correlated Data Streams

There is an increasing demand for real-time analysis of large volumes of data streams that are produced at high velocity. The most recent data needs to be processed within a specified delay target in order for the analysis to lead to actionable result. To this end, in this paper, we present an effective solution for detecting the correlation of such data streams within a micro-batch of a fixed time interval. Our solution, coined DCS, for Detection of Correlated Data Streams, combines (1) incremental sliding-window computation of aggregates, to avoid unnecessary re-computations, (2) intelligent scheduling of computation steps and operations, driven by a utility function within a micro-batch, and (3) an exploration policy that tunes the utility function. Specifically, we propose nine policies that explore correlated pairs of live data streams across consecutive micro-batches. Our experimental evaluation on a real world dataset shows that some policies are more suitable to identifying high numbers of correlated pairs of live data streams, already known from previous micro-batches, while others are more suitable to identifying previously unseen pairs of live data streams across consecutive micro-batches.
Foreign language interfaces by code migration

A foreign function interface (FFI) is a classical abstraction used for interfacing a programming language with another foreign language to reuse its libraries. This interface is important for a new (or non prevailing) language because it lacks libraries and thus needs to borrow libraries written in a foreign language when the programmer develops a practical application in that new language. However, a modern library often exploits unique language mechanisms of the implementation language. This makes the use of the library difficult through a simple function call from that new language. This paper presents our approach to this problem. We use an embedded domain specific language (DSL), which is designed to resemble the foreign language, and migrate the DSL code to access to the library written in the foreign language. This paper also presents our framework Yadriggy for developing the DSL from Ruby to a foreign language environment. The framework supports DSL-specific syntax checking for the migrated DSL code.
Leveraging writing systems changes for deep learning based Chinese affective analysis

Abstract Affective analysis of social media text is in great demand. Online text written in Chinese communities often contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and minor text using Latin letters, an alphabet-based writing system. This phenomenon is referred to as writing systems changes (WSCs). Past studies have shown that WSCs often reflect unfiltered immediate affections. However, the use of WSCs poses more challenges in Natural Language Processing tasks because WSCs can break the syntax of the major text. In this work, we present our work to use WSCs as an effective feature in a hybrid deep learning model with attention network. The WSCs scripts are first identified by their encoding range. Then, the document representation of the text is learned through a Long Short-Term Memory model and the minor text is learned by a separate Convolution Neural Network model. To further highlight the WSCs components, an attention mechanism is adopted to re-weight the feature vector before the classification layer. Experiments show that the proposed hybrid deep learning method which better incorporates WSCs features can further improve performance compared to the state-of-the-art classification models. The experimental result indicates that WSCs can serve as effective information in affective analysis of the social media text.
Managing Uncertainty in Ontology Mapping in E-Learning Context

Many mapping methods have been proposed in recent years, but every of them works well only for specific type of ontologies or in well-defined context. A grand number of mapping methods propose mappings, leading to inconsistency of resulting ontological network, and in such a way make it unusable in many reasoning-based applications. And most of automatically proposed mappings are uncertain in some sense. In this paper we make a short analysis of the approaches for modelling ontology mapping uncertainty and the ways for detecting logical errors, resulting from incorrect mappings. We discuss some important mapping, mapping evaluation and refinement strategies, well working in e-learning domain.
Generating incremental type services

In this vision paper, we propose a method for generating fully functional incremental type services from declarations of type rules. Our general strategy is to translate type rules into Datalog, for which efficient incremental solvers are already available. However, many aspects of type rules don't naturally translate to Datalog and need non-trivial translation. We demonstrate that such translation may be feasible by outlining the translation rules needed for a language with typing contexts (name binding) and bidirectional type rules (local type inference). We envision that even rich type systems of DSLs can be incrementalized by translation to Datalog in the future.
Dynamic Service Selection based on User Feedback in the IoT Environment

With the expansion of IoT technologies, the number of devices deployed around the world as well as the number of services is increasing rapidly. Service selection is a process to choose services that meet the functional and non-functional properties for each user. Some of the features of the IoT environment make this process more complicated. For example, user's mobility constantly changes the connected service collections. Because the dynamic nature of the environment leads to changes in the QoS (quality of service), judging a service that satisfies the needs of users in the future is difficult based on previous QoS static values. Moreover, an IoT service relies on its device, which is affected by CPU processing power and battery capacity. Hence, long-term and stable service cannot be provided to users. In order to solve the above problems, we need a service selection method that can consider the quality of the service and reflect the service context in real time. In this paper, we propose a novel IoT service selection approach by using the linear reward inaction (LRI) algorithm to learn all subjective assessments from the latest user feedback and objective assessments of service. In addition, to reduce the effect of unreasonable feedback from users in different contexts, the similarity between users is considered in the LRI algorithm.
An Information Integration System to Continuing of Care Case study Nongsung Hospital, Mukdahan THAILAND

