Reviewerly/exHarmony huggingface dataset:
An Effective Principal Singular Feature Information Extracting Algorithm

In this paper, a effective algorithm to effectively estimate the singular component and value from the cross-covariance kind of matrix from two data streams by using the neural network approach. Different from traditional algorithms, the newly proposed algorithm can estimate not only the principal singular vectors (PSVRs) but also the principal singular values (PSVEs). First, a dynamical system is obtained from the gradient flow, which is obtained from the optimization of a novel information criterion. Then, based on the dynamical system, a stable neural network algorithm, which can extract the left and right PSVRs, is obtained. Moreover, the PSVE can also be obtained based on the estimated left or (and) right singular vectors (SVRs), because the norm of the SVRs mainly depends on the corresponding singular value (SVE) of the cross-covariance matrix. In this case, the principal singular triplet (PST, PSVRs and the corresponding SVE) of the cross-covariance matrix can be extracted by using the algorithm proposed in this paper. What's more, the algorithm proposed in this paper can also be used for multiple PSTs extractions on the basis of the sequential method. Then, the convergence of the algorithm is analyzed, and the result illustrates that the algorithm proposed in this paper converges to the stable equilibrium point with probability 1. Last, experiment results show that the algorithm proposed in this paper is fast and stable in convergence, and can also extract multiple PSTs efficiently.
Deep Captioning Hashing Network for Complex Scene Image Retrieval

Hashing methods have been widely applied to approximate nearest neighbor search for large-scale image retrieval, due to its computation efficiency and retrieval quality. Deep hashing can improve the retrieval quality by representation learning and hash coding. Existing deep hashing methods only take image spatial features into account and result in the lack of accurate semantic similarities of images pairs. In this paper, a novel deep hashing network, Deep Captioning Hashing Network (DCHN), is proposed to enhance semantic similarities of hash codes. In DCHN, the binary hash codes are generated in a Bayesian learning framework by fusing deep spatial representation and deep content captioning representation obtained by image captioning. Our analysis and simulation results have demonstrated that DCHN can achieve better retrieval performance in complex scene images compared with other supervised hashing methods and unsupervised methods on two complex scene image datasets MS COCO and NUS-WIDE.
Deep Learning of Neuromuscular and Visuomotor Control of a Biomimetic Simulated Humanoid

We present a biomimetic framework for human neuromuscular and visuomotor control that promises to be of value to researchers developing humanoid robots. Our framework features a biomechanically simulated human musculoskeletal model, actuated by numerous skeletal muscles, with realistic eyes driven by extraocular and intraocular muscles, whose optic organs refract light, and whose retinas have many nonuniformly distributed photoreceptors. The humanoid's visuomotor control system comprises 24 trained deep neural networks (DNNs)-10 DNNs in its vision subsystem and 14 DNNs in its motor subsystem-plus an additional 4 trained shallow neural networks (SNNs) that control the irises and lenses of the eyes. Of the motor DNNs, a pair control the extraocular muscles, 6 per eye, responsible for eye movements, 2 control the 216 neck muscles of the cervicocephalic biomechanical complex, producing natural head movements, 2 control the 443 core muscles of the torso, and 2 control each limb; i.e., the 29 muscles of each arm and 39 muscles of each leg. Directly from the foveated retinal photoreceptor responses, a pair of foveation DNNs drive eye, head, and torso movements, while 8 limb vision DNNs extract the visual information needed to direct arm and leg actions. By synthesizing its own training data, our humanoid automatically learns efficient, online, active visuomotor control of its eyes, head, torso, and limbs in order to perform nontrivial tasks involving the foveation and visual pursuit of moving target objects coupled with visually-guided limb-reaching actions to intercept them. We also demonstrate that it can balance itself in an upright stance, take steps, and perform certain simulated sports activities.
Automatic Plan Generating System for Geriatric Care Based on Mapping Similarity and Global Optimization

The smart home is an effective means of providing geriatric care to increase the ability of the elderly to live independently and ensure their health in daily life. However, the smart home is not widely used because it is arduous to obtain a sensing devices selection plan. In this paper, the accuracy of service selection and cost savings assumes enormous importance. Therefore, we propose an automatically plan generating system for the elderly based on semantic similarity, intuitionistic fuzzy theory, and global optimization algorithm, aiming at searching for an optimized plan. Experiment results indicate that our approach can satisfy care demands and provide an optimized plan of sensing devices selection.
Experiments on Malayalam Language using Graph-based Data-driven Dependency Parser

