Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward User Engagement Optimization in 2D Presentation

Given a collection of items to display, such as news, videos, or products, how can we optimize their presentation order to maximize user engagements, such as click-through rate, viewing time, and the number of purchases? The problem becomes more complicated when the items are displayed in a grid-based, 2-dimensional presentation on a widescreen. For example, many E-Commerce websites such as Amazon and Etsy are displaying their products in a grid-like format, and so are streaming services like Youtube and Netflix. Unlike 1-dimensional space, where products can be naturally ranked in a vertical order, the presentation in 2-dimensional space poses a novel challenge about how to find the best presentation order - should we put the best listing on the top left corner, or the central position on the second row? We are aware that many traditional methods can be applied to solve the problem, such as conducting an attention heatmap web test, or a randomization experiment by shuffling positions of listings. However, both tests are costly to perform and they may downgrade the quality of users' search experience. By contrast, we focus on utilizing existing search log data to reveal propensity of positions, which is readily available and ubiquitously abundant. In a nutshell, the study presents how we find an optimal way of presentation in a grid-based environment - more relevant content should be placed in a more noticeable position. The position noticeability is further quantified to help ranking models better understand the signal of relevance manifested in user feedbacks. Our investigation paves the way for an end-to-end item presentation framework that learns the optimal layout for optimizing user engagements. Experimental results based on real-world data show the superiority of the proposed approach over state-of-the-art methods.
Predicting Crowd Flows via Pyramid Dilated Deeper Spatial-temporal Network

Predicting crowd flows is crucial for urban planning, traffic management and public safety. However, predicting crowd flows is not trivial because of three challenges: 1) highly heterogeneous mobility data collected by various services; 2) complex spatio-temporal correlations of crowd flows, including multi-scale spatial correlations along with non-linear temporal correlations. 3) diversity in long-term temporal patterns. To tackle these challenges, we proposed an end-to-end architecture, called pyramid dilated spatial-temporal network (PDSTN), to effectively learn spatial-temporal representations of crowd flows with a novel attention mechanism. Specifically, PDSTN employs the ConvLSTM structure to identify complex features that capture spatial-temporal correlations simultaneously, and then stacks multiple ConvLSTM units for deeper feature extraction. For further improving the spatial learning ability, a pyramid dilated residual network is introduced by adopting several dilated residual ConvLSTM networks to extract multi-scale spatial information. In addition, a novel attention mechanism, which considers both long-term periodicity and the shift in periodicity, is designed to study diverse temporal patterns. Extensive experiments were conducted on three highly heterogeneous real-world mobility datasets to illustrate the effectiveness of PDSTN beyond the state-of-the-art methods. Moreover, PDSTN provides intuitive interpretation into the prediction.
FinSense: An Assistant System for Financial Journalists and Investors

This paper demonstrates FinSense, a system that improves the working efficiency of financial information processing. Given the draft of a financial news story, FinSense extracts the explicit-mentioned stocks and further infers the implicit stocks, providing insightful information for decision making. We propose a novel graph convolutional network model that performs implicit financial instrument inference toward the in-domain data. In addition, FinSense generates candidate headlines for the draft, reducing a significant amount of time in journalism production. The proposed system also provides assistance to investors to sort out the information in the financial news articles.
Excluded by Robots:Can Robot-Robot-Human Interaction Lead to Ostracism

Robot-Robot-Human Interaction is an emerging field, holding the potential to reveal social effects involved in human interaction with more than one robot. We tested if an interaction between one participant and two non-humanoid robots can lead to negative feelings related to ostracism, and if it can impact fundamental psychological needs including control, belonging, meaningful existence, and self-esteem. We implemented a physical ball-tossing activity based on the Cyberball paradigm. The robots’ ball-tossing ratio towards the participant was manipulated in three conditions: Exclusion (10%), Inclusion (33%), and Over-inclusion (75%). Objective and subjective measures indicated that the Exclusion condition led to an ostracism experience which involved feeling “rejected”, “ignored”, and “meaningless”, with an impact on various needs including control, belonging, and meaningful existence. We conclude that interaction with more than one robot can form a powerful social context with the potential to impact psychological needs, even when the robots have no humanoid features.CCS CONCEPTS • Human-centered computing $\rightarrow$Empirical studies in HCI.
Reduced Learning Time with Maintained Learning Outcomes

