Reviewerly/exHarmony huggingface dataset:
SeCloudDB

With the advent of cloud computing, the interest of moving the databases from local to remote cloud servers have been increasing. Despite this popularity, data security becomes a major challenge in the way of adopting these Cloud services. Moreover, the heterogeneity and variety of the existing NoSQL DBs that are different from relational database represent one of the main obstacles. Consequently, it prevents designing one solution that can fit different data models. In this paper, we focus on the security of the cloud database as a service (DBaaS) environments. Due to the lack of a unified solution to enhance the security of both SQL and heterogeneous NoSQL cloud databases, we propose a framework for secure cloud Databases, SeCloudDB. We provide a standardized API to support encrypting the data before storing them in the cloud databases. Further-more, our framework is equipped with tools that support generating the encryption keys, storing the metadata and securely control the access policies.
A Chaotic Adaptive Particle Swarm Optimization for Robot Path Planning

In this paper, a chaotic adaptive particle swarm optimization (CAPSO) algorithm is proposed for global path planning of mobile robots under static environment. The fitness function of CAPSO synthesizes two evaluation functions which consider path length and obstacle risk degree separately. Then the beetle antennae search algorithm (BAS) is introduced to modify the particle position updating equation to strengthen the global search ability of the algorithm. The trigonometric function is adopted for the adaptive adjustment of the control parameters for CAPSO to achieve the best cooperation in each stage of the algorithm operation and improves the local search ability of the algorithm. The chaotic map is also used to replace the random parameters of the basic particle swarm optimization. This procedure can improve the diversity of particle swarm and maintain the original random characteristics. By CAPSO the global search ability and search speed are improved and optimal robot path planning under static environment is realized. The simulation results verify the effectiveness of the proposed algorithm.
Collaborative Interaction in Large Explorative Environments

Building collaborative VR applications for exploring and interacting with large or abstract spaces presents several problems. Given a large space and a potentially large number of possible interactions, it is expected that users will need a tool selection menu that will be easily accessible at any point in the environment. Given the collaborative nature, users will also want to be able to maintain awareness of each other within the environment and communicate about what they are seeing or doing. We present a demo that shows solutions to these problems developed in the context of a collaborative geological dataset viewer.
Internal Threat Prediction Algorithm Based on VASG Model

Network attacks have evolved from traditional external attacks to internal attacks. Internal threats have been considered as a very serious security problem. In view of the high hiding and difficult management of internal threats, the internal threat model of BIAG Bayesian network is first proposed in this paper to analyze internal threads. The model takes the attack behavior as the research object, uses the <;behavior> resource binary group to analyze the internal of the model, and to further optimize BIAG model by means of the relationship between resources and behaviors, behaviors and sources in quantitative analysis model. Then the internal threat calculation model of VASG based on Bayesian inference is proposed. And the MC-Alg algorithm is used to realize the transformation between models. Finally, the internal threat is predicted and analyzed by LW-Alg algorithm.
Intelligent contract design based on block chain in logistics management

Abstract Although the logistics management has been improving, the information management is still stagnant. The renewal mechanism of logistics information is the basic requirement of logistics system, but it is more important to solve the trust problem of logistics information. It is good for improving the logistics management to design a verifiable information management mechanism which can help relevant participants establish trust relationships. This paper analyzes the development status of block chain technology, then a decentralized and self-verifiable system management program model is designed. Finally, the application is implemented based on the intelligent contract design of Ethereum block chain.
Digital Expression and Representation of Rhythm

Music provides a means to explore time by sequencing musical events in a seemingly endless and expressive way. This potential often far exceeds the ability of digital systems to enable composers and performers to explore musical time, perhaps due to the influence of Western music on implementation or maybe due to the challenges involved in the notation of music itself. In this paper we look at ways to explore time within a musical context, looking to create tangible examples and methods for exploring complex rhythmic relationships using digital systems. We draw on the approach for describing sequences in terms of cycles, inspired by the live coding language Tidal Cycles.
Detection of Changes in Binary Sequences

In applications, such as biologicalDarkhovsky, Boris ones, the segmentation of very long binaryPiryatinska, Alexandra sequences is necessary. For example, such problems arise in DNA analysis. Some properties of a DNA sequence can be coded as a binary sequence and it should be separated into the homogeneous increments. In this paper, we propose a new approach for the segmentation of long binary sequences. Our approach is based on a transformation of an initial sequence into a sequence of real numbers. We will call such sequence a diagnostic sequence. After that, in the case of sequences generated by the stochastic mechanisms, we propose to apply the nonparametric change-point detection algorithm of Brodsky-Darkhovsky to the diagnostic sequence. If we don’t know the type of generating mechanism of the sequence, we propose to utilize our theory of $$\varepsilon $$ -complexity to create new diagnostic sequences of $$\varepsilon $$ -complexity coefficients. Subsequently, the change-point detection algorithm of Brodsky-Darkhovsky is applied to these diagnostic sequences. We verify the performance of the proposed methods on simulations.
Deep Visual Detection-Tracker for Aerial Target

