Reviewerly/exHarmony huggingface dataset:
Representing Boundary-ambiguous Scene Online with Scale-encoded Cascaded Grids and Radiance Field Deblurring

Implicit scene representations have recently shown promising results in photo-realistic 3D reconstruction and view synthesis based on calibrated views. However, their applications face several challenges, including unknown camera pose, boundary ambiguity, and observation noise. This paper proposes a novel online scene representation method that simultaneously learns to represent the target scene and estimates the camera poses from an RGB-D stream. An implicit scene representation function built with scale-encoded cascaded grids is proposed to represent scenes online from incremental observations. This implicit function is optimized in a reparameterized domain that provides defined boundaries. In this reparameterized domain, the cascaded grids are progressively distilled under geometric and photometric supervision to improve their model capacity and geometry accuracy. A radiance field deblurring module based on the physical imaging process is further proposed to restore a photo-realistic reconstruction against camera motion blur, which is the main component of the observation noise. The proposed method can produce sharp and photo-realistic representations of scenes under various shooting conditions without known camera poses. Experiments on multiple datasets have demonstrated the effectiveness of the proposed method in improving view synthesis and camera tracking results for online scene representation tasks.
MODERN APPROACHES TO PREDICTING STRATEGIES FOR THE FORMATION OF INFORMATION LITERACY OF STUDENTS IN THE CONTEXT OF DIGITAL TRANSFORMATION OF EDUCATION

The article discusses modern approaches to predicting strategies for the formation of information literacy of students. The phenomenon of information literacy, the genesis of this phenomenon, its criteria and elements are considered. The authors establish the correspondence of the levels of change in pedagogical practice in the context of digital transformation of education to the levels of interaction of students with digital educational materials.
Anti-corruption system 4.0: The adoption of blockchain technology in the public sector

Countries are using various sophisticated information technologies to help provide quality and transparent services. This research aims to explore the potential and models of using blockchain technology to overcome corruption problems in Indonesia. Proper utilisation of blockchain technology can increase accountability and reduce loopholes in corrupt practices. Blockchain is a distributed database with records (ledgers) shared by all network participants. Smart contract as a form of blockchain can help make the process more transparent and trackable. Since blockchain is a relatively new technology, very few studies have been done to explain its potential in the public sector, particularly with regards to issues of corruption. This study develops a model for the application of blockchain technology and conducts an in-depth analysis of its possibilities using a qualitative method and an exploratory approach. In-depth interviews, designed to get a big picture view of the blockchain development model in the public sector were conducted with several sources, namely the Director General of Asset Tracing KPK (Indonesian Anti-Corruption Commission), KPK Public Prosecutors, the KPK IT department, WIr Group, Financial and Development Oversight Agency (BPKP), and blockchain practitioners. The research was conducted between August and December 2022. The study's findings demonstrate how promising it is to use blockchain technology to lessen corruption. Distributed ledgers give governments new ways to improve transparency. The model that can be applied is ‘permissioned blockchain’ because it has a private nature. Blockchain application requires top management support, organisational readiness, and organisational support. To deepen understanding, further experimental research is needed to test the model.
Mathematical modeling of intelligent system for predicting effectiveness of premenstrual syndrome

Majority of reproductive-aged women experience some form of physical discomfort or emotional unease in the weeks leading up to the onset of menstruation. The symptoms are often not severe, but they can cause significant discomfort and disrupt day to day activities of the person experiencing them. It is estimated that between 5 and 8 percent of women experience severe premenstrual syndrome (PMS); the majority of these women may also fall under the category of premenstrual dysphoric disorder (PMDD). The most bothersome symptoms are those associated with the mood and behaviour, such as impatience, tension, sad mood, tearfulness, and mood swings. However, physical problems, such as breast soreness, indigestion and bloating, can also be problematic. Using the Gradian Boost regressor (GBR) method of machine learning, the researchers in this study made a prediction regarding the effects of premenstrual syndrome (PMS). Kelly Wallance classifies premenstrual syndrome as PMS-A, PMS-C, PMS-D, and PMS-H, in addition to other symptoms. Researchers circulated the Kelly Wallance questionnaire on Google Form, which was then used to collect the data for the dataset. The accuracy of the model was measured at 99.99% for PMS-A, 99.93% for PMS-C, 99.87% for PMS-D, 99.92% for PMS-H, and 99.97% for other symptoms.
xOS

Process and Thread are first-order abstractions of the operating system (OS), whose implementation is wired into the OS core. Several research works have shown the inadequacy of these two main abstractions for modern isolation needs, leading to the introduction of additional abstractions with new isolation and communication features. Despite their usefulness, these new proposals are introduced in a somewhat ad-hoc manner, compromising their broad and consensual adoption.
<b>Blockchain and supply chain logistics evolutionary case studies</b>

