Reviewerly/exHarmony huggingface dataset:
Introducing a Framework for Crafting E-Textiles: Exploring a Material Investigation into Technology

This paper introduces a framework and recommendations for crafting e-textiles, advocating craft as an embodied, knowledge-generating practice employing innovative materials and processes. It offers a material investigation into technology to create e-textiles that embed digital capabilities to support more meaningful and connected forms of expression. The framework and recommendations interweave concerns around collaborative, sensorial and aesthetic interaction to conceptualise the relationship between experiences and materials that are lively and animated. The framework conceives lively experiences as a central component of crafted e-textiles that aspire to be meaningful and worthwhile. A material practice activates our sensory awareness and facilitates embodied actions leading to experiences that have much to reveal about people and their interactions through participation and engagement. Applications of the framework and recommendations can structure a material approach to e-textiles and support open exploration and emergent participant behaviour. The framework and recommendations are intended to support more enriching, personally conceived design contexts engaging technology to craft e-textiles that contribute social value and stimulate emotional response in people.
Real Time Query Method of Cleaning Quality Database of Medical Experimental Instruments Based on PSO

With the continuous development of science and technology, the amount of data generated in various industrial automation fields has increased sharply. Due to the limitations of capacity and performance, traditional database query methods have difficulty meeting the query requirements of large-scale applications for massive data. Based on this, a real-time query method of cleaning a quality database of medical experimental instruments based on PSO is proposed. The temporal data model of cleaning quality of medical experimental instruments is constructed to manage the data; Design tsmtree data storage structure to achieve the aggregation of database; The real-time query method of medical experimental instrument cleaning quality database is designed based on PSO to achieve the real-time query goal of massive medical data. Experiments show that this database real-time query method can effectively improve the accuracy of query results, reduce the error in query, and is conducive to the rapid query of medical data.
Evolving Regulatory Frameworks: Blockchain as a Form of Trust—Comparative Evidence

Firms and governments are interested in regulatory experiments in order to minimise compliance costs and improve business and regulatory efficiency. Blockchain presents many hopes for achieving the aforementioned objectives but presents many risks in the area of privacy and cyber risks. Considering its regulatory and business organisational features, it can be considered a regulatory tool that supplements, complements and substitutes law. In the area of AML, Blockchain can be employed to verify, validates and transfer information between two or more stakeholders. The aim of this chapter is to understand the organisational features of the four most prominent Blockchain networks and understand how and when firms and their associated Blockchain-based system operate. In order to do so, this chapter compares Blockchain features with national and international current regulatory frameworks for Blockchain. Results show that Blockchain does not undermine the role of the government, but it reinforces its role when the rule of law is weak or absent. Moreover, considering that technological actors can evolve into a digital sovereign, governments need to carefully assess the risks of this novel technological framework and set the right rules of the games that benefits all consumers and businesses.
A Masked Self-Supervised Pretraining Method for Face Parsing

Face Parsing aims to partition the face into different semantic parts, which can be applied into many downstream tasks, e.g., face mask up, face swapping, and face animation. With the popularity of cameras, it is easier to acquire facial images. However, pixel-wise manually-labeling is time-consuming and labour-intensive, which motivates us to explore the unlabeled data. In this paper, we present a self-supervised learning method attempting to make full use of the unlabeled facial images for face parsing. In particular, we randomly mask some patches in the central area of facial images, and the model is required to reconstruct the masked patches. This self-supervised pretraining is capable of making the model capture facial feature representations through these unlabeled data. After self-supervised pretraining, the model is fine-tuned on a few labeled data for the face parsing task. Experimental results show that the model achieves better performance for face parsing assisted by the self-supervised pretraining, which greatly decreases the labeling cost. Our approach achieves 74.41 mIoU on the LaPa test set fine-tuned on only 0.2\% labeled data of the whole training data, surpassing the model that is directly trained by a large margin of +5.02 mIoU. In addition, our approach achieves a new state-of-the-art on the LaPa and CelebAMask-HQ test set.
A Comparative Study between Regression Algorithms to Visualize and Analyze the Scalability of Large-Scale Graphs in Big Data

