Reviewerly/exHarmony huggingface dataset:
C-3PO: Towards Rotation Equivariant Feature Detection and Description

Despite the recent advances in local feature matching, dealing with affine distortions remains a major challenge. While state-of-the-art methods have shown to perform well in the absence of rotation perturbations, some computer vision applications, such as object tracking and image stitching, require keypoint extraction methods that maintain high performance regardless of the image orientation. Current approaches perform extensive data augmentation to artificially acquire a degree of rotation equivariance. However, this does not only induce redundancy in the learned feature representations, but also does not provide any geometric guarantees. To address this issue, this work explores an alternative approach that instead instills rotation equivariance inside the model itself. Leveraging recent advances in group equivariant deep learning, we propose C-3PO, a family of feature detection-and-description models based on steerable group convolutions. We evaluate our method against prior work, and find that it outperforms its non-equivariant counterparts for most rotation perturbations. However, presumably due to the task's inherent sensitivity to interpolation artifacts, extending a discrete rotation equivariant model to a continuous variant provides only marginal performance gains.
First Steps in Updating Knowing How

We investigate dynamic operations acting over a knowing how logic. Our approach makes use of a recently introduced semantics for the knowing how operator, based on an indistinguishability relation between plans. This semantics is arguably closer to the standard presentation of knowing that modalities in classic epistemic logic. Here, we discuss how the semantics enables us to define dynamic modalities representing different ways in which an agent can learn how to achieve a goal. In this regard, we study two types of updates: ontic updates (for which we provide axiomatizations over a particular class of models), and epistemic updates (for which we investigate some semantic properties).
‘Our Online-Ness Matters’: The Construction of Social Media Presences by Malaysian LGBTQ Communities

There has been a recent surge of interest in and research into the use of social media among lesbian, gay, bisexual, transgender and queer/questioning (LGBTQ) communities in Malaysia. Most studies, however, tend to focus on the communities’ motivations for using social media, and much more needs to be known about their actual social media usage and impact—especially what “online-ness” means and why it matters to them, given the diversity within these marginalised communities. Therefore, this chapter examines social media usage among members of Malaysia’s LGBTQ communities, in terms of their positions on the spectrum and the particularities of their online-ness. Based on the findings of a nationwide survey of LGBTQ individuals regarding their social media experiences, it discusses what online-ness means to them, the ways in which it is managed, its benefits and drawbacks as well as their future use of social media. It ends with a summary of the ways through which open and supportive discussions of online-ness can be created between members of these communities and the mainstream Malaysian public. This chapter is not intended to cover any of these topics in great detail—rather, it lays the groundwork for future studies regarding the nuances that not only characterise local LGBTQ communities, but which also shape their social media realities and experiences.
Solar Radiation Forecasting Based on Artificial Neural Network: A Case Study of Bechar City, Southwest Algeria

Estimating solar irradiance is an essential step in the design of solar systems and the performance evaluation of their various applications. This work has the purpose of developing a model based on an Artificial Neural Network (ANN) to anticipate the global solar irradiance on a daily basis in the city of Bechar. The models were given seven input data. We developed four models using different training algorithms. Correlation coefficient (R) and mean absolute percentage error (MAPE) were used to assess these models' efficiency. The results over 6 years demonstrated that Model1, provides significantly better forecasts with (R = 0.9198 and MAPE = 7.57). Therefore, in the Multi-Layer Feed Forward Neural Network (MLF), using the Levenberg-Marquardt back-propagation training algorithm provides the best accuracy for estimating daily solar radiation and may be considered one of the fastest and most accurate algorithms. This model is useful for sizing and designing solar systems in Algeria.
Application Architecture of Accounting Information and Auditing System Based on Blockchain Technology and a Private Data Sharing Technology

Under the background of informatization and intelligence of financial management, the accounting information and auditing system based on blockchain technology has improved the quality of financial information.It is possible to explore the use of the blockchain technology as a basis to solve the information traceability problem of information transmission in accounting information systems, and the distributed storage architecture of blockchain, lightweight digital signature algorithm, consistency Algorithms and other technologies are used to make up for accounting and auditing information security loopholes, thereby preventing information from being tampered with.Nevertheless, the authenticity, reliability and integrity of blockchain information transmission can also be solved by the EPC system network technology and Hash Encryption Algorithm.Aiming at safer and more efficient accounting information and auditing system, this paper proposes our computing scheme based on fully homomorphic encryption technology and semi-trusted mechanism in the context of applying financial robots, which improve the security of internal information under the premise of full disclosure.
Big Data Based Transfer Learning for Sentiment Classification with Multiple Source Domains

