Reviewerly/exHarmony huggingface dataset:
Image-spherizing-based planeness detecting method for a micro-channel plate

Based on the mathematical models proposed by the image spherizing algorithm, a novel planeness detecting method for a micro-channel plate (MCP) is presented. After describing the theory of the image spherizing algorithm, a straight line image is chosen as a standard picture, and the imaging simulation for a deformed MCP is performed. According to the simulated results, the deformation characteristic of the MCP and the mechanism of the image twisting phenomenon were analyzed. Finally, the double lines method, which specializes in planeness detection, is proposed. The experimental result obtained by interferometer shows that the profile characteristics of a deformed MCP are consistent with the theoretical analysis. To verify the effectiveness of the double lines method, we measured the planeness of 100 pieces of MCP and then defined the detecting standard: a micro-channel plate will be treated as a defective product when the twisting angle of the two orthogonal lines in the reflected image is greater than 40°, whereas the product will meet the military standard when the twisting angle is smaller than 7°. In this method, the planeness detection could be completed only by observing the reflected image of the MCP, even in a harsh environment. In particular, the accuracy can reach 1 μm and it has been applied to the MCP production process.
Classification and comparison of on-line video summarisation methods

Many methods exist for generating keyframe summaries of videos. However, relatively few methods consider on-line summarisation, where memory constraints mean it is not practical to wait for the full video to be available for processing. We propose a classification (taxonomy) for on-line video summarisation methods based upon their descriptive and distinguishing properties such as feature space for frame representation, strategies for grouping time-contiguous frames, and techniques for selecting representative frames. Nine existing on-line methods are presented within the terms of our taxonomy and subsequently compared by testing on two synthetic data sets and a collection of short videos. We find that success of the methods is largely independent of techniques for grouping time-contiguous frames and for measuring similarity between frames. On the other hand, decisions about the number of keyframes and the selection mechanism may substantially affect the quality of the summary. Finally, we remark on the difficulty in tuning the parameters of the methods “on-the-fly”, without knowledge of the video duration, dynamic or content.
IWD Based Feature Selection Algorithm for Sentiment Analysis

Feature selection methods aim to improve the classification performance by eliminating non-valuable features.In this paper, our aim is to apply a recent optimization technique namely the Intelligent Water Drops (IWD) algorithm to select best features for sentiment analysis.We investigate the classification performances of our proposed IWD based feature selection method by comparing one of the well-known feature selection method using Maximum Entropy classifier.Experimental results show that Intelligent Water Drops based feature selection method outperforms than ReliefF method for sentiment analysis.
Levels Propagation Approach to Image Segmentation: Application to Breast MR Images

Accurate segmentation of a breast tumor region is fundamental for treatment. Magnetic resonance imaging (MRI) is a widely used diagnostic tool. In this paper, a new semi-automatic segmentation approach for MRI breast tumor segmentation called Levels Propagation Approach (LPA) is introduced. The introduced segmentation approach takes inspiration from tumor propagation and relies on a finite set of nested and non-overlapped levels. LPA has several features: it is highly suitable to parallelization and offers a simple and dynamic possibility to automate the threshold selection. Furthermore, it allows stopping of the segmentation at any desired limit. Particularly, it allows to avoid to reach the breast skin-line region which is known as a significant issue that reduces the precision and the effectiveness of the breast tumor segmentation. The proposed approach have been tested on two clinical datasets, namely RIDER breast tumor dataset and CMH-LIMED breast tumor dataset. The experimental evaluations have shown that LPA has produced competitive results to some state-of-the-art methods and has acceptable computation complexity.
Developing an Adaptive Language Model for Bahasa Indonesia

A language model is one of the important components in a speech recognition system.It is commonly developed using a statistical method called n-gram.However, a standard n-gram cannot be used for general domains with so many ambiguous semantics of sentences.This paper focuses on developing an adaptive n-gram language model for Bahasa Indonesia.First, a text corpus of ten million distinct sentences is crawled from hundreds of websites of news, magazines, personal blogs, and writing forums.The text corpus is then used to construct an adaptive language model using Latent Dirichlet Allocation (LDA) with Collapsed Gibbs Sampling (CGS) training method.Compare to the standard n-gram, the adaptive language model gives a better performance in the word selection to produce the best sentence.
Reasoning in Multi-agent Based Smart Homes: A Systematic Literature Review

