Reviewerly/exHarmony huggingface dataset:
Knowledge ontology system

The paper considers a purpose and features of ontologies use in describing of subject area, both from a theoretical and an applied point of view. Criteria for identifying ontologies types are indicated. Based on cognition schematism principles, a knowledge representation ontologies system has been developed, that combines language, forms of knowledge representation and process schemes. It is shown that it is exactly such a system of ontologies makes it possible for the practical use of ontologies in computing environments.
A lightweight Data Sharing Scheme with Resisting Key Abuse in Mobile Edge Computing

To achieve large-scale access control over the shared data, attribute-based encryption(ABE) is a good choice. However, in the existing ABE schemes, a data user which possessing a decryption-key can regenerate a new key since key randomization technique is introduced, which will incur key abuse without any responsibility. In addition, to decrypt the ciphertext, computational complexity of a user is linear to the size of attribute set, it is a formidable challenge for the resource-constrained users. To overcome the problem above, we proposed a lightweight data sharing scheme with Resisting Key Abuse in MEC base on CP-ABE. By using transforming key technique and unforgeability of signature, the proposed scheme can not only resist decryption-key regeneration but also offload decryption computation to MEC server in order to reduce the computation complexity of data user. For a data user, it only takes two exponential operations to decrypt the ciphertext. Security proofs show that our proposed scheme can provide data confidentiality and strong key unforgeability. Compared to several schemes, the proposed scheme is show to have more advantages in terms of computational cost and communication overhead by experiment simulation.
Research on Hand Detection in Complex Scenes Based on RGB-D Sensor

Human gesture has the characteristics of intuitive, natural and informative, and is one of the most commonly used interaction methods. However, in most gesture interaction researches, the hand to be detected is facing detection camera, and experiment environment is ideal. There is no guarantee that these methods can achieve perfect detection results in practical applications. Therefore, gesture interaction in complex environments has high research and application value. This paper discusses hand segmentation and gesture contour extraction methods in complex environments and specific applications: First, according to the characteristics of depth map, an adaptive weighted median filtering method is selected to process depth data; then use depth information to construct background model which can reduce interference of noise and light changes, and combine RGB information and depth threshold segmentation to complete hand segmentation; finally, region grow method is used to extract precise gesture contour. This paper verifies the proposed method by using vehicle environment material, and obtains satisfactory segmentation and contour extraction results.
Evaluating NLG-frameworks for multilingual surface realization in conversational assistants

Conversational voice assistants that are available in multiple countries need to be able to generate utterances in the language that their users speak. In open domains, in which messages can be variable, pre-writing and translating all utterances in advance is unfeasible because it is costly, error-prone, and inflexible when changes need to be made. Approaches to automatically generate multilingual surface forms of utterances have been developed in the field of Natural Language Generation (nlg), however, these are rarely used when developing skills for conversational voice assistants. In this paper, we describe an evaluation study that analyses the feasibility of integrating nlg surface-realization frameworks (SimpleNLG and RosaeNLG) into the development process of an existing commercial and multilingual (English, French, German, Italian, Spanish) home-automation skill, and compare it to a more traditional localization approach. The study uses methods and measures from human–computer interaction and software engineering, and takes into account the perspective of various stakeholders in the development process (conversation designers, language experts and developers).
Wavelet transform for cardiac image retrieval

In this chapter, we present an overview of use of various wavelet transforms in content-based image retrieval method for diagnosis aid in cardiac fields. We start by describing the wavelet properties that are the most important for biocardiac applications. The retrieval performance analysis is provided for various orthogonal, biorthogonal, and Gabor wavelet transforms. The feature vector, used to measure similarity, is derived by computing the energy and standard deviation from the various wavelet filtered coefficients. The relevance similarity is ranked according to the closest similar measures computed by the Manhattan distance. The performance of all the wavelet transforms is evaluated on three different publicly available cardiac image databases, namely NEMA, OASIS, and EXACT09. The performance in terms of average retrieval precision (ARP) and average retrieval rate (ARR) is compared for all the wavelet transforms.
Writing science for the general public

In this exercise, you will practice communicating a scientific study to a general audience by taking a recent primary scientific journal article in animal behavior and translating it into a compelling science news story. First, you will need to develop a deep understanding of the study itself to gain insight into what is the “big picture”. As you develop your story, you will find that writing for the public is very different from the careful, calculated manner in which you are taught to report your findings for other scientists. In the second part of the exercise, you will have the opportunity to receive and provide peer feedback to help you learn the balance between jargon-heavy overly detailed reports and oversimplified stories that diminish or misrepresent the importance of the study.
Blockchain Interoperability from the Perspective of Interdependent Networks