Sentiment classification, served as a curial technology in natural language processing and computational linguistics, has drawn a lot of attentions from researchers.However, due to the high cost of manual labeling in the era of big data, conventional methods of sentiment classification are unqualified to be employed in a new domain directly.Hence, in this paper, we explore big data based transfer learning for sentiment classification with multiple source domains.To solve the problem of inherent domain gap, we propose a novel framework Adversarial crossdomain sentiment classification with weighted domain-dependent fEAture learning dubbed AdEA.Specifically, AdEA involves an individual domain-invariant feature extractor and several domain-dependent feature extractors.To obtain the domain-invariant feature, we use a reversed discriminator loss for these extractors.Furthermore, we propose a weighted learning module to reinforce the relationship of domain-dependent features between source and target domains.Integrated with these two domain-related features, AdEA is able to achieve better capability of cross-domain sentiment classification.Experimental results show the effectiveness of our proposed method.
KG-BERTScore: Incorporating Knowledge Graph into BERTScore for Reference-Free Machine Translation Evaluation

BERTScore is an effective and robust automatic metric for reference-based machine translation evaluation. In this paper, we incorporate multilingual knowledge graph into BERTScore and propose a metric named KG-BERTScore, which linearly combines the results of BERTScore and bilingual named entity matching for reference-free machine translation evaluation. From the experimental results on WMT19 QE as a metric without references shared tasks, our metric KG-BERTScore gets higher overall correlation with human judgements than the current state-of-the-art metrics for reference-free machine translation evaluation.1 Moreover, the pre-trained multilingual model used by KG-BERTScore and the parameter for linear combination are also studied in this paper.
RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphs

Due to the open world assumption, Knowledge Graphs (KGs) are never complete. In order to address this issue, various Link Prediction (LP) methods are proposed so far. Some of these methods are inductive LP models which are capable of learning representations for entities not seen during training. However, to the best of our knowledge, none of the existing inductive LP models focus on learning representations for unseen relations. In this work, a novel Relation Aware Inductive Link preDiction (RAILD) is proposed for KG completion which learns representations for both unseen entities and unseen relations. In addition to leveraging textual literals associated with both entities and relations by employing language models, RAILD also introduces a novel graph-based approach to generate features for relations. Experiments are conducted with different existing and newly created challenging benchmark datasets and the results indicate that RAILD leads to performance improvement over the state-of-the-art models. Moreover, since there are no existing inductive LP models which learn representations for unseen relations, we have created our own baselines and the results obtained with RAILD also outperform these baselines.
Learning Outcomes Measurement on Image Processing Subject using Self Organizing Map based Fuzzy Logic

Outcome-based Education (OBE)-based learning has been implemented to improve the quality of education.One of the important things is to measure the achievement of PLO.The aim of this study is proposed an alternative method of measuring PLO which is measurement carry out used Self Organizing Map (SOM) based on Fuzzy Logic.The parameters used in SOM are the number of epochs of 500, and the learning rate of 0.5.The implemented fuzzy logic uses a centroid fuzzification parameter with a membership function triangle and fuzzy bisector fuzzification parameter with gaussian membership function.The experimental results show that measurements using a Fuzzy Logic-based Self Organizing Map (SOM) can be used but must go through a simulation process first.This is because the selection of fuzzy and rules-based parameters that are implemented greatly affects the final result.
Distributed News Crawler Using Fog Cloud Approach

Technology advanced quickly during the Industrial Revolution.4.0, makes the internet network also develop rapidly and become larger.So website technology that is constantly changing becomes a big challenge in using large and complex data information on the global Internet.Stand-alone web crawlers have traditionally been difficult to overcome the challenges of rapid information growth, therefore it's challenging to extract a lot of data in a short period.The research will use distributed technology to build a more effective web-distributed news system, to search for news.Crawler systems can work efficiently with Multi-Threads working together, and each node can work efficiently with Multithreading.This study applies a new web crawler fog cloud approach that is considered to be more efficient in navigating URLs by setting according to the domain used and dividing URL limitations into various priority URL queues so that URLs can be dispersed across concurrent crawler operations to get rid of the new building.In particular, the proposed model can effectively utilize resources optimally in the cloud-fog layer by deploying a crawler distribution in the cloud-fog infrastructure to detect news.With the fog cloud, analysis is dynamically distributed across the fog and cloud layers enabling real-time distribution.The research phase of the distributed news crawler starts from URL collection, URL filtering, scheduling, accessing URLs, and extracting news data.This research is focused on developing web crawlers to process distributed news crawlers..
Youth Empowerment Through Digital Literacy Education

