Reviewerly/exHarmony huggingface dataset:
Consensus Multiple Kernel K-Means Clustering With Late Fusion Alignment and Matrix-Induced Regularization

Multiple kernel clustering (MKC) attracts considerable attention due to its competitive performance in unsupervised learning.However, we observe that most of the existing MKC approaches do not sufficiently consider the correlation between different clustering partitions.As a result, the existing methods would cause redundant and low diversity of selected clustering partitions which deteriorate clustering performance.To address these issues, we propose an effective and efficient multiple kernel k-means clustering method termed Consensus Multiple Kernel Clustering with Late Fusion Alignment and Matrix-Induced Regularization (CMKC-LFA-MR).Specifically, the correlations between different clustering partitions are calculated as a matrix-induced regularization to encourage the diversity of clustering results.Moreover, we propose to maximally align the consensus partition with the weighted base partitions.The proposed algorithm jointly optimizes the basic clustering partitions and the optimal consensus clustering result.To solve the resultant optimization problem, a three-step alternate algorithm is proposed with both theoretically and experimentally proved convergence.As demonstrated by the experiments on six benchmark datasets, our algorithm outperforms the existing state-of-the-art multi-kernel methods in clustering performance with less time complexity, which demonstrates the effectiveness and efficiency of our proposed algorithm.
A novel methodology for optimal land allocation for agricultural crops using Social Spider Algorithm

In the current agricultural scenario, availability of suitable land for cultivation is less and profitable allocation of the land for cultivating crops seems to be a cumbersome task. Crop planning optimization is a major research field in agriculture, in which land optimization is a significant challenge, which falls under the category of combinatorial optimization problems. The main objective of the present research is to maximize the net income from agriculture through optimal land allocation. Bio-inspired algorithms are quite popular in solving combinatorial optimization problems. Social Spider Algorithm (SSA), a new bio-inspired algorithm, is used to solve land optimization problem in this research based on the simulation of cooperative behaviour of social spiders. The agricultural area chosen for case study is the Coimbatore region, located in Tamilnadu state, India and the relevant data for the crops are collected from Tamilnadu Agricultural University Coimbatore, India. The optimal planting area, crop productivity for various land holdings and the water requirements are computed by SSA and the results have shown better directions for agricultural planning to improve the profit with constrained land area and water limitations.
Clustering User Characteristics Based on the influence of Hashtags on the Instagram Platform

Instagram is a social media that has the potential to be used to increase awareness of a product. Approximately 70% of users spend their time searching for a product on Instagram. Many people promote their products with a lack of attention to the target. So that not infrequently the information distributed is inaccurate information and not following user characteristics. This study aims to cluster the characteristics of Instagram users based on hashtag compatibility. The method used in this study is the K-Means Clustering method. Based on the results of the experiment, this research succeeded in clustering Instagram users based on the hashtag match on the text caption. Besides, TF-IDF can be used as a feature suitable for the K-Means Klastering method. The results of the hashtag "#kopi" analysis resulted in hashtag suggestions that can be used for the promotion of a product related to coffee, including the hashtag #coffeeshop and #coffee with total usage of 14968 captions.
Wavelet Dual Function-Link Fuzzy Brain Emotional Learning System Design for System Identification and Trajectory Tracking of Nonlinear Systems

This paper proposes a new efficient identification system for nonlinear systems. The proposed wavelet dual function-link fuzzy brain emotional learning system (WDFLFBELS) is used as an identifier to identify the system and to track the trajectory of nonlinear systems. The WDFLFBELS consists of three sub-structures and a fuzzy inference system. The sub-structures include a prefrontal cortex, an amygdala, and a new dual function-link network, then it can efficiently reduce the identification and tracking errors, and obtain good performance. The gradient descent technique is used to find the adaptive laws to online tune the parameters of the system effectively. Simulation studies for identifying a time-varying system and tracking a chaotic trajectory are performed to validate the effectiveness and superiority of the proposed method.
A study of a clothing image segmentation method in complex conditions using a features fusion model

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Un estudio cualitativo sobre la competencia didáctica comunicativa de los docentes en formación

