Reviewerly/exHarmony huggingface dataset:
Play at work: Virtual conferencing in game space

The Covid-19 pandemic accelerated the adoption of video conferencing, making it a prominent space that our work and social lives are lived within.For many this transition to virtual co-presence has been joyless, highlighting the shortcomings of mainstream video conferencing.In contrast the video gaming community had already pleasurably adopted and occupied online shared spaces for decades prior to the pandemic.In this paper we discuss the designed affordances of the video conferencing platform Gather Town, which adapts some of the conventions of video games and implements them to better support video conferencing.In this research we consider whether gaming conventions-including interactive spatial design, use of avatars, and a retro art style-may enable us to inject more joy into remote working.By critically considering Gather Town's affordances through our own experimental spaces within it, we seek to encourage the design of more diverse and engaging digital spaces, which will enable more enjoyable remote work and social events.
A Stochastic Proximal Method for Nonsmooth Regularized Finite Sum Optimization

We consider the problem of training a deep neural network with nonsmooth regularization to retrieve a sparse and efficient sub-structure. Our regularizer is only assumed to be lower semi-continuous and prox-bounded. We combine an adaptive quadratic regularization approach with proximal stochastic gradient principles to derive a new solver, called SR2, whose convergence and worst-case complexity are established without knowledge or approximation of the gradient's Lipschitz constant. We formulate a stopping criteria that ensures an appropriate first-order stationarity measure converges to zero under certain conditions. We establish a worst-case iteration complexity of $\mathcal{O}(\epsilon^{-2})$ that matches those of related methods like ProxGEN, where the learning rate is assumed to be related to the Lipschitz constant. Our experiments on network instances trained on CIFAR-10 and CIFAR-100 with $\ell_1$ and $\ell_0$ regularizations show that SR2 consistently achieves higher sparsity and accuracy than related methods such as ProxGEN and ProxSGD.
Systematic review of community-led initiatives fighting the opioid crisis and opportunities for design

The opioid epidemic has been declared a health emergency in the US.This crisis has been addressed from multiple perspectives and by different stakeholders, like communities that have participated in self-organizing and mobilizing to fight back the crisis from a bottom-up approach.Community-led initiatives have been fundamental in tackling the opioid epidemic with education, service linkage, and distributing Naloxone, an overdose-reversing medicine.They are also the perfect space for social innovation because they can strengthen social relationships, however, little has been written about the role of Design in these organizations.The purpose of this paper is to conduct a systematic review of community-led initiatives tackling the opioid crisis, an assessment of their strategies of action, and the presence of Design.Finally, we discuss 3 of the gaps in the practice where design can generate impact and offer means of innovation.
Documenting new design ontologies

Ontological design -characterized by the understanding that what we design designs us -has been invoked as a necessary framing in redirecting the design discipline toward more pluralistic and sustainable ends.In this paper, I situate ongoing conversations about process documentation within emergent conversations surrounding ontological design, considering ways in which innovative documentation practices may support new ontological agendas.By considering process documentation as a hermeneutical, knowledge-making practice, I speculate ways that new, experimental modes of process documentation may afford designers -and design itself -new vantage points from which to (re)interpret design practice and the discipline at large.To this end, I sketch out some preliminary ideas of what ontological documentation may look like.In particular, I explore how deliberately open-ended (or even speculative) approaches to design documentation could invite critical reflection and collaborative meaning-making -inviting more voices to shape the narratives and ontologies of design.
An Accurate HDDL Domain Learning Algorithm from Partial and Noisy Observations

The Hierarchical Task Network ({\sf HTN}) formalism is very expressive and used to express a wide variety of planning problems. In contrast to the classical {\sf STRIPS} formalism in which only the action model needs to be specified, the {\sf HTN} formalism requires to specify, in addition, the tasks of the problem and their decomposition into subtasks, called {\sf HTN} methods. For this reason, hand-encoding {\sf HTN} problems is considered more difficult and more error-prone by experts than classical planning problem. To tackle this problem, we propose a new approach (HierAMLSI) based on grammar induction to acquire {\sf HTN} planning domain knowledge, by learning action models and {\sf HTN} methods with their preconditions. Unlike other approaches, HierAMLSI is able to learn both actions and methods with noisy and partial inputs observation with a high level or accuracy.
Sentence-Level Sentiment Classification A Comparative Study Between Deep Learning Models

