Reviewerly/exHarmony huggingface dataset:
The Simulation Research of Multi-model Adaptive KF and Target Tracking

The Kalman Filter (KF) has been applied to various fields in engineering. Since R. E. Kalman proposed this theory, many extended Kalman filtering theories have been developed to solve different problems in various situations, such as Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Cubature Kalman Filter (CKF), and interactive multiple model (IMM) Kalman filter, etc. This paper will use the scenario of target tracking to perform simulation verification study of IMM Kalman filter.
VLSI Implementation of Modified Twofish Algorithm and Its Comparative Analysis with Different Rijndael Variants

This study concerns about reducing the core area and power consumption of cryptographic module based on 256bit Twofish algorithm. In order to do so, the irreducible Galois Field polynomial in the algorithm is replaced with a simpler one that results in a better Maximum Distance Separable matrix. This matrix greatly reduces the computational cost at hardware level, which finally brings down the core area and power consumption. Cadence Genus Synthesis Solution and Cadence Innovus Implementation System have been used for assessment. This study then compares the obtained results with three different variants of Rijndael – the algorithm currently used in Advanced Encryption Standard.
Scale to estimate the aspect-oriented sentiment polarity under anaphors influence (SPAI)

Purpose The purpose of this paper is to handle the anaphors through anaphora resolution in aspect-oriented sentiment analysis. Sentiment analysis is one of the predictive analytics of social media. In particular, the social media platform Twitter is an open platform to post the opinion by subscribers on contextual issues, events, products, individuals and organizations. Design/methodology/approach The sentiment polarity assessment is not deterministic to conclude the opinion of the target audience unless the polarity is assessed under diversified aspects. Hence, the aspect-oriented sentiment polarity assessment is a crucial objective of the opinion assessment over social media. However, the aspect-oriented sentiment polarity assessment often influences by the curse of anaphora resolution. Findings Focusing on these limitations, a scale to estimate the aspects oriented sentiment polarity under anaphors influence has been portrayed in this article. To assess the aspect-based sentiment polarity of the tweets, the anaphors of the tweets have been considered to assess the weightage of the tweets toward the sentiment polarity. Originality/value The experimental study presents the performance of the proposed model by comparing it with the contemporary models, which are estimating the sentiment polarity tweets under anaphors impact.
Pidgin Metadata Framework as a Mediator for Metadata Interoperability

Abstract A pidgin metadata framework based on the concept of pidgin metadata is proposed to complement the limitations of existing approaches to metadata interoperability and to achieve more reliable metadata interoperability. The framework consists of three layers, with a hierarchical structure, and reflects the semantic and structural characteristics of various metadata. Layer 1 performs both an external function, serving as an anchor for semantic association between metadata elements, and an internal function, providing semantic categories that can encompass detailed elements. Layer 2 is an arbitrary layer composed of substantial elements from existing metadata and performs a function in which different metadata elements describing the same or similar aspects of information resources are associated with the semantic categories of Layer 1. Layer 3 implements the semantic relationships between Layer 1 and Layer 2 through the Resource Description Framework syntax. With this structure, the pidgin metadata framework can establish the criteria for semantic connection between different elements and fully reflect the complexity and heterogeneity among various metadata. Additionally, it is expected to provide a bibliographic environment that can achieve more reliable metadata interoperability than existing approaches by securing the communication between metadata.
Initiatives for Engineer Ethics Education at the Architectural Institute of Japan

As an initiative for engineer ethics education at the Architectural Institute of Japan, we started to systematize ethics education, which had been conducted independently in the past, with JABEE as a trigger. We have grasped the ethics education conducted by each educational institution / organization, and examined and constructed its program.
MPC-in-Multi-Heads: A Multi-Prover Zero-Knowledge Proof System

With the rapid development of distributed computing, the traditional zero-knowledge proofs (ZKP) are becoming less adequate for privacy-preserving applications in the distributed setting. Take “double financing” as an example: multiple financial providers jointly prove that the sum of their committed values is no more than a given threshold, which generalizes the “range proof” to the multiple-prover setting. Therefore, traditional zero-knowledge proof does not seemingly lend itself to this problem on its own. We identify and fill this gap by formalizing the ZKP system in the multi-prover setting (MPZK) that proves arbitrary NP statements with distributed witnesses. Our MPZK system offers zero-knowledge as long as one prover is honest (while others can collude arbitrarily), and thus is applicable to “double financing”, “credit checking”, and various other multi-prover applications. We then propose a generic black-box construction from multiparty computation, referred to as “MPC-in-Multi-Heads”, and prove its security under the simulation-based paradigm. We also offer a proof-of-concept implementation and present its experimental results.
Prediction Model for Cervical Cancer in Female Patients Using Machine Learning