Teachers must have didactic communicative competence to interact with students and facilitate their learning process. Students’ understanding of ideas as well as the interest and emotions they may feel from new learnings largely depend on the way teachers interact using their knowledge, competence and communication as well as their communicative skills. The objective of this study was to know and analyse pre-service teachers’ levels of communicative competence and identify what strategies and skills they developed during these didactic interactions. A qualitative research methodology was chosen to approach pre-service teachers’ reflections about the object of study. Semi-structured interviews were conducted leading to reflections on their training. The qualitative AQUAD program was used and enabled the analysis of participants’ narratives based on the designed code map. Results showed that despite the relevance of communication in the teaching-learning process, pre-service teachers mostly used verbal communication skills and techniques. Their didactic communicative competence continues to be insufficient when they start to teach. Interactions with students, however, constitute one of the key factors of quality in educational processes, therefore this study proposes ways to improve didactic communicative competence that can be integrated into initial teacher training curricula.
Positive Sequential Data Modeling Using Continuous Hidden Markov Models Based on Inverted Dirichlet Mixtures

The hidden Markov model (HMM) has long been one of the most commonly used probability graph models for modeling sequential or time series data. It has been widely used in many fields ranging from speech recognition, face recognition, anomaly detection, to gene function prediction. In this paper, we theoretically propose a variant of the continuous HMM for modeling positive sequential data which are naturally generated in many real-life applications. In contrast with conventional HMMs which often use Gaussian distributions or Gaussian mixture models as the emission probability density, we adopt the inverted Dirichlet mixture model as the emission density to build the HMM. The consideration of inverted Dirichlet mixture model in our case is motivated by its superior modeling capability over Gaussian mixture models for modeling positive data according to several recent studies. In addition, we develop a convergence-guaranteed approach to learning the proposed inverted Dirichlet-based HMM through variational Bayes inference. The effectiveness of the proposed HMM is validated through both synthetic data sets and a real-world application regarding anomaly network intrusion detection. Based on the experimental results, the proposed inverted Dirichlet-based HMM is able to achieve the detection accuracy rates that are about 4%~9% higher than those ones obtained by the compared approaches.
A New Provably Secure Certificateless Signature with Revocation in the Standard Model

The primitive of certificateless signature, since its invention, has become a widely studied paradigm due to the lack of key escrow problem and certificate management problem.However, this primitive cannot resist catastrophic damage caused by key exposure.Therefore, it is necessary to integrate revocation mechanism into certificateless signature.In this paper, we propose a new certificateless signature scheme with revocation (RCLS) and prove its security under the standard model.In the meanwhile, our scheme can resist malicious-but-passive Key Generation Center (KGC) attacks that were not possible in previous solutions.The theoretical analysis shows our scheme has high efficiency and practicality.
MEMORY-BASED GRAPH NETWORKS

Graph Neural Networks (GNNs) are a class of deep models that operates on data with arbitrary topology and order-invariant structure represented as graphs. We introduce an efficient memory layer for GNNs that can learn to jointly perform graph representation learning and graph pooling. We also introduce two new networks based on our memory layer: Memory-Based Graph Neural Network (MemGNN) and Graph Memory Network (GMN) that can learn hierarchical graph representations by coarsening the graph throughout the layers of memory. The experimental results demonstrate that the proposed models achieve state-of-the-art results in six out of seven graph classification and regression benchmarks. We also show that the learned representations could correspond to chemical features in the molecule data.
Obstacles and success factors for entrepreneurship: a comparative analysis between Egypt and Nigeria

This study investigates the obstacles and success for entrepreneurship at Africa The study also provides a framework for determinants of entrepreneurship that includes three groups of factors: (1) obstacle factors, which include financial and non-financial obstacles; (2) success factors, which include gaining an understanding of the economic environment, determining market gaps, ensuring capital adequacy, and creating a stable cash flow; and (3) business model characteristics, which include benefits for clients, suppliers, founders, investors, and other stakeholders.The study hypothesizes hat there is a relationship between the second and third groups, which creating a new value chain (tangible or intangible) to the society with support from stakeholders.A questionnaire survey was conducted with founders, investors, corporate managers, financial analysts, academics, and graduates who seek new projects from Egypt and Nigeria, and 395 questionnaires were received in the second half of 2018.The study found a difference in entrepreneurship nonbanking and non-financial obstacles between Egypt and Nigeria.This can be explained by differences in the level of efficiency of the stock exchanges and non-banking services, in addition to the differences in legal environments and levels of corruption between Egypt and Nigeria.Moreover, future directions of entrepreneurship are similar across countries, but age has an impact on these directions.
Face Recognition Using Laplacian Completed Local Ternary Pattern (LapCLTP)

