Reviewerly/exHarmony huggingface dataset:
THE EFFECT OF DRTA LEARNING MODEL ASSISTED BY COMIC MEDIA ON THE READING COMPREHENSION ABILITY OF STUDENTS CLASS IV PRIMARY SHCOOL

This study aims to determine the effect of the Direct Reading Thinking Activity (DRTA) learning model assisted by comics on the reading comprehension ability of fourth grade students at SDN Genuk Ungaran Barat. This study uses a quantitative approach with an experimental method, namely Quasi Experimental Design in the form of a Non-Equivalent Control Group Design. The population in this study were all fourth grade students at SDN Genuk Ungaran Barat. Meanwhile, the samples in this study were fourth grade students at SDN Genuk 01 and fourth grade students at SDN Genuk 02 Ungaran Barat. Data collection techniques used cognitive domain tests, observations, interviews and documentation. The data analysis technique used is the Independent Sample T-Test and Simple Linear Regression Test. The results of this study indicate: (1) There is an average difference in students' reading comprehension skills between the control class and the experimental class, which can be seen from the Independent Sample T-Test and the tcount &gt; ttable (3,611 &gt; 2,008) with a sig. (2-tailed) that is 0.001 &lt; 0.05 then H0 is rejected and Ha is accepted. (2) There is an influence of the Direct Reading Thinking Activity (DRTA) learning model assisted by comics media on students' reading comprehension skills, which can be seen from the Simple Linear Regression Test and obtained a significance level of 0.000 &lt; 0.05 with a percentage value of R2 = 0.573 = 57 ,3%, then this regression model can affect the participation variable. So it can be concluded that the Direct Reading Thinking Activity (DRTA) learning model assisted by comics media affects the reading comprehension ability of fourth grade students at SDN Genuk Ungaran Barat.&#x0D; Keywords: DRTA Learning Model; Comic Media; Reading Comprehension Ability.
Coordinated Motion Planning of Redundant Dual-Arm Robots with Self-collision Avoidance

To solve the self-collision problem when redundant dual-arm robots perform coordinated operations, a coordinated motion planning scheme based on self-motion and master-slave task switching is proposed in this paper. First of all, according to the actual motion of the operated object and the relative motion between the dual-arm, the coordinated operation equation of the redundant dual-arm robot is established. Then, the velocity-level inverse kinematics equation of redundant dual-arm robots for specific operational tasks is established by the pseudo-inverse formula. Finally, based on the principle of master-slave task switching, the self-motion of redundant robot manipulators is used to avoid self-collision in coordinated motion planning. The scheme is verified based on a virtual simulation platform composed of two 7-degree-of-freedom robot manipulators. The simulation results show that under the premise of satisfying the constraint between the dual-ram, the accurate planning of the end-effector trajectory is realized, and the joint motion is continuous and stable, which verifies the effectiveness of the dual-arm coordinated motion planning scheme.
A Multi-robot Collaborative Exploration Technology Based on Instance Segmentation

Multi-agents need to perform tasks in complex and unknown environments, such as exploratory missions in deep space environments and special operations missions in complex battlefields. How to intelligently and collaboratively perform exploration tasks in complex and unknown environments is a difficult point in the research of intelligent autonomous technology. This paper builds a multi-robot intelligent collaborative exploration framework, which integrates multi-robot intelligent instance segmentation and multi-view intelligent collaborative identification technology. Images obtained from different perspectives can improve the efficiency of target detection and recognition. The multi-view information of the environment is obtained by using the robot cluster, and the instance segmentation of the target detection network based on deep learning is performed. It can continuously adjust the optimal collaborative strategy of the cluster and improve the efficiency of target exploration. The final simulation experiments show that the active exploration technique proposed in this paper can achieve efficient path planning.
Tracker with Salient Aware Initialization Module