Cervical cancer is gynecological cancers which is associated with HPV infection. This disease is preventable if early diagnosis of symptoms is facilitated through health supervisors. Various risk factors are incorporated with this disease, so classification of test factors is necessary along with affected patients based on their symptoms. Predicting cancer in early stages is a preventive measure to any disease and can save life. This objective can be achieved by implementing a machine learning approach. The present paper predicts the risk of cervical cancer in female patients using a random forest algorithm with precise accuracy and calculating its precision value. To prepare a present prediction model, numerous feature selection techniques are explored to dig out essential attributes of cervical cancer prediction.
Collaborative Design of a Synchrotron Ontology

The paper presents an approach to collaborative analysis and design of the synchrotron ontology. The ontology building process requires the analysis of the subject of conceptualization, the definition of goals and tasks that ontology is to fulfill as well as the way of implementation of an ontology in the appropriate logic language. It often needs cooperation of many specialists: domain experts, analysts and engineers who implement the ontology. In this paper, the use of competency questions defined in cooperation with many specialists from different domains and usage of use cases to develop the synchrotron ontology, is presented. The competency questions are used to define the subject of conceptualization to be represented by the ontology, while collaborative analysis of requirements facilitates specification of use cases and implementation phases. The presented approach enables the cooperation of many engineers in defining ontology design patterns that could be reused in many domains. It is applied to build a prototype high level ontology that could be used to develop ontologies for specific synchrotrons.
An Improved Digital Image Encryption Algorithm Based on Sine Compound Chaotic System

The rapid development of digital image transmission technology requires a more secure and effective image encryption scheme to provide the necessary security. This paper introduces an improved sine-transform-based chaotic system (ISTBCS). By linearly weighting and nonlinearly multiplying the selected classic maps, the new chaotic maps with more complex dynamics are obtained through sinusoidal transformation. Performance evaluation shows that it has chaotic characteristics such as wide range of chaos, high complexity, and strong non-periodicity. On this basis, an image encryption algorithm that uses bidirectional pixel encoding, displacement and diffusion at the same time is designed. By pixel encoding the plaintext, the small changes in the ordinary image can be propagated to all the pixels of the encrypted image, which greatly improves the ability to resist known plaintext attacks and selected plaintext attacks. Simulation results and performance analysis show that the algorithm has a large key space, strong key sensitivity, strong performance to make the structural data correlation disappear, and then can increase the entropy, has good anti attack ability, and can effectively meet the needs of image encryption.
Supporting Elementary and Secondary Education During the Pandemic: A Case Study from the National Research University Higher School of Economics

Abstract The COVID-19 pandemic has presented a formidable challenge to the Russian school system. Such global challenges and crises highlight the significance of the National Research University Higher School of Economics (HSE)’s third mission: responsibility for the well-being of the community. As one of the first universities to offer support to elementary and secondary education systems in Russia during the pandemic, the HSE relied on its ability to create new scientific knowledge and make it useful in practice to provide versatile and targeted aid for students, teachers, regional administrators, and parents across the country. There were two main vectors of HSE activity at the time of the pandemic: (1) promoting the development of the Russian education system through research, monitoring, and coordination of scholars and analysts and (2) direct work with secondary and high school stakeholders using contemporary approaches for talent development and digital tools. The pandemic revealed the importance of developing new areas of research and analysis. In line with the first vector, the HSE focused on: Monitoring and studying the situation and collecting and promoting university and school case studies on organizing work during a pandemic Leading and participating in professional reflections and discussions regarding experiences and training practices in the context of a lockdown The institution organized an array of surveys with students, teachers, parents, and representatives from regional and municipal education organizations and analyzed the results as quickly as possible. The main areas of research were problems of educational inequality and digital transformation. Based on the collected data, the Institute of Education managed to publish more than 30 analytical works between April and June of 2020. For the second vector, the HSE developed programs that seek to expand its geographic reach, implement flexible recruitment, and digitize communication with school students. Such programs aimed at: Training personnel while consulting with administrators and educators on the technological and legal aspects of the work of schools. Providing online instruction and assistance for students learning software tools and preparing for exams. Helping parents arrange support for children in the transition to distance learning. For this period, the HSE quickly and effectively expanded cooperation with schools in Moscow and regions around Russia that the institution developed over the last 15 years. Key current projects working in this direction are: The HSE School District The Lyceum Distributed Schools The Higher Students Academy The Higher School for Parents
Autoencoder based Randomized Learning of Feedforward Neural Networks for Regression