Data Visualization is a powerful paradigm to enhance data analysis. Different type of data visualization techniques are widely used to facilitate graph analysis by taking advantage of humanâs strong ability in visual perception. Visualization of large-scale graphs suffers from different kind of problems such as lack of scalability, random access pattern, heavy data conflicts, and poor locality. Scalability analysis is an important factor in visualization of big data. Scalability of large-scale graphs results the irregular and unstructured relationships. Therefore, a comparative study is conducted between ridge regression algorithm and linear regression algorithm for scalability analysis. These algorithms are utilized in visualization of large datasets on scatter plot and distribution plot with respect to scalability analysis. For this purpose, two complex and large datasets are selected and their results are compared against mean standard deviations and execution time of the algorithms. The comparative study indicates that ridge regression technique performed better scalability analysis than linear regression technique.
Investigation on stroke deterministic parameters using intuitionistic fuzzy set environment

In this changing work-life environment, many kinds of diseases pop out. One of such life-threatening disease is stroke. Our research addresses how this disease can be recognised using one mathematical model using intuitionistic fuzzy set with its different measures. The mathematical model produces comparisons between the several measures seen in different genders also. The comparisons have been drawn through a statistical tool.
Application of Lean Principles in Software Development Processes

Lean Methodology was introduced to the world in the early 20th century by Toyota Motor company and has been present, adopted in the workflows of many other automotive and heavy industries from a very long time. It is only in the recent times that the importance of this principle has been realized globally and is being incorporated into the world of software engineering and software development lifecycle processes. In this paper we shall take a look into the basics of lean methodology and understand how the principles of lean in accordance with six sigma principles can be utilized in modern software development practices to achieve highly efficient workflows and performance results. We shall also compare and contrast the advantages of using lean methodology in software production.
Improved Attribute Manipulation in the Latent Space of StyleGAN for Semantic Face Editing

With the recent popularization of generative frameworks for producing photorealistic face images, we now have the ability to create a convincing graphical match for any particular individual. It is unrealistic, however, to rely solely on such generative methods to randomly produce the facial characteristics we are seeking. Instead, manipulation of facial attributes in the latent space, enabled by the InterFaceGAN framework, allows us to “tweak” these characteristics in the desired direction to improve the quality of the match. The challenge in this process is that attribute entanglement leads to a change of one feature having an undesirable impact on others. We explore several strategies to improve the results of these manipulations, and demonstrate how the automatic conditioning of attributes can be used to minimize the impact of such entanglement, and further, allow for improved control over complex (non-binary) attributes such as race or face shape.
Characteristic problems in the application of modern educational technology in teaching: The possibility of overcoming them

As a consequence of rapid technical, scientific and technological development, there was a need for changes in education and teaching. These changes are manifested both in the organization of educational content and in the introduction and application of new procedures, methods and tools. However, despite the knowledge that the goals and principles of the concept of education at the current stage of social development imply a complete reconstruction of the traditional educational system and teaching and that the application of modern educational technology creates conditions for acquiring more dynamic, quality and complex knowledge, there are numerous problems. Recognizing the fact that the application of modern educational technology can significantly contribute to the achievement of educational goals, our intention is to work, in addition to terminological and conceptual definition of educational technology, to focus on the causes of problems in this domain and to point out possible ways to overcome them.
Sparse LIDAR Measurement Fusion with Joint Updating Cost for Fast Stereo Matching

The complementary virtues of active and passive depth sensors inspire the LIDAR-Stereo fusion for enhancing the accuracy of stereo matching. However, most of the fusion based stereo matching algorithms have exploited dense LIDAR priors with single fusion methodology. In this paper, we intend to break these fetters, utilizing sparse LIDAR priors with multi-step fusion strategy for obtaining accurate disparity estimation more efficiently. At first, random sparse sampling LIDAR depth measurements are provided in Naive Fusion for updating the matching cost of Semi-Global Matching (SGM). Then Neighborhood Based Fusion is performed based on the former step for further updating the cost. Subsequently, Diffusion Based Fusion is utilized to update both the cost and disparities. At last, Tree Filtering is applied for removing speckle outliers and smoothing disparities. Performance evaluations on various stereo data sets demonstrate that the proposed algorithm outperforms other most challenging stereo matching algorithms significantly with approximately real-time implementation efficiency. Furthermore, it is worth pointing out that our proposal surprisingly possesses one of the top ten performances on Middlebury v.3 online evaluation system even if it has not been adopted any learning-based techniques.
IITG-INDIGO Submissions for Interspeech-2021 Multilingual and Code-Switching ASR Challenges for Low Resource Indian Languages

