Reviewerly/exHarmony huggingface dataset:
Imbalance Example-Dependent Cost Classification: A Bayesian Based Method

We propose to apply the recently introduced principled rebalancing classification algorithms in the first of the 2-steps that the well-known and effective methods of Bahnsen and colleagues use for solving binary example-dependent cost classification, whose practical importance appears clearly when considering application fields such as Finance or Health. The motivation is twofold: First, to avoid the degradation risks that non-principled rebalancing processes suffer from. Second, to explore if the high performance of the principled algorithms has positive effects on the results of the 2-step procedures when solving example-dependent cost classification problems. After a complete formulation of this new 2-step algorithms, we carry out detailed comparative experiments with 7 representative real-world example-dependent cost databases. The experimental results support that the proposed approach is competitive and that it provides an advantage in the less favourable situations: Cases with strong imbalance and highly nonlinear classification borders
Length-Normalized Representation Learning for Speech Signals

In this study, we proposed a length-normalized representation learning method for speech and text to address the inherent problem of sequence-to-sequence models when the input and output sequences exhibit different lengths. To this end, the representations were constrained to a fixed-length shape by including length normalization and de-normalization processes in the pre- and post-network architecture of the transformer-based self-supervised learning framework. Consequently, this enabled the direct modelling of the relationships between sequences with different length without attention or recurrent network between representation domains. This method not only achieved the aforementioned regularized length effect but also achieved a data augmentation effect that effectively handled differently time-scaled input features. The performance of the proposed length-normalized representations on downstream tasks for speaker and phoneme recognition was investigated to verify the effectiveness of this method over conventional representation methods. In addition, to demonstrate the applicability of the proposed representation method to sequence-to-sequence modeling, a unified speech recognition and text-to-speech (TTS) system was developed. The unified system achieved a high accuracy on a frame-wise phoneme prediction and exhibited a promising potential for the generation of high-quality synthesized speech signals on the TTS.
Reducing Web-Latency in Cloud Operating Systems to Simplify User Transitions to the Cloud

Data storage and algorithms are continuing to move toward cloud-based storage. This provides users with faster processing speeds and online access. The reduction in latency between client and server now makes it possible for servers to handle intensive algorithms and data, rather than running and storing information on client machines. Negative impacts like poor memory, storage and CPU speeds, can effect the desktop’s architectural performance, in addition to having a local storage dependency, desktops do not have the multi-platform and worldwide access capabilities of cloud based platforms. The proposed solution for this is the current/seen transition from local hardware to cloud platforms. However, this transition involves difficulties relating to network latency, storage retrieval and access-speeds. This study proposes a methodology to evaluate the validity of reducing web-latency in cloud platforms, so that they may simulate a local operating system (OS) environment. This research will compare web-architectures in categories that could positively impact network latency like prefetching, clustering and deep learning prediction models, as well as Markov algorithms and layered architectures, which will be reviewed to provide a development foundation. Surrounding studies on web-latency, storage access and architectural retrieval times will be reviewed to assess applicable technologies and real-world obstacles involved in reducing web-latency in cloud operating systems.
Multi-device Continuous Authentication Mechanism Based on Homomorphic Encryption and SVM Algorithm

In order to meet the higher security requirements of authentication technology under the current mobile Internet background, continuous identity authentication technology based on single authentication improvement came into being. At present, continuous authentication technology has problems such as low security, low efficiency, and lack of a scientific punishment mechanism. How to efficiently, safely and comprehensively evaluate the legal and illegal requests of the terminal is a huge challenge for continuous authentication. The multi-device continuous authentication mechanism based on homomorphic encryption and SVM algorithm can satisfy the server in a sufficiently secure environment to enable the terminal request to be continuously authenticated. Moreover, based on the existing research, this paper designs a new penalty protocol and applies it to the continuous authentication mechanism. For this purpose, an illegal request processing model is constructed, and the penalty protocol designed to cope with the illegal request Further processing solves the problem that the existing continuous authentication mechanism is insufficient to handle illegal requests and is not humane enough. Finally, the experimental results of SVM and convolutional network on a single device and multiple devices are compared from three dimensions. The comparison of the experimental results verifies the effectiveness of the proposed model and protocol.
Commercial Use of Distance Learning Technologies in the Digital Knowledge Economy: Trends and Prospects