Sentiment classification provides a means of analysing the subjective information in the text and subsequently extracting the opinion. Sentiment analysis is the method by which people extract information from their opinions, judgments and emotions about entities. In this paper we propose a comparative study between the most deep learning models used in the field of sentiment analysis; L-NFS (Linguistique Neuro Fuzzy System), GRU (Gated Recurrent Unit), BiGRU (Bidirectional Gated Recurrent Unit), LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory) and BERT(Bidirectional Encoder Representation from Transformers), we used for this study a large Corpus contain 1.6 Million tweets, as devices we train our models with GPU (graphics processing unit) processor. As result we obtain the best Accuracy and F1-Score respectively 87.36% and 0.87 for the BERT Model.
Design as an agent of narratives: A conceptual framework and a first exploration in the context of inclusive paediatric mobility design

Although much of human experience is qualitative, front-end design documentation typically defaults to quantification which can abstract, dilute or lose meaning and reasoning with regards to lived experiences.Narratives are a wellestablished channel for gathering rich qualitative insights around individual and collective experiences, perceptions and values.However, the potential to advance the role of design beyond simply an embodiment agent for dominant narratives -to an agent for uncovering, interrogating, speculating, and scaling a diversity of narrative 'classes' and 'statuses' -is yet to be fully explored.This paper proposes a conceptual framework positioning design as an agent of narratives through three strategic narrative roles: (1) acknowledgement and capture, (2) negotiation and speculation, and (3) embedding and scaling.A first exploration in the context of inclusive paediatric mobility design is used to explore initial insights, implications and limitations of incorporating narratives, as well as their potential to amplify marginalised voices, inform and steer design practice, and bring about transformative impact.
Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO

This work conducts the first analysis on the robustness against adversarial attacks on self-supervised Vision Transformers trained using DINO. First, we evaluate whether features learned through self-supervision are more robust to adversarial attacks than those emerging from supervised learning. Then, we present properties arising for attacks in the latent space. Finally, we evaluate whether three well-known defense strategies can increase adversarial robustness in downstream tasks by only fine-tuning the classification head to provide robustness even in view of limited compute resources. These defense strategies are: Adversarial Training, Ensemble Adversarial Training and Ensemble of Specialized Networks.
Examining performance of VR sketch modeling tool in personal sketches

This study examined the performance of the virtual reality (VR) sketch modeling tool Gravity Sketch in the early stage of design and explored its advantages and disadvantages.In the experiment, nine graduate students in the field of design with several months of experience with Gravity Sketch sketched ideas in VR and on paper.Qualitative analysis was performed using retrospective methods and grounded theory.The results revealed that fewer sketches were produced and less time was spent on design thinking in VR than on paper because (1) sketches created using the VR tool are three-dimensional models and (2) the high degree of simulation results in low-ambiguity visual information.The VR users subconsciously strove for accuracy when creating sketches.This suggests that the three-dimensional adjustability of VR sketching makes it suitable for the later stages of personal sketching.
An efficient decoder for a linear distance quantum LDPC code

Recent developments have shown the existence of quantum low-density parity check (qLDPC) codes with constant rate and linear distance. A natural question concerns the efficient decodability of these codes. In this paper, we present a linear time decoder for the recent quantum Tanner codes construction of asymptotically good qLDPC codes, which can correct all errors of weight up to a constant fraction of the blocklength. Our decoder is an iterative algorithm which searches for corrections within constant-sized regions. At each step, the corrections are found by reducing a locally defined and efficiently computable cost function which serves as a proxy for the weight of the remaining error.
Federated Learning aided Breast Cancer Detection with Intelligent Heuristic-based Deep Learning Framework

Abstract Breast cancer is the second largest cause of female cancer death and one of the most hazardous diseases that leads to a higher mortality rate. Breast cancer is initialized with the malignant stage, where the abnormal growth of cancerous lumps is initiated from the breast cells. Periodic clinical checks and self-tests assist in early identification and thus progress the survival rates considerably. One of the eminent medical approaches is breast cancer recognition, which offers scientists and researchers huge complications. Breast cancer detection at an early stage permits the patients to receive suitable treatment, which increases the chances of survival. Thus, this paper utilizes a new form of artificial intelligence training called Federated Learning (FL), especially for breast cancer detection, the most eminent technique in the last few years. FL permits individual hospitals to benefit from the rich datasets of multiple non-affiliated hospitals without centralizing the data in one place. Hence, FL utilizes numerous collaborators for building a strong deep-learning model using a large dataset. In this paper, a hybridization of this type of training with a meta-heuristic and deep learning is aimed to be proposed for breast cancer diagnosis. This model encloses diverse steps that include (a) image collection, (b) feature extraction, and (c) classification phase. Initially, the mammogram images related to breast cancer are collected with the concept of FL from the affected individuals. The federated learning helps in reducing the processing time and ensures better performance of the proposed model. The obtained images are considered for the feature extraction phase. The Densenet architecture is used to extract the features used in the classification phase with the help of Enhanced Recurrent Neural Networks (E-RNN) for detecting breast cancer. Here, the performance is enhanced by tuning the certain parameter in the RNN network using a hybrid optimization algorithm called Hybrid Dragon-Rider Optimization (HDRO) with Dragonfly Algorithm (DA) and Red deer algorithm (RDA) to achieve accurate classification results. The experimental results demonstrate the effectiveness of the suggested breast cancer diagnosis model compared with conventional approaches using diverse quantitative measures.
Studying interaction design practices with theory instruments