Multi-agent systems are widely used to model components of a smart living environment as autonomous intelligent agents. Accordingly, its advantages to achieve the comfort and efficiency goals of smart home systemsMekuria, Dagmawi Neway are well-documented in many studies. However, there is a clear lack of systematic investigation targeted at exploring the reasoningSernani, Paolo modules integrated into these systems. To close this gap, this paper examines the literature on multi-agent based smart home systems and provides a comprehensive overview of the essential requirements, Falcionelli, Nicola assumptions, strengths, limitations, challenges and future research directions of their proposed reasoning systems. Moreover, it identifies the main technologies used to representDragoni, Aldo Franco the home environment as a multi-agent system and the reasoning approaches utilized to bring decision-making ability into the smart living environment. As a result, this systematic literature review identifies the ability to learn, plan, predict, explain and reason with incomplete knowledge as the major elements of a smart home reasoning system. In addition, the findings of this work revealed the application of standard rule conflict resolution strategies and sensor data contextualization as principal solutions to address some of the problems caused by conflicting rules and agent goals. Further, it underlines the importance of utilizing hybrid reasoning approaches and the need to handle overlapping multi-inhabitant activities to realize the true potential of smart homes.
Socrative in Teaching Tenses: Indonesian Students and Lecturers’ Perceptions

Mastering a language not only involves four language skills-listening, speaking, reading, and writing-but also mastering the grammar and vocabulary of the language.This research was intended to investigate the perceptions of Indonesian undergraduates and their lecturers at the Islamic University of Riau toward the use of Socrative in grammar class specifically tenses.This study employed a quantitative research design.The respondents were 34 sixth-semester undergraduates who were randomly chosen and two grammar lecturers at the English Department of Islamic University of Riau in the academic year 2016/2017.The questionnaire was adapted from the technology acceptance model (TAM) by Davis, which consists of two specific variables: Perceived usefulness and perceived ease of use.Before the questionnaire was employed in this study, a pilot study was carried out to ensure the validity and reliability of the instrument.The findings of the research show the majority of respondents have positive perceptions toward Socrative.The students and lecturers provide positive responses with regard to "perceived usefulness and ease of use" of Socrative in grammar class.It is hoped that this tool not only could be used in grammar class but also in all aspects of English language learning.It is also expected that this research could assist Indonesian educators to teach more creatively.
Revisiting Conditional Functional Dependency Discovery: Splitting the “C” from the “FD”

Many techniques for cleaning dirty data are based on enforcing some set of integrity constraints. Conditional functional dependencies (CFDs) are a combination of traditional Functional dependencies (FDs) and association rules, and are widely used as a constraint formalism for data cleaning. However, the discovery of such CFDs has received limited attention. In this paper, we regard CFDs as an extension of association rules, and present three general methodologies for (approximate) CFD discovery, each using a different way of combining pattern mining for discovering the conditions (the “C” in CFD) with FD discovery. We discuss how existing algorithms fit into these three methodologies, and introduce new techniques to improve the discovery process. We show that the right choice of methodology improves performance over the traditional CFD discovery method CTane. Code related to this paper is available at: https://github.com/j-r77/cfddiscovery , https://codeocean.com/2018/06/20/discovering-conditional-functional-dependencies/code .
Efficient threshold password-authenticated secret sharing protocols for cloud computing

Threshold password-authenticated secret sharing (TPASS) protocols allow a client to distribute a secret s amongst n servers and protect it with a password pw, so that the client can later recover the secret s from any subset of t of the servers using the password pw. In this paper, we present two efficient TPASS protocols, one is built on two-phase commitment and has lower computation complexity, and another is based on zero-knowledge proof and has less communication rounds. Both protocols are in particular efficient for the client, who only needs to send a request and receive a response. In addition, we have provided rigorous proofs of security for the proposed protocols in the standard model. The experimental results have shown that the proposed two TPASS protocols are more efficient than Camenisch et al.’s protocols and save up to 85%–95% total computational time and up to 65%–75% total communication overhead.
Correction to “Improving Privacy and Security in Decentralizing Multi-Authority Attribute-Based Encryption in Cloud Computing”

In 2018, Yang et al. proposed a decentralized multi-authority attribute-based encryption scheme for cloud computing applications and proved its security using the dual system encryption technique. In this comment, we show that Yang et al.'s scheme does not achieve encryption one-wayness under the key-only attack and the user collusion attack, respectively. In the key-only attack, with the knowledge of public parameters only, an adversary can impersonate the attribute authorities to forge user attribute secret keys. In the user collusion attack, malicious users can collude by sharing their secret keys to unauthorizedly decrypt a ciphertext. In order to fix the scheme, we suggest adopting a pairing-based proof of knowledge protocol and the decryption algorithm from Lewko and Water's ABE scheme.
Quantum Neural Machine Learning: Theory and Experiments

