Reviewerly/exHarmony huggingface dataset:
A novel framework for face recognition using robust local representation–based classification

Face recognition via representation-based classification is a trending technique in the recent years. However, the recognition performance of the systems using such a technique degrades in an unconstrained environment. In this article, a novel framework is proposed for representation-based face recognition. To deal with the unconstrained environment, a pre-process is used to frontalize face images, and aligned downsampling local binary pattern features of the frontalized images are used for classification. A dimension reduction is then adopted in order to reduce the computation complexity via an optimized projection matrix. The recognition is carried out using an improved robust sparse coding algorithm. Such an algorithm is expected to avoid the overfitting problem. The open-universe test on labeled faces in the wild data sets shows that the recognition rate of the proposed system can reach 95% with a recall rate of 80%, which is best among those representation-based classification face recognition systems.
Web Scraping Crawling-based Automatic Data Augmentation for Deep Neural Networks-based Vehicle Classifications

In this paper, we propose a novel data augmentation using the web scraping crawling for Deep Neural Networks (DNNs). First, we collect training data through the proposed web scraping crawler and automatically increase given data by the image processing techniques optimized for the data augmentation. In addition, for the evaluation, we compare the effect of each image processing technique through the cross-validation. In the simulation results, the validation accuracy of the DNN classifier using the augmented data through the optimal augmentation method was about 23.58% higher than that of the DNN classifier using original data.
Enhanced face alignment using an unsupervised roll estimation initialization

We propose a novel and efficient initialization method for generalized facial landmark localization with an unsupervised roll-angle estimation based on B-spline models. We first show that the roll angle is crucial for an accurate landmark localization. Therefore, we develop an unsupervised roll-angle estimation by adopting a joint 1st -order B-spline model, which is robust to intensity variations and generic for application to various face detectors. The method consists of three steps. First, the scaled-normalized Laplacian of Gaussian operator is applied to a bounding box generated by a face detector for extracting facial feature segments. Second, a joint 1 st -order B-spline model is fitted to the extracted facial feature segments, using an iterative optimization method. Finally, the roll angle is estimated through the aligned segments. We evaluate four state-of-the-art landmark localization schemes with the proposed roll-angle estimation initialization in the benchmark dataset. The proposed method boosts the performance of landmark localization in general, especially for cases with large head pose. Moreover, the proposed unsupervised roll-angle estimation method outperforms the standard supervised methods, such as random forest and support vector regression by 41.6% and 47.2%, respectively.
Multi-qubit controlled NOT gates for artificial intelligence natural languages processing

Natural language processing is efficient using quantum neural networks including multiqubit controlled NOT gates with multiple control qubits and a single target qubit. We propose here a photonic miltiqubit controlled NOT gate based on a multi-wave mixing process in a cavity. Theory of such a multiqubit gate is constructed using input-output formalism. Parameters matching condition is found that must be fulfilled for successful gate operation. Recommendations are given for the construction of quantum neural networks that are able to solve various practical problems of natural language processing.
The Eighth Nerve Guitar

This paper describes the Eighth Nerve Guitar, a combined hardware / software instrument designed for computer-mediated improvisational performance. Key conceptual, aesthetic, and technical concerns will be discussed and multiple projects that utilize this live performance instrument will be referenced. A new instrument, the Guitar-Like Object (in development) will also be introduced.
TASKS FOR THE PRACTICAL WORKS ON THE THEME “PROCESSING OF INFORMATION IN SPREADSHEETS”

The article presents the tasks for the practical works on informatics. The tasks can be used as independent practical works in the framework of studying the theme “Electronic (dynamic) tables” of the informatics course in secondary general education, as well as in the framework of studying the elective (facultative) ICT course.
NEWS: Optimizing the Service Performance for Workflows with Replications in Virtualized Cloud Environments

Cloud computing environments facilitate applications by providing virtualized resources. It is attracting increasing interests for its elasticity and high quality of service, especially in "big time". For applications, execution time is crucial for the quality of service, because works finished after the deadlines will be useless. Nevertheless, typical cloud environments do not present regular performance in terms of execution and data transmission. And cloud service providers cannot guarantee the performance of execution time, which would take large default risk. In this paper, a performaNce guaranteE Workflow Scheduling (NEWS) algorithm is proposed to address these issues by taking replications based on reconstructed workflow. Characteristics of these issues are summarized to further describe the problems and challenges, which have always been ignored. The algorithm employs the parameter of partial critical path to optimize the workflow scheduling performance. On the analysis of challenges brought up by taking replications, NEWS also optimizes the choice of tasks and the number of replications. Simulation experiments show that the proposed algorithm reduces the variation of total execution time, which makes it possible to provide performance guarantee services.
Super-mask-based object localization for auto-contouring in head and neck radiation therapy planning

