Reviewerly/exHarmony huggingface dataset:
Robust and Secure Lucas Sequence-Based Video Watermarking

Currently, video copyright protection has become a challenging issue due to the growth of Internet technology and extensive use of multimedia. Digital watermarking plays an important role in protecting multimedia objects as they become easier to modify, copy and exchange data. Embedding the watermark in the video is done in different ways by many researchers. Most of the existing video watermarking (V-W) schemes directly apply image watermarking methods to raw and compressed video. Due to redundancy in the video frames, watermark embedding and extraction become time-consuming. To sort out this problem, the watermark is embedded into the selective key-frames. In this paper we use Lucas sequence (LS) for selection of key-frames and the watermark is embedded in selected key-frames only. The proposed scheme is the combination of DWT and DCT transforms that improves the performance. This approach not only saves time required for selecting key-frames but also proves to be better in terms of resistance to various types of attacks like frame dropping, frame averaging and noise addition. The proposed LS-based scheme is compared with [1–3]. It is observed that it outperforms in terms of security, robustness and imperceptibility. The performance of the proposed algorithm is evaluated using commonly used parameters PSNR and NCC. It is found that their average values are improved considerably. The extensive experimental simulations show that the proposed scheme is more efficient and robust against common attacks such as rotation, scaling, cropping, common image processing operations and geometric distortion.
Library & Cultural Services: Open Research: Open access publishing

Introduces the open research movement, and goes into more detail about different aspects of this movement, including open access.
Deep Graph Library: Overview, Updates, and Future Developments [GrAPL 2020 Keynote Speaker]

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Learning from graph and relational data plays a major role in many applications including social network analysis, marketing, ecommerce, information retrieval, knowledge modeling, medical and biological sciences, engineering, and others. In the last few years, Graph Neural Networks (GNNs) have emerged as a promising new supervised learning framework capable of bringing the power of deep representation learning to graph and relational data. This ever-growing body of research has shown that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations. Deep Graph Library (DGL) is an open source development framework for writing and training GNN-based models. It is designed to simplify the development of such models by using graphbased abstractions while at the same time achieving high computational efficiency and scalability by relying on optimized sparse matrix operations and existing highly optimized standard deep learning frameworks (e.g., MXNet, PyTorch, and TensorFlow). This talk provides an overview of DGL, describes some recent developments related to high-performance multi-GPU, multi-core, and distributed training, and describes our future development roadmap.
Library & Cultural Services: Open Research: Open access content

Introduces the open research movement, and goes into more detail about different aspects of this movement, including open access.
Distance labeling schemes for K4-free bridged graphs

k-Approximate distance labeling schemes are schemes that label the vertices of a graph with short labels in such a way that the k-approximation of the distance between any two vertices u and v can be determined efficiently by merely inspecting the labels of u and v, without using any other information. One of the important problems is finding natural classes of graphs admitting exact or approximate distance labeling schemes with labels of polylogarithmic size. In this paper, we describe a 4-approximate distance labeling scheme for the class of K4-free bridged graphs. This scheme uses labels of poly-logarithmic length O(log⁡n3) allowing a constant decoding time. Given the labels of two vertices u and v, the decoding function returns a value between the exact distance dG(u,v) and its quadruple 4dG(u,v).
Students’ perceptions towards multimedia elements in Sounds and Pronounce Interactive Software (SPISE) for English / Sueb Ibrahim, John Francis Noyan, Lilly Metomand, Ismariani Ismailand Lenny Yusrina Bujang Khedif

This paper presents students’ perceptions towards multimedia elements in Sounds andPronounce Interactive Software (SPISE) for English. SPISE is an interactive multimediasoftware, which attempts to introduce learners to the English phonetic system. It is a learningaid that teaches learners the correct pronunciation of English words. The collection of data forthis research paper involved the administration of questionnaires to 74 students of SK TASUDA(Tan Sri Sulaiman Daud), Kuching, Sarawak, comprising Primary 5 and 6 students. Prior to thedistribution of questionnaires, the participants were guided on how to use SPISE, and theyexperienced learning the English phonetics using the software. From the findings, highresponses were obtained for the software’s interface, text, graphic, audio, interactivity andeffectiveness (above 3.80). In addition, as SPISE promotes self-directed learning by providingimmediate feedback, the students found the learning fun and interactive. Hence, it could beconcluded that SPISE is a useful, fun and interactive software for a teaching and learning aid,which could help learners master their English phonetics.
Virtual Tribes: Analyzing Attitudes Toward the LGBT Movement by Applying Machine Learning on Twitter Data

