Miracle-dz/UniFAR-FTUs huggingface dataset:
Notes on CEPII’s Distances Measures: The GeoDist Database

GeoDist makes available the exhaustive set of gravity variables used in Mayer and Zignago (2005). GeoDist provides several geographical variables, in particular bilateral distances measured using citylevel data to assess the geographic distribution of population inside each nation. We have calculated different measures of bilateral distances available for most countries across the world (225 countries in the current version of the database). For most of them, different calculations of “intra-national distances” are also available. The GeoDist webpage provides two distinct files: a country-specific one (geo_cepii)and a dyadic one (dist_cepii) including a set of different distance and common dummy variables used in gravity equations to identify particular links between countries such as colonial past, common languages, contiguity. We try to improve upon the existing similar datasets in terms of geographical coverage, quality of measurement and number of variables provided.
Internet Research Ethics: A CSCW Community Discussion

Research ethics continues to be an important topic of conversation within the HCI and social computing communities, especially regarding the specific ethical challenges raised by internet research. Developing a shared understanding of ethical norms is complicated by the diverse disciplinary traditions, evolving technologies and methods, and multiple geographic and cultural settings of researchers in these communities. Drawing on experiences of the SIGCHI Research Ethics Committee as well as empirical work towards developing best ethical practices for internet research, this Special Interest Group will discuss current practices and challenges in studying people and data online, covering the full research life-cycle from the crafting of research ideas, through study design, data collection, analysis, and then dissemination. This conversation will benefit from diverse voices and perspectives to help us all learn from each other and critically engage with our research community’s values and ethical commitments.
Prototyping Kodi: Defining Design Requirements to Develop a Virtual Chat-bot for Autistic Children and Their Caregivers

Autistic children’s emotional meltdown is a common challenge faced by their caregivers, including their teachers and families. Communicating their problems can be extremely challenging for many autistic children, making it difficult for caregivers to identify the triggers of these meltdowns. Previous studies have revealed that autistic children find it easier to communicate with non-human objects, such as comfort dolls or social robots. In light of this, we have designed Kodi, a mobile virtual conversational agent aimed at bridging the communication gap between autistic children and their caregivers during distressing situations. Kodi’s appearance can be personalized to match the child’s favorite characters, such as a bunny or a dinosaur. To provide additional comfort, Kodi comes with furry phone cases that offer haptic stimulation. When the child faces stressful situations, caregivers can prompt Kodi on children’s mobile device, allowing the child to express their emotions with various input modalities (emojis, text, voice) and listen to soothing stories. Caregivers can tailor these stories to suit each child’s unique needs. Furthermore, Kodi will employ keyword analysis to summarize conversations, identify potential triggers for emotional meltdowns (e.g., loud noises in a shopping mall, unexpected changes at school), and alert caregivers of potential problems, such as bullying. The data collected through Kodi holds the potential to be leveraged for predicting emotional meltdowns using machine learning techniques. This paper discusses the implications of the Kodi platform and outlines future research directions in this area.
“It's the one thing that makes my life tick”: Security Perspectives of the Smartphone Era

As smartphones overtake personal computers as the device of choice for internet access and everyday digital tasks, cybersecurity becomes a pressing issue for the platform. Research has found that smartphone users appear to act less securely than they would on a PC, but the reasons for this are unclear. The technology, the threats, and the role of smartphones have all developed in recent years, and this paper examines what smartphone security looks like to users in the 2020s. We interviewed 27 smartphone users about their security attitudes and behaviours. We find that users place great emphasis on, and take responsibility for, the physical security of their device, but minimise their responsibility for dealing with digital threats. We observe key contextual factors that influence how users protect their smartphones. The increasing monetary cost of smartphones and users’ functional reliance on them, causes participants to be highly concerned with protecting the physical safety and integrity of their devices. However, users appear to have a high level of trust in apps, based on the vetting processes of official app stores, yet they are still vulnerable to abuse from malicious/unnecessary permissions, and exhibit poor security habits when accessing illegitimate, pirated media outside of their smartphone's app store.
Interpretable Imitation Learning with Symbolic Rewards