The Malayalam language possesses characteristics like morphological richness and agglutination, which makes the processing of language a tiresome task. The resources like treebank allow the syntactic-semantic analysis of Malayalam. In this work, the validation and optimization of Malayalam Treebank Data_ Iiithwere done with various parsing settings. The improvement in the values of qualitative analysis metrics was the main result of the optimization experiments. 10-cross-validation of the data was also performed for the analysis of the attainment of models generated. The results were found to be promising.
Cloud Computing Security using by Applying Cryptography Technique

Cloud computing is the provision of computing and storage capacity to users as a service. Cloud storage is a type of networked online storage where data is stored in virtualized storage pools as a subservice of infrastructure as a service (IaaS) in cloud computing. Cloud computing plays a significant role in the efficient use of resources and in the utilization of service. Regardless of the cloud category (e.g. private, public, hybrid or inter-cloud), all service providers rely on domain server data. As a rapid development and deployment of cloud computing and cloud storage, users are increasingly concerned about security and privacy issues involved in these techniques. This paper provides a summary of basic security problems that consist of conventional security issues. It also addresses the additional challenges resulting from the cloud computing paradigm being used by cloud system providers and consumers. In addition, solutions suggested by some researchers are presented with a focus on cryptographic techniques which support secure storage of the cloud.
Field-less Fieldwork in Archaeology’s Digital Age

Fieldwork is evolving; no longer is it something which has to be done in the field, or even outdoors. While traditional field techniques such as observations, planning and excavation still have their place today, the development of new technologies means that archaeologists can learn so much about a site before putting a trowel in the earth.
Linear Distribution-based SHADE with Variable Population Size

In adaptive DE algorithms, the parameter p is used to strike a balance between the algorithm's exploration and exploitation abilities. In this paper, an effective method for generating a suitable p value is proposed. The probability distribution function (PDF) of p is viewed as a linear function. At the start of the evolutionary process, the PDF of p is a linearly increasing function. Hence, there is a high probability of p having a high value. With successive generations, the PDF of p changes to a linearly decreasing function, and there is a high probability of p having a smaller value. Furthermore, a method for allowing a variable archive size is also proposed. After each generation, the archive size for the next generation is computed. If the archive size has to be reduced, the worst-ranking individuals are deleted from the archive. The accuracy of the proposed method is computationally compared with those of eight other advanced DE variants using benchmark functions from the IEEE Congress on Evolutionary Computation 2017. The results show that the proposed algorithm achieves the best performance among all the variants for most of the benchmark functions in the case of 30-dimensional problems.
An Offline Mahjong Support System Based on Augmented Reality with Context-aware Image Recognition

The diffusion of the Augmented Reality framework has facilitated the implementation of a support system for a real world task. This paper introduces a system that supports the Mahjong game for beginners. However, it is difficult to calculate the score for beginners in the real Mahjong game. We develop an offline system to get the score by recognizing the Mahjong tiles. This is a difficult task because Mahjong tiles have classes and attributes that are the context of their positions. The system needs context-aware image recognition. The system detected tiles using OpenCV and Convolutional Neural Network to classify them and self-localization indirectly found in attributes. The experimental results show that the system can be used effectively.
Stability and Hopf Bifurcation of Multi-ring Coupling Neural Network with Delays

Most previous studies with respect to bifurcation dynamics of neural networks are limited to low dimensional or simple single ring structure. It should be noted that neural networks are composed of thousands of neurons, and these network structures are so complex that cannot be accurate representation by a single ring structure or several nodes. Therefore, it has greater practicality and the potential to study the model of high-dimensional or multi-ring coupling neural network. However, how to obtain characteristic equations of high dimensional systems is an unavoidable problem for researchers. In current paper, a model with four rings coupling neural network is proposed, and the stability and Hopf bifurcation of this model are studied. In order to overcome the obstacle of solving characteristic equation by higher order determinant, the Coates flow graph method is used to obtain the characteristic equations of higher order neural network model. Finally, some numerical simulation examples are given to demonstrate the validity of proposed theoretical results, meanwhile the relation between the number of rings and bifurcation point is given by simulation.
Availability Analysis of Individuals using IoT

Facial detection and recognition system is basically used for authentication purpose. Every human face is unique, complicated multidimensional visual model and hence it is difficult in fabricating it. The face recognition is capable of performing task like identification and verification of a person. The face detection process is concerned with finding whether there is any faces in the images and if present then the image is recognized, compared and contents are stored in the database. This technique is used to find whether the individuals are available or not in the respective government organizations. The proposed system uses the HAAR face library cascade algorithm for face detection, recognition, comparison purposes.
A New Automatic Prediction System of Optimal Machine Learning Algorithm for Text Classification