In this study, we defined and critically analyzed the commercial use of online resources and e-learning technologies via systemic, neo-institutional, comparative analysis. These resources and technologies include the following: multifunctional e-learning platforms (University Without Borders, Moodle, iSpring), online tools (Kahoot, Buncee, Quizizz), virtual boards (MIRO, Twiddla, Scribblar), etc. We concluded that the current use of digital media in education is primarily aimed at retaining and attracting consumers, while the social responsibility aspect of e-learning is neglected. This phenomenon endangers the long-term sustainable development of society. Consequently, more equitable revenue redistribution is needed for those educational and scientific initiatives that cannot be efficiently marketed (basic research, teaching students from low-income families, etc.) but are crucial for the educational system and for the intangible assets in a digital knowledge-based economy. The use of free scholarly databases and open source software is particularly promising in this area. The study was supported by the Interdisciplinary Scientific-educational School “Preservation of the World Cultural and Historical Heritage” at Lomonosov State University.
A Dual-Projector Three-Dimensional Measurement Model for Shading Problem of Micro-Scale Object Complex Surface

By considering the inherent defects of the traditional triangulation method, always a shadow area is produced in the image collected by the camera, which eventually leads to data loss. Along these lines, in this work, based on the traditional monocular vision measurement system, an auxiliary light source was added to alleviate the shadow problem, while a measurement system with dual projectors and a monochromatic camera was established. Two symmetrical digital light processing (DLP) projectors were also employed to project the modulated monochrome red fringe patterns and the monochrome blue fringe patterns onto the object, whereas the distorted fringe patterns were captured by the color camera. The image collected by the color camera was separated , whereas the deformation fringe information of the measured object in the red channel and the blue channel was obtained, respectively. The mask of the red channel fringe patterns information was generated pixel by pixel, which was used to remove and retain the effective information invalid information and of the red channel fringe and the blue channel fringe patterns, respectively. In order to verify the feasibility of this method, experiments were also carried out and its accuracy was evaluated. The root mean square error (RMSE) of the system was about 6 μm. From the extracted results, it was demonstrated that the proposed method can effectively solve the shadow problem for small object measurement applications.
Cancel culture or Hate culture? When influencers decide to hold other influencers accountable

Influencers can alter their audience's attitudes by endorsing products, brands, and individuals.On the other hand, influencers also have the power to publically not support that brand orindividual and even hold them accountable for their actions. While cancel culture can producepositive societal changes, it is often misused to target competitors with detrimental effects ontheir careers in the influencer community. This paper describes the changes in attitudehomophily toward selected influencers in response to confrontation content published by otherinfluencers. Four testing sessions with 87 participants were conducted using software forsentiment recognition based on facial features, completed by homophily and personalityassessment questionnaires. Using this combined method of data collection, the presentresearch examines how influencers' credibility, attitude homophily, and personalcharacteristics of the testers affect the susceptibility to become part of the cancel culture.
Critical Analysis of Intelligent IoT in Creating Better Smart Waste Management and Recycling for Sustainable Development

The Internet of Things (IoT) has found extensive use in areas such as water management, waste management, and sustainable development. With its broad connectivity, the Internet of Things is a new and promising technology that has the potential to positively transform human existence globally. IoT allows low-energy devices to exchange information and interact with one another. Waste management is a daily chore that necessitates a huge number of labour resources and has an impact on natural, fiscal, efficient, and social elements. The rate of garbage generation has been magnified at an alarming rate as a result of fast urbanisation and expanding population. As a result, as the world faces global environmental problems, it is necessary to develop changes in waste management systems and technology to address issues that have never been addressed in such innovative ways. Many applications throughout the world have been using IoT to conduct various activities to provide unique services for wastage handling and maximise energy efficiency. These improvements allow IoT technologies to serve as a bridge between basic network-based systems and technologies that scan and gather data from the real environment, as well as deliver new services and applications that help people in a variety of ways. As a result, this research conducts a study of existing IoT-enabled waste management solutions and sustainable development. The goal is to get an understanding of the strengths and weaknesses so that changes and innovations may be made to effectively and efficiently manage waste while also maintaining a healthy environment in the communities. The impact of waste management on long-term economic, social, and environmental sustainability is also depicted in the literature review. The secondary data has been used in the research to gain a reliable conclusion of the study.
Development of Educational Independence of Transport Universities Students

