Reviewerly/exHarmony huggingface dataset:
Adaptive Pure Pursuit: A Real-Time Path Planner Using Tracking Controllers to Plan Safe and Kinematically Feasible Paths

Path planning is an essential function in an intelligent vehicle, especially when driving in scenarios cluttered by large-scale static obstacles. Traditional path planners often struggle to find a balance among speed, accuracy, and optimality in their solutions. In this paper, we introduce an Adaptive Pure Pursuit (APP) planner, which is designed to be fast and near-optimal for autonomous driving in cluttered environments. The APP planner generates feasible paths through a simulated closed-loop tracking control process of a virtual vehicle. If a derived path encounters obstacles, an adaptive refinement step is taken to locally reduce these collisions. Unlike search-based planners that suffer from the “curse of dimensionality” and optimization-based methods that often run slowly, the APP planner operates extremely fast. The high speed stems from the fact that both the virtual controller simulation and the refinement step involve computations with zero degrees of freedom. The proposed APP planner outperforms the prevalent optimization-based and search-based path planners, as shown by comparative simulations. Real-world experiments were also conducted to validate the APP planner, and its source codes are provided at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/libai1943/Adaptive_Pure_Pursuit_Planner</uri> .
Blockchain Technology in Banking as a Tool Towards the SDGs

Purpose of this paper is to investigate potential implications of blockchain in banking and its contribution towards the Sustainable Development Goals (SDGs). We use qualitative analysis based on case study analysis and netnography for determining existing and potential implications of blockchain in banking and their possible contributions towards the SDGs. This paper’s originality is finding specific potential contributions of blockchain technology in banking towards SDGs, such as towards ending poverty with increasing financial inclusion, promoting well-being for all with improving banking institutions’ performance and towards climate action by reducing banking institutions’ environmental print with digitalizing their processes and services.
Unexpected Events in IT Projects: A Conceptual Review and Research Agenda

When unexpected events occur, IT project teams need to react appropriately in order to limit the potentially negative, and sometimes disastrous consequences of such events. Yet, while past research has identified unexpected events as being an important aspect to consider when managing projects, the existing literature does not provide a clear conceptualization of this concept which limits the development of new theories and thus the ability to guide practitioners. Based on a literature review and to advance our understanding of unexpected events in IT projects and their effective management, and facilitate future theoretical developments on IT project management, the present paper develops a conceptualization that clearly distinguishes this concept from other closely related ones, as well as proposing a research framework that links it to IT project team responses and their consequences. A detailed research agenda, integrating current advances in different fields, is developed to highlight future research avenues.
Holistic Prototype Attention Network for Few-Shot Video Object Segmentation

Few-shot video object segmentation (FSVOS) aims to segment dynamic objects of unseen classes by resorting to a small set of support images that contain pixel-level object annotations. Existing methods have demonstrated that the domain agent-based attention mechanism is effective in FSVOS by learning the correlation between support images and query frames. However, the agent frame contains redundant pixel information and background noise, resulting in inferior segmentation performance. Moreover, existing methods tend to ignore inter-frame correlations in query videos. To alleviate the above dilemma, we propose a holistic prototype attention network (HPAN) for advancing FSVOS. Specifically, HPAN introduces a prototype graph attention module (PGAM) and a bidirectional prototype attention module (BPAM), transferring informative knowledge from seen to unseen classes. PGAM generates local prototypes from all foreground features and then utilizes their internal correlations to enhance the representation of the holistic prototypes. BPAM exploits the holistic information from support images and video frames by fusing co-attention and self-attention to achieve support-query semantic consistency and inner-frame temporal consistency. Extensive experiments on YouTube-FSVOS have been provided to demonstrate the effectiveness and superiority of our proposed HPAN method. Our source code and models are available anonymously at https://github.com/NUST-Machine-Intelligence-Laboratory/HPAN.
Collaborative Filtering Based on Non-Negative Matrix Factorization for Programming Problem Recommendation

This paper explores the use of Non-Negative Factorization for adapting collaborative filtering to programming exercises. Traditional collaborative filtering uses user preferences as ratings, but user feedback is expressed differently in programming exercises. The proposed approach captures user satisfaction based on the number of attempts and the final verdict. The NMF algorithm is used to factorize a non-negative input matrix into two non-negative matrices, and user-based collaborative filtering is implemented to make recommendations. The proposed model achieved an Mean Absolute Error of 0.06, Root Mean Squared Error of 0.2, a Recall of 51%, and a Precision of 54%, demonstrating its capability to make accurate and relevant recommendations for programming exercises.
Emotion Prediction Based on Conversational Context and Commonsense Knowledge Graphs