Machine learning is an important research area of artificial intelligence application technology. It has also become a hot spot in science, technology, engineering and mathematics (STEM) education. For beginners, it is crucial to select a training algorithm with high accuracy. This paper first proposes a method for processing data sets by using term frequency-inverse document frequency technology (TF-IDF). Then, we analyze the matching relationship between different model training sets and algorithms. Additionally, a training method is given to obtain the automatic prediction model with high accuracy. This model implements the automatic matching process from training set to training algorithm. Finally, we prove the availability of this method by analyzing the experimental results.
Recommender System Using Machine Learning

The data mining is one of the technique that is used to find the useful information from thousands of available data or the rough data. This is the technique that is mostly used nowadays. As the digital information is flowing these days, the ecommerce sites are also increasing. The people using them are also increasing. The product reviews on these websites play a major role in finding the best product. The other customers buy the products looking at the reviews at these sites. Firstly, the data collected will be processed and in our paper we have developed the `modified SentiWordNet algorithm' that mainly is on machine learning is used to get the features of the product. These features can then be stored and can be made available for the users while they are searching for the products.
<i>Waste Tide</i>: The Haunted Shadowlands of Industry 4.0

Previous articleNext article No AccessMedia Essay ReviewWaste Tide: The Haunted Shadowlands of Industry 4.0 Waste Tide by Chen Qiufan. Translated by Ken Liu. London: Head of Zeus, 2019.Euan AuldEuan Auld Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Comparative Education Review Volume 64, Number 1February 2020 Sponsored by the Comparative and International Education Society Article DOIhttps://doi.org/10.1086/706847 Views: 179Total views on this site © 2020 by the Comparative and International Education Society. All rights reserved.PDF download Crossref reports no articles citing this article.
ATMOSPHERE: Adaptive, Trustworthy, Manageable, Orchestrated, Secure, Privacy-Assuring, Hybrid Ecosystem for Resilient Cloud Computing

This paper describes the goals of the ATMOSPHERE project, which is a multi-institutional research and development (R&D) effort aiming at designing and implementing a framework and platform to develop, build, deploy, measure and evolve trustworthy, cloud-enabled applications. The proposed system addresses the federation of geographically distributed cloud computing providers that rely on lightweight virtualization, and provide access to heterogeneous sets of resources. In addition, the system also considers both classic trustworthiness properties from the systems community, such as dependability and security, and from the machine learning community, such as fairness and transparency. We present the architecture that has been proposed to address these challenges and discuss some preliminary results.
Markov Augmented Neural Networks for Streaming Video Classification

With the growing number of streaming services, internet providers are increasingly needing to be able to identify the types of data and content providers that are being used on their networks. Traditional methods, such as IP and port scanning, are not always available for clients using VPNs or with providers using varying IP addresses. As such, in this paper we explore a potential method using neural networks and Markov Decision Process in order to augment deep packet inspection techniques in identifying the source and class of video streaming services.
A House of Quality (HOQ) matrix of assistive technology for deaf students at elementary school to enhance basic-level language competencies

Abstract Communication skill is a human basic ability to interact with one another. People usually have no difficulties in communication, yet not for deaf people. The basic communication ability of deaf people tends to be poor due to their hearing impairment. Thus, the technological role is an effective way to enhance communication skill for deaf people or so-called as assistive technology. This study generated a house of quality (HOQ) matrix which can be adopted to develop an assistive technology in the form of language instructional media. The HOQ matrix generated significantly contributes to translating from profound need analysis to assistive technology features to enhance communication ability through Indonesian language competencies for deaf students at elementary school. The user needs were formulized from a preliminary assessment at previous research which took twelve best deaf students from three special elementary schools in Special Region of Yogyakarta, Indonesia. This study has found that the priority of assistive technology development is visual and interactive media, either application or educational game, which comprises the vocabulary of concrete thematic nouns and non-affix transitive verbs and composing a simple sentence. Therefore, the HOQ generated is particularly noteworthy for Indonesian language major competencies by deaf students, which are: understanding reading text skill, writing, and speaking.
Building Tag Systems Based on Advanced Semantic Hierarchical Clustering

