Reviewerly/exHarmony huggingface dataset:
An Efficient Hashing Algorithm for NN Problem in HD Spaces

Nearest neighbor (NN) search is a fundamental issue in many computer applications, such as multimedia search, computer vision and machine learning. While this problem is trivial in low-dimensional search spaces, it becomes much more difficult in higher dimension because of the phenomenon known as the curse of dimensionality, where the complexity grows exponentially with dimension and the data tends to show strong correlations between dimensions. In this paper, we introduce a new hashing method to efficiently cope with this challenge. The idea is to split the search space into many subspaces based on a number of jointly-independent and uniformly-distributed circular random variables (CRVs) computed from the data points. Our method has been tested on datasets of local SIFT and global GIST features and was compared to locality sensitive hashing (LSH), Spherical Hashing methods (HD and SHD) and the fast library for approximate nearest neighbor (FLANN) matcher by using linear search as a baseline. The experimental results show that our method outperforms all state-of-the-art methods for the GIST features. For SIFT features, the results indicate that our method significantly reduces the search query time while preserving the search quality and outperforms FLANN for datasets of size less than 200 K points.
HECMI: Hybrid Ensemble Technique for Classification of Multiclass Imbalanced Data

Imbalanced data is a problem which is observed in many real-world applications. Although a lot of research is focused on achieving a solution to handle this problem, most of them assume binary classes. However, occurrence of multiple classes in most of the applications is not uncommon. Multiclass classification with imbalanced data poses additional challenges. This paper proposes a hybrid ensemble approach for classification of multiclass imbalanced data (HECMI). A hybrid of data based and algorithm based approach is proposed to deal with the imbalance and multiple classes. The ensemble created focuses on misclassified instances that are added to the partitioned dataset. HECMI proves to be more accurate than traditional algorithms.
Non-rigid Registration of Brain MR Images for Image Guided Neurosurgery Using Cloud Computing

Image guided Surgery (IGS) has become familiar on enhancing it with computerized technologies which makes processing much easier. These computerized systems are highly sophisticated and improvised which orients the surgeon to perform the surgery with three dimensional images of the patients MR images. This work presents the Non Rigid Registration of Brain MRI with the help of Cloud computing. There exist geometric ambiguities among individuals due to the varying shape of the cortex and this is a challenging task when it comes to registration process. Intensity based registration is not alone enough to tackle the issues hence anatomical knowledge is highly important. Segmentation and labelling of cortex sulci with non-parametric approach that enables capturing of its shape and topology. The intensity and feature points are matched using the registration energy. A linear combination of compact smooth linear field and branched basic function is formed. The addition of the sulci from the upper border till its bottom part makes the registration process even more efficient. Cloud computing has been used in two scenarios, Firstly, the complete workflow of registration is enabled at the cloud to handle hypothetical computations and obtain precise results. Secondly, storage and computational nodes are used to handle large amount number of images nearly 7TB. The Deformable registration is evaluated and its feasibility has been analysed using cloud resources which resulted in timely execution of complex components during registration. The result obtained has proved that Cloud Computing provides practical and cost effective support for telemedicine while performing Image guided Surgery. The accuracy level has been improved upto 62% while performing Non Rigid Registration.
Strategies for Development and Commercialization of Medical Devices: Taking Artificial Trachea as an Example

Universities, academic institutes and hospitals in Japan are home to some excellent basic research. However, often, the outcomes of basic research cannot easily be translated into drug discovery or practical use of medical devices, the so- called “valley of death.” Furthermore, some latest medicines that have already been introduced for practical use overseas are sometimes difficult to use in Japan, the so- called “device lag.”
Gray-Level Feature Based Approach for Correspondence Matching and Elimination of False Matches

Matching of interest points (feature points) is a basic and very essential step for many image processing applications. Depending on the accuracy of the matches, the quality of the final application is decided. There are various methods proposed to tackle the problem of correspondence matching. In this paper, a method that makes use of the textural features, mainly gray-level features with respect to a pixel’s neighborhood has been discussed for point matching with an emphasis on its use for image registration. Feature points are obtained from the images under consideration using SURF and are matched using gray-level features. Then, the false matches are removed using a graph-based approach.
Improved Intrinsic Image Decomposition Technique for Image Contrast Enhancement Using Back Propagation Algorithm