Existing studies on emotion recognition in conversations have generally analyzed and classified emotions based on a speaker’s utterances from conversations. On the other hand, little research has predicted emotions without such speaker utterances. In this study, we propose an emotion prediction model that forecasts a speaker’s emotion before she makes a statement utilizing conversational context and commonsense knowledge graphs. In an evaluation experiment, we rate our proposed model’s performance using an emotion recognition dataset in conversations.
Framework of Data Governance Interoperability in National Capital Based on Indonesia's One Data Policy

One Data Indonesia (SDI) is a government data management policy that aims to create quality data, which is easily accessible, and can be shared between Central and Regional Agencies. With the relocation of the Indonesian State Government Capital to the one Province of Kalimantan, the new National Capital (IKN) must be ready to synergize with the other government institutions of Indonesia. Through One Data Indonesia policy, all central government data can lead to the One Data Indonesia Portal as stated in Presidential Regulation no. 39 of 2019 concerning One Data Indonesian. This paper aimed to study the model of the data governance to support IKN in the One Data Indonesia Policy. The study used a descriptive qualitative approach with systematic literature review method. This study also discussed the data interoperability framework in the National One Data Policy. The results of this study are recommendations for the implementation of interoperability data governance in IKN based on the related Policy.
Unsupervised Defogging for Rotary Kilns Image

Image processing of rotary kilns is extremely important for their combustion efficiency. Most of the current image processing on kilns is focused on image classification, but image defogging is also one of the key aspects, which is still a challenging problem. Due to the harsh combustion environment, the large amount of smoke and dust generated during the combustion process leads to poor visualization of rotary kiln images. To solve its combustion uncertainty and real-time, We propose an unsupervised layer-based defogging network that employs a graph decomposition defogging approach, which decomposes images into three layers for defogging. In particular, the proposed UPSCA-Net, which is a U-shaped network combining an attention mechanism and focus loss, uses a long connection, jump fusion of different features for extracting image features and balancing the uncertainty of fogging, and achieves zero-sample kiln image defogging, making the defogging effect closer to the ground truth. In addition to this, flame images do not have a fog-free image for reference and cannot be measured with a referenced baseline, so we have conducted extensive experiments using a reference-free image quality metric based on real-world rotary kiln combustion images. The defogging results show that our proposed method outperforms several different defogging methods in various indicators. Excellent visual effects are also achieved.
Towards Enhanced Functionality and Usability of Giving Manikin Task Instructions in a DHM Tool

There are many approaches on how to make digital manikins replicate how real humans perform tasks. The manikin motions can, for instance, be computed by algorithms based on task instructions from the DHM tool user. In this study, we investigate possibilities for improving the task instruction language used in the DHM software tools. The study focuses on identifying opportunities and challenges for how the task instruction language can be improved, and the goal of the study is to establish research questions and to create a research roadmap. The aims of the research questions and associated research and development are: (i) to make it easier to give task instructions; (ii) to reduce the variance in simulations results between different DHM tool users; and (iii) to improve the trustworthiness of the simulation results, related to issues such as manikin behavior and estimated motion times. The potential approaches that have been identified, and will be elaborated and discussed in this paper, with the DHM software tool IPS IMMA as base, are: (i) to enable the DHM tool user to give task instructions on a higher abstraction level than today; (ii) to incorporate functionality to automatically represent likely human behavior; and (iii) to improve the accuracy of time estimation of task performance.
Augmented Operators and Virtual User Manuals for Complex Industrial Systems: User Research and Scenario-Building Activities

Over the last decades, rapid technological advances are radically changing the manufacturing environment. Although the impact of Industry 4.0 intends to equip traditional factories with interconnected and reconfigurable machines to avoid errors and maximize production process efficiency, maintenance processes are still suffering from inconsistent and unstructured documentation. In this scenario, Augmented Reality (AR) appears as a promising tool to support the operators in real-time during manual operations by reducing human errors and dependence on printed work instructions. AR applications can enable maintenance operators to speed up production efficiency and reduce or prevent equipment downtime. Considering this, the paper presents a design research collaboration with an industrial company to convert the existing documentation into an accessible augmented interface. The activities adopted a human-centered design perspective to explore the influencing factors that can improve technological accessibility and acceptance of AR applications. User research and scenario-building activities will help the research to define proper guidelines to augment workers in complex maintenance operations.
Translation Ontology of optimal Decision Making along a Distributed Production Chain by Example

