Reviewerly/exHarmony huggingface dataset:
Usage of HMM-Based Speech Recognition Methods for Automated Determination of a Similarity Level Between Languages

The problem of automated determination of language similarity (or even defining of a distance on the space of languages) could be solved in different ways – working with phonetic transcriptions, with speech recordings or both of them. For the recordings, we propose and test a HMM-based one: in the first part of our article we successfully try language detection, afterwards we are trying to calculate distances between HMM-based models, using different metrics and divergences. The Kullback-Leibler divergence is the only one we got good results with – it means that the calculated distances between languages correspond to analytical understanding of similarity between them. Even if it does not work very well, the conclusion is that this method is usable, but usage of some other methods could be more rational.
Maxima-Turn-Switching Strategy of Sensor-Equipped UAVs for Target Localization in 2-D and 3-D Environments

This paper presents an inter-loop switched-turn strategy to drive a mobile sensory aerial agent to the unknown source of a scalar signal field in planar and 3-D environments. The source may be stationary, moving or maneuvering, but assumed to be of constant signal strength. No range, bearing angle or gradient information is considered to be available. The only available information are the scalar measurements of the signal field taken by the on-board sensor of the UAV at each time-instant, and the signal strength is assumed to lessen with distance from the source. The effectiveness of the presented method in improving estimation of the direction to the source and in approaching closer to the source in each subsequent loop is shown, and verified by simulation results, both in the presence and absence of noise in signal measurement. The time-efficiency of the proposed method for varied initial conditions, and stationary and mobile sources, is shown through simulation results by comparison with two existing methods.
Recognition of the Remote Sensing Scenes From Unseen Classes

Existing scene classification models tend to be only capable of recognizing scene images from classes which have been learned by the models. This implies that testing images from classes which are not used for training the models cannot be recognized. There are tens of thousands of scene classes in the real world and it is quite infeasible for us to label all these classes for model training. Therefore, how to develop a classification model that recognizes scene images from unseen classes has been an open problem. To address this issue, we investigate the underlying relations between scene attributes and scene classes. We observe that one scene class can be characterized by a few scene attributes, and furthermore, one unseen scene class can be depicted by seen attributes. In the light of this observation, we train support vector machines to classify scene attributes rather than scene classes. We determine the attributes of a scene image from an unseen class by the support vector machines. This enables us to infer the unseen class of the scene image with respect to the attribute-class relations. Experimental results validate our framework.
DESIGN OF PEDAGOGICAL ABILITY DEVELOPMENT PROGRAM: PROCESSING AND CONTENT ASPECTS

The article has been devoted to one of the most critical issues of the modern psychological science – development of pedagogical skills. The described program is a response to the lack of effective models of development of pedagogical skills. Besides the presentation of the program, the article also has described the integration of the existing models of professional development into one program, which encouraged positive changes among lecturers and teachers. It has been emphasized that awareness of the own mission in relation to pedagogical activity is critical in the development of pedagogical skills. The program included reflexive discussions, informational sessions and seminars aimed at updating and elaborating various content aspects of pedagogical activity, pedagogical thinking and self-awareness of the pedagogues.&nbsp
Multi-scale Convolutional Neural Network Based on 3D Context Fusion for Lesion Detection

Lesion detection is an essential technique in medical diagnostic systems. Since there are great differences in intensity and appearance within a same lesion category, lesion detection from computed tomography (CT) scans is still a challenging task. Sufficiently using 3D context information become the research hotpot in lesion detection area, since algorithms can benefit from geometry and texture of lesions. Motivated by this trend, we propose a multi-scale CNN based on 3D context fusion, called M3DCF, for extracting lesion area from CT scans. In order to speed up the algorithm, the one-stage regression-based detector, rather than region proposal network, is adopted. Specifically, we employ 3D context fusion strategy that allows M3DCF fusing features from neighboring slices. Finally, we use a multi-scale scheme to combine low-level and high-level features. This strategy allows us to get more meaningful semantic information. The experimental results conducted on DeepLesion dataset indicates that the proposed method outperformed state-of-the-arts, including RetinaNet, Faster R-CNN, and 3DCE. The source code is available on https://github.com/JMUAIA/M3DCF .
Multi-Variant Execution Research of Software Diversity