In this paper, we investigate the application of machine learning techniques in the context of social media. Specifically, we aim at drawing conclusions from users’ Twitter behavior and language to users’ attitudes toward the LGBT movement. By using an adjusted procedure of the Cross Industry Standard Process for Data Mining (CRISP-DM) process, we create a prediction model for investigating and identifying those attitudes. Furthermore, we formulate step-by-step instructions for its deployment. We provide the reader with a theoretical background for our research domain and describe the methods that we use. Results show that there are two groups of contrary attitudes toward the LGBT community and that the language and behavior of users in the groups, respectively, differ from each other. Also, we identify word analyses as a valuable means for prediction. We also apply our model on another dataset to investigate its interspersion with the previously identified groups and demonstrate its effectiveness for predicting attitudes of a single actor on Twitter. Finally, we critically assess our findings and propose further fields of investigation in this area.
Impact of Near-Time Information for Prediction on Microeconomic Balanced Time Series Data using Different Machine Learning Methods

Instead of relying solely on data of a single time series it is possible to use information of parallel, similar time series to improve prediction quality. Our data set consists of microeconomic data of daily store deposits from a large number of different stores. We analyze how prediction performance regarding a given store can be increased by using data from other stores. First we compare several machine learning methods, including Elastic Nets, Partial Least Squares, Generalized Additive Models, Random Forests, Gradient Boosting and Neural Networks using only data of a single time series. Afterwards we show that Random Forests are able to better utilize parallel time series data compared to Partial Least Squares. Using near-time data of parallel time series is highly beneficial for prediction performance. To allow a fair comparison between different machine learning methods, we present a novel hyper-parameter optimization technique using a regression tree. It enables a fast and flexible determination of optimal parameters for a given method.
Automated characterisation of multi-view fringe projection system for three- dimensional measurement of additively manufactured parts

Digital fringe projection is a non-contact method that is widely used for the dimensional characterisation of complex manufactured parts. However, single camera-projector fringe projection systems struggle to acquire the full three-dimensional point cloud in one acquisition due to their relatively small field-of-view, and the typically freeform geometry, potentially with multiple occlusions, of additively manufactured parts. In this paper, we demonstrate that a multi-view fringe projection system is an effective solution to address form measurement of complex additively manufactured parts. However, the global geometric characterisation of multiple sets of cameras and projectors is a challenge due to the lack of a common field-of-view and overlapping of the projected fringes. We use a cost-effective multi-view fringe projection system to characterise an assembly of multiple sets of cameras and projectors with different perspectives. We present an automated characterisation method that uses a checkerboard which is moved in the measurement volume. The absolute phase information from the captured phase-stepped images is used to establish the global geometric properties by automated image processing and parameter optimisation. The geometric characterisation method is implemented and the multi-view system has been used to measure a range of additive parts. In this paper, we present the three-dimensional reconstruction results from different views that are combined to optimise the global geometric parameters.
Competencies for Creating an Integrated Approach in the Educational Process of Preschool Student Teachers

The preschool teacher is the main initiator of the dynamics and efficiency of the educational process.The children need to acquire more functional knowledge, which raises questions about the preschool teacher students' competencIes.The paper emphasizes the necessity of raising awareness of preschool student teacherS about the importance of their role in enriching children's experiences, and bringing scientific, musical and linguistic phenomena closer.This further implies modernization of university education by encouraging and raising students' motivation to apply innovative approaches to work and the creation of specific competencies and skills.The competence of preschool student teachers in the realization of activities is not only reflected in building moral values and habits among children, but also in activating children's potentials and encouraging creativity.The aim of the research was to examine the attitudes of preschool student teachers towards their competencies necessary for implementation of an integrated approach.The method of theoretical analysis and scaling techniques are used.The paper emphasizes the importance of extending specific professional competencies of preschool teacher students, as well as the importance of the continuous development of pedagogical knowledge for supporting holistic development of children, through monitoring, listening and supporting various children's expression (sound and voice, dance, movement, narration, etc).
Scalable Deep Learning Inference: Algorithmic Approach

Large-scale deep learning training has made significant progress in the last few years: more powerful systems/accelerators are delivered (i.e., Summit cluster), innovative training mechanisms are designed (i.e., sophisticated hyper-parm tuning), and advantage communication techniques are exercised (i.e., async-SGD). However, deep learning inference has rather limited options when it comes to scaling up the model density per device. Quantization to lower precision can be helpful along with sparsification such as pruning and compression yet suffers from the underlying hardware architecture and efficacy. In this talk, I like to highlight some of the early efforts to scale up the inference from algorithm perspectives, specifically based on compute sharing and reuse. Compute sharing across multiple models enables a highly dense inference platform by fundamentally reducing the resource demand, and compute reuse based on sensory inputs further improves the performance of an individual model.
A Hybrid Cost-Effective Genetic and Firefly Algorithm for Workflow Scheduling in Cloud