Visual tracking of aerial target in complex battle environments is a challenging task. To meet this challenge, we develop a deep visual detection-tracker, which incorporates a deep learning detector, a correlator and a Kalman filter. The powerful capability of existing visual tracking algorithm based on deep learning attributes to a robust discriminative appearance model, which is established by integrating hierarchical convolutional features from a deep convolutional network. For our task a series of measures are applied to insure the tracking performance. First, the performance of the detector, in terms of both accuracy and speed, is improved by using techniques of local labeling for training dataset, adaptive threshold for detection scores and quantity compression of region proposals. Second, to eliminate the distracters, a brand-new correlator of target attributes and pooling features is designed for calculating matching degrees of the target and candidates. Besides, a Kalman filter is used to produce a reasonable prediction when the detector fails to detect the target. To demonstrate our deep tracker, a customized videos /images dataset of aerial target is built for training and test. Experiment results show that the developed deep visual detection-tracker is more robust and computationally more efficient than existing tracking algorithms based on deep learning.
HeatMat

Recently, adopting a hands-on approach to conventional 3D fabrication has been attracting attention due to its advantages in design activity. In this context, we aim to support hands-on design activity in digital fabrication by designing internal structures for alleviating issues of external heating for shape deformation. As a first step, we simulate four simple structures with Computational Fluid Dynamic (CFD) simulation to investigate effective structural parameters such as cavity's ratio, its geometry, exposure to the heat source for influencing thermal properties, and deformation in a malleable state. Through the pilot experiment, we figured out that the simulation results of the basic structures are valid, the structure is stable in a malleable state, and the parameters are effective. In the future, we will design functional structures based on the explored parameters and embed them on various topologies.
Robust fast nonsingular terminal sliding mode control strategy for electronic throttle based on extreme learning machine

This paper proposes an extreme-learning-machine-based robust fast nonsingular terminal sliding mode control (FNTSMC) strategy for an electronic throttle (ET) system. Distinguished from the conventional implementations of sliding mode control (SMC), the prior knowledge of disturbance bound is not required but estimated by the novel neural networks titled as extreme learning machine (ELM) which features in the fast learning rate and excellent generalization. The unique of the proposed control strategy lies on that both the sliding variable and system state enjoy a finite-time convergence without the information of predetermined bound of system nonlinearities and disturbances. The comparative simulations are conducted to verify the effectiveness and robustness of the proposed control strategy.
Investing in Prevention or Paying for Recovery - Attitudes to Cyber Risk

Broadly speaking an individual can invest time and effort to avoid becoming victim to a cyber attack and/or they can invest resource in recovering from any attack. We introduce a new game called the prevention and recovery game to study this trade-off. We report results from the experimental lab that allow us to categorize different approaches to risk taking. We show that many individuals appear relatively risk loving in that they invest in recovery rather than prevention. We find little difference in behavior between a gain and loss framing.
Charlier and Meixner Moments and Their Application for Texture and Image De-noising Problems

Moment-based features have been employed for solving various problems in pattern recognition and image processing. In this paper, we choose Charlier and Meixner moments separately, to solve texture segmentation, classification, and de-noising of images corrupted with Gaussian noise. In order to solve the texture classification problem, we propose to use lower-order Charlier and Meixner moments of 12 Haralick’s texture features and a two-class support vector machine. But, for texture segmentation problem, we use these moment-based texture energy features suggested for geometric moments by Mihran Tuceryan. Further, these energy features are used in K-means, Fuzzy K-means, and Kohonen’s neural network for solving texture segmentation problem, whereas for image de-noising problem, we use non-local mean (NLM) filter with Charlier and Meixner moments-based similarity values instead of pixel-based similarity values used in NLM for filter weights calculation. The simulation results show that the proposed applications worked well for two-class texture segmentation, classification of texture images and de-noising of images corrupted with a small amount of noise.
Attainments of the Bayesian information bounds