Many online learning initiatives have failed to reach beyond the environments in which they were first developed. One exception is the Open Learning Initiative (OLI) at Carnegie Mellon University (CMU). In an attempt to validate the question-based learning methodology implemented in OLI, we developed online material for an introductory course in object-oriented programming, and tested it on two course offerings with a total of 70 students. As our course has been given in the same format for several years, we also had comparable assessment data for two classes prior to our intervention in order to determine that we did not introduce any obvious harm with this methodology. Findings show a reduced teaching and learning time by 25%. No statistically significant differences could be found in the results of the assessment quizzes nor confidence surveys completed by the students. The two teachers (the same who handled the classes before the intervention) took different paths to teaching preparations with this new methodology. One teacher increased preparations, whilst the other reduced them, but both teachers were convinced that using online question-based learning was superior to the previous lecture and textbook-based approach, both for the students and themselves in terms of overall satisfaction. We also gathered time logs from the development to estimate return on investment.
Changing Teacher Perceptions about Computational Thinking in Grades 1-6, through a National Training Program

Chile is a medium-sized developing country, with approximately 3,500,000 school-aged children. Computational thinking (CT) has been recognized as a tool for developing XXI century skills, but Chile has a large population of in-service teachers that do not know what CT is, limiting its application in classrooms. In response, the government has enacted a training program for in-service primary school teachers, focusing on changing their perceptions about CT, and showing them how it can be used in classroom. This training program was designed for grades 1-6 and piloted in the capital region during 2018. The program initially focused on coding activities, but was then redesigned to show how CT can be used to help meet the learning objectives of different subjects in the curriculum. The program was then scaled up nationwide. In this experience report, we discuss the outcomes of this program. The training program scaled as intended and we identified positive changes in teacher perceptions about CT, especially among female participants. We conclude with the lessons we learned from this experience.
A Theoretical and Empirical Reflection on Technology Acceptance Models for Autonomous Delivery Robots

In this work, we provide an argument and first empirical insights that existing technology acceptance models fall short when it comes to explaining spontaneous, unplanned and unsolicited encounters between humans and delivery robots on the street. Since technology acceptance models have been defined by the technology’s perceived ease of use, perceived usefulness and behavioural intention to use, they are not well suited to explain acceptance in situations in which humans meet robots without any prior intention to use. Nevertheless, acceptance of delivery robots might be a driving force for safe navigation. Thus, the concept of acceptance should not be limited to its current focus on (planned) usage. In consequence, we i) expand the understanding of technology acceptance, ii) propose the concept of Existence Acceptance for autonomous systems, and iii) explore a new model for acceptance in an online study (n=185). Theoretical considerations hint towards the relevance of existence acceptance models for autonomous systems.CCS CONCEPTS•Human-centered computing $\rightarrow$User models; User studies; Laboratory experiments; HCI theory, concepts and models; Empirical studies in HCI.
Looking at CS1 through Three Colour-tinted Glasses

This poster examines learning to program from three different angles or colour-tinted glasses. These somehow overlapping angles are (1) learning a new language, using principles of second language acquisition, (2) mastering a skill, based on skill acquisition theory and (3) solving problems in a range of contexts, thus frequently requiring both near and far transfer of programming strategies. The goal of this project is to identify effective practice strategies that support multiple perspectives of learning. We approach this goal by exploring any commonalities between these three angles that can have a positive effect on CS1 learning. This analysis has already identified a common factor: the need for repetition and variation in practice. Next steps are to evaluate the diversity in current coding practice and find ways to support more variation.
Data-Sharing Economy: Value-Addition from Data meets Privacy

The need for improved segmentation, targeting, personalization fuel the practice of data sharing among companies. Concurrently, data sharing faces the headwind of new laws emphasizing users' privacy in data. Under the premise that sharing of data occurs from a provider to a recipient, we propose a practicable approach of generating representational data for sharing that achieves value-addition for the recipient's tasks while preserving privacy of users. Prior art shows that the mechanism to improve value-addition inevitably weakens privacy in the generated data. In a first of a kind contribution, our system offers tunable controls to adjust the extent of privacy desired by the provider and the extent of value-addition expected by the recipient. Our experiments on a public data show that under common organizational practice of data-sharing, data generation for value-addition is achievable while preserving privacy. Our demonstration starkly shows the trade-off between privacy-protection and value addition, through user-controlled knobs and offers a prototype of a platform for data sharing which is mindful of this trade-off.
Effects of Gaze and Arm Motion Kinesics on a Humanoid’s Perceived Confidence, Eagerness to Learn, and Attention to the Task in a Teaching Scenario