Visual tracking is to perform position and scale estimation based on a given initial position of an object. Correlation filters (CFs) are one of the state-of-the-art trackers for non-classified objects. Due to the fact that the initial tracking window has a huge impact on tracking performance, and the tracker would not provide a good description of the target when tracker with an inexact bounding box of the target. Thus, a stable and precise bounding box estimation plays an important part on track performance, especially on mobile platforms with inexact initial tracking point. This work brings up a salient aware module with transferability and generality for tracking window refinement. By exploring the salient mask of object, this work leads to a strengthened tracker. Experiments show that the tracker with salient aware module is efficient and very versatile for many trackers.
Robust Real-Time Object Tracking System for Human-Following Quadruped Robot

Quadruped robot needs a real-time robust object tracking algorithm and an accurate and stable servo controller for human following task. The object tracking algorithm in this paper used the Kalman filter to generate the pre-sampling area for Kernel Correlation Filter (KCF) according to the motion state, and the object detection is used to periodically evaluate and update the tracking box of KCF according to the scale change rate and the center offset error, so as to realize the scale adaptation and prevent missed detection. Based on the visual information provided by the target tracking algorithm, the kinematics of the target following task are modeled, and the kinematic controller satisfying the Lyapunov stability is designed based on backstepping. Through simulation experiments, the effectiveness and stability of the target tracking system proposed in this paper are verified.
A Vehicle Counting and Road Condition Analysis System Based on Multiple Object Tracking

Traffic data collection is crucial for the management of road networks. Based on YOLOv5s and DeepSORT, optimizations are made in the vehicle detection stage and the vehicle tracking stage to improve the accuracy of the system. At the same time, Support Vector Machine(SVM) is introduced to divide the upper and lower lanes, and the reference lines are automatically defined for videos taken from different perspectives. This solves the shortcomings of traditional vehicle counting methods that need to use manual annotation for each video to distinguish upper and lower lanes and set reference lines. Finally, the experimental results show that the system is able to achieve an average accuracy of 95.5% in real time in daytime, evening, foggy, and sunny conditions.
Mitigating the Backdoor Attack by a Weight-Based Federated Aggregation

Federated learning (FL) provides a new learning paradigm without compromising client privacy to solve contradictions between privacy-preserving concerns and big data requirements. Clients do not need to transmit personal data and train locally in FL. However, there is a challenge against backdoor attacks on client sides. To overcome this challenge, we proposed a weight-based federated aggregation (WFA) that strengthens regular models and decreases corrupted modes for the global model, which leads to high robustness to resist backdoor attacks. We get weights by minimizing the sum of deviations between client models and the geometric median of client models. To avoid the impact of the curse of dimensionality, we use a nonlinear principal component analysis method to reduce the dimension of received model data before computing deviations in WFA. Extensive experiments demonstrate that WFA generally has good robustness in defeating backdoor attacks, and the robust aggregation approach of WFA is agnostic to the level of corruption.
Ring-Overlap: A Storage Scaling Mechanism for Hyperledger Fabric

Currently, blockchain is facing a serious storage explosion problem. While most storage scaling schemes are focused on permissionless blockchain, we propose the ring-overlap mechanism for consortium blockchain and use it to scale Hyperledger Fabric. In our scheme, all accounting nodes are divided into clusters, and each cluster contains several nodes; then, a portion of the entire block data is stored in a cluster. Block data is stored overlappingly on some cluster nodes, and each block is guaranteed to have some copies in a cluster. Theoretical analysis and simulation show that the storage occupied by nodes is significantly reduced in blockchain applications with frequent transactions, and the mechanism can still guarantee data integrity in the case of partial node failures in a single cluster. Furthermore, for transaction-frequent applications, storage space consumption can be significantly reduced without increasing excessive query time overhead.
Because Fathers Matter: How Tools and Materials Mediate the Collaboration between Father and Child in Maker Activities

