Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical Guidelines for Intent Recognition: BERT with Minimal Training Data Evaluated in Real-World HRI Application

Intent recognition models, which match a written or spoken input’s class in order to guide an interaction, are an essential part of modern voice user interfaces, chatbots, and social robots. However, getting enough data to train these models can be very expensive and challenging, especially when designing novel applications such as real-world human-robot interactions. In this work, we first investigate how much training data is needed for high performance in an intent classification task. We train and evaluate BiLSTM and BERT models on various subsets of the ATIS and Snips datasets. We find that only 25 training examples per intent are required for our BERT model to achieve 94% intent accuracy compared to 98% with the entire datasets, challenging the belief that large amounts of labeled data are required for high performance in intent recognition. We apply this knowledge to train models for a real-world HRI application, character strength recognition during a positive psychology interaction with a social robot, and evaluate against the Character Strength dataset collected in our previous HRI study. Our real-world HRI application results also confirm that our model can produce 76% intent accuracy with 25 examples per intent compared to 80% with 100 examples. In a real-world scenario, the difference is only one additional error per 25 classifications. Finally, we investigate the limitations of our minimal data models and offer suggestions on developing high quality datasets. We conclude with practical guidelines for training BERT intent recognition models with minimal training data and make our code and evaluation framework available for others to replicate our results and easily develop models for their own applications. CCS CONCEPTS • Computing methodologies → Natural language processing; • Human-centered computing → Systems and tools for interaction design.
Designerly Ways of Knowing in HRI : Broadening the scope of design-oriented HRI through the concept of intermediate-level knowledge

Interest in design methods and tools has been steadily growing in HRI. Yet, design is not acknowledged as a discipline with specific epistemology and methodology. Designerly HRI work is validated through user studies which, we argue, provide a limited account of the knowledge design produces. This paper aims to broaden current understanding of designerly HRI work and its contributions by unpacking what designerly knowledge is and how to produce it. Through a critical analysis of current HRI design literature, we identify a lack of work dedicated to understanding the conceptual implications of robotic artifacts. These, in fact, are implicit carriers of crucial HRI knowledge that can challenge established assumptions about how a robot should look, act, and be like. We conclude by discussing a set of practices desirable to legitimize designerly HRI work, and calling for further research addressing the conceptual implications designerly HRI work. CCS CONCEPTS •Human-centered computing ~ Interaction design theory, concepts and paradigms • Computer systems organization ~ Robotic components
Broadening Participation and Success in AP CSA: Predictive Modeling from Three Years of Data

The AP Computer Science A course and exam continually exhibit inequity among over- and under-represented populations. This paper explored three years of AP CS A data in the Chicago Public School district (CPS) from 2016-2019 (N = 561). We analyzed the impact of teacher and student-level variables to determine the extent AP CS A course taking and exam passing differences existed between over- and under-represented populations. Our analyses suggest four prominent findings: (1) CPS, in collaboration with their Research-Practice Partnership (Chicago Alliance for Equity in Computer Science; CAFÉCS), is broadening participation for students taking the AP CS A course; (2) Over- and under-represented students took the AP CS A exam at statistically comparable rates, suggesting differential encouragement to take or not take the AP CS A exam was not prevalent among these demographics; (3) After adjusting for teacher and student-level prior experience, there were no significant differences among over- and under-represented racial categorizations in their likelihoods to pass the AP CS A exam, albeit Female students were 3.3 times less likely to pass the exam than Males overall; (4) Taking the Exploring Computer Science course before AP CS A predicted students being 3.5 times more likely to pass the AP CS A exam than students that did not take ECS before AP CS A. Implications are discussed around secondary computer science course sequencing and lines of inquiry to encourage even greater broadening of participation in the AP CS A course and passing of the AP CS A exam.
Lane Change Decision Making for Automated Driving

For the success of automated vehicles, in addition to legal, safety, and technical aspects, user acceptance and trust in automation systems are considered to have a significant impact. The driving style can decisively influence these factors. Besides driving dynamics, such as velocity and accelerations, tactical decisions (e.g. for lane changes) also define the driving style on highways. In order to get a better understanding of lane change behavior expected by passengers during automated driving, participants (N=35) determined and initiated the desired point in time to perform lane changes in a study under real highway driving conditions. Subsequently, a logistic regression analysis could determine the probability of a lane change considering different environmental variables as predictors. Thus, the most important factors influencing lane change decisions could be identified. The results indicate that for lane changes from the right to the middle lane as well as from the middle to the right lane, the preceding and approaching vehicle on the target lane as well as the preceding vehicle on the current lane have a significant influence on the lane change decision. In addition, vehicles entering the highway and presence of more than one preceding vehicle on the right lane were revealed as significant predictors for the lane change decision to the right. Contrarily, for the lane change decision to the left, the relative velocity to the preceding vehicle as well as a speed limit equal to the target velocity have decisive influence. The results give a first important insight for a user-centered automated lane change behavior. Following, individual aspects of these results can be considered to be evaluated more specifically. CCS CONCEPTS • Human-centered computing $\rightarrow$ Empirical studies in HCI; HCI design and evaluation methods; User studies; • Applied computing $\rightarrow$ Transportation.
Charting Sequential Person Transfers Between Devices, Agents, and Robots