From previous research we learned that (1) interaction designers are experiencing new challenges in response to increasing digitalization, and that (2) current methods and conceptual frameworks seem insufficient for addressing these challenges.In this paper, we explore how playing with theoretical concepts in tangible ways may allow practitioners to gain new perspectives on current practices, challenges and possibilities.We introduce a set of Design Anthropological Theory Instruments to bring social theory into conversations with practitioners.Via the instruments, the researchers' data collection process and the participants' reflection processes come together in a collective sense-making experience.Our analysis of video recordings from experimental sessions in 10 companies provides insights into interaction designers' practices and inspires further inquiry into the potential of theory instruments for transforming design practice.Furthermore, we see potential for development of theory instruments to open new avenues for practicing Design Anthropology.
Macroeconomic effects of artificial intelligence on emerging economies: Insights from Bangladesh

Artificial intelligence (AI) has the potential to increase economic growth substantially across the world. This paper explores the predicted potentials of AI from macroeconomic perspectives, employing theoretical analysis and contextualizing Bangladesh. The study employs time series data to examine the effects of AI on the labor market and productivity. The findings indicate that Bangladesh was yet to realize the expected economic merits though the integrated number of AI-induced industry robots has been insignificant. For AI to be effective in Bangladesh, the country shall adopt more AI robots across the various sectors, mainly manufacturing and service. Besides, the country may complement the human labor force with AI labor instead of relying on only one as a source of labor.
The future of money as a design material

For many years the primary representation of value has been money.However complex we perceive its material, social and symbolic characteristics, money is now undergoing significant change as it becomes data.This paper explores the implications for design as a series of technological and regulatory shifts are taking place that are changing the representation of money into data.The paper anticipates that it won't be long before personal bank accounts will be better understood to be personal data stores, and monies held within them are connected to data-driven systems to 'pay' for services that we require.By charting the changes that are taking place, and introducing a series of design case studies that make tangible the design opportunities, the paper suggests an emerging design space in which designers should anticipate new forms of money as an entirely new design material.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Double Anonymous • Conference submission management system : Morressier • Number of submissions received : 30 • Number of submissions sent for review : 30 • Number of submissions accepted : 19 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 63.3 • Average number of reviews per paper : 1.95 • Total number of reviewers involved : 21 • Contact person for queries : Name : Andri Putra Kesmawan Email : info@idpublishing.org Affiliation : Indonesian Journal Publisher
Storing cultural archives in synthetic DNA: An integrated prospective design investigation

As cultural institutions digitize their archives, they become more accessible and versatile. However, there are significant economic and environmental costs to maintaining large databases over time. Synthetic DNA is an emerging technology that can store data at high density, with almost no energy, for thousands of years. Yet, as technical advances bring DNA storage closer to everyday use, little has been done to understand how we will interact with the technology and accept it into society. We developed an integrated prospective design approach to investigate these challenges. This included participative workshops, narrative building and the design of three tangible DNA storage objects. User evaluations showed how summarized information within the object strengthens understanding and appreciation of the technology. We also gathered insights around material and societal perception. This work opens perspectives for the adoption of long-term sustainable preservation for cultural archives, and pushes prospective design methodologies into far-future contexts.
Effective Leadership Enhancing Financial Sustainability and Good Governance in Public Sector South Sudan, Juba: Case Study of Juba County

