Economical Accommodations for Neurodivergent Students in Software Engineering Education: Experiences from an Intervention in Four Undergraduate Courses

Neurodiversity is an umbrella term that describes variation in brain function among individuals, including conditions such as Attention deficit hyperactivity disorder (ADHD), or dyslexia. Neurodiversity is common in the general population, with an estimated 5.0% to 7.1% and 7% of the world population being diagnosed with ADHD and dyslexia respectively. Neurodivergent (ND) individuals often experience challenges in specific tasks, such as difficulties in communication or a reduced attention span in comparison to neurotypical (NT) individuals. However, they also exhibit specific strengths, such as high creativity or attention to detail. Therefore, improving the inclusion of ND individuals is desirable for economic, ethical, and for talent reasons. In higher education, struggles of ND students are well-documented. Common issues in this area are a lack of awareness among other students and staff, forms of assessment that are particularly challenging for some students, and a lack of offered accommodations. These factors commonly lead to stress, anxiety, and ultimately a risk of dropping out of the studies. Accommodations for ND students can require substantial effort. However, smaller changes in course material can already have major impact. In this chapter, we summarise the lessons learned from an intervention in four courses in undergraduate computer science programmes at Reykjavik University, Iceland, over a period of two terms. Following accessibility guidelines produced by interest groups for different ND conditions, we created course material in the form of slides and assignments specifically tailored to ND audiences. We focused on small, economical changes that could be replicated by educators with a minimal investment of time. We evaluated the success of our intervention through two surveys, showing an overall positive response among ND students and NT students

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

Greining á sneiðmyndum úr heila með notkun stoðvigravéla og sjálfnykraðra eiginda sniðmáta

A more detailed knowledge of the human brain is desirable. Automating the process of reviewing brain images and detecting abnormalities of those would be a significant effort in that process. In this research, a labelled data set of high resolution brain slice images is analysed. Each pixel of the images has been manually labelled, depending on which area of the brain the pixel belongs to. The aim of this research is to perform pixel-wise classification, utilizing the data set of brain images. The objective is to recognise cortex regions from other areas of the brain. The challenge of the work lies in the magnitude of an image, that is approximately 37.000.000 pixels per image. The volume of the images introduce computational complexity in terms of memory and processing time, which encourages the employ of a High Performance Computing (HPC) system. The proposed approach is to perform feature extraction with Self-Dual Attribute Profiles (SDAP) and to use Support Vector Machines (SVM) for the classification. The obtained classification results indicate that Support Vector Machines are suitable for classification of brain images.Að öðlast yfirgripsmeiri þekkingu á mannsheilanum er eftirsóknarvert fyrir heimsbyggðina. Að finna leið til að yfirfara heilasneiðmyndir sjálfvirkt og uppgötva frávik í þeim væru stór framför í því ferli. Í þessari rannsókn fer fram greining á gagnabanka með heilasneiðmyndum af hárri upplausn. Pixlar myndanna hafa verið merktir handvirkt, eftir því hvaða svæði heilans þeir tilheyra. Tilgangur rannsóknarinnar er að framkvæma pixla flokkun á heilasneiðmyndunum. Markmiðið er að þekkja heilabörkinn frá öðrum hlutum heilans. Áskorun verkefnisins liggur í stærð myndanna, en þær eru um 37.000.000 pixlar af stærð, hver. Stærð myndanna veldur þunga í vinnslu þeirra, en þess vegna er notuð ofurtölva. Aðferðin sem lögð er til er að framkvæma útdrátt sérkenna (e. Feature Extraction) með tví-sjálfvöldum eiginda sniðmátum (e. Self Dual Attribute Profiles) og flokkunin gerð með stoðvigravélum (e. Support Vector Machines). Rannsóknin bendir til þess að stoðvigravélar séu fýsilegur kostur til flokkunar á heilasneiðmyndum

Wilf-flokkun möskvamynstra

Heildartexti lokaskýrslu. Prentuð útgáfa og öll fylgi gögn á CD eru varðveitt í bókasafni HRThis B.Sc. project deals with mesh pattern avoidance in permutations. A mesh pattern is a pair P=(p,R), where p is a permutation of length k and R is a subset of [0,k]x[0,k]. The elements of R denote filled boxes in a mesh pattern. For a permutation u to contain a mesh pattern, it has to contain the underlying classical pattern p, and no points in u can be located in the shaded boxes. In this paper we begin the study of Wilf-classifying mesh patterns by classifying 776 out of the 1024 mesh patterns of length 2. Two mesh patterns P and Q are said to be equivalent if for any permutation u, u avoids P if and only if u avoids Q. The paper introduces a new operation that preserves pattern equivalence and provides rules determining which additional boxes in a mesh pattern P can be shaded. This is useful to lower the number of patterns one needs to look at in the process of Wilf-classifying patterns. We also have some observations on the only non-trivial interval pattern of length 3.Í þessu B.Sc. verkefni vinnum við með mynstraforðun möskvamynstra í umröðunum. Möskvamynstur er par P=(p,R) þar sem p er umröðun af lengd k og R er hlutmengi í [0,k]x[0,k]. Stökin í R tákna skyggða ferninga í möskvamynstrinu. Umröðun u er sögð innihalda möskvamynstrið P ef hún inniheldur grunnmynstrið p og engir punktar u eru inn í skyggðu ferningum mynstursins P. Í þessu verkefni hefjum við Wilf-flokkun möskvamynstra, þar með hafa 776 mynstur af 1024 verið Wilf-flokkuð. Tvö möskvamynstur P og Q eru sögð vera jafngild ef fyrir sérhverja umröðun u gildir að u forðast P ef og aðeins ef u forðast Q. Verkefnið kynnir nýja aðgerð sem varðveitir jafngildi mynstra og gefur reglur um hvaða ferninga í mynstri P má skyggja aukalega í möskvamynstri. Þessi aðgerð er hentug til að minnka fjölda þeirra mynstra sem þarf að skoða við Wilf-flokkun mynstra. Einnig höfum við skoðað eina bilamynstrið af lengd 3 sem er ekki augljóst, og setjum fram athugasemdir um það

State of the Art of Audio- and Video-Based Solutions for AAL

The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted

State of the art of audio- and video-based solutions for AAL

It is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.peer-reviewe