Feedforward neural networks are widely used as universal predictive models to fit data distribution. Common gradient-based learning, however, suffers from many drawbacks making the training process ineffective and time-consuming. Alternative randomized learning does not use gradients but selects hidden node parameters randomly. This makes the training process extremely fast. However, the problem in randomized learning is how to determine the random parameters. A recently proposed method uses autoencoders for unsupervised parameter learning. This method showed superior performance on classification tasks. In this work, we apply this method to regression problems, and, finding that it has some drawbacks, we show how to improve it. We propose a learning method of autoencoders that controls the produced random weights. We also propose how to determine the biases of hidden nodes. We empirically compare autoencoder based learning with other randomized learning methods proposed recently for regression and find that despite the proposed improvement of the autoencoder based learning, it does not outperform its competitors in fitting accuracy. Moreover, the method is much more complex than its competitors.
Representation of Characters’ Directed relationships in Comics with Speech-roles

This paper describes a novel way to represent characters’ relationships in comics: as edges of directed graphs labeled with speech-role frequency distribution. In comics, the story progresses through characters’ actions and speeches. The relationships of characters are usually represented as non-directed: e.g., they are “lovers,” or “friends.” However, directionality is an essential component of complicated relationships; the combination of directed relationships often determines the attractiveness of a story. In this study, relationships between characters are assumed to be directed, and are further specified using speech roles: the types of speech that pass between two characters can typify their relationship. This idea has been realized experimentally, and its accuracy has been verified against subjective evaluation by readers. We also suggest the use of directed relationships for analogy retrieval of characters. These methods should be applicable in fields other than comics.
Effects of Binary Vectors Similarities on the Accuracy of Multi-Criteria Collaborative Filtering

Recommender systems offer tailored recommendations by employing various algorithms, and collaborative filtering is one of the well-known and commonly used of those. A traditional collaborative filtering system allows users to rate on a single criterion. However, a single criterion may be insufficient to indicate preferences in domains such as restaurants, movies, or tourism. Multi-criteria collaborative filtering provides a multi-dimensional rating option. In similarity-based multi-criteria collaborative filtering schemes, existing similarity methods utilize co-users or co-items regardless of how many there are. However, a high correlation with a few co-ratings does not always provide a reliable neighborhood. Therefore, it is very common, in both single- and multi-criteria collaborative filtering, to weight similarities with functions utilizing the number of co-ratings. Since multi-criteria collaborative filtering is yet growing, it lacks a comprehensive view of the effects of similarity weighting. This work studies multi-criteria collaborative filtering and the literature of binary vector similarities, which are frequently used for weighting, by giving a related taxonomy and conducts extensive experiments to analyze the effects of weighting similarities on item- and user-based multi-criteria collaborative filtering. Experimental findings suggest that prediction accuracy of item-based multi-criteria collaborative filtering can be boosted by especially binary vector similarity measures which do not consider mutual absences.
Regression Analysis Models Under the Hesitant Fuzzy Environment

The complexity of practical problems generates lots of uncertain information, which brings great challenges to the study of regression analysis.
Multi-dimensional Data Correlation Analysis Method Based on Neighborhood Preserving Embedding Mechanism

Due to the complexity of network structure and the existence of weak nodes, the possibility of abnormal or attacked in the network is greatly increased. Therefore, it is necessary to carry out feature extraction on the multi-dimensional data of network nodes and extract the key features, to improve the speed of data analysis and find the abnormal behaviors in the network in time. In the past feature extraction methods, data fusion is often used to compress and merge multidimensional data to form new data. However, this approach cannot guarantee the influence of the original attribute dimension in the whole data set, and each dimension cannot be clearly analyzed one by one. Therefore, this paper proposes a feature extraction method based on multi-core canonical correlation analysis. The improved multi-core canonical correlation analysis algorithm is introduced into the neighborhood preserving embedding algorithm to obtain the MultiCCA-NPE feature extraction algorithm. According to the characteristics of the network operating data, in order to better extract the features, this paper first uses the improved multi-core learning CCA algorithm to calculate the correlation between the attributes of each dimension. Taking the correlation size as the basis of the NPE weight value, the improved MultiCCA-NPE algorithm is obtained, and then effective feature extraction is realized. Compared with the KCCA algorithm and the NPE-GNN algorithm, this method has a better feature extraction effect.
Multi-modality Image Registration Models and Efficient Algorithms