The technique of intrinsic image decomposition is based on the illumination value of the image. The histogram equalization value of the input image is calculated to increase the image contrast. In this research work, the back propagation algorithm is applied for the calculation of histogram equalization. The iterative process of back propagation is executed until error is reduced for the histogram equalization calculation. The simulation of the proposed modal is performed in MATLAB. The performance of proposed modal is compared in terms of PSNR and MSE.
Business Case as a Tool for Educating Schoolchildren

The practice of working with business cases contradicts all basic school education organizations' canons. Judge for yourself: the authors of the cases do not know initially how to solve them. They often do not even guess which methods they should use to do so. Moreover, they are not always sure that they formulate the problem correctly. However, students for some reason find such problems to be the most interesting to solve. The middle adolescence is the age when young people are in search of themselves. Therefore, these cases, dictated by the chaos of a changing life, serve as a real window to the world of future destiny for them. The chapter will consider the process of selecting enterprises that can become the authors of cases, reveal the stages of case creation, describe the problems that the designers of cases are faced with, analyze in detail the experience of organizing the educational process based on cases with schoolchildren, and provide examples of high quality scientific and technological cases.
Yet Another Size Record for AES: A First-Order SCA Secure AES S-Box Based on $$\mathrm {GF}(2^8)$$ GF ( 2 8 ) Multiplication

It is well known that Canright’s tower field construction leads to a very small, unprotected AES S-box circuit by recursively embedding Galois Field operations into smaller fields. The current size record for the AES S-box by Boyar, Matthews and Peralta improves the original design with optimal subcomponents, while maintaining the overall tower-field structure. Similarly, all small state-of-the-art first-order SCA-secure AES S-box constructions are based on a tower field structure. We demonstrate that a smaller first-order secure AES S-box is achievable by representing the field inversion as a multiplication chain of length 4. Based on this representation, we showcase a very compact S-box circuit with only one $$\mathrm {GF}(2^8)$$ -multiplier instance. Thereby, we introduce a new high-level representation of the AES S-box and set a new record for the smallest first-order secure implementation.
Relevance, Need, and Opportunity

This chapter shows the results of the market study developed at the request of the Faculty of Economics of the University of Colima, interested in participating in the online education environment, to know the demand that would have a Bachelor's degree in economics, under this modality. To carry out the analysis, there was the participation of 425 people who voluntarily answered a multiple-choice survey in person. Based on their answers, the aim is to identify, as mentioned above, the demand that a new distance education offer (online) would have for the economy area. Additionally, the chapter offers a review of the Mexican public policy in which these types of initiatives are framed; reflections on the environment and trends of development of online education and didactic-pedagogical trends in the area. This case study can be useful for researchers in the educational area who wish to know about online initiatives in countries with developing economies.
What’s that Sound? Culture, Significance and Interpretation of Electronic Sound and Noise

Electronic and acoustic sound and noise, including signal processed acoustic instruments, and autonomous IMSs (Interactive Music Systems), are key features of many online jam sessions. However, the types and qualities of sound that this introduces to an improvisation may be alien to many cross-cultural performers. This chapter explores the role of un-pitched, sound and noise, across musical cultures and how performers interpret them in intercultural tele-improvisation. Included in this examination, is how online cross-cultural performers perceive electronic sound, and the ways in which this shapes their interpretation and improvisatory responses. The investigation draws on the findings from the performance case studies and perspectives from practitioners and authors who incorporate these elements into their work. While exploratory in approach, consideration is also given to how networked performers engage with an IMS as a collaborative partner in intercultural tele-improvisatory context.
Do we have the time for IRM?

Distributed sensor networks such as IoT deployments generate large quantities of measurement data. Often, the analytics that runs on this data is available as a web service which can be purchased for a fee. A major concern in the analytics ecosystem is ensuring the security of the data. Often, companies offer Information Rights Management (IRM) as a solution to the problem of managing usage and access rights of the data that transits administrative boundaries. IRM enables individuals and corporations to create restricted IoT data, which can have its flow from organisation to individual control - disabling copying, forwarding, and allowing timed expiry. We describe our investigations into this functionality and uncover a weak-spot in the architecture - its dependence upon the accurate global availability of time. We present an amplified denial-of-service attack which attacks time synchronisation and could prevent all the users in an organisation from reading any sort of restricted data until their software has been re-installed and re-configured. We argue that IRM systems built on current technology will be too fragile for businesses to risk widespread use. We also present defences that leverage the capabilities of Software-Defined Networks to apply a simple filter-based approach to detect and isolate attack traffic.
Telematics, Art and the Evolution of Networked Music Performance