Sample inefficiency of deep reinforcement learning (DRL) methods is a major obstacle for their use in real-world tasks as they naturally feature sparse rewards. In fact, this from-scratch approach is often impractical in environments where extreme negative outcomes are possible. Recent advances in imitation learning have improved sample efficiency by leveraging expert demonstrations. Most work along this line of research employs neural network-based approaches to recover an expert cost function. However, the complexity and lack of transparency make neural networks difficult to trust and deploy in the real world. In contrast, we present a method for extracting interpretable symbolic reward functions from expert data, which offers several advantages. First, the learned reward function can be parsed by a human to understand, verify and predict the behavior of the agent. Second, the reward function can be improved and modified by an expert. Finally, the structure of the reward function can be leveraged to extract explanations that encode richer domain knowledge than standard scalar rewards. To this end, we use an autoregressive recurrent neural network that generates hierarchical symbolic rewards represented by simple symbolic trees. The recurrent neural network is trained via risk-seeking policy gradients. We test our method in MuJoCo environments as well as a chemical plant simulator. We show that the discovered rewards can significantly accelerate the training process and achieve similar or better performance than neural network-based algorithms.
Data Advocacy for Visibility of Home Care Workers

Technology solutions have been proposed to address the “care gap” that the United States has been facing due to the increasing demand for home and community based services that far exceeds the supply of caregivers. However, these technology solutions do not fully address the crux of the crisis—the poor working conditions of home care workers (HCWs). Rather, these technology solutions further burden, invisibilize, and surveil workers. My research challenges this dynamic by proposing the design of technology that can be used to advocate for the workers, amplifying their voices and highlighting their work. In this paper, I describe the three phases of my research where I: (1) examine the relationship between visibility and technology, (2) explore different design approaches to see how technology could complicate questions of visibility, and (3) build and deploy a system to evaluate how data could be used for advocacy. Helping workers collect and share data could build awareness and solidarity, expose employer violations and uncompensated work, and challenge existing narratives of care.
Usable Security Model for Industrial Control Systems - Authentication and Authorisation Workflow

Industrial Control Systems (ICS) run critical large-scale systems that are needed in everyday society. These include systems such as: power, water treatment and manufacturing. However, legacy systems are widely utilized in ICS settings and updating, regular patching and modern cryptographic and authentication systems are not often feasible due to safety and real-time constraints. Therefore, ICS rely on Operators and Control Engineers to work on legacy systems which lack usable security. There has been a shortage of work which examines usable security challenges within ICS and a lack of empirical insights that bring to the fore the specific challenges and constraints impacting usable security in such systems. What may make perfect sense from an HCI standpoint may not be feasible due to the security and control systems constraints. We, therefore, conducted a participatory study where we asked participants from Human Computer Interaction (HCI) and Security background to draw their ideal authentication and authorisation workflow - as these mechanisms are seen to be first line of defence and are used in all systems. We recruited participants from these speciality backgrounds in an attempt to identify different perspectives on usable security challenges and what threats emerge due to different design choices or constraints. We elicit the threats emerging from our study, categorise them using STRIDE threat modelling analysis and refine models from theoretical studies. We found a lack of usable security factors such as satisfactory when designing these security configurations. This raises concerns that users are not confident whether they have completed their configurations accurately therefore leading to misconfigurations which raises the risk of an attack. Prior studies stated this is due to users’ lack of abilities such as knowledge/skills. But, little work points out to these legacy systems’ lack of abilities - to provide appropriate feedback which can contribute and nurture users’ knowledge to cope with the environment. This study alters the existing theoretical security usability model for ICS and offers insights into mechanisms for conveying semantics to minimise misconfigurations.
Bringing Emotions into Practice: A Framework for AI Design to Support Emotion Work

Informal or family caregivers receive no professional training and are overburdened. The CSCW community has increasingly examined ways to support informal caregivers. However, the solutions have primarily focused on managing the negative emotions of informal caregivers. Informal caregivers go through a range of emotional experiences, which are often invisible to others. In this work, we aim to use the context of informal caregiving needs and experiences to understand how we can design future Artificial Intelligence (AI) systems for supporting emotion work. We conducted user studies and interviews to understand what informal caregivers perceive as emotion work in two caregiving contexts: parenting and Alzheimer’s Disease and Related Dementias (ADRD). Based on the findings, we propose a framework for generating design requirements for AI systems that support emotion work. Our work contributes to how we can design AI to support people’s practices that involve emotion work.
T-distributed Stochastic Neighbor Embedding for Co-Representation Learning