Traditionally, product design is done from within a company. From a purely economic point of view, it will focus on optimising product performance while minimising costs and waste. The optimisation uses key performance indicators, which often result from modelling the production processes and the product’s properties. Increasingly, though, product design and product manufacturing involve groups of companies, each of which has its own objectives and strategies. The central idea is to couple the single company-based silo optimisation to a collaborative optimisation and decision-making to unlock hereto hidden product innovation potentials on a digital platform. This platform supports the modelling process from describing the distributed manufacturing and the involved materials to product design, which we call Translation. In this contribution, we discuss a joint translation ontology, capturing the translation process and the recursive business-to-business process implemented in an H2020-funded innovation platform VIPCOAT.
Legality Identification of Japanese Online Advertisements Using Complex-Valued Support Vector Machines with DFT-Coded Document Vectors

As the Internet advertising market expands, the number of advertisements containing inappropriate language is increasing. Advertisements that exaggerate the efficacy of products may contravene the Pharmaceutical Affairs Law and the Act against Unjustifiable Premiums and Misleading Representations. Therefore, a system that can detect problematic expressions is required. Some advertisements cannot be classified using only the statistics of words. Therefore, embedding other information, such as word order and word period in the features, is effective to categorize documents. However, the number of labeled data in advertising documents is limited; consequently, models with complex structures tend to overlearn. In addition, features and discriminant models with high generalization performance must be found even if the number of data is small. To address these severe issues, we propose a document feature based on the discrete Fourier transform (DFT) of word vectors weighted using an index previously proposed in a study that attempted to categorize Chinese online advertisements. We also propose a document discriminant model based on a complex-valued support vector machine. We demonstrate that the proposed model outperforms previous models in terms of discriminative performance of F-measure. We found that the proposed index emphasizes word vectors of specific nouns and verbs in Japanese advertisements. In addition, we found that certain words appeared periodically, and such words are highlighted by the discrete Fourier transform. These factors contributed to the better performance of the proposed model.
Domain-Specific Knowledge Graph Adaption with Industrial Text Data

We suggest an interactive approach towards knowledge acquisition based on neural text mining. Our method enables experts to extend a given knowledge graph-which is general and scarce-with knowledge discovered in a text collection covering a particular customer or subdomain. Our tool suggests text passages that contain new concepts and links, which the domain expert inspects and adds to the graph. The underlying discovery model combines a transformer-based contextualized text encoder with a knowledge graph completion model. End-to-end training of both components is bootstrapped by sampling mentions of the given knowledge graph’s concepts from the text collection. We evaluate our approach quantitatively based on manual annotations of model predictions, including a comparison with fastText. Furthermore, we conducted an expert annotation session using the tool and a subsequent interview. The resulting observations show the potential of the approach for knowledge engineering in the wild.
Link-Aware Link Prediction over Temporal Graph by Pattern Recognition

A temporal graph can be considered as a stream of links, each of which represents an interaction between two nodes at a certain time. On temporal graphs, link prediction is a common task, which aims to answer whether the query link is true or not. To do this task, previous methods usually focus on the learning of representations of the two nodes in the query link. We point out that the learned representation by their models may encode too much information with side effects for link prediction because they have not utilized the information of the query link, i.e., they are link-unaware. Based on this observation, we propose a link-aware model: historical links and the query link are input together into the following model layers to distinguish whether this input implies a reasonable pattern that ends with the query link. During this process, we focus on the modeling of link evolution patterns rather than node representations. Experiments on six datasets show that our model achieves strong performances compared with state-of-the-art baselines, and the results of link prediction are interpretable. The code and datasets are available on the project website: https://github.com/lbq8942/TGACN .
The Development of Learning Media using Wordwall Application in Social Science Learning