This chapter explores the evolution of practices and research in the fields of networked music performance (NMP) and telematic art with a focus on tele-improvisation. It outlines the development of telecommunications technologies in tandem to the creative projects and systems devised by artists and musicians. The emphasis here is on the innovations of the practitioner, adapting and developing approaches and technologies for creative purpose. It is not intended to be an exhaustive survey but rather an overview of telematic sound, music and arts practices, and the technological agenda that has shaped NMP research. This chapter highlights how artists have been at the forefront of exploring the conceptual and philosophical characteristics of remote networked interaction. Musicians, on the other hand, have primarily focused on the technical aspects of interface development, synchronisation, and connectivity. The literature reveals the need for a greater understanding of the interactive and experiential characteristics of the telematic musical experience, which this book addresses. The chapter begins with a summary and history of relevant network technologies and architectures to provide an overview of key terms and concepts referred to throughout the book.
Enhancing Teamwork Behavior of Services

Nowadays, many large software systems that are developed for business are mainly built from services leveraging the benefits of interoperability. However, the development of new technologies such as Cloud Computing, Internet of Things and Cyber Physical Systems create additional concerns that claim to be integrated into the existing approaches of modeling services, their interaction and cooperation. In this research, we propose web services to automatically cooperate using the role modeling approach by enhancing service’s interoperability through novel service teamwork roles. Teamwork contribution to the organizational performance has tracked attention of various research groups from several disciplines. In this direction, we contribute by determining the dominant teamwork roles that prevail during service group cooperation, link them with fifteen major teamwork factors that are recognized in agent-based teamwork, and indicate their primary teamwork behavior. A simulation using Monte Carlo presents results about how teamwork roles could affect and benefit the service cooperation.
Text-Independent Handwriting Classification Using Line and Texture-Based Features

This paper addresses the problem of making a machine recognize the writer by means of the handwriting. It delineates the preprocessing methods used to enhance handwritings, so as to ease the process of feature extraction. It discusses six statistical texture-based features that characterize a handwriting. Once these features are extracted, a nearest neighbor approach is used to classify a sample handwriting into one of those in the database. The methods are verified on a self-compiled database, and a performance evaluation is also performed. This method can be used to identify an unknown handwriting and is in specific demand in the forensic domain. Unlike other biometric identification methods, handwriting-based identification is the least intrusive.
Publisher's Note: Convergence of the multimode quantum Rabi model of circuit quantum electrodynamics [Phys. Rev. B 95 , 245115 (2017)]

Received 18 December 2018DOI:https://doi.org/10.1103/PhysRevB.99.039902©2019 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasLight-matter interactionQuantum description of light-matter interactionQuantum opticsQuantum optics with artificial atomsTechniquesFirst-principles calculationsRabi modelAtomic, Molecular & Optical
Incidence of mobile devices in the level of learning in students of the University Católica de Santiago de Guayaquil in the Faculty of Arts and Humanities in the career of Multimedia Arts

The development of technology has evolved the way to create and develop knowledge, this is why it is necessary to take advantage of the benefits brought by mobile devices, commonly known as Smartphones, and apply them in the learning processes through a virtual community that will facilitate the development and the sharing of information; this knowledge is based on cooperative learning supported by fast and effective communication services, easy access to the information through wireless connection and the application in modern society of the use and sharing of information created through knowledge that facilitates an inflected learning. This projects aims to create a methodological guide for the use of mobile devices in the teaching-learning process for the School of Multimedia Arts at Catholic University of Santiago de Guayaquil. The use of the application Evernote is highlighted as an effective tool for instant communication amongst the different members of a community conformed by students and teachers where the collaboration for creating new knowledge is a must.
An Estimation Method of Intellectual Concentration State by Machine Learning of Physiological Indices

Although recent information society has improved the value of intellectual work productivity, its objective and quantitative evaluation has not been established. It is suggested that intellectual productivity can be indirectly evaluated by estimating intellectual concentration states when giving cognitive load. In this study, therefore, the authors have focused on physiological indices such as pupil diameter and heart rate which are supposed to be closely related to cognitive load in office work, and an estimation method of intellectual concentration states from the measured indices has been proposed. Multiple patterns of classification learning methods such as Decision Tree, Linear Discrimination, SVM, and KNN were employed as the estimation method. Based on the estimation method, an evaluation experiment was conducted where 31 male university students participated and the measured psychological indices were given to the classification learning estimators.
Symmetric Decomposition of Convolution Kernels