An improved method based on semantic analysis and clustering algorithm for building tag systems was proposed. First, Hive is employed to process data. Then, Advanced Semantic Hierarchical Clustering (ASHC), which is an adaptation of Semantic Hierarchical Clustering (SHC), is used to build synonym relationship and hypernym-hyponym relationship of the tag trees and enhance the precision and efficiency of tag systems. In the end, removing some obviously incorrect paths and isolated nodes. For evaluating the performance of the method, the tag coincidence rate, the hypernym-hyponym coincidence rate and the accuracy are used to assess the precision of merging and constructing tag systems. The results show that compared with SHC, the accuracy of ASHC increases 2.7% averagely, and after adjusting tags, these metrics are improved more than 3.9%. Based on this, tag systems with higher precision and applicability can be built.
The generalized complexity of linear Boolean functions

Abstract We study generalized (in terms of bases) complexity of implementation of linear Boolean functions by Boolean circuits in arbitrary functionally complete bases; the complexity of a circuit is defined as the number of gates. Let L * ( n ) be the minimal number of gates sufficient for implementation of an arbitrary linear Boolean function of n variables in an arbitrary functionally complete basis. We show that L * (0) = L * (1) = 3 and L * ( n ) = 7( n – 1) for any natural n ≥ 2.
Multi-task Learning for Relation Extraction

Distantly supervised relation extraction leverages knowledge bases to label training data automatically. However, distant supervision may introduce incorrect labels, which harm the performance. Many efforts have been devoted to tackling this problem, but most of them treat relation extraction as a simple classification task. As a result, they ignore useful information that comes from related tasks, i.e., dependency parsing and entity type classification. In this paper, we first propose a novel Multi-Task learning framework for Relation Extraction (MTRE). We employ dependency parsing and entity type classification as auxiliary tasks and relation extraction as the target task. We learn these tasks simultaneously from training instances to take advantage of inductive transfer between auxiliary tasks and the target task. Then we construct a hierarchical neural network, which incorporates dependency and entity representations from auxiliary tasks into a more robust relation representation against the noisy labels. The experimental results demonstrate that our model improves the predictive performance substantially over single-task learning baselines.
A Methodology for Intuitive and Low-Attention TV Remote Control on Smart Phones

This article proposes a new methodology regarding TV remote-control applications on smart phones, which employs a paradigm based on simple and intuitive gestures. Experiments with users were performed and showed its feasibility and effectiveness.
Research on Intelligent Evaluation Method of Transient Analysis Software Function Test

In transient distributed cloud computing environment, software is vulnerable to attack, which leads to software functional completeness, so it is necessary to carry out functional testing. In order to solve the problem of high overhead and high complexity of unsupervised test methods, an intelligent evaluation method for transient analysis software function testing based on active depth learning algorithm is proposed. Firstly, the active deep learning mathematical model of transient analysis software function test is constructed by using association rule mining method, and the correlation dimension characteristics of software function failure are analyzed. Then the reliability of the software is measured by the spectral density distribution method of software functional completeness. The intelligent evaluation model of transient analysis software function testing is established in the transient distributed cloud computing environment, and the function testing and reliability intelligent evaluation are realized. Finally, the performance of the transient analysis software is verified by the simulation experiment. The results show that the accuracy of the software functional integrity positioning is high and the intelligent evaluation of the transient analysis software function testing has a good self-adaptability by using this method to carry out the function test of the transient analysis software. It ensures the safe and reliable operation of the software.
Improving End User Experience in Software Application Using a Design Scheme for Effective Exception Handling

We have performed research from context dependent exception handling point of view for perspective conscious applications and enhanced robustness in it to bring better experience to the end user. We have developed software applications like intelligent melody player, smart health care system and also explored interactive smart museum applications. In our work, we identify the specific exceptional scenario in individual applications which can be handled more diligently and provides better service and/or better experience, to a scholar, to a patient in healthcare scenario and to a visitor in a museum. We used the pixel position for intelligently identifying the boundaries, person, object etc. and to improve the software system for specific issues. We have proposed an abstract design scheme for applications for advance recovery from exceptional scenario.
Age Estimation of Person Based on Face Features

In the era of information technology the social media and other sources of evidence are usually used to spot suspects and victims of crimes by police. Investigators generally use individual characteristics like gender, age, weight, height, etc. from unknown speculate to testimony in their perception. For example in child abuse investigations victim age is essential in the detection of the offenses' classification. Machine learning methods are used in the analysis of digital photographs to identify soft biometric. Investigator benefits from this to improve their cases. This paper presents an efficient intelligent system for age estimation of a person's by employing the facial features. The facial information employing the deep learning technique that is transfer learning based on Convolutional Neural Networks will estimate the age of the person. The method contains pre-trained model, convolutional base, and classifier. The proposed method is efficient and outer performs the state-of-the-art techniques.