Co-clustering is the simultaneous clustering of the samples and attributes of a data matrix that provides deeper insight into data than traditional clustering. However, there is a lack of representation learning algorithms that serve this mechanism of co-clustering, and the current representation learning algorithms are limited to the sample perspective and lack the use of information in the attribute prespective. In order to solve this problem, in this paper, a co-representation learning model based on t-distributed stochastic neighbor embedding (ctSNE) is proposed for unsupervised co-clustering, where ctSNE makes the dataset representation outputted more discriminative of row and column clusters, i.e. co-discrimination. On the basis of tSNE retaining the sample data distribution and local data structure, the philosophy of collaboration is introduced, i.e., row and column hidden relationship information, so that the ctSNE model is equipped with co-representation learning capability, which can effectively improve the performance of co-clustering. To prove the effectiveness of the ctSNE model, several classic co-clustering algorithms are used to check the co-representation performance of ctSNE, and a novel internal index based on an internal clustering index, known as total inertia, is proposed to demonstrate the effect of co-clustering. The numerous experimental results show that ctSNE has tremendous co-representation capability and can significantly improve the performance of co-clustering algorithms.
The Kinect: a low‐cost, high‐resolution, short‐range 3D camera

We present a novel application of the Kinect™, an input device designed for the Microsoft® Xbox 360® video game system. The device can be used by Earth scientists as a low‐cost, high‐resolution, short‐range 3D/4D camera imaging system producing data similar to a terrestrial light detection and ranging (LiDAR) sensor. The Kinect contains a structured light emitter, an infrared camera (the combination of these two produce a distance image), a visual wavelength camera, a three‐axis accelerometer, and four microphones. The cost is ~ US $100, frame rate is 30 Hz, spatial and depth resolutions are mm to cm depending on range, and the optimal operating range is 0.5 to ~5 m. The resolution of the distance measurements decreases with distance and is ≤1 mm at 0.5 m and ~75 mm at 5 m. We illustrate data collection and basic data analysis routines in three experiments designed to demonstrate the breadth and utility of this new sensor in domains of glaciology, stream bathymetry, and geomorphology, although the device is applicable to a number of other Earth science fields. Copyright © 2012 John Wiley & Sons, Ltd.
Performance Evaluation of the Docker Technology on Different Raspberry Pi Models

On the one hand, Single-Board Computers (SBCs) are gaining acceptance due to their small form factor, low energy requirements, affordability, versatility, and increasing computing power. On the other hand, containerization is transforming how software applications are developed, managed, and deployed. In particular, containerization has emerged as an innovative solution to the problem of running software applications across diverse environments by encapsulating them into single, lightweight units called containers (binary code, libraries, configuration files, etc), that can be executed consistently on different platforms without dependency issues (missing or incompatible dependencies). In this paper, several SBCs from the Raspberry Pi Foundation were selected and an empirical assessment was performed to analyze how far these SBCs can be used with container technologies (aka Docker). The authors conducted performance tests that included containerizing CPU-intensive applications, web servers, and database servers. According to these tests, even low-profile SBCs such as the Raspberry Pi 3 Model B and Raspberry Pi 3 Model B+ can be considered to run a Docker engine, and the maximum number of containers will depend on the containerized applications. For these two SBCs, the most frequent limitation encountered in the experiments was the small RAM capacity.
Towards a Greener and Fairer Transportation System: A Survey of Route Recommendation Techniques

In recent years, ride-hailing services have emerged as a popular means of transportation for the residents of urban areas. There is an inequality in the spatio-temporal distribution of demand and supply, which requires the proper recommendation of routes to drivers in order to guide them towards riders optimally. This paper provides a review of different route recommendation strategies that have been applied in ride-hailing platforms with the main focus on fairness, and environmental issues. It is important to consider the environmental aspects of route recommendation systems as the transportation sector is one of the major sources of air pollution and has reduced the life expectancy of people around the globe. Moreover, there is an unfair distribution of resources and opportunities among the drivers and riders of the platform which has affected their long-term sustainability in the market. In this paper, we highlight the critical challenges and opportunities inherent in the design of green and fair route recommendation systems and indicate some possible directions for future research.
Encouraging Organisational Information Security Incident Reporting

21st-century organisations can only learn how to respond effectively to, and recover from, adverse information security incidents if their employees report any incidents they notice. This should happen irrespective of whether or not they themselves triggered the incident. Organisations have started to inform their employees about their incident reporting obligations. However, there is little research that organisations can benefit from to make their reporting provisions maximally effective. For this work, we follow a multi-step approach. (1) We review the related research on reporting, including reporting reluctance, and the legalities of incident reporting in the European Union. (2) We explain how we developed variations of information texts that raise awareness of incident reporting obligations and aim to ameliorate reporting reluctance. (3) We conducted an online user study (n=257) to identify the most effective information text. (4) The most effective text was deployed by the CISO of a German energy company and we collected feedback from 24 employees to support a qualitative analysis. We discuss our experiences and the implications of such information text design. We make recommendations for encouraging information security incident reporting and suggest future work.
Fostering Data Worker Inclusion and Well-Being: Identifying Barriers and Designing Interventions