This research is stimulated by optimizing the material for cultural diversity of traditional houses in Indonesia in the fourth grade of elementary school in social science learning through the digital media of the Wordwall web application. The use of learning media that can add students' interest in learning in social science learning in elementary schools as well as decreasing students' awareness in preserving culture. This research has mission to produce a product in the form of a Wordwall web application learning media on social science material for fourth grade of elementary school. This research used the development of D&amp;D (Design and Development) with the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The instrument used is a questionnaire to validate material experts, linguists and media experts, as well as teacher and student responses. This research produces a product in the form of learning media for the Wordwall web application material for cultural diversity of traditional houses in Indonesia. Based on the results of the feasibility test through validation by experts, namely material experts, linguists, and media experts using a validation questionnaire, they obtained a "Excellent" score category with indicators of presentation feasibility, language, usability, and illustration design (content). Likewise, the responses of teachers and students also acquire an average of 90.3% in the classify of "Excellent" scores with indicators of material content, presentation, language, and design quality. Therefore, the development of Wordwall application learning media in social science subject theme 7, the beauty of diversity in my country has become one of the optimizations of learning media. Keywords: Wordwall Application, Learning Media, social science learning in Elementary School
The age of blended classical and quantum networks

Quantum networking is upon us, which brings unforeseen rewards and challenges. One such challenge is the coexistence of quantum and classical information in a single standard fiber. Indeed, most quantum networking protocols rely on auxiliary classical communication be it synchronization or a “public channel” traffic – a classical link that operates contemporarily with the quantum link. I will talk about the coexistence limits due to classical-quantum crosstalk and introduce a concept of classical communication via a quantum channel and with the help of quantum measurement. I will show how quantum measurement can improve energy-per-bit requirements and introduce the first quantum-enabled error correction protocol.
Quantum networks: from the quantum internet to the brain

I will give my personal perspective on recent progress in quantum networks and quantum communication, including some mentions of efforts towards distributed quantum computing. In this context of moving towards a global 'quantum internet', I will discuss some of our own recent work on quantum repeaters, quantum memories, quantum transducers, and sources of entanglement. I will discuss approaches to the quantum internet involving satellites. Finally I will also briefly talk about our work on the question whether there could be quantum networks in our brains.
Expectations of Ecuadorian higher education in a time of uncertainty: a comparison between the perceptions of students and teachers during the COVID-19 pandemic (2020/21)

Since March 2020, higher education students in Ecuador have been learning through digital classes. Digital education conceptually represents an educational modality that provides autonomy and a freeway for knowledge construction synchronously and asynchronously. It includes the students’ self-development in the medium and long term, with the support of previously trained teachers, standardized didactic-pedagogical material, and specific platforms. However, especially in emerging countries, adopting digital classes may neglect the students and teachers’ needs and the universities’ ability to offer that. To consider the inherent challenges of digital education, that is economic, social and educational, we used three online validated questionnaires on distance learning, the use of information technologies and skills with particular apps with a convenience sample of students (200) and teachers in International Studies (25). Building on our participatory action research in 2020, in which teachers used the opportunity of teaching online with new digital tools in university courses to analyze their problematic frames of reference and develop their ideas and practices of teaching and learning, the three questionnaires, with slight modifications to ensure their response are reflections of how they think students will respond, was also applied to teachers. The students participating in this research were from the third and fourth year of International Studies at the University of Azuay, Cuenca, Ecuador. We were particularly curious in what ways global trends are affecting these students’ lives with experiences that have been shaped by interactions with international digital peers through games and apps rather than campus-based activities. How are these worldwide student interactions geopolitically mediated by the cultural imperialism flowing from the Global North to the South? What are students and teachers’ perceptions of students’ experience at the interstices of power, privilege and oppression associated with being emerging ‘third’ world citizens in Ecuador?
Really useful knowledge in a postdigital age

This chapter presents a close reading of Richard Johnson’s seminal work 'Really useful knowledge', 1790–1850. Published 40 years before writing this chapter, Johnson’s work has left a huge legacy in radical education until day. The chapter moves on to reinventing and reimagining Johnson’s ideas for the postdigital context through a complete rewrite, mirroring the exact structure of Johnson’s work. This methodological choice is burdened by two limitations: there is no way to know whether presented interpretations of Johnson’s themes are at all faithful to Johnson’s ideas, and Johnson himself would surely not write the same text today. These limitations are counter-balanced by an honest approach to the rewrite, in which the author reveals the importance of randomness, chance, and human emotion, in postdigital research, and it welcomes new workers, human and non-human, into knowledge work. The chapter concludes by reinventing and reimagining Johnson’s work in the concept of postdigital really useful knowledge which is a concept in flux, a set of practices, and a battlefield for social justice, that can be found in streets, art galleries, activist organizations, libraries, the academia, and many other places. Mirroring Johnson’s seminal work, it is hoped that his important messages will be taken forward into today’s postdigital context.