In this chapter, we discuss blockchain interoperability from the perspective of interdependent networks. At first, we define blockchain interoperability, describe the design philosophy of interoperability explaining its entire life-cycle (including data generation, cross-chain mechanism, verification, smart contract, and API), and present a high-level representation of blockchain interoperability. After that, we describe how the blockchain interoperability concept can be beneficial for interdependent networks. We analyze the possible challenges we may face while enabling interoperability amongst multiple blockchains and show a comparison of existing blockchain interoperability schemes. To the end, we present open issues with challenges and highlight some research directions on the blockchain interoperability domain.
Environments and contexts STEM – STEAM education : A Systematic Literature Review

The objective of this research is to describe STEM education, even more so with the incorporation of art being STEAM, taking into account studies in science, technology, engineering, art and mathematics worldwide through a systematic literature review.83 articles were taken from the Scopus database, selected through a methodology for engineering and education, based on the environments in which they are developed, the intervention of art and the contexts of STEAM education.The results indicate that STEAM education is carried out in settings under educational policies and reforms to improve teaching - learning, requiring change and its integration into the curriculum, STEAM learning environments should be created based on the study plans of the Ministry of Education to integrate all subjects. The incorporation of art has allowed the development of new skills such as creativity and collaboration. On the other hand, the case studies that have given good results in countries such as: Malaysia, United Kingdom, Mexico, United States, China, Hong Kong, Australia, India and Indonesia, Japan, Germany, Qatar, South Korea, Taiwan, Turkey where they make an adaptation of the curriculum and learning policies based on projects and technological design.
Medical Image Watermarking Based on Secret Sharing and Integer Wavelet Transform

Abstract In telemedicine, the medical data are shared and distributed between the whole world with different specialists and for many purposes through an unsafe medium. So protecting the medical data during the transmissions becomes an important issue. Many image secret sharing schemes with steganography have been proposed. Unfortunately, each of these schemes has one or more drawbacks. First, the large size of a stego images. Second, the visual quality of the stego images evaluated by the peak signal-to-noise ratio (PSNR) is degraded too much. To overcome such drawbacks, a new scheme based on secret sharing and IWT is proposed in this paper. The new scheme can optimize both the size of the stego images and its quality. The proposal scheme involves a dispersion of medical image into shadow images using Lin and Thien’s technique. The size of each shadow size is reduces to 1/k from the overall of the secret image size and k is the number of shadows. After that, the shadows are embedded in a host image by using Integer wavelet transform (IWT) technique. In the reconstruction the secret medical image is reconstruct by pooling at least k shadows The experimental results of the proposed algorithm are shown for many medical images the effectiveness of analyzed with the help of the peak signal-to-noise ratio and normalized correlation.
Text Summarization Using Natural Language Processing

It is very difficult for human beings to manually extract the summary of a large document of text. There are plenty of text materials available on the Internet. So, there is a problem of searching for relevant documents from the number of documents available and absorbing relevant information from it. In order to solve the above two problems, the automatic text summarization is very much necessary. Text summarization is the process of identifying the most important meaningful information in a document or set of related documents and compressing them into a shorter version preserving its overall meanings. The different dimensions of text summarization can be generally categorized based on its input type (single or multi-document), purpose (generic, domain specific, or query-based), and output type (extractive). The main purpose is to provide reliable summaries of web pages or uploaded files depend on the users. This paper summarizes how a web application can convert generate keywords from the entered text or file or URL. It also converts the resultant summary into the audio file (.mp3).
The Stream Exchange Protocol: A Secure and Lightweight Tool for Decentralized Connection Establishment