"Blockchain and supply chain logistics evolutionary case studies." Production Planning & Control, ahead-of-print(ahead-of-print), pp. 1–2 Additional informationNotes on contributorsManjot Singh BhatiaDr. Manjot Singh Bhatia is currently an Assistant Professor of Operations and Supply chain management. He has over 5 years of experience in academics and Industry. His current research interests include circular economy, and industry applications of Blockchain implementation. He has published over 20 articles in reputed Journals such as Industrial Marketing Management, International Journal of Production Research, IEEE Transactions on Engineering Management, Production Planning & Control, etc.Kishore Kumar GangwaniProf. (Dr.) Kishore is an Assistant Professor in the area of Marketing at the Management Development Institute Gurgaon. Dr. Kishore participated in the Wharton Global Faculty Development Program in 2021, hosted and led by the Wharton School of the University of Pennsylvania. He earned his doctorate in Marketing from the Indian Institute of Management, Bangalore, in March 2019. In his doctoral thesis, Kishore has examined the role of regulatory focus on Pay What You Want pricing. His current areas of research include consumer decision-making, offloading consumer experiences to digital memory, and Technological advancements and interventions in productivity enhancement.
Cultural and Socio-Economic Impact of the Indigenous Garment Industry: A Case of Smock Dealership in Tamale, Ghana

This study explores the socioeconomic benefits and challenges of smock dealership in Tamale. Despite the relevance of the African traditional garment sector in the socio-cultural and economic development of local and national economies, the industry is bedeviled with a plethora of challenges. Empirical evidence also confirms that African management practices have been largely dependent on western ideologies without taking cognisance of the unique features of indigenous practices. This case study provides an account of successful indigenous business practices among smock dealers in the Tamale Metropolis of Ghana, employing mainly qualitative descriptive research methods. Manual thematic analysis was utilised on the qualitative data. Findings showed that smock businesses offer economic/financial benefits such as income generation, family up-keep, trade, serves as wedding attires, social networks, projects northern cultures, sources of income and livelihood. We also found various advantages of deploying technology in smock business and these include efficiency, productivity, fraud prevention, financial benefits to the producers, competitiveness and globalisation via social media, and customer relationship building. It is recommended that the government make wearing made-in-Ghana clothing a policy and enforce its use. Also, the Ghanaian government and NGOs should make industrial sewing machines available to encourage the business, thereby increasing smock production.
Transforming Hand Gesture Recognition Into Image Classification Using Data Level Fusion

Hand gesture recognition (HGR) is a form of perceptual computing with applications in human-machine interaction, virtual/augmented reality, and human behavior analysis. Within the HGR domain, several frameworks have been developed with different combinations of input modalities and neural network architectures to varying levels of efficacy. Such frameworks maximized performance at the expense of increased computational and hardware requirements. These drawbacks can be mitigated by a skeleton-based framework that transforms the hand gesture recognition task into an image classification task. This chapter explores several temporal information condensation (via data-level fusion) methods for encoding dynamic gesture information into static images. The efficacies of these methods are compared, and the best ones are aggregated into a generalized HGR framework which was extensively evaluated on the CNR, FPHA, LMDHG, SHREC2017, and DHG1428 benchmark datasets. The framework's performance shows competitiveness compared to other frameworks within the state-of-the-art for the datasets.
Visual question answering model based on fusing global-local feature

In order to solve the problem of insufficient learning ability of mutual representation of two modal information in existing visual question answer models, the Global-Local Attention Network (GLAN) approach to visual question answer model with global-local features is proposed by analyzing the reasons why the model cannot distinguish the relationship between similar targets in images and the reasons for the model's reliance on text information. To supplement the missing local information in the global features, the model is designed with a convolution guided attention module to enhance the image representation capability and increase the bias of image information in the model. Experiments are conducted on VQAv2.0 and GQA datasets, and the accuracy of the proposed model is improved by 0.60% and 0.38%, respectively, compared with Baseline, which can effectively improve the inference ability of the model.
Data Re-Balancing using Fuzzy Clustering and SMOT Mechani