Youth as pioneers of community development need to be stimulated through programs to strengthen youth capacity and increase competence following the demands of the digital technology era.The PPM program was youth empowerment through digital literacy education.It aimed to 1) increase understanding and awareness of the youth to ITE Law, 2) improve the ability of youth in using information technology for learning resources, and 3) improve the ability of youth in using information technology for online business.This PPM program was well implemented through the methods of 1) capacity building of youth in seminars, 2) training on the use of digital technology, and 3) online business mentoring.The PPM program was well implemented through seminars, training, and mentoring in the use of digital technology.PPM activities increased the understanding and awareness of the youth to ITE Law, improve the ability of youth in using information technology for learning resources, and improve the ability of youth in using information technology for online business.
A Collection of Tangible Theory Instruments for Design Anthropology

We propose Theory Instruments, a novel kind of tangible tool, to bring perspective shifts and new vocabulary into design projects and organisations. They aim to bridge disciplinary gaps among practitioners. In this pictorial, we present six Theory Instruments with instructions in cutouts, so practitioners can put them into action. To test the Theory Instruments, we conducted workshops with ten companies from various sectors. With the tangibles, practitioners investigated the interconnectedness of their products, the use contexts, and user experiences. They also unfolded internal interaction design practices. We contribute to Design Anthropology by mapping anthropological theories onto material structures that increase sensitivity to human practices.
Bringing Movement to Digital Tasks at the Office: Designing an Acceptably Active Interface Interaction for Sending Emails

While working on a computer typically involves sitting for prolonged periods of time, sedentary work routines are associated with numerous health issues. To address this societal concern, existing solutions trigger physical activity as a break from work, rather than a part of it. In this research, we explore a vision for physically active ways of working, by transforming mundane digital tasks into physically active ones. As a research artifact and design exemplar, we present A2-I2, an innovative tangible system for sending emails. After loading their email onto a physical “letter” token, office workers must walk to a physical mailbox located in the office space. Understanding what design qualities influence the experience and acceptability of such systems is a necessary step toward the design of acceptably active interface interactions. We report on a preliminary user test with 8 participants. With this project, we aim to inspire future tangible and embodied systems addressing the timely issue of sedentary behavior at work.
My Body, My Baby, and Everything Else: An Autoethnographic Illustrated Portfolio of Intra-Actions in Pregnancy and Childbirth

I have been interested for many years in technology and its impact in everyday moments, I had not yet had the chance to critically and systematically find a coherent and self-contained experience to focus on. In March 2021, I bought a digital pregnancy test for the first time, and an autoethnographic journey started. It was the first year of my PhD and I was expecting my third child. In this pictorial, I offer an illustrated and annotated portfolio of my pregnancy, from test to birth, with an emphasis on the technology entangled in the stories. Framed by Agential Realism, I identify the agential cuts in the illustrations. I conclude with an appeal for annotated portfolios of intra-actions, and for other HCI researchers to share their own socio-technical assemblages around fertility, pregnancy, and childbirth. I use my account of this process as a step towards making the intra-actions in pregnancy and childbirth a matter of care for the TEI community.
FloraWear: Wearable Living Interface

While nature can benefit people both mentally and physically, contemporary society has become increasingly disconnected from nature. To rebuild a stronger connection with nature in our everyday life, we introduce FloraWear, a do-it-yourself, wearable living interface, that enables people to easily and closely connect with plants. This pictorial introduces how knowledge is built and shared with others using hybrid craft and fabrication, illustrates the material experiments and design development for FloraWear, and discusses how it affects wearers. Then we summarize how FloraWear can help catalyze a shift in people's perspectives towards nature. By developing emotional ties to their wearable plants, FloraWear wearers begin to understand that both they and their plants are part of an ecosystem.
A Practice-Based Approach to Post-Human Computer Interaction: Design Notes from Nature Scenes

This pictorial explores the processes and methods of designing for non-human animal users and human visitors through the example of Nature Scenes. Nature Scenes are a set of urban outdoor interspecies shelters and feeding stations designed by us, the Interaction Research Studio, commissioned by Jane Withers Studio for Brompton Design District 2019, as part of London Design Festival (LDF). In this pictorial we document the various steps of designing, prototyping and deploying the ‘scenes’, and examine the animal-human relationship in context of product and interaction design with the intention to advance the current discourse on interspecies and post-human practice-based design research. We unpick the complexities and contradictions that come with designing for animals as well as for humans, and introduce suggestions of how Post-Human Computer Interaction (PHCI) can be approached going forward.
Investigating a Force-Based Selection Method for Smartwatches in a 1D Fitts’ Law Study and Two New Character-Level Keyboards