Nowadays, the face is one of the typical biometrics that has high-security technology in the biometrics field. In face recognition systems, feature extraction is considered as one of the important steps. In feature extraction, the important and interesting parts of the image are represented as a compact feature vector. Many features had been proposed in the image processing fields such as texture, colour, and shape. Recently, texture descriptors are playing an important and significant role as a local descriptor. Different types of texture descriptors had been proposed and used for face recognition task, such as Local Binary Pattern (LBP), Local Ternary Pattern (LTP), and Completed Local Ternary Pattern (CLTP). All these texture features have achieved good performances in terms of recognition accuracy. In this paper, we propose to improve the performance of the CLTP and use it for face recognition. A Laplacian Completed Local Ternary Pattern (LapCLTP) is proposed in this paper. The image is enhanced using a Laplacian filter for pre-processing image process before extracting the CLTP. JAFFR and YALE standard face datasets are used to investigate the performance of the LapCLTP. The experiment results showed that the LapCLTP outperformed the original CLTP in both datasets and achieved higher recognition accuracy. The LapCLTP achieved 99.24%, while CLTP achieved 98.78% with JAFFE dataset. IN YALE, the LapCLTP achieved 85.13%, while CLTP, only 84.46%.
Automated Machine Learning Overview

Abstract This paper aims at deeper exploration of the new field named auto-machine learning, as it shows promising results in specific machine learning tasks e.g. image classification. The following article is about to summarize the most successful approaches now available in the A.I. community. The automated machine learning method is very briefly described here, but the concept of automated task solving seems to be very promising, since it can significantly reduce expertise level of a person developing the machine learning model. We used Auto-Keras to find the best architecture on several datasets, and demonstrated several automated machine learning features, as well as discussed the issue deeper.
Associative Variational Auto-Encoder with Distributed Latent Spaces and Associators

In this paper, we propose a novel structure for a multi-modal data association referred to as Associative Variational Auto-Encoder (AVAE). In contrast to the existing models using a shared latent space among modalities, our structure adopts distributed latent spaces for multi-modalities which are connected through cross-modal associators. The proposed structure successfully associates even heterogeneous modality data and easily incorporates the additional modality to the entire network via the associator. Furthermore, in our structure, only a small amount of supervised (paired) data is enough to train associators after training auto-encoders in an unsupervised manner. Through experiments, the effectiveness of the proposed structure is validated on various datasets including visual and auditory data.
Deep learning model with low-dimensional random projection for large-scale image search

Developing deep learning models that can scale to large image repositories is increasingly gaining significant efforts in the domain of image search. The current deep neural networks rely on the computational power of accelerators (e.g. GPUs) to tackle the processing limitations associated with features extraction and model training. This paper introduces and investigates a deep model of Convolutional Neural Networks (CNNs) to efficiently extract, index, and retrieve images in the context of large-scale Content-Based Image Retrieval (CBIR). Random Maclaurin projection is used to generate low-dimensional image descriptors and their discriminating efficiency is evaluated on standard image datasets. The scalability of deep architectures is also evaluated on one million image dataset over a High-Performance Computing (HPC) platform, which is assessed in terms of the retrieval accuracy, speed of features extraction and memory costs. Additionally, the controlling GPU kernels of the proposed model are examined under several optimization factors to evaluate their impact on the processing and retrieval performance. The experimental results show the effectiveness of the proposed model in the retrieval accuracy, GPU utilisation, speed of features extraction, and storage of image indexing.
An Improved Selective Facial Extraction Model for Age Estimation

In this paper, we propose an improved end-to-end learning algorithm to address the aggregation of multiclass classification and regression for age estimation by using deep Convolutional Neural Networks (CNNs). Inspired by Soft Stagewise Regression Network (SSR-Net), we take residual units embedded with channel and spatial feature response correlation values and dynamically adopted the kernel corresponding to these feature maps into our model. In addition, we used weight normalization at each layer by using input samples at the beginning of training and this weight normalization was beneficial both in terms of accuracy as well as training time. We validate the proposed model based on benchmark datasets and compare the MAE with seven other mainstream networks. The results reveal that our model achieves an improved performance. Our contribution is an updated algorithm for age estimation by adopting the latest attention and normalization mechanisms for balancing the efficiency and accuracy of the proposed model.
High-speed 3D digital image correlation for rolling deformation of a tire sidewall and measuring dynamic contact patch length