In the not-so-distant future, people in service experiences are likely to interact with more than a single intelligent system, often sequentially, including different robots and devices. However, there has been sparse work exploring the characteristics of transferring people from one intelligent system to another. This paper aims to create a context-independent taxonomy to differentiate and categorize the transfer of users across robots, devices, and human staff in service interactions. We conducted two sets of design workshops where participants generated scenarios of human-multi-robot interactions and existing person transfers. Using the outcomes of both workshops, we analyzed scenarios and constructed a taxonomy for person transfers with 4-dimensions: Rationale, Type, Design, and Information Shared. We showcase different ways to utilize the taxonomy, and, through it, we discuss the trade-offs and design considerations in the implementation of person transfers.CCS CONCEPTS • Human-centered computing → Interaction design theory, concepts and paradigms.
Expanding Opportunities: Assessing and Addressing Geographic Diversity at the SIGCSE Technical Symposium

The ACM Special Interest Group on Computer Science Education (SIGCSE) is one of the oldest and largest SIGs, and the SIGCSE Technical Symposium is the oldest and largest of the four SIGCSE conferences. However, the vast majority of Symposium attendees and contributors are from the United States. Because SIGCSE is an international organization, this lack of geographic diversity and representation is troubling because it may stifle collaboration, membership, professional development, and dissemination of research, and have many other adverse effects. In this paper we substantiate the position that there are many reasons to be concerned about poor geographic representation, supported with facts demonstrating that both attendance and authorship at the Symposium are not geographically diverse, particularly for a global organization. We provide evidence that the geographic diversity of SIGCSE membership is substantially more limited than ACM membership, demonstrating that the field of Computing Education is not as diverse as it should be given that the topic is of global interest and importance. We conclude by offering some suggestions that may improve inclusivity and participation in SIGCSE's flagship conference, as well as the Computing Education community in general.
Human-inspired Motion Planning for Omni-directional Social Robots

Omni-directional robots have gradually been popular for social interactions with people in human environments. The characteristics of omni-directional bases allow the robots to change their body orientation freely while moving straight. However, human spectators show dislike when observing robots behave unnaturally. In this paper, we observed how humans naturally move to goals and then developed a motion planning algorithm for omni-directional robots to resemble human movements in a time-efficient manner. Instead of treating the translation and rotation of a robot separately, the proposed motion planner couples the two motions with constraints inspired from the observation of human behaviors. We implemented the proposed method onto an omni-directional robot and conducted navigation experiments in a shop with shelves and narrow corridors at width of 90cm. Results from a within-participants study of 300 human spectators validated that the proposed human-inspired motion planner provided people with more natural and predictable feelings compared to the common rotate-while-move or rotate-then-move strategies. CCS CONCEPTS• Human-centered computing → Social navigation; User studies; • Computer systems organization → Robotic control.
TwiScraper: A Collaborative Project to Enhance Twitter Data Collection

In past years several works have noted that Twitter data are essential in diverse fields and may have a lot of applications. Nevertheless, the API proposed by Twitter sternly restricts access to public data generated by users. These restrictions have the consequences of greatly slowing down the contributions of researchers and of limiting their scope. In this paper we introduce TwiScraper, a collaborative project to enhance Twitter data collection by scraping methods. We present a module allowing user-centered data collection: Twi-FFN.
Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks

Sharing recommendation is becoming ubiquitous at almost every e-commerce website, where a user will be recommended a list of users when he wants to share something with others. With the tremendous growth of online shopping users, sharing recommendation confronts several distinct difficulties: 1) how to establish a unified recommender model for large numbers of sharing scenarios; 2) how to handle with long-tail even cold start scenarios with limited training data; 3) how to incorporate social influence in order to make more accurate recommendations. To tackle with the above challenges, we firstly build multiple expert networks to integrate different scenarios. During model training one specific scenario can learn to differentiate importance of each expert network automatically based on corresponding context information. With respect to the long-tail issue, we propose to maintain a complete scenario tree such that each scenario can utilize context knowledge from root node to leaf node to select the expert networks. At the same time, making use of the tree-based full path message contributes to alleviating training data sparsity problem. Moreover, we construct a large-scale heterogeneous user-to-user graph which is derived from various social behaviors at e-commerce websites. Then a novel scenario-aware multi-view graph attention network is leveraged to augment user representations socially. In addition, an auxiliary inconsistency loss is applied to balance the load of expert networks, along with main click-through rate (CTR) prediction loss, the whole framework is trained in an end-to-end fashion. Both offline experiments and online A/B test results demonstrate the superiority of proposed approach over a bunch of state-of-the-art models.
Explainable Recommendation with Comparative Constraints on Product Aspects

To aid users in choice-making, explainable recommendation models seek to provide not only accurate recommendations but also accompanying explanations that help to make sense of those recommendations. Most of the previous approaches rely on evaluative explanations, assessing the quality of an individual item along some aspects of interest to the user. In this work, we are interested in comparative explanations, the less studied problem of assessing a recommended item in comparison to another reference item. In particular, we propose to anchor reference items on the previously adopted items in a user's history. Not only do we aim at providing comparative explanations involving such items, but we also formulate comparative constraints involving aspect-level comparisons between the target item and the reference items. The framework allows us to incorporate these constraints and integrate them with recommendation objectives involving both types of subjective and objective aspect-level quality assumptions. Experiments on public datasets of several product categories showcase the efficacies of our methodology as compared to baselines at attaining better recommendation accuracies and intuitive explanations.
Can You Trust Your Trust Measure?

Trust in human-robot interactions (HRI) is measured in two main ways: through subjective questionnaires and through behavioral tasks. To optimize measurements of trust through questionnaires, the field of HRI faces two challenges: the development of standardized measures that apply to a variety of robots with different capabilities, and the exploration of social and relational dimensions of trust in robots (e.g., benevolence). In this paper we look at how different trust questionnaires [18, 30, 35] fare given these challenges that pull in different directions (being general vs. being exploratory) by studying whether people think the items in these questionnaires are applicable to different kinds of robots and interactions. In Study 1 we show that after being presented with a robot (non-humanoid) and an interaction scenario (fire evacuation), participants rated multiple questionnaire items such as “This robot is principled” as “Non-applicable to robots in general” or “Non-applicable to this robot In Study 2 we show that the frequency of these ratings change (indeed, even for items rated as N/A to robots in general) when a new scenario is presented (game playing with a humanoid robot). Finally, while overall trust scores remained robust to N/A ratings, our results revealed potential fallacies in the way these scores are commonly interpreted. We conclude with recommendations for the development, use and results-reporting of trust questionnaires for future studies, as well as theoretical implications for the field of HRI.CCS CONCEPTS•Human-centered computing $\rightarrow$ Interaction paradigms; •Computer systems organization $\rightarrow$Robotics.
Faculty Performance on Simple Programming Tasks

We generally accept that some students will struggle on simple tasks at the end of an introductory programming course. We investigate the disturbing possibility that many CS faculty in India also struggle on such tasks. Based on initial data from a pan-India unproctored online programming test, we find that the 193 self-identified faculty participants performed poorly on this test. Our preliminary findings are consistent with recent national policies that acknowledge the lack of requisite expertise and depth among many faculty in India.
Chebyshev Accelerated Spectral Clustering

Spectral clustering is widely used in modern data analysis. Spectral clustering methods speed up the computation and keep useful information by reducing dimensionality. Recently, graph signal filtering (GSF) has been introduced to further speed up the dimensionality reduction process by avoiding solving eigenvectors. In this work, we first prove that the non-ideal filter not only affects the accuracy of GSF, but also the calculation speed. Then we further propose a modified Kernel Polynomial Method (KPM) to help GSF set the filter properly and effectively. We combine the main steps of KPM and GSF, and propose a novel spectral clustering method: Chebyshev Accelerated Spectral Clustering (CASC). In CASC, we take advantages of the excellent properties of Chebyshev polynomials: Compared with other spectral clustering methods using GSF, CASC spends negligible time on estimating the eigenvalues, and achieves the same accuracy with less computation. The experiments on artificial and real-world data prove that CASC is accurate and fast.
Evaluation of an Online Intervention to Teach Artificial Intelligence with LearningML to 10-16-Year-Old Students