When human students practise new skills with a teacher, they often display nonverbal behaviours (e.g., head and limb movements, gaze, etc.) to communicate their level of understanding and expressing their interest in the task. Similarly, a student robot’s capability to provide human teachers with social signals to express its internal state might improve learning outcomes. This could also lead to a more successful social interactions between intelligent robots and human teachers. However, to design successful nonverbal communication for a robot, we first need to understand how human teachers interpret such nonverbal cues when watching a trainee robot practising a task. Therefore, in this paper, we study the effects of different gaze behaviours as well as manipulating speed and smoothness of arm movement on human teachers’ perception of a robot’s (a) confidence, (b) eagerness to learn, and (c) attention to the task. In an online experiment, we asked the 167 participants (as teachers) to rate the behaviours of a trainee robot in the context of learning a physical task. The results suggest that splitting the robot’s gaze between the teacher and the task not only affects the perceived attention, but can also make the robot appear to be more eager to learn. Furthermore, perceptions of all three attributes tested were systematically affected by varying parameters of the robot’s arm movement trajectory while performing task actions. CCS CONCEPTS •Human-centered computing $\rightarrow$Empirical studies in interaction design; Interaction techniques; Empirical studies in HCI.
Exploring the Inchworm Problem's Ability to Measure Basic CS Skills

The Rainfall problem has been widely used to explore plan composition and programming errors by CS1 students. This work introduces the Inchworm problem, a conceptually straightforward task which must be solved using three interrelated skills: problem solving, algorithmic design and code implementation. The Inchworm is a holistic low stakes assessment of those three skills that provides insights on students' use of abstraction in problem solving while exposing the intricacies of code design. We tested this task with three cohorts of undergraduate students that have passed our core CS2 course. Most of them solved it in their first attempt, showing a reasonable mastery of basic algorithmic skills. In regards to problem solving strategies, most students chose to simulate the process described in the problem, step by step, with less than 10% choosing the more efficient mathematical approach. Their simulations used 4 different modelling strategies, all correct but with varying abstraction and efficiency levels. Simulation designs with poor performance indicate possible gaps in software development skills by disregarding the memory and computational cost of their solutions.
Evolving a K-12 Curriculum for Integrating Computer Science into Mathematics

Integrating computing into other subjects promises to address many challenges to offering standalone CS courses in K-12 contexts. Integrated curricula must be designed carefully, however, to both meet learning objectives of the host discipline and to gain traction with teachers. We describe the multi-year evolution of Bootstrap, a curriculum for integrating computing into middle- and high-school mathematics. We discuss the initial design and the various modifications we have made over the years to better support math instruction, leading to our goal of using integrated curricula to cover standards in both math and CS. We provide advice for others aiming for integration and raise questions for CS educators about how we might better support learning in other disciplines.
Cybersecurity Education in the Age of Artificial Intelligence: A Novel Proactive and Collaborative Learning Paradigm

This Innovative Practice Work-in-Progress paper presents a virtual, proactive, and collaborative learning paradigm that can engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary AI-cybersecurity knowledge. While progress has been made to better understand the trustworthiness and security of artificial intelligence (AI) techniques, little has been done to translate this knowledge to education and training. There is a critical need to foster a qualified cybersecurity workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. To address this import issue, in our proposed learning paradigm, we leverage multidisciplinary expertise in cybersecurity, AI, and statistics to systematically investigate two cohesive research and education goals. First, we develop an immersive learning environment that motivates the students to explore AI/machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects. Second, we design a proactive education paradigm with the use of hackathon activities based on game-based learning, lifelong learning, and social constructivism. The proposed paradigm will benefit a wide range of learners, especially underrepresented students. It will also help the general public understand the security implications of AI. In this paper, we describe our proposed learning paradigm and present our current progress of this ongoing research work. In the current stage, we focus on the first research and education goal and have been leveraging cost-effective Minecraft platform to develop an immersive learning environment where the learners are able to investigate the insights of the emerging AI/ML concepts by constructing related learning modules via interacting with tangible AI/ML building blocks.
Diverse Approaches to School-wide Computational Thinking Integration at the Elementary Grades: A Cross-case Analysis