Necessary and sufficient conditions for the attainments of the Borovkov–Sakhanenko and the van Trees information bounds are given when the underlying distribution is an exponential family and the conjugate prior, and the estimand is a function of the parameter. Moreover, the attainments under the Jeffreys prior are also considered. Furthermore, some examples are given.
Clustering Penilaian Kinerja Dosen Menggunakan Algoritma K-Means (Studi Kasus: Universitas Dehasen Bengkulu)

This research was conducted to classify lecturer's performance by utilizing data mining technique. This study also aims to provide ease of information and evaluation to the lecturers and decision makers in making decisions. The application of the method used in this research is the K-Means Clustering algorithm. Several stages performed in analyzing and classifying lecturer's performance begins with the determination of the centroid value of the center at random. The K-Means algorithm process ends if there is no change of centroid value between one iteration with another iteration. The test is done by using RapidMiner Studio 7.5 application with 60 lecturer's data as input. The results of the test can be seen that the group of excellent lecturer performance amounted to 12 members with a total value of the highest 48,550 centroid, good lecturer group performance amounted to 29 members with a total value of 40,340 centroid, lecturer performance good enough group amounted to 10 members with total centroid value 37,963, poor lecturer performance amounted to 9 members with the lowest centroid value 37,033. Key w ords: Data Mining, Clustering, K-Means, Performance Lecturer
Small Objects Detection with Multi-layer Laser Radar Based on Projection Dimensionality Reduction

Small objects on the road is extremely dangerous obstacles especially when driving at high speed. Detection of such small obstacles is crucial to the safety of autonomous car user. Therefore, a novel of small objects detection algorithm by using multi-layer laser radar is proposed in this paper. Firstly, the road edge point-clouds was extracted from the numerous raw point-clouds based on Hough Transform, and the drivable area and non-drivable area was separated by filtering the point-clouds outside the road edge. Secondly, projection-dimensionality reduction and Hough Transform was applied to recognize and filter the road point-clouds and the remaining point-clouds can be considered as the objects and some interference points. Then, the noise point-clouds was filtered by the outliers filtering algorithm. Finally, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm was used to cluster the small objects. The proposed algorithm was tested on the platform of driverless formula one car and the experimental results show that the algorithm is efficient and robustness.
Trust Decision Model and Trust Evaluation Model for Quality Web Service Identification in Web Service Lifecycle Using QSW Data Analysis

In secure web application development, the role of web services will not continue if it is not trustworthy. Retaining customers with applications is one of the major challenges if the services are not reliable and trustworthy. This article proposes a trust evaluation and decision model where the authors have defined indirect attribute, trust, calculated based on available direct attributes in quality web service (QWS) data sets. After getting training of such evaluation and decision strategies, developers and customers, both will use the knowledge and improve the QoS. This research provides web-based learning about web service quality which will be utilized for prediction, recommendation and the selection of trusted web services in the pool of web services available globally. In this research, the authors include designs to make decisions about the trustworthy web services based on classification, correlation, and curve fitting to improve trust in web service prediction. In order to empower the web services life cycle, they have developed a quality assessment model to incorporate a security and performance policy.
Based on the comparison and analysis of the economic characteristics of transformers in an operating substation

Abstract The technical parameters of transformer are the basic data for analyzing and calculating the comparative analysis of transformer economic characteristics, The comparative analysis of transformer economic characteristics is to seek for reducing transformer active power and improving its operating efficiency, That is to reduce the transformer loss rate, And reduce the reactive power loss of the transformer. The active power loss, reactive power loss and integrated power loss of transformer are calculated. The quantitative calculation formula of economic operation mode of transformer in substation is obtained through analysis, The economic operation mode arrangement and load adjustment can be carried out for the transformer of a certain substation, The purpose of economic operation of transformer and reduction of network loss can be achieved.
Video Media-Based Learning to Improve The Story Writing Ability of the 4th Graders of Public Elementary Schools in Cilacap Regency

This research aimed to design a video media-based learning to improve the writing ability of the 4th graders of Elementary School in Cilacap Regency. The procedure of development referred to educational research and development model. The output (product) was an interactive learning media in the form of learning CD that is usable to teachers in the class. The application in the learning showed that this learning model could improve writing ability with high category for experiment class, and with medium category only for control class. Teacher responded positively to this learning model as it could improve the students’ ability. CD resulting from this development was equipped with children story and was organized systematically and in order. This learning media could be developed further into on-line learning. The development of video media-based children story writing learning model in Public Elementary Schools in Cilacap Regency. This research and development aimed to provide a children story writing learning model to improve writing ability. The procedure used included preliminary study, product development, expert validation, small-scale trial, and large-scale trial. The research and development had produced a video media-based children story writing learning model. This story writing learning model consisted of the stages of video media-based learning.
Informetrics Research Methods Outlined