In this research, “the authors present an innovative method for rebalancing data to eliminate bias in confidential features. It is possible for individuals with protected attributes, such as race, gender, or age, to be subjected to discrimination in the fields of job selection and loan approval. The suggested method utilizes fuzzy clustering in conjunction with the Synthetic Minority Over-sampling Technique (SMOTE) to rebalance the data set and reduce the amount of bias that is there. Fuzzy clustering is applied to discover groups of similar instances based on specific characteristics, while SMOTE is utilized to construct fake examples in the underrepresented group. Fuzzy clustering is also utilized to locate groups of comparable cases based on certain characteristics. The method is assessed in contrast to other rebalancing strategies that already exist and based on many standard data sets. The findings indicate that the suggested approach outperforms the methods that are state-of-the-art in terms of accuracy, precision, recall, and f1-score metrics, demonstrating its effectiveness in reducing bias in the protected” attributes.
Project Management Perspective on UAV Data Collection: Water Management Case Study Baroch

Currently, environmental protection is one of the main priorities. Vegetation cover as a complex system and water bodies are examples of land cover types that are observed and understood as important parts of our environment and need to be cared for. It is very important to collect up-to-date data on such systems. The use of unmanned aerial vehicles for data collection has become very used in recent years due to its scalability, efficiency, and instrumentality in various fields. Managing data collection projects using UAVs can be quite complex and challenging. This text examines the use of the Agile Scrum methodology in realization such a project.
On Visual Servoing under Field-of-View Limits and Impulsive Perturbations<sup>*</sup>

We consider the problem of image-based visual servoing (IBVS) under inelastic visibility constraints that arise from the field-of-view (FOV) of the camera. The target is affected by possible aperiodic impulse perturbations. Under the assumption that a minimum time needs to elapse after each impulse, the proposed controller guarantees that all image features will remain strictly inside the FOV and will converge to the desired feature values with prescribed performance characteristics, related to accuracy and convergence time, between any two consecutive impulses. Simulation results clarify and verify the theoretical findings.
Quadcopter Tracking Using Euler-Angle-Free Flatness-Based Control

Quadcopter trajectory tracking control has been extensively investigated and implemented in the past. Available controls mostly use the Euler angle standards to describe the quadcopter’s rotational kinematics and dynamics. As a result, the same rotation can be translated into different roll, pitch, and yaw angles because there are multiple Euler angle standards for the characterization of rotation in a 3-dimensional motion space. To address this issue, this paper will develop a flatness-based trajectory tracking control without using Euler angles. We assess and test the proposed control’s performance in the Gazebo simulation environment and contrast its functionality with the existing Mellinger controller, which has been widely adopted by the robotics and unmanned aerial system (UAS) communities. Our simulations also show that, for both controllers, the main cause of loss of stability is not the theoretical domain of stability, but it is instead the inability of quadcopter rotors to provide negative thrust as is requested by controllers for aggressive trajectories.
Visualizing Uncertainty in Sets

Set visualization facilitates the exploration and analysis of set-type data. However, how sets should be visualized when the data are uncertain is still an open research challenge. To address the problem of depicting uncertainty in set visualization, we ask 1) which aspects of set type data can be affected by uncertainty and 2) which characteristics of uncertainty influence the visualization design. We answer these research questions by first describing a conceptual framework that brings together 1) the information that is primarily relevant in sets (i.e., set membership, set attributes, and element attributes) and 2) different plausible categories of (un)certainty (i.e., certainty, undefined uncertainty as a binary fact, and defined uncertainty as quantifiable measure). Following the structure of our framework, we systematically discuss basic visualization examples of integrating uncertainty in set visualizations. We draw on existing knowledge about general uncertainty visualization and previous evidence of its effectiveness.
DIALOGIC COMMUNICATION IN PSYCHOLOGICAL SUPPORT OF PERSONAL DEVELOPMENT OF CLIENTS

The article discusses the theoretical aspects of dialogic communication implemented in the process of consulting clients, describes its features and the changes being formed. The conducted research will become a basis for the authors’ dialogical model of psychological support of people with personal development traumas.
Development of an Application for Automated Testing of Web Forms

The stages of automating the process of regression testing of a web form are considered. A feature of the work presented for consideration is a description of the main features of the development of an application for automated testing of web forms performed on the server side. The essence of the application concept is to use the algorithm proposed by the authors, which allows to significantly increase the speed and accuracy of tasks. The proposed algorithm is easily optimized for any browser by replacing the driver, and it can also be used with minor changes to test any other form. All algorithms of generation and subsequent analysis are universal. The paper shows that the key factors in the choice of testing automation tools are: the ability to manage databases of libraries used in the project; cost; supported Python programming language. The development features are related to the fact that the main information for testing is the data obtained from the Selenium WebDriver driver locators. The received data are stored on the server. The authors have developed an algorithm and sequential stages of testing. Testing of the form should be as efficient as possible, productive and cover all possible defects before the release of the final version of the project. Using the example of a specific form, it is shown that the proposed development is a simple and effective tool that can significantly increase the speed of testing filling out web forms on the server side and the correctness of filling out forms on the user side. The authors conducted a comparative analysis of manual and automated testing and showed the effectiveness of the proposed solution.
Comparison of Performance of Four Distance Metric Algorithms in K-Nearest Neighbor Method on Diabetes Patient Data