Abstract As more and more software products are threatened by malicious reverse analysis, along with software products are pirated, tampered with and so on, it is of great significance to study software security protection technology in depth. As a software security protection technology, software diversification introduces uncertainty into the target program and provides probabilistic protection for the target program. Multi-Variant Execution (MVE) are fine-grained implementation of software diversification that produces functionally identical variants at the system call level. This article first introduces the related concepts of multi-variant execution. Secondly, it expounds the key technologies of multi-variant execution implementation-variant generation, variant monitor, input/output and synchronization, monitor-variant communication. Security of monitors and different variant communication technologies are analyzed and compared, their advantages and defects are pointed out respectively. Several implementation methods of multi-variant execution design are summarized. Finally, the reasons for the false negatives and false positives of attack events are analyzed, and the prospects and challenges of using multi-variant execution techniques to implement security systems are summarized.
Fast inference services for alternative deep learning structures

AI inference services receive requests, classify data and respond quickly. These services underlie AI-driven Internet of Things, recommendation engines and video analytics. Neural networks are widely used because they provide accurate results and fast inference, but it is hard to explain their classifications. Tree-based deep learning models can provide accuracy and are innately explainable. However, it is hard to achieve high inference rates because branch misprediction and cache misses produce inefficient executions. My research seeks to produce low latency inference services based on tree-based models. I will exploit the emergence of large L3 caches to convert tree-based model inference from sequential branching toward fast, in-cache lookups. Our approach begins with fully trained, accurate tree-based models, compiles them for inference on target processors and executes inference efficiently. If successful, our approach will enable qualitative advances in AI services. Tree-based models can report the most significant features in a classification in a single pass. In contrast, neural networks require iterative approaches to explain their results. Consider interactive AI recommendation services where users seek to explicitly order their instantaneous preferences to attract preferred content. Tree-based models can provide user feedback much more quickly than neural networks. Tree-based models also have less prediction variance than neural networks. Given the same training data, neural networks require many inferences to quantify variances of borderline classifications. Fast tree-based inference can explain variance in seconds (versus minutes). Our approach shows that competing machine learning approaches can provide comparable accuracy but desire wholly different architectural and platform support.
A Fast Public Key Searchable Encryption Scheme Against Inside Keyword Attacks

With the advent of the era of cloud computing technology, the security and search efficiency of ciphertext retrieval have has become the focus of research. However, in the traditional encryption schemes, most of them only solve the problem of defending against external keyword guessing attacks. We tend to ignore malicious third party cloud server provider, which sometimes tries to guess the user’s ciphertext information through the trapdoor or keywords. For the sake of improving the security of ciphertext, an inside keyword attack scheme on the basis of inverted index is proposed. Firstly, When building inverted indexes, the private key of the data owner is added to protect against the attack of malicious server. Secondly, an efficient public key ciphertext search scheme of parallel encryption index structure is introduced to realize the parallel search task of keywords. Compared with the traditional public-key searchable encryption, our scheme not only improves the search efficiency but also improves the security of the system.
Hierarchical Identity-Based Signature over Verifiable Random Function

Hierarchical computation makes an important role in constructing identity-based signature (IBS) since it provides a delegation mechanism to IBS, which results in the Hierarchical identity-based signature (HIBS). HIBS has widely potential applications in the large networks. However, the constructions available cannot propose a good trade-off for the private keys and signatures since the size of private keys or signatures depends on the identity depth. In this paper, a new hierarchical computation algorithm is introduced to construct HIBS scheme. The new scheme achieves O(1)-size private keys and signatures, which are independent of identity depth. It is the best trade-off at present. Furthermore, under the $$n+1-weak$$ Computational Diffie-Hellman Exponent ( $$n+1-wCDH$$ ) assumption, the scheme is provably secure against existential forgery in the standard model.
Framing Choice Architectures

This chapter is about cloud architectures, freedom of choice, and the legitimate scope of cloud brokers in softly nudging end-users and cloud providers—as a new form of soft paternalism—that can help them make better decisions without coercing or neglecting their choices. I attempt to examine these issues by taking into account the recent theories within behavioral law and economics, which are steadily on the rise and increasingly relevant as a point of reference in policy-making and regulation over the past decade. In this regard, behavioral economics offers a normative framework, which helps us better understand the pitfalls of the decision-making process.
A Framework for Real-Time 3D Freeform Manipulation of Facial Video