We have presented a variety of methods for object recognition based on the Automatic Anatomy Recognition (AAR) framework at previous SPIE conferences, including AAR recognition via optimal threshold on intensity, AAR recognition via composite information from intensity and texture, and AAR recognition with the optimal hierarchical structure design, and via neural networks to learn object relationships. The purpose of this paper is to introduce new features for the AAR-based recognition procedure and improve the performance of object localization for autocontouring in head and neck (H&amp;N) radiation therapy planning, specifically for some of the most challenging objects. The proposed super-mask technique first registers images used for model building among themselves optimally by using a minimal spanning tree in the complete graph formed with images as nodes to determine the order of registering images. Subsequently, we build a super-mask by combining the similarly registered binary images corresponding to each object by taking (S1) union of all binary images, (S2) intersection among all binary images, or (S3) the votingbased fuzzy mask created by adding the binary images. The super-mask is then used to confine search for optimum localization of the object in the given image. A large-scale H&amp;N computed tomography (CT) data set with 216 subjects and over 2000 3D object samples were utilized in this study. The super-mask-based object localization approach within the AAR framework improved the recognition accuracy by 25-45% compared with the previous AAR strategy, especially for the most challenging H&amp;N objects. On low quality images, the new method achieves recognition accuracy within 2 voxels on 50-60% of the cases.
A Novel Approach to Generate OWL2 Models Using Case Based Reasoning

This research paper investigates the novel approach to use CBR approach to generate OWL2 language. The idea of semantic web is described by Tim Berners Lee. He is also inventor of WWW. A book “Weaving the Web” by Tim Berners Lee, in this book he describes about his dream. The dream of Tim is to linking and connecting all things in this way that humans and computers understand the information CBR is a case base reasoning system in which we use previous cases to solve new case. To start a work from the scratch is always a time consuming and it requires extra effort and it can also produce complexity. To avoid complexity we can reuse the existing work, so it is the main purpose of CBR approach to utilize the existing work rather than start from the scratch. CBR approach provides much functionality and features to efficiently and accurately utilize the existing work. The term of “Semantic Web” is basically used for two things linked data and meaningful data. To make data meaningful for both humans and computers we use annotations. Annotations add additional information with the objects. The humans and computers understand the meaning of data through annotations. To make information link together we create ontologies. Ontologies create connection between classes, subclasses, object properties and data properties of knowledge domain. It creates a relationship among all entities. So, we can use CBR approach to generate ontologies. We can get help from existing ontologies to create new one. In semantic web we linked entities through ontologies. To create new ontologies we can use existing ontologies through CBR approach. CBR provides the opportunity to Retrieve ontology, Reuse ontology, Revise ontology and Retain ontology.
Obtaining the potential number of models/atlases needed for capturing anatomic variations in population images

Many medical image processing and analysis operations can benefit a great deal from prior information encoded in the form of models/atlases to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. However, the fundamental question “How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor in a given population?” has remained a difficult and open problem. We propose a method to seek an answer to this question assuming that a set Ι of images representative of the population for the body region is given. Our approach, after images in Ι are trimmed to the exact body region, is to create a partition of Ι into a specified number n of groups by optimizing the collective similarity of images in each group. We then ascertain how the overall goodness of partition P<sub>n</sub>(Ι) varies as we change n from 1 to |Ι|. Subsequently, values of n at which there are significant changes in the goodness value are determined. These breakpoints are taken as the recommended number of groups/ models/ atlases. Our results on 284 thoracic computed tomography (CT) scans show that at least 8 groups are essential, and 15, 21, or 32 could be optimum numbers if a finer classification is needed for this population. This method may be helpful for constructing high quality models/atlases with a proper grouping of the images from a sufficiently large population and in selecting optimally the training image sets needed for each class in deep learning strategies.
Basic and Refined Nomic Truth Approximation by Evidence-Guided Belief Set Revision