All governments of the world are required by their constitutions to provide good governance According to Dickson Charles (2011); the world is in a better position of implement democratic ruling system.The aim of Good Governance in the Public Sector is to encourage better service delivery and improved accountability by establishing a benchmark for good governance (Dutch government, report2009).This situation has fostered the need for extending collaboration among multiple policy-making agents, taking sustainability and concern about the needs of future generations to the center of the debate, good governance has become a central point of discussion of economic and political development.South Sudan Transitional Constitution (2011) mandates three levels of government, with decentralised system, which is responsible for delivering good governance to its people equitably and accessible.The concept of leadership is as old as human history, interest in leadership increased during the early part of the twentieth century, Northouse G. ( 2007) Since good governance is the process of decision-making and the process by which decisions are implemented, as the exercise of economic, political and administrative authority to manage a country's affairs at all levels at faire accountably manner.Which citizens exercise their legal rights, meet their obligations and mediate their differences.Ikpe's (2006).Without effective leadership, financial sustainability and Good Governance at all levels in private, public and civil organizations, it is impossible to achieve effective administration, to sustain quality and deliver first-rate services IFAC's (2008).Constant change in society, require effective leadership.Good governance and effective financial sustainability in
CorticalFlow$^{++}$: Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability

The problem of Cortical Surface Reconstruction from magnetic resonance imaging has been traditionally addressed using lengthy pipelines of image processing techniques like FreeSurfer, CAT, or CIVET. These frameworks require very long runtimes deemed unfeasible for real-time applications and unpractical for large-scale studies. Recently, supervised deep learning approaches have been introduced to speed up this task cutting down the reconstruction time from hours to seconds. Using the state-of-the-art CorticalFlow model as a blueprint, this paper proposes three modifications to improve its accuracy and interoperability with existing surface analysis tools, while not sacrificing its fast inference time and low GPU memory consumption. First, we employ a more accurate ODE solver to reduce the diffeomorphic mapping approximation error. Second, we devise a routine to produce smoother template meshes avoiding mesh artifacts caused by sharp edges in CorticalFlow's convex-hull based template. Last, we recast pial surface prediction as the deformation of the predicted white surface leading to a one-to-one mapping between white and pial surface vertices. This mapping is essential to many existing surface analysis tools for cortical morphometry. We name the resulting method CorticalFlow$^{++}$. Using large-scale datasets, we demonstrate the proposed changes provide more geometric accuracy and surface regularity while keeping the reconstruction time and GPU memory requirements almost unchanged.
LANGUAGE AND VISION

Natural language processing (NLP) and computer vision (CV) are two of the most essential areas in the artificial intelligence field (AI). CV is a topic of study that looks into the methods for teaching computers to perceive and interpret digital stuff like images and movies. NLP (natural language processing) is a discipline of linguistics that allows computers to process, interpret, and even synthesise human language. With the advent and development of deep learning over the last decade, there has been a continual flow of innovation and breakthroughs that convincingly push the boundaries and improve the state-of-the-art in both vision and language modelling. An noteworthy point is that research in the two areas is beginning to interact, and many previous experiences have demonstrated that this is a good thing. In general, visual and language interactions have taken place in two dimensions: vision to language and language to vision. In the form of tags [Reference Yao, Mei, Ngo and Li1], answers [Reference Anderson2], captions [Reference Yao, Pan, Li, Qiu and Mei3–Reference Yao, Pan, Li and Mei5], and comments [Reference Li, Yao, Mei, Chao and Rui6], the former primarily recognises or describes the visual content with a set of individual words or a natural sentence. In visual content, a tag, for example, usually represents a specific object, action, or event. An answer is a statement made in response to a question concerning the facts portrayed in a photograph or video. A caption is a natural-language utterance (typically a sentence) that goes beyond tags or answers. The purpose of this programme is to convert text into an image or a video. For example, given a textual description of "this small bird has a short beak and dark stripe down the top, the wings are a mix of brown, white, and black," text-to-image synthesis' purpose is to create a bird image that fits all of the requirements. This study examines recent state-of-the-art AI advancements that improve both vision to language, particularly image/video captioning, and language to vision. Real-world deployments in both domains are also shown as good instances of how AI improves user engagement and transforms consumer experiences in industrial applications. The following is a breakdown of the remaining sections. Section II outlines the evolution of vision to language by laying out a quick road map of significant image/video captioning technologies, distilling a typical encoder–decoder structure, and summarising the results of a common benchmark. Further, the practical applications of vision to language are discussed. Section III describes the technological advances in language to vision in terms of various situations and generation methodologies, and then concludes with a summary Progress in language to image, language to video, and AI-powered applications is summarised. Finally, Section IV brings the paper to a close