This research study presents information on the integration system from several data sources of health units in Thailand in order to support the continuity of patient care using the web services and metadata concepts. The purpose of this system is to provide information to support doctors and nurses in evaluating the long-term healthcare plans for patients by using patient records from other health promotion hospitals. However, there are many different health information systems (HIS) presently being used in Thailand, and each system may have a separate structure. This has led to difficulty in integrating and exchanging information between them. To solve this problem, we proposed architecture to integrate information by using metadata and web services. We implemented the proposed architecture for the continuity of patient care in the Nongsung District Hospital along with three district health promotion hospitals in the Nongsung District, which had been using different HIS systems. The accuracy of the system and users' degree of satisfaction were evaluated. The results showed that the system could be correctly integrated and the assessments for satisfaction from the users had been very good.
Graph cutting and its application to biological data

Abstract In this paper we introduce the concept of segmentation based on mathematical approach using graph theory algorithms using the family of augmenting paths algorithms. We present a new program, an implementation, algorithms and obtained results devoted to segmentation of biomedical data. We implement our program for handling with segmentation, counting a measure of the existence of the minerals in the biomedical data. As a consequence we prove the existence of minerals in the data obtained from the brain of rabbits.
Intelligent Classification and Recognition of Acoustic Targets Based on Semi-tensor Product Deep Neural Network

Traditional acoustic target recognition is mainly based on artificial feature construction. In many cases, there are difficulties in feature construction and low recognition rate. Referring to computer vision technology, this paper proposes a method of acoustic target classification based on semi-tensor product deep neural network. First, the acoustic signal is transformed into Lofargram. Then a semi-tensor product deep neural network model (SPNN) is established. After that the parameters of the SPNN are determined by actual data. Finally, the classification and recognition of sound source targets are realized. Moreover, the recognition accuracy is much higher than that of traditional manual feature extraction and classification by support vector machine (SVM). The recognition rate of underwater target is higher than that of convolution neural network (CNN). The accuracy of air sonar target and CNN is similar, but the training speed of network is much faster.
New Methods of Training Two-Layer Sigmoidal Neural Networks with Regularization

We propose an algorithm for training two-layer sigmoidal artificial neural networks (ANN) in the presence of significant interference and low-informative variables. To obtain an efficient initial ANN parameters approximation, the algorithm applies a uniform distribution of the neuron work areas in the data domain, followed by training of fixed neurons. The proposed algorithm for ANN learning in combination with non-smooth regularization allows us to obtain efficient ANN models for classification problems.
Developing a monadic type checker for an object-oriented language: an experience report

Functional programming languages are well-suited for developing compilers, and compilers for functional languages are often themselves written in a functional language. Functional abstractions, such as monads, allow abstracting away some of the repetitive structure of a compiler, removing boilerplate code and making extensions simpler. Even so, functional languages are rarely used to implement compilers for languages of other paradigms.
Establishing a refinement relation between binaries and abstract code

This paper presents a method for establishing a refinement relation between a binary and a high-level abstract model. The abstract model is based on standard notions of control flow, such as if-then-else statements, while loops and variable scoping. Moreover, it contains high-level data structures such as lists and records. This makes the abstract model amenable for off-the-shelf verification techniques such as model checking or interactive theorem proving. The refinement relation translates, e.g., sets of memory locations to high-level datatypes, or pointer arithmetic to standard HOL functions such as list operations or record accessors. We show applicability of our approach by verifying functions from a binary containing the Network Security Services framework from Mozilla Firefox, running on the x86-64 architecture. Our methodology is interactive. We show that we are able to verify approximately 1000 lines of x86-64 machine code (corresponding to about 400 lines of source code) in one person month.
An Efficient Strategy for Face Clustering use in Video Surveillance System

The importance of knowledge discovery from data has been increased dramatically with the increase of data over the past few years. A small video file contains more information compared to text documents and other media files such as audio, images. For this reason, extracting useful information from video i.e. automated video surveillance system has become a hot research issue. The presence of human in different frame of a video is a common scenario. In the security based application, identification of the human from the videos is an important issue. Face pattern is the most widely used parameter to recognize a person. A system with the ability of gathering the information about the presence of the same person in different frame of a video is highly demanding. In this study we have proposed a clustering algorithm which is variation of Hierarchical Agglomerative Clustering algorithm that is able to cluster face image of human. It tries to cluster based on both similarities and dissimilarities. Our proposed algorithm performs better compared to traditional Hierarchical Agglomerative Clustering algorithm in terms of accuracy and time complexity.
Quantum Computer Search Algorithms: Can We Outperform the Classical Search Algorithms?