Straightforward theory revision, taking into account as effectively as possible the established nomic possibilities and, on their basis induced empirical laws, is conducive for nomic truth approximation. The question this paper asks is: is it possible to reconstruct the relevant theory revision steps, on the basis of incoming evidence, in the terms of so-called belief revision and, more specifically, in the terms of belief set revision, also called the AGM-approach? Assumin g exclusion theories, a positive answer will be given in two rounds, first for the case in which the initial theory is compatible with the established empirical laws, then for the case in which it is incompatible with at least one such a law.
Multiple-exposure adaptive selection algorithm for high dynamic range 3D fringe projection measurement

In order to cover the high dynamic exposure range requirement of high reflective surface, a multiple-exposure adaptive selection algorithm of phase shifting 3D measurement is developed. At first, the camera response curve function is calibrated with the sequence images of different exposure times. The nonlinear curve function shows the relationship between the image intensity value and exposure amount. The relative irradiance value of all the image pixels is computed based on the linear relation between the exposure time and exposure amount. Both relative irradiance value cover the highest and lowest gray value of the fringe image is set the threshold. Then, the adaptive selection algorithm for exposure time is proposed based on the double threshold principle. At last, different exposure time node is selected adaptively. The sequences images with multi-exposure time are fused. In order to evaluate the performance of this method, some typical metal parts and blade with high range of reflectivity surfaces are used for 3D measurement and construction. The experiment results verify the feasibility of the proposed method.
A Dynamic Resource Pricing Scheme for a Crowd-Funding Cloud Environment

With the rapid development of cloud computing and the exponential growth of cloud users, federated clouds are becoming increasingly prevalent based on the idea of resource cooperation. In this paper, we consider a new resource cooperation model called “Crowd-funding”, which is aimed at integrating and uniformly managing geographically distributed resource-limited resource owners to achieve a more effective use of resources. The resource owners are rational and maximize their own interest when contributing resources, so a reasonable pricing scheme can incentivize more resource owners to join the Crowd-funding system and increase their service level. Therefore, we propose a dynamic pricing scheme based on a repeated game between the “Crowd-funding” system and the resource owners. The simulation results show that our resource pricing scheme can achieve more effective and longer-lasting incentivizing effects for resource owners.
Restricted Similarity Functions, Distances and Entropies with Intervals Using Total Orders

The paper deals with theoretical investigation targeting in practical utilization in image processing. First the interval valued restricted equivalence functions together with interval valued dissimilarity functions with respect to total order are introduced. The novelty of the approach lays in the using of total orders of intervals and the fact that the outputs of the functions are intervals and not numbers as in previous works. Then both interval valued restricted equivalence functions and interval valued dissimilarity functions are aggregated and similarity measures, distance measures and entropies for interval-valued fuzzy sets are yielded, with respect to total order as well. The investigation is a continuation of previous works of the authors aiming in improvement of existing tools in image processing.
Towards multi-class detection: a self-learning approach to reduce inter-class noise from training dataset

This paper proposes a novel self-learning framework, which converts a noisy, pre-labeled multi-class object dataset into a purified multi-class object dataset with object bounding-box annotations, by iteratively removing noise samples from the low-quality dataset, which may contain a high level of inter-class noise samples. The framework iteratively purifies the noisy training datasets for each class and updates the classification model for multiple classes. The procedure starts with a generic single-class object model which changes to a multi-class model in an iterative procedure of which the F-1 score is evaluated to reach a sufficiently high score. The proposed framework is based on learning the used models with CNNs. As a result, we obtain a purified multi-class dataset and as a spin-off, the updated multi-class object model. The proposed framework is evaluated on maritime surveillance, where vessels need to be classified into eight different types. The experimental results on the evaluation dataset show that the proposed framework improves the F-1 score approximately by 5% and 25% at the end of the third iteration, while the initial training datasets contain 40% and 60% inter-class noise samples (erroneously classified labels of vessels and without annotations), respectively. Additionally, the recall rate increases nearly by 38% (for the more challenging 60% inter-class noise case), while the mean Average Precision (mAP) rate remains stable.
Adaptive Cluster Based Discovery of High Utility Itemsets

Utility Itemset Mining (UIM) is a key analysis technique for data which is modeled by the Transactional data model. While improving the computational time and space efficiency of the mining of itemsets is important, it is also critically important to predict future itemsets accurately. In today’s time, when both scientific and business competitive edge is commonly derived from first access to knowledge via advanced predictive ability, this problem becomes increasingly relevant. We established in our most recent work that having prior knowledge of approximate cluster structure of the dataset and using it implicitly in the mining process, can lend itself to accurate prediction of future itemsets. We evaluate the individual strength of each transaction while focusing on itemset prediction, and reshape the transaction utilities based on that. We extend our work by identifying that such reshaping of transaction utilities should be adaptive to the anticipated cluster structure, if there is a specific intended prediction window. We define novel concepts for making such an anticipation and integrate Time Series Forecasting into the evaluation. We perform additional illustrative experiments to demonstrate the application of our improved technique and also discuss future direction for this work.
Body part and imaging modality classification for a general radiology cognitive assistant