Selecting small targets is difficult on tiny displays due to the “fat-finger problem”. In this paper, we explore the possibility of using a force-based approach to target selection on smartwatches. First, we identify the most comfortable range of force on smartwatches. We then conduct a 1D Fitts’ law study to compare the performance of tap and force-tap. Results revealed that force-tap is significantly better in selecting smaller targets, while tap outperforms force-tap for bigger targets. We then developed two new force-based keyboards to demonstrate the feasibility of force input in practical scenarios. These single-row alphabetical keyboards enable character-level text entry by performing slides and variating contact force. In a user study, these keyboards yielded about 4 wpm with about 2% error rate, demonstrating the viability of force input on smaller screens.
Embodying Wind through Flare: How Natural Phenomena Can Contribute to Enriching the Design of Interactive Systems

Since the divide between nature and society, humans have objectified nature. This continuing separation has corrupted the relationship with our natural habitat. While various design research projects bring elements of nature into our cultivated environment, most incorporate nature as a separate entity. Although integrative collaborations with nature can be found in interactive projects designed from non-anthropocentric perspectives, they are not specifically aimed to connect humans with nature. We argue that the same forces that shape our natural habitat allow us to be closer to nature and propose the participation of natural phenomena as strong concept for the design of interactive systems. To illustrate this concept, we present Flare, a silk dress embroidered with dandelions made up of LED's which react to wind movements. With Flare as an evocative object, we draw parallels between existing theories and explore opportunities for designers to create interactions which help us to reconnect with nature.
Interactive Stained-Glass: Exploring a new design space of traditional hybrid crafts for novel fabrication methods

Stained-glass is a craft with a wealth of opportunities that blends seamlessly into our everyday environments. Despite sharing similar tools and materials with other types of hybrid crafting, authentic stained glass is underexplored in HCI. We introduce stained-glass to TUI researchers, explain the fabrication process thoroughly (using the traditional methods of both copper foil and lead came), and explore its potential as a conductive substrate for interactivity. We contribute fabrication techniques to support various circuit connection traces, light diffusion methods, interactivities, and aesthetic qualities. We also introduce three potential applications as proof of method validity in different contexts. We follow this with a discussion on experiential outcomes and the importance of creative practices in the development of interactive artefacts.
Improving Collaboration Experiences and Skills: An Open-ended, User-Driven Self-Tracking Approach for Education

Collaboration is considered an essential skill necessary for work and life in the 21st century. It is hence necessary to support students in developing this skill, whose necessity is amplified by hybrid work and globalization. In this vision, we bring insights and practices from the personal informatics field to the education domain, in order to trigger self-awareness and collective sensemaking. We propose CoSensUs, a physical self-tracking kit for teams of students to track and reflect on their collaboration practices and experiences. We argue for a user-driven, open-ended, playful, and privacy-centered solution, which would track and visualize data on a group level. This original and underexplored focus on group-level tracking also aims to account for the special needs of students, subject to social pressure and potential control in institutional settings. Through this vision, we contribute to the development of essential social and collaboration skills, in an original, playful and inclusive way.
Generative non-autoregressive unsupervised keyphrase extraction with neural topic modeling

Unsupervised keyphrase extraction (UKE) aims to detect out a set of keyphrases of a document without using any annotation signal for training the UKE model. Existing UKE models however either fail to solve the issue of low keyphrase relevance, or suffer from the problem of unsatisfactory keyphrase coverage. Besides, the current strong-performing neural based UKE methods unfortunately come with the cost of lower decoding efficiency. In this paper we propose a novel framework for UKE. We model the task as a fully generative process with an encoder–decoder framework, which unifies the prediction of both the present and absent keyphrases in an end-to-end manner. We accelerate the decoding by installing a non-autoregressive decoder, which yields all keyphrase extraction in parallel fast. To enhance the keyphrase relevance, we investigate a neural topic module that unsupervisingly learns the latent topic information, aiding the semantic understanding of the input document. To strengthen the keyphrase coverage, we present an anchor-region scheme for the extraction of present keyphrases, which first locates the anchor points of present phrases and then determines the final boundaries of keyphrases. The initial anchor phrases are organized into an anchor-aware graph, which is modeled by our proposed topic-guided graph attention network which captures the document contexts at global level. Over six benchmark datasets of UKE our system shows better experimental results than existing strong-performing baselines, becoming the new state-of-the-art UKE model. Further in-depth analyses show that our model can effectively resolve both the relevance and coverage of keyphrase extraction, meanwhile being faster on decoding.