Quantum Computers are not limited to just two states. Qubits, the basic unit of quantum computing have the power to exist in more than one state at a time. While the classical computers only perform operations by manipulation of classical bits having two values 0 and 1, quantum bits can represent data in multiple states. This property of inheriting multiple states at a time is called superposition which gives quantum computers tremendous power over classical computers. With this power, the algorithms designed on quantum computers to solve search queries can yield result significantly faster than the classical algorithms. There are four types of problems that exist: Polynomial (P), Non-Deterministic Polynomial (NP), Non-Deterministic Polynomial Complete (NP-complete) and Non-Deterministic Polynomial hard (NP-hard). P problems can be solved in the polynomial amount of time like searching a database for an item. However, when the size of the search space grows, it becomes difficult to compute solutions even for P problems. Quantum algorithms like Grover's algorithm has reduced the time complexity of some of the classical algorithm problems from N to \(\sqrt{N}\). Variants of Grover's algorithm like Quantum Partial Search propose changes that yield not exact but closer results in time even lesser than Grover's algorithm. NP problems are the problems whose solution if known can be verified in polynomial amount time. Factorization of prime numbers which is considered to be an NP problem took an exponential amount of time when solved using the classical computer while the Shor's quantum computing algorithm computes it in polynomial time. Factorization is also a class of bounded-error quantum polynomial time (BQP) problems which are decision problems solved by quantum computers in polynomial time. There are problems to which if a solution is found can solve every problem Of NP class, these are NP-complete problems. The power of Qubits could be exploited in the future to come up with solutions for NP-complete problems in the future.
PRINCIPLES AND TECHNOLOGIES OF IMPROVING THE EDUCATIONAL AUTONOMY OF STUDENTS IN THE CONDITIONS OF TWO-LEVEL EDUCATION

Purpose of the study: The urgency of the problem under study is due to the need to ensure the substantial unity of the two stages of vocational education: undergraduate and graduate. In connection with the increase in the proportion and importance of independent learning activities of students, the problem of self-regulation and self-control of students in the organization of this process and the provision of conditions for the formation of educational autonomy of bachelors and undergraduates from the side of university teachers becomes more acute. The aim of the article is to determine the principles and effective technologies of organizing activities for the formation of the educational autonomy of students in a two-level education.&#x0D; Methodology: The leading principles of the study were the principles of human-like education and student-centered education.&#x0D; Results: The authors formulated the principles, presented copyright technology to ensure the educational autonomy of students. The results of the use of the technological complex in the framework of the educational activities of physical culture students are indicated. The created scientific and methodological complex allowed to improve the level of self-organization and self-government of students in educational activities.&#x0D; Applications of this study: Materials may be applied to solve a wide range of vocational education tasks. The results of the study may be applied by teachers of higher education in order to develop the professional creativity of students and the formation of their professional identity.&#x0D; Novelty/Originality of this study: The problem of the formation of the educational autonomy of students is exacerbated in the conditions of modern higher education due to the increasing proportion of independent work of students and the possibilities of an open educational environment. The originality of the research lies in the development of technology for the use of anthropic educational technologies, focused more on students' activities, rather than regulating the activities of university teachers.
An Analysis of Live VM Migration Based on OpenStack Cloud: A Survey

Generally, cloud computing offers various services to users such as storage, software services, network, pay-per-use services and memory by means of virtualizations provided through the internet. Thus it provides a meaningful computing environment; however, there is an adequate pursuit of services. The significant inconveniences are the best approach to manage the assets in distributed computing when heaps of the virtual machines have been ascended. The live relocation (migration) of these VMs is acquainted with acquiring different advantages which primarily incorporate high accessibility, equipment maintenance, adaptation to internal failure and the remaining task at hand adjusting. This paper solidifies different effective techniques of live VM movement (migration) along with their positive points and negative points.
Fuzzy Cognitive Network by Adaptive Fuzzy PID Controller and Hybrid Optimization Algorithm

Objectives: To improve the power produced by a photovoltaic system under varying climatic circumstances and thus improving the convergence speed.Methods: To improve power in this work a Fuzzy PID regulator is implemented which is tuned by hybrid Artificial Neural Network -Particle Swarm Optimization -Simulated Annealing (ANN-PSO-SA) and FCN (Fuzzy Cognitive Network) optimization algorithm.Additionally a DC/DC help converter is employed to regulate the yield intensity of the photovoltaic system.Findings: The proposed technique works on maximum power point and improves the performances of solar energy conversion capability and maintains system stability in case of quickly unstable atmospheric rules.To the best of knowledge no PID controller or regulator has implemented this hybrid optimization algorithm along with fuzzy concepts and works with differing climatic conditions.This method achieves the advantages of the fuzzy techniques along with optimization techniques.Along with achieving maximum power the proposed controller achieves constant voltage control.The DC-DC boost converter makes use of the output of PV panel and is responsible for regulating the output power.The FCN utilized is responsible for maintaining an equilibrium condition at varying climatic conditions.Improvements: The tuned controller is compared to the conventional MPPT algorithms with specification rise time, overshoot and delay time.This is demonstrated in the comparison results shown in the results section.
A Dynamic Hybrid Logic for Followership

This paper formalises the followership in networks that agents following or unfollowing each other dynamically. The semantics is based on the basic hybrid logic and we extend the logic with a propositional action modality $$[{a}\uparrow {\theta }]$$ for the changes of followership. The main contribution of this paper is the completeness result. Moreover, we have proved that all pure axiomatic extensions have completeness and discussed some possible future works and extensions, in particular, some features of the extended action modality $$[{a}\uparrow {\varphi }]$$ , like repetition regrets are discussed.