With the growing availability and prevalence of internet-capable devices, the complexity of networks and associated connection management increases. Depending on the use case, different approaches in handling connectivity have emerged over the years, tackling diverse challenges in each distinct area. Exposing centralized web-services facilitates reachability; distributing information in a peer-to-peer fashion offers availability; and segregating virtual private sub-networks promotes confidentiality. A common challenge herein lies in connection establishment, particularly in discovering, and securely connecting to peers. However, unifying different aspects, including the usability, scalability, and security of this process in a single framework, remains a challenge. In this paper, we present the Stream Exchange Protocol (SEP) collection, which provides a set of building blocks for secure, lightweight, and decentralized connection establishment. These building blocks use unique identities that enable both the identification and authentication of single communication partners. By utilizing federated directories as decentralized databases, peers are able to reliably share authentic data, such as current network locations and available endpoints. Overall, this collection of building blocks is universally applicable, easy to use, and protected by state-of-the-art security mechanisms by design. We demonstrate the capabilities and versatility of the SEP collection by providing three tools that utilize our building blocks: a decentralized file sharing application, a point-to-point network tunnel using the SEP trust model, and an application that utilizes our decentralized discovery mechanism for authentic and asynchronous data distribution.
Vmemcpy: Parallelizing Memory Copy with Multi-core in the Cloud

Memory copying is one of the common operations in modern operating systems and servers. It is simple but synchronous and deterministic. However, with the coming of big data era, the size of memory data to be copied is increased rapidly in the cloud servers, resulting in blocking the copying thread/process and eventually hurting the responsiveness of a system. In this paper, we propose Vmemcpy, a method for parallelizing memory copy with multi-core CPU, which is the standard hardware equipment among current cloud infrastructures. Vmemcpy mainly improves memcpy function in user space, dividing the copying data into several sub-blocks. Then each of them is executed by a separated thread running on the dedicated CPU core, forming parallel copying and thus reaping performance improvement. Due to the necessary split procedure, Vmemcpy manifests its own cost on copying small data. To deal with this issue, Vmemcpy intelligently determines the copying threshold on different servers, then uses the original copying routine to copy the small data while employing multi-thread to handle with the large one. We implement Vmemcopy on both servers of Linux and Windows. The experimental results show that Vmemcpy brings up to 38.9% reduction on the latency of memory copying. At the same time, moderate overhead is generated when dealing with the small data.
Characterizing Student Proficiency in Software Modeling in Terms of Functions, Structures, and Behaviors

Software modeling is an integral practice for software engineers, especially as the complexity of software solutions increases. Unified Modeling Language (UML) is the industry standard for software modeling. however, it is often used incorrectly and misunderstood by novice software designers. This study is centered around understanding patterns of student proficiency of abstraction and systems thinking within a software modeling context. The participants of this study (n = 97) belonged to a systems analysis and design course that is primarily taken by second-year university students. The exam solutions to a case study from the course were evaluated for modeling proficiency. As evidence of proficiency in abstract thinking and systems thinking, we evaluated UML activity diagrams, class diagrams, and sequence diagrams and the alignment between these representations in terms of functions, structures, and behaviors. The results suggest students being proficient in modeling the functional aspects of an information system while facing some difficulty in capturing the structural and behavioral aspects of an information system. Clustering analysis revealed two groups within the sample, with one group displaying a significantly higher abstraction and systems thinking ability. Statistically significant correlations were also found between student proficiency of abstraction and their modeling proficiency in terms of functions, structures, and behaviors.
A Completely Distributed Blockchain Period Authentication Framework

Abstract The time capsule that would be opened in the future without third-party intervention was always a difficult issue. Although many researchers work on various systems, there are potential limitations, such as unreliable decryption period not entirely decentralised, which are difficult to estimate the needed data resources. In this post, we introduced a protocol and a safe cryptographic way to open a timely message in an advanced, decentralised environment to match in with several computing power conditions. The methodology also allows participants to gain extensive benefits of adding their computing resources, making our system more suited for applications in real life.
Inductive and Effective Privacy-preserving Semi-supervised Learning with Harmonic Anchor Mixture

Distributed privacy-preserving data mining (DPPDM) has been attracting enormous attention. It allows multiple participants to jointly use their datasets as a whole to train a model while preserving data privacy. Many works have been looking into the semi-supervised learning in DPPDM, to combine both labeled and unlabeled data for better performance. However, these works only provide transductive solutions, which means they can only give predictions for instances in the training set, and not for any new data sample beyond the set. Meanwhile, these methods are constructed with approximate calculations for security concerns, leading to sub-optimal results and limited effectiveness. In this paper, a mixture-model-based solution is proposed for inductive and effective semi-supervised learning in DPPDM. Our motivation lies in combining mixture models and graph-based methods to construct an anchor mixture with the ability of label prediction. We also propose an optimization process, which is accurately calculated through secure computation protocols, to achieve effectiveness. Experiments on synthetic and real-world datasets demonstrate that our proposal outperforms state-of-the-art methods in both transductive and inductive tasks.
Mobile-based Speech Recognition for Early Reading Assistant