The submissions made by the INDIGO team at Indian Institute of Technology Guwahati (IITG) to the Interspeech-2021 multilingual and code-switching Automatic Speech Recognition (ASR) challenges for low resource Indian languages are described in this publication. For multilingual ASR case, the CTC-loss based end-to-end ASR systems are developed for 6 target languages, namely, Gujarati, Hindi, Odia, Marathi, Telugu and Tamil. For the code-switching case, separate ASR systems are developed for Hindi-English and Bengali-English code-switching data using the time delay neural network (TDNN) based acoustic model employing lattice-free, maximum mutual information (LF-MMI) training. The language wise results and the average of all the 6 languages and the other having 5 languages (all except Marathi) and their average was 73.24%. Without Marathi, the average %WER obtained was 65.60%. The TDNN system was found to give better results than the baseline on the basis of average Transliterated %WER. But the average %WER of TDNN system was slightly higher than the baseline. The raw average scored 30.73 %, whereas the transliterated average scored 27.49% (better than baseline)
Multi-Party Security Collaboration Model of Electric Power Data Based on Federated Learning

Under the background that the country implements the big data strategy to promote the open sharing of data resources, the open sharing of power data and multi-party collaboration face many privacy protection and data security risks. This paper carried out the research on the multi-party security collaboration technology of power data based on federated learning, sorted out the typical application scenarios and data security requirements of power multi-party collaboration, and proposed a basic model of power data security collaboration based on federated learning. On the basis of technical research, a prototype system for power data federation learning has been developed to build the multi-party security and collaborative protection capability of power data, and support the multi-party collaborative application of power data.
Blockchain Smart Contract Virtual Machine Optimization Technology for Domain Name Systems

The existing domain name system relies heavily on the root domain name server and has unilateral management problems, which is not conducive to the open and equal development of the Internet environment. Blockchain technology provides new technical means to solve the centralization problem of the root resolution system but induces high consumption and slow query issues. In this paper, we studied optimizing the blockchain smart contract virtual machine, specifically EVM (Ethereum Virtual Machine), for realizing a domain name system through a smart contract. We abstracted the key data structure of domain name management and added core attributes to the account structure to accelerate domain name management through transactions. Furthermore, we modified the EVM structure and added EVM opcodes that can directly call the underlying logic related to upper-level domain name management to simplify the transaction processing flow. The evaluations show that the system has achieved a two orders of magnitude improvement in the performance of the domain name resolution system based on blockchain.
Developing a Decision-Making Support Information System for the Operational Control of Industrial Technological Processes

Fuzzy logic is a new and innovative technique that was used to develop an engineering control realization. In recent years, fuzzy logic has demonstrated its immense promise, particularly in the automation of industrial process control, where it allows the control design to be developed based on expert experience and experimental results. The projects that have been completed show that using fuzzy logic in technical process control has already resulted in better decisions than using standard control techniques. Fuzzy logic allows for the creation of an advisory system for decision-making that is based on operator experience and experiment results rather than a mathematical model. The current research focuses on a specific technological process: developing a support decision-making information system for the operational control of a lime kiln using fuzzy logic and a related expert-objective knowledge base.
Seamless Hybrid Science Learning: Streamlining the Techno-Pedagogical Designs for Wider Diffusion

We narrate a qualitative descriptive study on the translation of the pedagogical model of primary school seamless science inquiry learning (SSIL), originally with the mandatory requirement of 1:1 (one-mobile-device-per-student) and 24 × 7 (24 h a day, 7 days a week) setting, to a more scalable and sustainable model that fits the conditions of typical schools in Singapore and elsewhere. Seamless learning as a hybrid learning model refers to a continuous, holistic learning process across learning contexts, such as formal and informal learning settings, individual and collaborative learning, and learning in both the physical and digital realms. We discuss the adaptation of the SSIL model which remove the constraint of the requirement of one device per student and its implementation in three schools over the duration of two academic years. Through a process of scaffolding by the researchers, as manifested by five pedagogical design principles known as C2FIP (Connectivity, socio-Constructivist learning, Formative assessment, leveraging resources in Informal settings, Personalised learning), primary science teachers were empowered to design good SSIL lesson plans that adhered to techno-pedagogical design principles. Data analysis showed teacher growth in designing and implementing SSIL lessons and evidence of student learning.
Multi-scale Profile-HMM action recognition based on double matching states