Cloud computing is developing as a new platform that gives high-quality information over the Internet at a very low cost. But still, it has numerous concerns that need to be focused. Workflow scheduling is the main serious concern in cloud computing. In this paper, we propose a Hybrid Cost-Effective Genetic and Firefly Algorithm (CEFA) for Workflow Scheduling in Cloud Computing. In the existing approach, the number of iteration was very large which increases the total execution cost and time which we will optimize in the proposed algorithm. The performance is estimated on scientific workflows and the results show that the proposed algorithm performs better than the existing algorithm. Three parameters are used to compare the performance of the existing and proposed algorithm; (1) execution time, (2) execution cost, and (3) termination delay.
Numerically Robust Maximum Correntropy Criterion Kalman Filtering Using the UD Covariance Factorization Approach

The paper explores the maximum correntropy criterion Kalman filtering (MCC-KF) approach to estimating an unknown dynamic state of linear state-space models in the presence of non-Gaussian uncertainties and/or outliers. A widespread application of the MCC-KF estimator for solving practical problems has highlighted a special attention to the numerical stability issue. In this paper, we give a solution to an open problem formulated in our recent work for the class of UDU <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> factorization-based MCC-KF methods. More precisely, we reveal a new stable MCC-KF implementation that outperforms the existing Cholesky factorization-based algorithms for estimation quality. The results of numerical experiments are also provided.
Formation of Research Skills at Younger Schoolchildren: Experience of Kazakhstan

The article considers the experience of schoolchildren of Kazakhstani schools in teaching research skills. The relevance of the problem is due to the creation of an educational space favorable for harmonious formation and development of the personality of schoolchild, possessing the basics of the following skills of a wide spectrum: functional and creative application of knowledge; critical thinking; carrying out research works.The novelty of the work consists in the development and justification of organizational-pedagogical conditions affecting the efficiency of formation of research skills at younger schoolchildren in the educational space in the conditions of the new educational paradigm. The mechanism of implementation of organizational-pedagogical conditions is based on the identification of specifics of formation of research skills; development and testing of organizational-pedagogical conditions on the formation of research skills at younger schoolchildren in educational institutions of the Republic of Kazakhstan. The research results confirmed the effectiveness of the suggested program on formation of research skills at younger schoolchildren. Developed and experimentally tested organizational-pedagogical conditions on the formation of research skills at younger schoolchildren can be widely used in primary school.
Assessing the quality of evolving Haskell systems by measuring structural inequality

Software metrics are used to measure the quality of a software system, and to understand the evolution of the system's quality over time. In this paper we report on an empirical study that investigates whether structural degradation in Haskell systems is related to decreasing software quality. For our study we use three metrics that measure internal attributes at the level of Haskell modules: intra-modular complexity (cohesion), inter-modular complexity (coupling), and module size. For these metrics, we calculate the Gini coefficient, which is a measure of the inequality in a distribution of values within a certain population, and the deviation of the population's central tendency from an empirically established ideal value. We develop a method to track the evolution, and measure the correlation between the calculated system-level information and post-release defects.
How QBF Expansion Makes Strategy Extraction Hard.

In this paper we show that the QBF proof checking format QRAT (Quantified Resolution Asymmetric Tautologies) by Heule, Biere and Seidl cannot have polynomial time strategy extraction unless P=PSPACE. In our proof, the crucial property that makes strategy extraction PSPACE-hard for this proof format is universal expansion, even expansion on a single variable.
PARTIAL FACE RECOGNITION USING DYNAMIC FEATURE MATCHING AND CNN

As the computer generation grows, security and safety are being an active topic. The most active and most advanced problem is face detection and face recognition. Nowadays, detection and recognition of frontal face images are achieved in all computer fields, from identification to authorization and authentication. Still, the problem is to deal with a partial face. Sometimes the obtained picture may be occlusions, large-viewing angles, or out-of-view. Identifying a partial face and recognition isn't an easy task. We propose a technique in this paper to detect the partial face and recognize them using various steps. Our method helps to identify the partially appearing faces with very low computational complexity. We couple the detected facial segment, which allows detecting partially visible faces. Once a partial face is detected, the facial sections can be used in many applications. The partially detected probe is sent to a face recognition model to discover the person. We use FCNs (Fully Convolutional Networks) and SRC (Sparse Representation Classification) combined using DFM (Dynamic Feature Matching) to handle the recognition of partial face regardless of different sizes.
An IoT Framework for Healthcare Monitoring and Machine Learning for Life Expectancy Prediction