Decision support systems built for radiologists need to cover a fairly wide range of image types, with the ability to route each image to the relevant algorithm. Furthermore, the training of such networks requires building large datasets with significant efforts in image curation. In situations where the DICOM tag of an image is unavailable, or unreliable, a classifier that can automatically detect the body part depicted in the image, as well as the imaging modality, is necessary. Previous work has shown the use of imaging and textual features to distinguish between imaging modalities. In this work, we present a model for the simultaneous classification of body part and imaging modality, which to our knowledge has not been done before, as part of the larger work to create a cognitive assistant for radiologists. This classification network consists of 10 classes built from a VGG network architecture using transfer learning to learn generic features. An accuracy of 94.8% is achieved.
Spatio-temporal super-resolution implementation based on camera array

Multi video super-resolution algorithms reconstruct high spatio-temporal resolution video by exploiting complementary information in multiple low-resolution video sequences. Aiming at improving spatio-temporal resolution of video for real-world applications, an algorithm is proposed using Maximum Posterior Likelihood - Markov Random Field (MAP-MRF) and implemented on camera array. Compared with the current algorithms for super-resolution reconstruction, the suggested algorithm is advantageous in keeping the edge sharpness and detailed texture, and robust against the noises. The experimental result has confirmed the effectiveness of the proposed method under the practical conditions such as large displacement and motion aliasing.
Methodological confusions in universal grammar: Reply to Kim

Kim (2018) argues that the method adopted in universal grammar (UG) is the same as that employed in the empirical sciences such as physics, chemistry and biology, and that my rejection of UG in Lin (2017a) amounts to a rejection of these sciences. In this reply I show that Kim has misunderstood the main point of my original paper, which is that UG is crucially different from typical scientific theories in that it does not consider causal factors. I further show that once this misunderstanding is cleared his objections can be dissolved smoothly, that the fatal problem with UG will become more evident, and that the methodological confusions embedded in Kim's arguments will be dispelled.
Security and Risks for Cloud Environments

Cloud computing has been considered a revolutionary approach towards the computing and a promising solution to increasing demand for using resources provisioned over the Internet which becomes more risky than ever. The cloud computing is a powerful technology in IT environment to provide cost effective, easy to manage, elastic, and powerful resources over the Internet. The Cloud computing offers several advantages and conveniences for today’s organizations. Personnel can work together in documents in real time from their phone or tablet from any location, and communicate instantly with teammates via video, voice, instant message, or email. Even though there are innumerable advantages to approaching the cloud computing, security standards are emerging—and constantly evolving—that directly address many of the challenges we already see today. This paper explores the key issues surrounding cloud security for providers, and passes on valuable guidance about not only information security, but more broadly risk. Also, it will help providers to make informed security decisions about their diverse cloud set-ups and better understand how to reap all the benefits of cloud without compromising their organizations’ security.
Revealing Correlations Between a System and an Inaccessible Environment

How can we detect that our local, controllable quantum system is correlated with some other inaccessible environmental system? The local detection method developed in recent years allows to realize a dynamical witness for correlations without requiring knowledge of or access to the environment that is correlated with the local accessible quantum system. Here, we provide a brief summary of the theoretical method and recent experimental studies with single photons and trapped ions coupled to increasingly complex environments.
The use of modular technology in the design of basic educational programs in accordance with the FES of HE 3 ++

The article shows the problems of developing educational programs and curricula in accordance with the Federal State Educational Standards of Higher Education (FES of HE 3++). Federal Law №273 «On Education in the Russian Federation» states that the introduction of basic educational programs implies the use of structures for the design of educational activities, based on the principle of a modular understanding of the basic educational program. and curriculum design. The article proposed one of the options for the formation of such modules in the design of basic educational programs in accordance with the FES of HE 3 ++. With this approach to educational programs formation in various areas of training students of higher education institutions, problems can be solved in the development of basic educational programs of previous FES of HE++, the labor costs of the teaching staff in developing various educational and methodical documentation can be optimized, the requirements of employers for the effectiveness of training future professionals can be taken into account.