Cloud-based access to quantum computers opens up the way for the empirical implementation of quantum artificial neural networks and for the future integration of quantum computation in different devices, using the cloud to access a quantum computer.The current work experimentally implements quantum artificial neural networks on IBM's quantum computers, accessed via cloud.Examples are provided for the XOR Boolean function representation problem and decision under risk; in the last case, quantum object-oriented programming using IBM's Qiskit Python library is employed to implement a form of quantum neural reinforcement learning applied to a classical decision under risk problem, showing how decision can be integrated into a quantum artificial intelligence system, where an artificial agent learns how to select an optimal action when facing a classical gamble.A final reflection is provided on quantum robotics and a future where robotic systems are connected to quantum computers via cloud, using quantum neural computation to learn to optimize tasks and act accordingly.
A Population-Based Optimization Method Using Newton Fractal

We propose a deterministic population-based method for a global optimization, a Newton particle optimizer (NPO). The algorithm uses the Newton method with a guiding function and drives particles toward the current best positions. The particles’ movements are influenced by the fractal nature of the Newton method and are greatly diversified in the approach to the temporal best optimums. As a result, NPO generates a wide variety of searching paths, achieving a balance between exploration and exploitation. NPO differs from other metaheuristic methods in that it combines an exact mathematical operation with heuristics and is therefore open to more rigorous analysis. The local and global search of the method can be separately handled as properties of an associated multidimensional mapping.
A Comprehensive Diagnosis of Components of Pedagogical Students’ Research Competency

Introduction. Nowadays, the aspects of development of research competencies in future experts belong to the most relevant topics, which are widely being discussed both in Russian and in foreign scientific community. One of burning issues in this thematic block is diagnosis of such competencies without which their successful development is impossible. Today, we are witnessing the disparity between a large number of the methods presented in scientific sources and technologies of the formation of students’ research competency and limitation of its criteria and indicators. Moreover, there is lack of adequate methodological tools to estimate objectively the level and quality of scientific literacy and degree of students’ research activity. The aim of the research was to define the content of a comprehensive diagnosis of the components of pedagogical students’ research competency and to justify the criteria, indicators and methods of its evaluation. Methodology and research methods. The methods of analysis, synthesis, generalisation and modelling were employed. The systematic approach was applied, taking into account the provisions of plurality, integrity and structurisation. Results and scientific novelty. The authors presented and described the model of formation of pedagogical students’ practice-focused research competency. The knowledge-based, value-oriented and practical components of the model are correlated to the methods of psycho-pedagogical monitoring, which are relevant to the content of these components. The complex of particular techniques for comprehensive, precise and complete assessment of future teachers’ research competency is designed. The complexity of measurement tools is provided with homogeneity between a theoretical construct (research competency) and operationalised constructs (psychological phenomena estimated in diagnostic techniques). Cumulative application of the selected diagnostic procedures and techniques allows: objective and subjective indicators of formation of research abilities and skills to be compared; motivation and readiness of students for implementation of independent research activity to be defined, taking into account social and professional contexts of its realisation. Practical significance. The authors of the present publication propose to introduce into practice of the higher school the approaches to contents, structure and organisation of the psychological diagnostics of level of students’ research competency, which will give the chance not only to carry out the internal regular and continuous monitoring of formation of one of key characteristics of the identity of the modern expert in educational institutions, but also to optimise the process of multilevel high school preparation, having adjusted work on early identification of its problems and crisis stages to take timely effective correctional measures.
Preordering for Chinese-Vietnamese Statistical Machine Translation

Word order is one of the most significant differences between the Chinese and Vietnamese. In the phrase-based statistical machine translation, the reordering model will learn reordering rules from bilingual corpora. If the bilingual corpora are large and good enough, the reordering rules are exact and coverable. However, Chinese-Vietnamese is a low-resource language pair, the extraction of reordering rules is limited. This leads to the quality of reordering in Chinese-Vietnamese machine translation is not high. In this paper, we have combined Chinese dependency relation and Chinese-Vietnamese word alignment results in order to pre-order Chinese word order to be suitable to Vietnamese one. The experimental results show that our methodology has improved the machine translation performance compared to the translation system using only the reordering models of phrase-based statistical machine translation.
Performance prediction of data streams on high-performance architecture