The beginning of the IoT era, shrinking of devices and the concept of intelligent independently learning machines have led to improvements in the quality of human life. The application of machine learning to IoT data has led to the automation of the creation of analytical models. One key area of research has seen such a revolution in the health care sector. This work aims to design a wireless healthcare system that detects patients vitals using sensors, transfers data to cloud, and predicts the approximate life expectancy using machine learning techniques. The notion of the Internet of Things (IoT) interconnects devices and offers effective health care service to the patients. Here the IoT architecture gathers the sensor data and transfers it to the cloud where processing and analyses take place. Based on the analyzed data, feedback inputs are sent back to the doctor and using the present pulse rate of the patient, nominal or approximate value of life expectancy is predicted using machine learning algorithms.
Modelling and Resource Scheduling approaches on Cloud Computing

Cloud computing is the dominant service delivery paradigm over the last decade. The rapid development of the Internet of Things (IoT)-based applications shifts the cloud model to more decentralized approaches, such as Edge Computing and Fog Computing. The resource allocation in such distributed environments must guarantee both the Quality of Service (QoS) requirements of each application and the optimal utilization of the underlying computing resources. Thus, the static resource allocation policies are not adequate to fulfill these objectives and lead to over or under-provisioning. On the other hand, control-theoretic approaches guarantee important system properties, such as stability. This presentation will present already proposed modeling and resource scheduling methodologies on cloud computing and the future challenges of this interesting research area.
DIGITAL EDUCATIONAL ENVIRONMENT: ARE NEW FORMS OF DIDACTOGENY POSSIBLE?

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 7217-7220ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.1853Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
How do checkpoint mechanisms and power infrastructure failures impact on cloud applications

With the growth of cloud computing usage by commercial companies, service providers are looking for ways to improve the quality of their services. Failures in the power subsystem represent a major risk of cloud data centre unavailability at the physical level. At same time, software-level mechanisms (such as application checkpointing) can be used to maintain the application consistency after a downtime and also improve its availability. This paper analyses the impact of power subsystem failures on cloud applications availability, as well as the impact of having checkpoint mechanisms to recover the system from software-level failure. We propose stochastic models to represent the cloud power subsystem, the cloud application, and also the checkpoint-based retrieval mechanisms. The results show that the selection of a given type of checkpoint mechanism does not present a significant impact on the observed metrics. On the other hand, improving the power infrastructure implies gains in performance and availability.
Convergence of Particle Filter for Output Feedback Control

In the existing literature, convergence results for particle filters are given explicitly only for the case when the underlying dynamic model is a Markov process. When output feedback control is used, the evolution of the state process is no longer Markovian due to the dependence of inputs on the outputs. In this paper, it is shown that the random probability measures produced by the particle filter converge to the true prior and posterior measures in this nonMarkovian case. Firstly, it is proved that the recursive equations relating the prior and posterior measures continue to hold for output feedback control. These recursive equations are then used to show the required convergence of the random measures. Finally, the convergence is also illustrated using simulations on a nonlinear dynamical system.
A Revocable Certificateless Aggregate Signature Scheme with Enhanced Security

In certificateless public key cryptosystem, a tough problem is how to revoke a user when the user's private key is compromised or expired. So the revocable certificateless schemes come into being. Certificateless aggregate signature (CLAS) is an efficient way to verify a large amount of signatures from different users simultaneously. However, none of CLAS schemes considers the user revocation currently. In this paper, we firstly demonstrate that an efficient certificateless aggregate signature (abbreviated to ECLAS) scheme proposed by Kang et al. is vulnerable to forged signature attack from the type II adversary by a concrete example, although they claimed that their scheme is existentially unforgeable against the adaptively chosen-message attacks. Furthermore, based on the ECLAS scheme and the revocable idea, we proposed a revocable certificateless aggregate signature scheme, which was proved to be existentially unforgeable against adaptive chosen-messages attacks under the hardness assumption of computational Diffie-Hellman problem. As far as we know, this is the first revocable CLAS scheme. Finally, numerical analyses and performance comparisons show our scheme saves computational cost, communication bandwidth and storage space than some related schemes.