This paper proposes a framework that allows 3D freeform manipulation of a face in live video. Unlike existing approaches, the proposed framework provides natural 3D manipulation of a face without background distortion and interactive face editing by a user’s input, which leads to freeform manipulation without any limitation of range or shape. To achieve these features, a 3D morphable face model is fitted to a face region in a video frame and is deformed by the user’s input. The video frame is then mapped as a texture to the deformed model, and the model is rendered on the video frame. Because of the high computational cost, parallelization and acceleration schemes are also adopted for real-time performance. Performance evaluation and comparison results show that the proposed framework is promising for 3D face editing in live video.
KUALITAS LAYANAN KOLEKSI LANGKA Dl PERPUSTAKAAN GRHATAMA PUSTAKA YOGYAKARTA: STUDI KASUS PADA AKSESIBILITAS KOLEKSI

The title of this research is Service quality of rare collections in Grhatama Pustaka Library in Yogyakatta: a case study on the collection accessibility. In this research, the researcher analyzed the service quality of rare collections in Grhatama Pustaka Library with reference to indicators of accessibility, viz. easy to access, easy to use, relevant, easy to find location, up to date, reliable, quick to obtain and complete in collections. This research aimed to describe how an ideal service of rare collections is to be applied in a library. The researcher used qualitative research method with descriptive approach. From the research and the discussion conducted by the researcher, it concluded that the service of rare collections in Grhatama Pustaka Library had met the indicators of accessibility and availability or completeness of collection, however the service provided had not met the rest indicators as easy to use, relevant, easy to find location, up to date, reliable, quick to obtain.
An Improved Fully Convolutional Network for Learning Rich Building Features

Many efficient approaches are proposed to detect building in remote sensing images. In this paper, in order to learning rich building features better, we propose a full convolutional network with dense connection. There contributions are made: 1) To strengthen feature propagation, an improved dense network is introduced to the full convolution network. 2) We have designed top-down short connections to facilitate the fusion of high and low feature information. 3) In addition, we add the weighted cross entropy edge loss function to make the network pay more attention to building edge in detail. Experiments show that the proposed method achieves excellent performance on the remote sensing image data taken by the QuickBird satellite.
Open Science and Its Impact on Libraries, Publishers, and Authors

The open science movement enables the accessibility and reusability of research output across the globe. Researchers and other stakeholders in the research process can now easily collaborate to add to the body of knowledge. This chapter documents how open science is impacting the role of libraries, publishers, and authors in the digital era. A structured document analysis and web analysis were done to find out how authors, publishers, and librarians are affected by open science. It was found that librarians are taking advantage of open science to provide various information sources to patrons, the publishers are now charging article processing fees to make the journal articles open access upon publishing, and authors are now able to access many information sources during the research process and enjoy greater visibility of their research output. The author recommends the adoption of open science especially in the developing countries and the enactment of policies that support open science at national, regional, and international levels.
Online Trajectory Generation for Differential Drive Mobile Robots Using Multi-Dimensional Rate Limiters

This research considers rate limiters in a novel way to generate parameterized trajectories for differential drive mobile robots such that the wheel velocity and acceleration constraints are fulfilled. In order to obtain feasible wheel velocity reference signals for following a geometrical path, the classical second-order rate limiter is generalized to a two-dimensional second-order rate limiter. The target points are mapped onto a desired direction vector, which is then mapped to desired linear and angular velocities. The Kalai-Smorodinsky bargaining solution inspires the proposed mapping to a feasible solution that addresses the tradeoff between linear and angular motion while yielding a short running time.
L1 norm based double-prior learning for image denoising

Image denoising problem has attracted a large number of researchers. Generally speaking, there are two kinds of image priors considering the source of training sets, external priors and internal priors. The realistic image priors can be obtained from a large number of external example images or the corrupted internal image itself. However, external priors cannot give accurate image representations towards various corrupted images because the total number of example images is limited. While internal priors may bring too much noise along with useful information for denoising, which leads to unexpected denoising results. The most common assumption in denoising problem is that the image noise obeys Gaussian distribution, which is simple and ideal. If there are outliers in the corrupted images, Laplace distribution is more suitable to model the image noise. This paper proposes a denoising model towards image noise in Laplace distribution utilizing both external priors and internal priors. Gaussian Mixture Model (GMM) is used to model external priors and l1 norm is aimed to deal with outliers. Experiments on some publicly available databases show the performance of proposed method, resulting in denoised image of high quality.
Design Considerations for a Mobile Sensor-Based Learning Companion