Diabetes is a chronic disease that occurs when the pancreas no longer produces insulin or when the body cannot effectively use the insulin it produces. The aim of this study is to analyze and compare the classification performance on diabetes patient dataset using four distance metric algorithms in the K-Nearest Neighbor (K-NN) method. Based on previous research, the performance values obtained were not sufficiently high, not exceeding 80%. Therefore, some actions are needed with the hope of obtaining new performance values and making comparisons with previous studies. Based on the test results using the confusion matrix, the accuracy level using Euclidean distance measurement obtained the best performance value at k=17 with 10-k fold, with an accuracy of 85.71%, precision of 86.24%, recall of 85.71%, and F-measure of 85.12%. The Manhattan distance measurement obtained the best performance value at k=25 with 10-k fold, with an accuracy of 85.53%, precision of 85.54%, recall of 85.53%, and F-measure of 85.10%. The Minkowski distance measurement obtained the best performance value at k=17 with 10-k fold, with an accuracy of 85.71%, precision of 86.24%, recall of 85.71%, and F-measure of 85.12%. On the other hand, the Hamming distance measurement obtained the best performance value at k=23 with 10-k fold, with an accuracy of 75.32%, precision of 79.27%, recall of 75.32%, and F-measure of 71.45%.
Thinking Big About Going Small: Conceptualizing the Human-Technology Integration Spectrum

As materials are advanced and the distal relationship between consumers and material is diminished, relevant theories (e.g., extended-self and posthumanism) must be revisited to explain the human-technology integration. This research does this by introducing the human technology integration (HTI) spectrum, which describes eight distal levels that material can be integrated within the body (i.e., no contact, touch non-wearable, touch wearable, surface barrier, oral consumption, inhalation, injection, constant contact). We highlight findings of the HTI spectrum by providing relevant examples for each category based on nanotechnology. Well-being, public policy, managerial implications, and future research suggestions are then discussed. This framework helps marketers and policymakers to consider how technology integration impacts the existing institutional political, social, and economic structures and guides them to implement best practices related to human technology integration for consumers and society.
A Feminist Postdigital Analysis of Misogyny, Patriarchy and Violence Against Women and Girls Online

In this chapter, I offer a postdigital feminist analysis of misogyny and its harmful manifestations online. The Internet is a powerful tool in the systemic and structural dissemination of gender-based violence against women and girls (VAWG). This violence includes technology-facilitated harmful behaviour, along with technological tools to violate victims’ rights, using devices like smartphones and surveillance cameras. I will offer a postdigital critique of VAWG, arguing that at the root of these behaviours is misogyny, a conceptual, descriptive, and analytical account of which I will give here. I will also analyse this phenomenon from the perspective of epistemic injustice to show how asymmetries in testimonial exchanges and hermeneutical resources sustain misogynistic, patriarchal practices. Despite the scale and prevalence of digital and cyber gender-based misogyny and violence, Big Tech are under little to no legal obligation to address the abuse, though legal measures such as the Online Harms Bill (UK) are in progress, and should, I argue, incorporate a VAGW Code of Practice. I conclude with a tentative formulation of what feminist postdigital analysis could consist in and its relevance to the postdigital condition.
The Implementation of Problem-Based Learning (PBL) Model to Improve Students’ Vocabulary Mastery

Background: Vocabulary is an important aspect of learning English for students, Mastering vocabulary allows students to understand English and to produce sentences easily. This research aims at improving students’ vocabulary mastery by implementing Problem-Based Learning (PBL) Model. Methods: It applied Classroom Action Research. The sample was 17 students of eleventh grade at SMA Utama Widya Pasraman Wira Dharma Palu. The research was conducted in two cycles. Each cycle consists of planning, action, observation and reflection. The researcher employed some instruments to collect data, the instruments were test, observation checklist and field notes. Result: The result of data analysis indicated that the percentage of students who passed score above KKM which is 70 in cycle 1 was 47.06 percent or 8 students. In cycle 2, the result increased to 82.35 percent or 14 students. It significantly increased 35.29 percent compared to cycle 1. Conclusion: Problem-Based Learning (PBL) is effective to improve students’ vocabulary mastery of grade XI students at SMA Utama Widya Pasraman Wira Dharma Palu.