Problem and purpose. The study of the problems of the development of educational independence of students of transport universities is associated with the understanding of the contradictions within the modern system of higher education, the need to create pedagogical conditions for the development of educational independence of future transport specialists. The author’s goal is to present the main conceptual provisions describing the system of pedagogical conditions for the development of educational independence of students of transport universities. Methodology. The research is carried out on the basis of the methods of theoretical analysis. The methodological basis is the competence-activity approach. The results are in the formulation of the main provisions of the concept of the development of educational independence of students of transport universities, which is the basis for building the educational process in a transport university. Conclusions. It is concluded that the learning process aimed at developing the educational independence of students in transport universities should correspond to the psychological nature of the process of mastering professional knowledge and skills, which consists of two psychological processes: interiorization and exteriorization.
Letting the Light Shine in: A Tapestry of Digital Literacies in Canadian Faculties of Education

The fabric of digital literacy learning and the measurement of digital competence in Canadian faculties of education is multi-colored and multi-textured. The threads connecting these digital literacy practices and research are loosely stitched together into a national tapestry, full of holes and imperfections. Yet, just as Canadian singer/songwriter Leonard Cohen suggests, these cracks allow the light to shine through. By reviewing research from across the country, this chapter illuminates distinctive patterns in teaching, learning, and research into digital literacies and digital competencies in faculties of education. Singular threads reveal trends that enhance digital literacy learning and digital competency development. Research into digital readiness, a digital competence profile, and self-study scholarship reveals the patchy nature of measurement of digital literacies in Canadian faculties of education. This chapter concludes with insights into contextual factors that impact teaching and learning in faculties of education in Canada. This chapter illuminates limitations and barriers, the cracks in the development of digital literacies in teacher education programs, which allow individual lights of innovation to shine across this vast and diverse country.
On-Demand Biometric Authentication for System-User Management

To realize a secure system-user management, continuous authentication must be implemented in the system. In addition, only limited biometrics that can be measured passively are applicable for continuous authentication. However, continuous authentication is a heavy processing load for the system. In this study, possible methods for conducting a continuous authentication are examined from the viewpoint of reducing the processing load, and two types of on-demand authentication approaches are confirmed to be effective.
Texture Transfer Attention for Realistic Image Completion

Over the last few years, the performance of inpainting to fill missing regions has shown significant improvements by using deep neural networks. Although the recent inpainting works create visually plausible structure, but insufficient expression of the texture of objects or color distortion make feel heterogeneity. Motivated by these observations, we propose a method for transferring texture patches using skip-connection that Texture Transfer Attention network that better produces the missing region inpainting with fine details. The network is a single refinement network and takes the form of U-Net architecture that transfers fine texture features of encoder to coarse semantic features of decoder through skip-connection. Texture transfer attention is used to create a new reassembled texture map using fine textures and coarse semantics that can efficiently transfer texture information as a result.
Information Extraction for Knowledge Graph of ISO 19650 Standards

Engineering project development requires all participants timely communicate explicit information. Besides the IFC file format, they also need an information management framework to support their collaboration. ISO 19650 standard series provide such a framework to establish a reliable information source. Since the 5-part ISO 19650 series constitute a complex system, the AEC community expect a knowledge graph to capture the key concepts and rules in those standards in order to facilitate the application of the collaborative management framework. Unfortunately, manual development of ISO 19650 knowledge graph is costly and time consuming. Therefore, a NLP-based information extraction method has been specifically developed for automatic construction of the ISO 19650 knowledge graph. In particular, the difficulty arising from the lack of corpus is greatly overcome by reasoning out domain semantic relationships from the syntactic relationships with the mapping rules specifically developed in this study. Finally, the experiment verified the feasibility and usefulness of the developed information extraction method.
Hybrid Spaces, Hybrid Methodologies: Finding Ways of Working with Social Sciences and Humanities in Human-Computer Interaction

Starting from the need for responsible research and innovation in computing, this paper addresses the challenges that come from working with the social sciences and the humanities in human-computer interaction. In this paper, we attempt to move beyond interdisciplinarity towards transdisciplinarity. In particular, we explore concepts of hybridity and third spaces that are used in HCI participatory design practices. We trace back the postcolonial, decolonial and feminist scholarship basis of these concepts and look at how their critical potential can inform a conceptualization of what we call hybrid methodology in its own right. For this, we present three case studies. The first two work with experimental spaces in artistic research and research through design, the third works with intersectional critical and speculative methodologies in machine learning systems design. The first two case studies center around the discussions of disciplinary openness through experimentation as a way of moving beyond interdisciplinarity towards transdisciplinarity; the third zooms in on the role of critical theoretical knowledge from the social sciences and the humanities as knowledge that enables new orientations in research and design of IT systems.
Three-Dimensional Morphometric Analysis and Statistical Distribution of the Early Kimmeridgian Hanifa Formation Stromatoporoid/Coral Buildups, Central Saudi Arabia