In recent years, we have seen great advancements in artificial intelligence. Given this new “Age of AI”, an increase in annotated data for training advanced models requires increased support of a large, diverse, and robust population of workers. Fostering inclusivity and well-being within this space of data work must then be a top priority. In my thesis, I start with identifying barriers to inclusion and well-being across three groups of data workers with differing challenges. First, I study crowd workers whose disabilities encumber their ability to participate in data work. Next, I explore the effectiveness of a strategy for training crowd workers to complete complex tasks, as advanced AI models may elevate the required complexity of future data work. Lastly, I will briefly discuss a work-in-progress characterizing the productivity and well-being of a trained annotation team. Through these studies, I find a common shared barrier: a lack of shared knowledge of productive behaviors among workers. I conclude with future work involving designing interventions to mitigate this barrier.
Cooperative Operations in Urban Terrain (COUNTER)

The Air Force Research Laboratory (AFRL) has an ongoing investigation to evaluate the behavior of Small Unmanned Aerial Vehicles (SAVs) and Micro Aerial Vehicles (MAVs) flying through an urban setting. This research is being conducted through the Cooperative Operations in UrbaN TERrain (COUNTER) 6.2 research and flight demonstration program. COUNTER is a theoretical and experimental program to develop the technology needed to integrate a single SAV, four MAVs, and a human operator for persistent intelligence, reconnaissance and surveillance for obscured targets in an urban environment. The research involves development of six-degree-of-freedom models for integration into simulations, modeling and integration of wind data for complex urban flows, cooperative control task assignment and path planning algorithms, video tracking and obstacle avoidance algorithms, and an Operator Vehicle Interface (OVI) system. The COUNTER concept and the contributing technologies will be proven via a series of flight tests and system demonstrations. The first of six planned COUNTER flight demonstrations occurred in July of 2005. This demonstration focused on the simultaneous flight operations of both the SAV and the MAV while displaying their respective telemetry data on a common ground station (OVI). Current efforts are focused on developing the architecture for the Cooperative Control Algorithm. In FY 2006, the COUNTER program will demonstrate the ability to pass vehicle waypoints from the OVI station to the SAV and MAV vehicles. In FY 2007, COUNTER will focus on solutions to the optical target tracking (SAV) and obstacle avoidance (MAV) issues.
Exposing impropriety and the degeneration of ubuntu through satire in selected isiZulu poems

This article examines the skill employed by poets in creating satirical isiZulu poems and the extent to which these satirical elements have been used in exposing indecent and immoral acts that reveal the degeneration of ubuntu culture. IsiZulu writers and researchers have not dealt with the aspects of satire as a literary genre much. As a result, this article discusses model satirical poems to unearth rare skills used in isiZulu satire. This is beneficial to reluctant writers and those who have little exposure to satire. The article is centred on five poems by four prominent writers chosen for their richness in elements of satire. The study adopts a discourse analysis method as a qualitative approach to textual analysis. Critical discourse analysis is used to support the interpretation and analysis of the idiomatic language used in the poems. The author argues that some of the satirical isiZulu poems resemble traditional praise poems where the poets criticise and ridicule those in power for their stupidity or vices. The study concludes that the poets use certain elements of satire depending on the context of the poems.
Characterization of Cancer Evolution Landscape Based on Accurate Detection of Somatic Mutations in Single Tumor Cells

Accurate detection of somatic mutations in single tumor cells is greatly desired as it allows us to quantify the single-cell mutation burden and construct the mutation-based phylogenetic tree. Here we developed scNanoSeq chemistry and profiled 842 single cells from 21 human breast cancer samples. The majority of the mutation-based phylogenetic trees comprise a characteristic stem evolution followed by the clonal sweep. We observed the subtype-dependent lengths in the stem evolution. To explain this phenomenon, we propose that the differences are related to different reprogramming required for different subtypes of breast cancer. Furthermore, we reason that the time that the tumor-initiating cell took to acquire the critical clonal-sweep-initiating mutation by random chance set the time limit for the reprogramming process. We refer to this model as a reprogramming and critical mutation co-timing (RCMC) subtype model. Next, in the sweeping clone, we observed that tumor cells undergo a branched evolution with rapidly decreasing selection. In the most recent clades, effectively neutral evolution has been reached, resulting in a substantially large number of mutational heterogeneities. Integrative analysis with 522-713X ultra-deep bulk whole genome sequencing (WGS) further validated this evolution mode. Mutation-based phylogenetic trees also allow us to identify the early branched cells in a few samples, whose phylogenetic trees support the gradual evolution of copy number variations (CNVs). Overall, the development of scNanoSeq allows us to unveil novel insights into breast cancer evolution.