Tires play a vital role in a vehicle’s operational stability, comfort, and security. However, due to the influence of test equipment and tire operating conditions, the perception of rolling tire characteristics is still in the stage of gradual improvement, especially the analysis of sidewall rolling deformation and dynamic contact peculiarities of tires/road interactions, which has restricted the analysis of rolling energy dissipation and the accurate observation of tire forces. In this paper, the high-speed stereo-vision system was created by high-speed cameras, and the relative rigid and flexible displacement, strain, and trajectory trend of marker points and the real-time global displacement field of the sidewall during the tire-rolling cycle were acquired utilizing the improved digital image correlation algorithm under different rolling velocities. Meanwhile, the periodic dissipation of the strain concentration region was observed, and the phenomenon of strain resonance appeared at the overlap of the periods. The relative flexibility strain and shear strain of the marker point were obtained on the plane element, which resulted in the relative flexibility major principle strain of the plane element, and then it achieved recognition of the leading and trailing points of the contact patch and the accurate measurement of dynamic contact patch length.
College English Language Teaching Reform and EFL Lecturers' Teaching Practice: A Case Study in China

The rapid expansion and development of newly-upgraded undergraduate colleges and universities across China witnessed a corresponding demand for the teaching and learning of English as Foreign Language (EFL).Accordingly, the Ministry of Education of China implemented the College English Language Teaching Reform (CELTR) to improve the teaching and learning of English language at higher institutions in China.The aim of this paper was to investigate EFL lecturers' perspectives of the CELTR and to what extent the CELTR had enhanced the EFL lecturers' teaching practice, which on converse influenced the effect and implementation of the CELTR.The study involved a total of 92 EFL lecturers from a newly-upgraded university located in Nanyang City in China.Data for the study were collected by using a questionnaire and semi-structured interviews with eight EFL lecturers adopting purposive sampling.The findings indicated that on the whole the EFL lecturers had moderate level of understanding and knowledge of the CELTR, and the lecturers' teaching practice had been satisfactorily enhanced due to the implementation of the CELTR.The college EFL lecturers considered and constantly adjusted their teaching contents, modes, methods and strategies to meet the students' specific needs better, and they tried to motivate the students' interests and participation in the EFL classroom teaching.
Blockchain Technology and Decentralized Governance: The Pitfalls of a Trustless Dream

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Compression of Convolutional Neural Network for Natural Language Processing

Convolutional Neural Networks (CNNs) were created for image classification tasks. Quickly, they were applied to other domains, including Natural Language Processing (NLP). Nowadays, the solutions based on artificial intelligence appear on mobile devices and in embedded systems, which places constraints on, among others, the memory and power consumption. Due to CNNs memory and computing requirements, to map them to hardware they need to be compressed.This paper presents the results of compression of the efficient CNNs for sentiment analysis. The main steps involve pruning and quantization. The process of mapping the compressed network to FPGA and the results of this implementation are described. The conducted simulations showed that 5-bit width is enough to ensure no drop in accuracy when compared to the floating point version of the network. Additionally, the memory footprint was significantly reduced (between 85% and 93% comparing to the original model).
Mechanical Performance and Microstructure Prediction of Hypereutectoid Rail Steels Based on BP Neural Networks

Rapid development of railway has heightened the need for the researches on hypereutectoid heavy rail steels. Artificial intelligence method has become an effective tool to realize materials composition design. In this paper, BP neural network models are constructed to determine the relationship among (Cr, P, S, V) alloying elements, mechanical performance and microstructure of hypereutectoid rail steels. Analysis based on this model reveals that Cr is the most prominent element for mechanical properties. The tensile strength, yield strength and hardness can be improved with the increasing content of Cr and V. The addition of P and S seems to decrease the strength and hardness of rail steels. Furthermore, the addition of (Cr, P, S, V) has a slight impact on the content of pearlite dual phases. The increase of (Cr, V) and decrease of (P, S) can contribute to an increase in ferrite content with the associated decrease in cementite. Experimental results agree well with the prediction based on the BP neural network model. This work provides an excellent basis for assessing the mechanical performance and microstructure of hypereutectoid heavy rail steels.
Accounting Information Systems in the Blockchain Era

In this paper we analyse the advantages that the application of blockchain technology brings to accounting information systems (AIS), but also highlight the potential issues with its use. We examine the use of blockchain against the background of the historical evolution of AIS and explain the operational fit of this technology in AIS. We then analyse the pros and cons of the highly probable use of blockchain technology in AIS. For this purpose, we review the relevant contributions on this subject in the accounting academic literature to date and classify them into four categories on the basis of their focus: governance, transparency and trust; continuous audit; smart contracts; and roles of accountants and auditors. We also analyse the early reactions of the accounting industry and regulators to this new technological environment.