Abstract Worldwide sensor streams are expanding continuously with unbounded velocity in volume, and for this acceleration, there is an adaptation of large stream data processing system from the homogeneous to rack-scale architecture which makes serious concern in the domain of workload optimization, scheduling, and resource management algorithms. Our proposed framework is based on providing architecture independent performance prediction model to enable resource adaptive distributed stream data processing platform. It is comprised of seven pre-defined domain for dynamic data stream metrics including a self-driven model which tries to fit these metrics using ridge regularization regression algorithm. Another significant contribution lies in fully-automated performance prediction model inherited from the state-of-the-art distributed data management system for distributed stream processing systems using Gaussian processes regression that cluster metrics with the help of dimensionality reduction algorithm. We implemented its base on Apache Heron and evaluated with proposed Benchmark Suite comprising of five domain-specific topologies. To assess the proposed methodologies, we forcefully ingest tuple skewness among the benchmarking topologies to set up the ground truth for predictions and found that accuracy of predicting the performance of data streams increased up to 80.62% from 66.36% along with the reduction of error from 37.14 to 16.06%.
Moving objects classification via category-wise two-dimensional principal component analysis

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects classification problem via an extended version of two-dimensional principal component analysis (2DPCA), named as category-wise 2DPCA (CW2DPCA). A key component of the CW2DPCA is to independently construct optimal projection matrices from object-specific training datasets and produce category-wise feature spaces, wherein each feature space uniquely captures the invariant characteristics of the underlying intra-category samples. Consequently, on one hand, CW2DPCA enables early separation among the different object categories and, on the other hand, extracts effective discriminative features for representing both training datasets and test objects samples in the classification model, which is a nearest neighbor classifier. For ease of exposition, we consider human/vehicle classification, although the proposed CW2DPCA-based classification framework can be easily generalized to handle multiple objects classification. The experimental results prove the effectiveness of CW2DPCA features in discriminating between humans and vehicles in two publicly available video datasets.
An Efficient Hybrid Optimization Approach Using Adaptive Elitist Differential Evolution and Spherical Quadratic Steepest Descent and Its Application for Clustering

In this paper, a hybrid approach that combines a population-based method, adaptive elitist differential evolution (aeDE), with a powerful gradient-based method, spherical quadratic steepest descent (SQSD), is proposed and then applied for clustering analysis. This combination not only helps inherit the advantages of both the aeDE and SQSD but also helps reduce computational cost significantly. First, based on the aeDE’s global explorative manner in the initial steps, the proposed approach can quickly reach to a region that contains the global optimal value. Next, based on the SQSD’s locally effective exploitative manner in the later steps, the proposed approach can find the global optimal solution rapidly and accurately and hence helps reduce the computational cost. The proposed method is first tested over 32 benchmark functions to verify its robustness and effectiveness. Then, it is applied for clustering analysis which is one of the problems of interest in statistics, machine learning, and data mining. In this application, the proposed method is utilized to find the positions of the cluster centers, in which the internal validity measure is optimized. For both the benchmark functions and clustering problem, the numerical results show that the hybrid approach for aeDE (HaeDE) outperforms others in both accuracy and computational cost.
On the Space Between Critical Points

The vertices of the neighborhood graph of a digital picture P can be interpolated to form a 2-manifold M with critical points (maxima, minima, saddles), slopes and plateaus being the ones recognized by local binary patterns (LBPs). Neighborhood graph produces a cell decomposition of M: each 0-cell is a vertex in the neighborhood graph, each 1-cell is an edge in the neighborhood graph and, if P is well-composed, each 2-cell is a slope region in M in the sense that every pair of s in the region can be connected by a monotonically increasing or decreasing path. In our previous research, we produced superpixel hierarchies (combinatorial graph pyramids) that are multiresolution segmentations of the given picture. Critical points of P are preserved along the pyramid. Each level of the pyramid produces a slope complex which is a cell decomposition of M preserving critical points of P and such that each 2-cell is a slope region. Slope complexes in different levels of the pyramid are always homeomorphic. Our aim in this research is to explore the configuration at the top level of the pyramid which consists of a slope complex with vertices being only the critical points of P. We also study the number of slope regions on the top.
Self-Adaptive Deep Multiple Kernel Learning Based on Rademacher Complexity

The deep multiple kernel learning (DMKL) method has caused widespread concern due to its better results compared with shallow multiple kernel learning. However, existing DMKL methods, which have a fixed number of layers and fixed type of kernels, have poor ability to adapt to different data sets and are difficult to find suitable model parameters to improve the test accuracy. In this paper, we propose a self-adaptive deep multiple kernel learning (SA-DMKL) method. Our SA-DMKL method can adapt the model through optimizing the model parameters of each kernel function with a grid search method and change the numbers and types of kernel function in each layer according to the generalization bound that is evaluated with Rademacher chaos complexity. Experiments on the three datasets of University of California—Irvine (UCI) and image dataset Caltech 256 validate the effectiveness of the proposed method on three aspects.