The inclusion of artificial intelligence (AI) in education is increasingly highlighted by international organizations and governments around the world as a cornerstone to enable the adoption of AI in society. That is why we have developed LearningML, aiming to provide a platform that supports educators and students in the creation of hands-on AI projects, specifically based on machine learning techniques. In this investigation we explore how a workshop on AI and the creation of programming projects with LearningML impacts the knowledge on AI of students between 10 and 16 years. 135 participants completed all phases of the learning experience, which due to the COVID-19 pandemic had to be performed online. In order to assess the AI knowledge we created a test that includes different kinds of questions based on previous investigations and publications - resulting in a reliable assessment instrument. Our findings show that the initiative had a positive impact on participants? AI knowledge, being the enhancement especially important for those learners who initially showed less familiarity with the topic. We observe, for instance, that while previous ideas on AI revolve around the term robot, after the experience they do around solve and problem. Based on these results we suggest that LearningML can be seen as a promising platform for the teaching and learning of AI in K-12 environments. In addition, researchers and educators can make use of the new instrument we provide to evaluate future educational interventions.
Observations on the heat-induced salt balance changes in milk I. Effect of heating time between 4 and 90°C

Summary Milk permeate was separated at various temperatures by means of a hollow fibre ultrafiltration unit coupled to a stainless steel heat exchanger. Milk samples conditioned at 4°C were heated to 20, 40, 60, 80, 85 or 90°C prior to ultrafiltration. Ca, P, Mg, Na, K and citrate concentrations were measured in the permeate samples. Ca and P contents of the permeate decreased as the temperature increased. The pH was measured after cooling the permeate to room temperature. Smaller losses of Mg and citrate were also observed with increase in temperature. Na and K levels were not affected. A two-step time-concentration relationship was apparent for the species under study. An initial sharp decrease in concentration occurred in the first minute of holding time and was followed by a slower reaction. The possible occurrence of a two-step mechanism in the heat-induced salt balance changes is discussed. Dicalcium phosphate precipitation is believed to be coupled with tricalcium citrate precipitation upon heating.
Teaching Embedded Systems by Constructing an Escape Room

Embedded systems are the foundation of many of today's consumer products and industrial applications - and they are increasingly connected. To teach this topic we created a course with the overarching goal of designing and constructing an automated escape room. This provided motivation and opportunity for students to learn the engineering and soft skills required for building networked embedded systems. The game was open for faculty members and friends of the students after the course concluded. By dividing the building process into multiple tasks, such as individual puzzles, the presented concept encourages inter- and intra-group work, including conceptualizing, designing and developing reliable, connected embedded systems. In this paper we first present the motivation, context, and pedagogical approach of the course, then describe the course structure and conclude with experiences from constructing an escape room with multiple groups of students.
Computer Science Student Selection  A Scoping Review and a National Entrance Examination Reform

Background: Higher education student selection has significant impact both on lives of young individuals but also on national economies. Current digital era calls for digital professionals. Countless applicants apply to study Computer Science (CS) and Information Technology (IT) each year worldwide. Thousands of them are left without the university place they aspire to. At the same time, these disciplines suffer from numerous dropouts. Objective: Could performance and persistence in CS and IT higher education be fostered by more accurate acceptance criteria? Academic studies on skills assessed in entrance examinations along with their predictive power were systematically reviewed in this study to ultimately propose evidence based student selection criteria for the discipline. Methods: Scoping review gathered peer-reviewed studies from four academic databases. Their findings on skills assessed in entrance examinations and their predictive value were extracted and synthesized. The results were evaluated in a national consultation round utilizing the Delphi method. Findings: The review discovered seven skill categories assessed in CS and IT entrance examinations. However, the predictive values of these skills were contradictory. The Delphi process agreed on reasoning skills, verbal skills and mathematics as the most important skills to be assessed in the reformed national entrance examination. Discussion: The skills studied were usually limited to mathematics and verbal skills. Reasoning and problem solving skills were seldom examined separately. Critical thinking skills were not mentioned in any of the articles reviewed. It seems that research is tethered with school subjects learned at secondary level and tested by the legacy methods.
Local Collaborative Autoencoders

This work presents a generalized local factor model, namely Local Collaborative Autoencoders (LOCA). To our knowledge, it is the first generalized framework under the local low-rank assumption that builds on the neural recommendation models. We explore a large number of local models by adopting a generalized framework with different weight schemes for training and aggregating them. Besides, we develop a novel method of discovering a sub-community to maximize the coverage of local models. Our experimental results demonstrate that LOCA is highly scalable, achieving state-of-the-art results by outperforming existing AE-based and local latent factor models on several large-scale public benchmarks.