Elementary schools throughout the United States are attempting to integrate computational thinking (CT) into their instruction, often without guidance from research about effective approaches for achieving particular CT goals. This cross-case study investigated the school-wide integration of CT in three elementary schools in a large urban school district in the Northeast that has a district-led CS for All initiative. Data included interviews with teachers, professional development providers, and school administrators as well as surveys from teachers and classroom observations in each participating school. Findings revealed three distinct approaches to integration: (a) single teacher leader-driven model, (b) scaffolded professional development model, and (c) intensive coaching model. These approaches reflect the visions set by administrators and teachers, methods used by professional development providers, and cultures of each school. Across the case studies, common pedagogical approaches included strategic use of both unplugged and plugged activities with a range of computational tools, a focus on collaborative project-based learning, and the use of CT-specific academic language to anchor new CT learning within the academic disciplines. The study highlighted advantages and challenges within each integration approach with implications for schools considering CT integration.
WoMG: A Library for Word-of-Mouth Cascades Generation

Studying information propagation in social media is an important task with plenty of applications for business and science. Generating realistic synthetic information cascades can help the research community in developing new methods and applications, testing sociological hypotheses and different what-if scenarios by simply changing few parameters. We demonstrate womg, a synthetic data generator which combines topic modeling and a topic-aware propagation model to create realistic information-rich cascades, whose shape depends on many factors, including the topic of the item and its virality, the homophily of the social network, the interests of its users and their social influence.
About a Practical Synthesis of Ambrox® from Sclareol: a New Preparation of a Ketone Key Intermediate and a Close Look at its Baeyer–Villiger Oxidation

The latest route to Ambrox® (1) starting from sclareol (2) proceeds through an oxy ketone 4 (Scheme 1). A new practical synthesis of the key intermediate 4 is described; it is equivalent to a partial oxidative degradation of sclareol (2) with peracetic and periodic acids (Scheme 2). The final product of the BaeyerVilliger oxidation of the oxy ketone 4 with commercial peracetic acid is decisively dependent on the reaction conditions because the expected acetate 9 reacts with any nucleophile, especially the peracid (Scheme 3). Furthermore, this acetate 9 is very prone to eliminative coupling (Scheme 4).
Programming for Children and Teenagers in Brazil: A 5-year Experience of an Outreach Project

There has been a worldwide surge in programming education initiatives for children and teenagers. In Brazil, this trend faces some challenges, namely inadequate infrastructure of most schools, notably public ones, that lack access to computers and tablets, and basic education curricular requirements not contemplating computer science concepts. This article reports on the five-year experience of an outreach project from a public university in Brazil. The project aims to promote computer science education and to teach programming to children and teenagers. Undergraduate engineering students who participate in the project as members engage in activities such as planning the courses and their schedules, creating partnerships with local schools and other educational projects, giving lectures, producing scientific research and educational materials, as well as promoting the project on social media. The courses use free online programming tools, Python, MIT App Inventor, and Arduino to cover fundamental concepts of programming and computational thinking. They vary approaches and tools according to the age range and available technological resources of the target audience. The use of unplugged activities means to assist in learning and to circumvent computer access problems. Furthermore, they serve for introducing basic programming concepts in classes and motivating students with dynamic activities. Over its five-year existence, the project has achieved its purpose, by reaching a total of 2639 students through 45 workshops and 94 courses. It has provided courses in eleven public schools, created two booklets and one app as free educational material, along with presented papers and posters in scientific conferences.
Bridging Professional Development to Practice: Using School Support Visits to Build Teacher Confidence in Delivering Equitable CS Instruction

CS for All initiatives have grown around the country, aiming to help build a technologically skilled workforce, increase diversity in the CS field, and provide students with the computing skills needed to be successful. However, we do not currently have enough educators to meet the swelling demand for CS instruction across grade levels. Further, schools of education are not offering sufficient CS courses to prepare new teachers in anywhere near the volume required. To address this gap, many districts are providing in-service professional development to build teachers' capacity to deliver high-quality CS instruction at scale. This paper presents findings from one large urban district's capacity-building efforts, which have consisted of a variety of in-service professional development (PD) opportunities, along with follow-up school support visits (SSVs) that include coaching and the use of a teacher-learner-observer (TLO) model. Specifically, we explore the influence of the SSVs on teacher's confidence in their ability to provide equitable, high-quality CS instruction. These findings contribute to our understanding of effective strategies to build teachers? capacity at the scale that is necessary to accomplish the goals of CS for All.