This chapter provides a conceptual scope of informetrics by defining the concept and demonstrating its relationship with bibliometrics, scientometrics, webometrics, cybermetrics, and altmetrics. It demonstrates that informetrics is a quantitative research design that assumes a realistic ontology and objectivism as the epistemological perspective. Based on the data that was extracted from Scopus as well as a content review of selected calls for papers, the chapter highligts methods and areas of informetrics research as reflected in the literature that was published in the subject domain and its sub-domains between 1991 and 2018. The author-supplied keywords, which were the items of analysis, yielded 96 interconnected research methods and 361 areas in which informetrics research can be applied or undertaken. Finally, the chapter provides informetrics students and developing researchers with an outline of the elements that would constitute their research proposals and research methodology chapters of their theses and dissertations.
Evaluation of Data Similarity using Data Jackets based on Users’ Recognition

Data jacket (DJ) emerged as a method to make use of data without revealing any actual content. In a DJ Store, one can search DJs, and learn the types of solution these DJs are used for. In this study, we propose a model that calculates and acquires similar DJs under the assumption that it may be used for similar solutions by acquiring DJs that are similar to a certain DJ. The experimental results show that the similarity model generated by the proposed method is the model that most reflects the similarity of the DJ’s outline.
Industry Trusted Network Communication Based on Quantum Encryption

Industrial control systems are typical Cyber-Physical Systems which have wide social impacts but under increasing cyber security threats. At present, many researching efforts have been made on industrial control system for improving its security and these methods mainly rely on the classical encryption algorithm which is heavily based on mathematical theory. These cryptographic algorithms are designed around computational hardness assumptions and the risk of the classic security communications are being attacked continues to increase. Quantum cryptography has been proved in theory to ensure the security of communication. We proposed a secure industrial field bus transmission scheme based on quantum key distribution system. The proposed method is verified by experiments on a test-bed.
Sentence Simplification from Non-Parallel Corpus with Adversarial Learning

In this study, we propose sentence simplification from a non-parallel corpus with adversarial learning. In recent years, sentence simplification based on a statistical machine translation framework and neural networks have been actively studied. However, most methods require a large parallel corpus, which is expensive to build. In this paper, our purpose is sentence simplification with a non-parallel corpus in open data en-Wikipedia and Simple-Wikipedia articles. We use a style transfer framework with adversarial learning for learning by non-parallel corpus and adapted a prior work [by Barzilay et al.] to sentence simplification as a base framework. Furthermore, from the perspective of improving retention of sentence meaning, we add pretraining reconstruction loss and cycle consistency loss to the base framework. We also improve the sentence quality output from the proposed model as a result of the expansion.
Real-time 3D human motion capture from binocular stereo vision and its use on human body animation

Existing motion-capture technologies are implemented through expensive and specialized equipment, which typically requires the wearable devices or the mark of human body joint. This leads to a distinct limitation on the accessibility and popularity for mass market consumer. To address these problems, we present a pipeline for 3D human motion capture from binocular stereo vision, which is based on MobilePose, a supervised learning method for 2D skeletal joint detection in real time. Due to the short time of 2D joint detection and 3D reconstruction, our approach can fulfill 3D human motion capture in real time. Besides, we use the captured 3D motion information to implement a simple application on human body animation. Compared with Microsoft's Kinect method using depth camera, our method is implemented with the available cameras, which is low cost and widely used.
Software Component Reusability Classification in Functional Paradigm

Reusing the software components can save time, money and resources, and upgrade the quality of the software being developed. Functional paradigm seems to be a platform that can support code reuse better than object-oriented or procedural programming paradigms. This work focuses on identifying potentially reusable components in functional programming paradigm. To conduct the experiments, various software metrics of software components written in Haskell have been used. In this research work, the Self-Organizing Map algorithm is used to cluster the functions from three Haskell packages into three different classes of reusability. Clustering of functions is based on metrics threshold and their average values.
Slippery hill-climbing technique for ciphertext-only cryptanalysis of periodic polyalphabetic substitution ciphers

We present a stochastic method for breaking general periodic polyalphabetic substitution ciphers using only the ciphertext and without using any additional constraints that might come from the cipher’s structure. The method employs a hill-climbing algorithm for individual key alphabets, with occasional slipping down the hill. We implement the method with a computer and achieve reliable results for a sufficiently long ciphertext (150 characters per key alphabet). Because no constraints among the key alphabets are used, this method applies to any periodic polyalphabetic substitution cipher.