This paper presents the concept of a mobile learning companion which uses sensor data to support self-regulated learning. Based on design considerations derived from previous work, a prototype of a mobile learning companion (Charlie) was developed as a student project at HTW Berlin. A first qualitative study with 4 students aimed at validating Charlie’s character as a friendly learning companion and its benefits and limitations for self-regulated learning. Future work will focus on improving Charlie to provide a positive learning support as a mobile learning companion.
A discretely adaptive connection logic network

This paper proposes a new model for a theoretical neural network that can be used as a guide for the design of future (quantum or optical) computational technologies. The model utilizes a uniformly connected nodal structure, where the connections are discretely adaptive and the nodes act as simple gatekeepers. The model replicates all known logics used in current electronics, such as AND, OR, XNOR, XOR, NOR, XNOR, NAND and NOT. Additionally, by using recurrent negating connections the model easily creates XOR gates, and adds novel sided and favoured gates. This model also facilitates the creation of ternary to n-ary gates, and it simplifies the creation of a number of majority functions (especially for an odd number of inputs). Also, as a multi-layered neural network, the model allows learning back propagation through the use of its negating connections. Finally, because of its adaptive connections, parts of the network can be used as internal memory. Overall, the model provides backward compatibility to existing CMOS circuitry, while opening up a number of new logics and architectures for neural computing.
Certification Attainment - A Gizmo to Evaluate Provider's Trust

The relevance and recompense offered by cloud services make it obligatory for researchers to address the issues faced by this paradigm. The two major issues faced by this fast growing and most rising technology of the last one and a half decades is trust and security. Although the literature has witnessed and practiced a lot of models that can address these issues, despite the efforts, a sharp increase can be seen in data leakage incidences and its leads to hamper trust of the consumers in the cloud services and service providers. In order to remove the obstacles that are hampering the growth of the technology. The authors have tried to work on the areas which are not yet explored to solve the trust problem which is an indirect outcome of security lapse instances. The idea is to utilize the power of certifications or implications of the following standards or working according to the guidelines mentioned. The objective of this research article is to analyze the service provider on these aspects and generate its trustworthiness.
Pedagogical reflection as a factor of professional development of future teachers of preschool education institutions

Formation and development of pedagogical reflection in the process of professional development of the future teacher of preschool education institutions to the needs of societyto become creative, able to take personal responsibility not only for the results of their professional activities, for their physical, mental and social health, the formation of the life competence of children of preschool age, but also for their professional self-realization and self-training, the personality of the future professional. The solution of this problem allows us to form, disclose and realize the ability of the future teacher of preschool education to forecast, design, implement and analyze their professional and quasi-professional activities. Analysis of the scientific foundation has allowed, in the broadest terms, to characterize reflection as a mirror, research of the cognitive act, a source of special knowledge, when observation is directed to the internal actions of consciousness; as a person’s ability to self-analyze, understanding and rethinking their subject-social relations with the outside world and as a necessary component of the developed human intelligence. Pedagogical reflection in modern studies is understood as self-analysis by a teacher of his professional activity, behavior, personal and professional experience, is considered in the context of intellectual and communicative processes, and therefore, is characterized by a combination of knowledge, action and experience. They are characterized by specific features that allow to reveal certain aspects of the future teacher’s reflective activity, and at the same time are interrelated and interdependent. The article highlighted the criteria and described the levels of formation of pedagogical reflection of future teachers of preschool education institutions (reproductive, analytical, prognostic, philosophical and constructive) and gave them a qualitative characteristic. The article suggests the forms, means and methods of organizing the educational process of the institution of higher education, contributing to the formation and development of the abilities of the future teacher of the preschool education institution for pedagogical reflection.
A vision for autonomous blockchains backed by secure hardware

Blockchains have emerged as a potential mechanism to enable immutable and consistent sharing of data across organizational boundaries. While much of the discussion on blockchains to date has been structured around public versus permissioned blockchains, both of these architectures have significant drawbacks. Public blockchains are energy inefficient, hard to scale and suffer from limited throughput and high latencies, while permissioned blockchains depend on specially designated nodes, potentially leak metainformation, and also suffer from scale and performance bottlenecks. This raises the question if blockchains, in their current form, are the only class of datastores that can provide such strong integrity guarantees.