In this Chapter we discuss multi-modality image registration models and efficient algorithms. We propose a simple method to enhance a variational model to generate a diffeomorphic transformation. The idea is illustrated by using a particular model based on reformulated normalized gradients of the images as the fidelity term and higher-order derivatives as the regularizer. By adding a control term motivated by quasi-conformal maps and Beltrami coefficients, the model has the ability to guarantee a diffeomorphic transformation. Without this feature, the model may lead to visually pleasing but invalid results. To solve the model numerically, we present both a Gauss-Newton method and an augmented Lagrangian method to solve the resulting discrete optimization problem. A multilevel technique is employed to speed up the initialization and reduce the possibility of getting local minima of the underlying functional. Finally numerical experiments demonstrate that this new model can deliver good performances for multi-modal image registration and simultaneously generate an accurate diffeomorphic transformation.
Blockchain-based Traceability Management System Using Split Root Hash Method

Traceability management service utilizing blockchain guarantees the tamper resistance of data. However, it is a problem to balance the performance of writing to blockchain and the data confidence of other company’s information when data of multiple vendors are mixed at the time of data disclosure. In the proposed method, we obtain split root hashes which are divided by manufacturer from multiple traceability data generated at a certain site, and multiple split root hashes are put into one transaction in blockchain. It is confirmed that write performance of approximately 3,000 transaction/sec could be achieved by reducing the number of writing to blockchain, and that the information of other companies at the time of data disclosure could be concealed.
Towards Explainability of non-Convolutional Neural Networks

Artificial intelligence has risen in popularity in research and applications in the past years. Explainability is a topic that has been proven to work on human interpretable data like images or sentences, but the research is narrow whenever such data is missing.
Designing Analytic Serious Games: An Expert Affordance View on Privacy Decision-Making

Abstract With advancing digitalisation and the associated ubiquitous data processing, people face frequent privacy decisions. As personal data is often collected and processed in non-transparent ways, decision-making is tedious and regularly results in unthoughtful choices that resign privacy to comfort. Serious Games (SG) could be instrumentalised to raise awareness about privacy concerns and investigate how better privacy decisions can be encouraged. However, creating a SG that can research and promote better privacy choices while providing exciting gameplay requires carefully balanced game design. In this study, we interviewed 20 international experts in privacy, psychology, education, game studies, and interaction design to elicit design suggestions for analytic Serious Games that can be applied to research and improve privacy decision-making. With a mixed-method approach, we conducted a qualitative affordance analysis and quantified the findings to determine each expert groups’ perceptions of how to investigate and educate privacy decision-making with games while keeping an engaging experience for players. The findings suggest that privacy decision-making is best analysed by storytelling that extends to a real-world context and engages the player with curiosity. Decision-making investigation is suggested to either apply unobtrusive in-game monitoring with story-aligned character interrogation, switching to a meta-context or include personal data and devices from daily routines. Conclusively, design implications for analytic SG targeting privacy are synthesised from the experts’ suggestions.
NAVIGATING VIDEO-BASED LEARNING IN SCIENCE – HOW DO WE CLOSE THE GAP BETWEEN ONLINE AND PHYSICAL CLASSROOMS?

Online learning increases the physical distance between instructors and students and depending on the mode of delivery it can be challenging to close this gap. In the face of a global pandemic institutions are rapidly increasing their portfolio of online and blended courses, which increases the potential risk of student attrition across the sector. To ameliorate this potential for student isolation, instructors need to communicate to students in a variety of ways, blending original online resources with synchronous interactive learning activities. In 2020, 55 videos designed for lectures, tutorials, and laboratory sessions were created for 400 undergraduate microbiology students at The University of Queensland. The videos collectively received over 35,000 views, and through learning analytics it was observed that videos featuring instructor presence on screen, frequent scene transitions, and dynamic pacing increased the consistency of student engagement (>80% average audience retention). This session will outline the design principles underlying a framework for developing video-based learning resources in science to maximise their utility as part of blended and online courses, as well as the academic digital upskilling required in the current Higher Education landscape.