Abstract In Malaysia, English language is the secondary language for most of the people and they usually rely on the education system to learn the language especially in learning how to read and pronounce the words correctly. As a multiracial country, Malaysians have many dialects according to the different states, which can affect the children’s pronunciation of the language. Consequently, their own style of dialect causes the children to struggle in developing the right pronunciation during the early years of school. The children will be exposed to the proper language at school but limited to the weekdays and school hours. Therefore, the technology of computerized applications has a high potential to help young kids in reading with the correct pronunciation. This paper presents the development of a mobile application with speech implementation based on Flutter framework for early reader assistant. The project is evaluated based on the subjective satisfaction towards the application in terms of overall system and functionality of the application, ease of login into the application and the difficulty of the words provided in the application. Based on the study, an average mean score of 3.36 is obtained in which most of the users feel that the application can deliver its function and purpose to the users. In conclusion, the mobile application should be able to aid the users in improving their reading skills and pronunciation.
The exploration of PBL mixed teaching mode in secondary vocational classes

Abstract The The large area coverage of network and the upgradation of intelligent software promote the further reform of network teaching. With the discussion of hot topics of Online teaching and Network teaching, teachers are also experiencing how to adopt appropriate teaching methods to solve the teaching problem of “being apart from each other” between teachers and students. The rise of platform technology and technical resources also promote the innovation of teaching. This paper will research on the mixed teaching mode based on PBL learning method, using platform tools to explore the practical significance of this teaching mode in secondary vocational classes, it provides a kind of thinking framework for teachers’ reference.
Synthesis and study of evolutionary optimised sensor linearisation with translinear &amp; FPGA circuits

Linear response characteristics of sensors are affected by ageing and changes in material properties. However, existing methods for improving these characteristics are disadvantaged by recurrent full-scale errors and insufficient speed. Accordingly, this study aims to reduce temperature sensor non-linearity through implementation of evolutionary optimised non-linear functions via a translinear-based application-specific integrated circuit. Moreover, the translinear-based analogue computation method is compared to a proposed field programmable gate array (FPGA)-based digital computation methodology to develop an alternative method for linearisation at sensor nodes (edge computing). The optimal values of the linearisation parameters for selected thermocouples are determined using the covariance matrix adaptation evolutionary strategy. Specifically, these optimal values are input in order to realise the non-linear functions. Experimental results reveal that FPGA offers a lower full-scale error than that of the translinear-based computation, although the translinear method can accomplish the required linearisation with fewer components (e.g. Data Converters), less delay and less power. Further, analogue computation through the proposed single chip-based method can utilise the latest processes, e.g. gallium nitride or pseudomorphic high electron mobility, for an enhanced error reduction. The comparative result shows that the translinear method with recommended processes can correct the non-linearity of sensors in all aspects.
Comparison of Consolidation Methods for Predictive Learning of Time Series

In environments where various tasks are sequentially given to deep neural networks (DNNs), training methods are needed that enable DNNs to learn the given tasks continuously. A DNN is typically trained by a single dataset, and continuous learning of subsequent datasets causes the problem of catastrophic forgetting. Previous studies have reported results for consolidation learning methods in recognition tasks and reinforcement learning problems. However, those methods were validated on only a few examples of predictive learning for time series. In this study, we applied elastic weight consolidation (EWC) and pseudo-rehearsal to the predictive learning of time series and compared their learning results. Evaluating the latent space after the consolidation learning revealed that the EWC method acquires properties of the pre-training and subsequent datasets with the same distribution, and the pseudo-rehearsal method distinguishes the properties and acquires them with different distributions.
MDPeQA: A Pediatric Question Answering System Based on Multiple Data Sources

Child healthcare and disease diagnosis and treatment are a focus of most families in our society. The goal of our research is to provide accurate and useful pediatric information consultation. In this paper, we report our development of a pediatric question answering system, which generates answers from multiple data sources including knowledge graphs, medical encyclopedia, medical textbooks and medical records. When a user inputs a question, the system first uses AC automata to map the question into a logical form and then retrieves information accordingly from knowledge graphs and medical documents. We formulate a scoring mechanism to decide which answer to be returned for a question. One month after the system was put into trial use in a Grade III Level A hospital in China, its satisfaction rate reached 86.7% by user rating.