The high porosity and permeability stromatoporoid/coral facies partly characterize some of the most productive supergiant Late Jurassic carbonate reservoirs in the Middle East. Reservoir models often overlook detailed morphology and distributions of this facies due to the limited resolution of subsurface data. 3D morphological architecture and distribution of the Late Jurassic stromatoporoid/coral buildups are poorly understood as not many studies specifically investigate these aspects. This study performs a full three-dimensional outcrop investigation of the stromatoporoid/coral complex part of the Late Jurassic Hanifa reservoir analog in Wadi Birk, Saudi Arabia. We investigated spatial correlation, clustering tendencies, scaling relationships, and comprehensive statistical description over a 1.2 x 1 km area analog to a small-scale sectoral model of a typical Middle Eastern oilfield. This study utilizes integrated datasets including measured sections, drone-based digital outcrop model (DOM), core, ground penetrating radar (GPR), and shallow seismic to perform a full three-dimensional outcrop investigation. We identify and map three levels of the scaling hierarchy of the buildups: S1, S2, and S3. S1-scale buildups (a few meters in length) combine to form S2 clusters ( ~35 m in length), which in turn may grow into larger S3 complexes (~128 m in length). S1-scale buildups are near-circular and isotropic. However, as the S1 buildups cluster and grow into S2 and S3, their circular shape evolves into pseudo-ellipsoids with a strong preferential orientation towards northwest-southeast. The S2 and S3-scale amalgamated buildups are associated with asymmetric flank accumulation towards the northwest. We propose that the orientation of the S2 and S3-scale buildups and associated flanks are attributed to hydrodynamic currents driven by paleo-trade winds. This study also performs a brief static connectivity assessment to demonstrate the subsurface implications of these facies to field development planning. Results show that a vertical 5-spot pattern with a 1 km spacing can, in the best case, only access 14% of the Gross Rock Volume of the build-up facies due to the extreme lateral heterogeneity. Some 84% of the build-ups would be connected via flank and other facies with significantly different reservoir quality. This result underlines the extreme heterogeneity of the late Jurassic shallow-water platform carbonates and the potential for substantial quantities of bypassed hydrocarbons remaining in subsurface analog reservoirs
A Runtime Framework of Self-Programming System

Following our previous article’s discussion of the storage system for the self-programming system, we construct a runtime framework in this article. And we also introduced the learning mechanism of this framework. Under this framework, the storage system plays the role of recording the functions originating from the body and the brain. In this way, it solved the long-standing question in computer science: how to build a self-improving artificial intelligence. The basic idea is that since the way to learn is nothing but some information that can be recorded, thus it can be handled the same as other normal information. Beyond the self-improvement, we also show part of the runtime states space has similar functionality as our consciousness.
A Space-Time Harmonic Potential-Based Navigation Control With Guaranteed Time of Arrival and Obstacle Avoidance

This paper suggests a novel context-aware, goal-oriented controller that suits realistic agents operating amidst predictable, time-varying obstacles. The controller can guarantee safe, feasible and on-time mobility to a reachable target where the operator can <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">priori</i> specify arrival time without violating an upper limit on speed. If the target is not reachable within the speed limit or time window, the control action degenerates and motion terminates at the start point. The navigation controller makes use of the concept of Machian state space-time. The space-time control action is induced from a special type of harmonic potential fields that is called: nonlinear anisotropic harmonic potential. The approach is developed, proofs of correctness and performance are supplied and simulation results are provided to demonstrate the controller’s capabilities. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The paper suggests a novel space-time navigation controller to meet the stringent requirement on a limited speed, autonomous agent of specifying time of arrival to a target in a cluttered environment. Space-time navigation enables, along with better process management, better utilization of space in terms of increasing traffic density. The approach reduces the need for re-planning in case of unexpected events by providing a plan to reach the target safely and on-time from each point in the feasible space.