It is a hot issue that speeding up the network layers and decreasing the network parameters in convolutional neural networks (CNNs). In this paper, we propose a novel method, namely, symmetric decomposition of convolution kernels (SDKs). It symmetrically separates k×k convolution kernels into (k×1 and 1×k) or (1×k and k×1) kernels. We conduct the comparison experiments of the network models designed by SDKs on MNIST and CIFAR-10 datasets. Compared with the corresponding CNNs, we obtain good recognition performance, with 1.1×-1.5× speedup and more than 30% reduction of network parameters. The experimental results indicate our method is useful and effective for CNNs in practice, in terms of speedup performance and reduction of parameters.
Stochastic Drop of Kernel Windows for Improved Generalization in Convolution Neural Networks

We propose a novel dropout technique for convolutional neural networks by redesigned Dropout and DropConnect methods. Conventional drop methods work on the individual single weight value of the fully connected network. When they are applied to convolution layers, only some kernel weights are removed. However, all the weights of the convolutional kernel windows together constitute a specific pattern, so dropping part of kernel window weights may cause change of the learned patterns and may model completely different local patterns. We assign the basic unit of drop method for convolutional weights to be the whole kernel windows, so one output map value is dropped. We evaluated the proposed DropKernel strategy by the object classification performance on CIFAR10 in comparison to conventional Dropout and DropConnect methods, and showed improved performance of the proposed method.
An Easy-Parallelizable Evolutionary Optimization Algorithm via Probabilistic Bit-Wise Operation for Template Matching

Pixel-based dense template matching still plays an important role in object localization tasks especially in the case of feature-less sequences. Instead of exhaustive search methods, optimization-based search strategy can converge to the global optimum faster by avoiding unnecessary tests. However, due to the presence of local optimums, conventional methods usually fail to converge in practice. In this paper, in order to prevent the optimization procedure from falling into a local optimal solution, we introduce a novel evolutionary operation called probabilistic bit-wise operation (PBO) into the framework of genetic algorithm (GA). Specifically, utilizing a natural phenomenon that the change of a higher-bit in an individual affect more than the lower-bit, the diversity of a population-based evolution algorithm can be well controlled by flipping each bit with different probabilities being assigned. Also, unlike crossover in GA, PBO only requires a single individual as the input, thus the parallelization can be easily implemented without considering the dependence between individuals. In the experiment, we compare against several classic optimization methods such as particle swarm optimization, GA, particle filter to show the superiority of PBO with a specific simulated benchmark. We also apply our method to a real sequence to show the practicality.
On Searching Maximal-Period Dynamic LFSRs With at Most Four Switches

Dynamic linear feedback shift registers (DLFSRs) are a scheme to transfer from one LFSR to another. In cryptography each LFSR included in a DLFSR should generate maximal-length sequences, and the number of switches transferring LFSRs should be small for efficient performance. This corresponding addresses on searching such conditioned DLFSRs. An efficient probabilistic algorithm is given to find such DLFSRs with two or four switches, and it is proved to succeed with nonnegligible probability.
Big Data, Privacy, and Healthcare

In the era of big data, large amounts of data are generated from different areas like education, business, stock market, healthcare, etc. Most of the available data from these areas are unstructured, which is large and complex. As healthcare industries become value-based from volume-based, there is a need to have specialized tools and methods to handle it. The traditional methods for data storage and retrieval can be used when data is structured in nature. Big data analytics provide technologies to store large amounts of complex healthcare data. It is believed that there is an enormous opportunity to improve lives by applying big data in the healthcare industry. No industry counts more than healthcare as it is a matter of life and death. Due to rapid development of big data tools and technologies, it is possible to improve disease diagnosis more efficiently than ever before, but security and privacy are two major issues when dealing with big data in the healthcare industry.
Discover, Imagine, Change: Community Place-Based Activities Using Unique Mobile Apps

Following the results of a study focusing on the unique affordances of mobile technologies that support their informed integration in learning environments, a novel pedagogy has been developed and tried out within the context of cultural, geographical, and archaeological heritage in three different communities of learners in Israel. The first part of this paper briefly presents the main findings of the study and sketches the emergent uniqueness profile of mobile apps for learning. The second part outlines the DICE model (Discover, Imagine, ChangE) for designing educational place-based mobile activities.
Risk Engineering and Blockchain: Anticipating and Mitigating Risks

Complex systems require an integrated approach to risks. In this paper, we describe risk engineering, a methodology to incorporate risks at the planning and design stage for complex systems, and introduce some of its components. We examine, at a high level, how risk engineering can help improve the risk picture for blockchain technologies and their applications and outline challenges and benefits of this approach.