Human action recognition is still a challenging task, partly because of the complex behavioral differences in the execution of actions. In order to solve this problem, we propose a multi-scale Profile-HMM action recognition method with double matching states. First, the action sequence is segmented according to the motion trajectory of the joint points, and the action characteristics of the segmentation point and the action segment are extracted. Then, the improved K-means algorithm is used to cluster the action features, and the action segments are coded into action sequences with alternating uppercase and lowercase letters. Finally, the encoded sequence uses the Profile-HMM of the double matching state for action recognition. The method is evaluated on MSRACtion 3D and self-built datasets, and the results show that the recognition accuracy of this method is significantly improved compared with the traditional HMM algorithm.
Adaptive Digital Educational Environments as Drivers of Remote Staff Training Technologies

The contribution presents a comparison of modern digital educational environments of the three main classes according to the characteristics of the possibility of integration into the educational process, automation of knowledge testing, compliance with the requirements of the state educational standard, as well as the configuration of training courses for specific learning conditions. The study analyzes the systems of mass online education based on open software of online systems, and adaptive educational environments suitable for teaching natural science disciplines. According to the results of the study, the authors conclude that modern subject-oriented adaptive digital educational environments, unlike traditional systems of mass online education and systems based on open software, are initially focused on use in the Russian Federation, implement both all the requirements for storing personal data and the requirements of the state educational standard, have advanced capabilities for automating the verification of tasks and configuring training courses for the specifics of an educational institution or trainees. Thus, the development of domestic adaptive learning environments, provided that common standards for data exchange between information systems are developed, will ensure the creation of highly competitive technologies for distance training and retraining of the country’s personnel potential.
CNN-based Evaluation Method of Academic Innovation Effect of American Research Universities

Aiming at the problem of fuzzy characteristics of academic achievements in the existing evaluation methods of academic innovation effects of American research universities, resulting in low evaluation accuracy, a CNN-based evaluation method of academic innovation effects of American research universities is designed. First, we obtain innovation influence indicators, calculate the breadth of innovation absorption in papers, and identify the characteristics of academic achievements of American research universities; then, we convert scientific and technological achievements into patent licenses, measure keyword coverage, and finally use CNN to design innovation effect evaluation algorithms. We completed the design of the evaluation method for the academic innovation effect of American research universities. The experimental results show that the average evaluation accuracy of the evaluation method of the academic innovation effect of the American research university and the other two evaluation methods are 73.59%, 62.51%, and 60.28%, respectively, which proves that the American research university integrates CNN technology. The evaluation method of the academic innovation effect is more practical.
Joint Domain Adaption and Pseudo-Labeling for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is a hot research topic in recent years. The inconsistent data distribution between source and target projects and lack of labels for most of target instances bring a challenge for defect prediction. Researchers have developed several CPDP methods. However, the prediction performance still needs to be improved. In this paper, we propose a novel approach called Joint Domain Adaption and Pseudo-Labeling (JDAPL). The network architecture consists of a feature mapping sub-network to map source and target instances into a common subspace, followed by a classification sub-network and an auxiliary classification sub-network. The classification sub-network makes use of the label information of labeled instances to generate pseudo-labels. The auxiliary classification sub-network learns to reduce the distribution difference and improve the accuracy of pseudo-labels for unlabeled instances through loss maximization. Network training is guided by the adversarial scheme. Extensive experiments are conducted on 10 projects of the AEEEM and NASA datasets, and the results indicate that our approach achieves better performance compared with the baselines.
MHCNC: A Novel Framework for Multi-Source Heterogeneous Cross-Network Node Classification

In this study, we consider the issue of multi-source heterogeneous cross-network node classification, which employs the plentiful labeled information from multiple source networks to assist in classifying unlabeled nodes in a target network. Traditional single-source cross-network node classification methods mainly focus on the scenario where the source and target networks share the same feature spaces. The current multi-source transfer learning methods are generally unable to model the network&#x0027;s structural information. Thus, both of them cannot be applied to the problem of multi-source heterogeneous cross-network node classification. In this study, we introduce a multi-source heterogeneous cross-network node classification (MHCNC) framework, which integrates multi-source heterogeneous transfer learning with graph convolutional neural network to learn network-invariant and label-discriminative node representations. In MHCNC, we first devise the heterogeneous feature transformation that transforms the multiple source feature spaces onto the target network to obtain new feature representations to mitigate the distribution discrepancy between networks. In addition, we incorporate an inductive learning-based convolutional neural network to classify the unlabeled nodes in a target network. Moreover, we propose a novel model fusion strategy based on the classification weight of each model. Comprehensive experiments conducted on real-world social networks verify that our algorithms outperform the most advanced methods in terms of classification accuracy.