The Maker Movement is a social and material movement aspiring to lower the barriers to participation in technology construction. Material engagement, learning, and collaboration are important values in the maker movement. Aspiring to further the understanding of makerspaces as places for meaningful collaborations, this research asks how tools and materials mediate the collaboration between father and child in the makerspace? Based on a case study comprising three workshops designed to support father-child collaboration in a social neighborhood, this research presents an in-depth video interaction analysis of father-child collaborations. Although relying on a small sample of father-child pairs (n=2), this research depicts situations in which computational tools and creative materials shape individual actions and social and collaborative interactions. The results inform a discussion of the makerspace as a meaningful place for social interaction and the material implications on improvisation, articulation of ideas, and sharing of tasks.
PONI: A Personalized Onboarding Interface for Getting Inspiration and Learning About AR/VR Creation

New creators of augmented reality (AR) and virtual reality (VR) applications often face a steep learning curve during the onboarding stage of creation and struggle in identifying suitable learning materials that are appropriate for their skillsets. To support the initial learning needs of new AR/VR creators from different backgrounds, we designed and implemented a novel personalized onboarding interface (PONI) that allows users to locate relevant projects based on their programming and 3D modeling skills, development goals, and any constraints, such as time or budget. Our usability evaluation (n=16) showed that most creators found PONI to be intuitive, useful, and saw its potential to be used as a knowledge hub for inspiration and self-directed exploratory learning. We discuss ways in which the personalization could be further enhanced and how the potential of PONI could be explored to improve onboarding in contexts beyond AR/VR development.
The Superhero Pose: Enhancing Physical Performance in Exergames by Embodying Celebrity Avatars in Virtual Reality

Altered virtual reality self-representations can augment human capabilities by exploiting the Proteus effect. For example, previous work has shown that embodying an avatar that signifies super-intelligence, Einstein, enhanced the users’ performance in cognitive tasks. In this paper, we show that embodying an avatar that signifies superior athletic skills enhances the users’ physical performance. We conducted a between-subject experiment (n = 50) where participants played a soccer game while being embodied in a famous soccer avatar and in a generic avatar. We reflect on how self-esteem, embodiment illusion, and presence moderate the Proteus effect. Our results showed that participants embodied as a professional soccer celebrity performed better in the tasks and had a higher sense of embodiment compared to those with a generic avatar. Our results can be used to design engaging exergames and technological interventions to expand human capabilities.
Creating More Personas Improves Representation of Demographically Diverse Populations: Implications Towards Interactive Persona Systems

Personas represent distinct user types. However, while online user data can be demographically and behaviorally heterogeneous, most studies generate less than ten personas, regardless of how heterogeneous the data is. Because all persona creation efforts need to assign a number of personas to create, assigning this number evokes a fundamental question, How many personas to create?. To address this question, we apply data-driven persona creation in a dataset with 250 million YouTube views from a global news and media organization. We focus on a statistically optimal number of personas, namely, how the distribution of demographic persona attributes deviates from the baseline user data. Altering the number of generated personas, ranging from 5 to 160 personas per set, we find that more personas cover more age groups and countries, thus improving the statistical correspondence with the raw user data, and increasing the representation of demographic diversity by including more fringe user segments. While the user representation continuously improved with more personas, the relative diversity gain was maximal with 40 personas, implying that, using our data, one ought to create more than 4 times more personas than generally advocated. The results imply that organizations with heterogeneous online audiences benefit from many personas in terms of more inclusive user representation. We further demonstrate how an interactive persona system can help stakeholders navigate many personas with possibly smaller cognitive effort.
Tales from Corrodia

In our continuous strive to expand, improve and ever develop this world, it is important to take a step back and consider: what might come of this? Is this truly a sustainable solution? Could it be misused or even abused? What will the future be like, with this innovation in it? Or, at a larger scale, what could a world be like, hundreds of years from now, given the speed of technological advance that we have today? What might happen if this technology – computer science, biotech, mechanical engineering, nanotech, medicine, space travel, all of it – goes haywire?
Combining Momentary and Retrospective Self-Reflection in a Mobile Photo-Based Journaling Application

The concept of self-reflection is investigated in a wide range of fields, from Psychology to HCI. Different fields use different methods to trigger reflection, such as visualizing accumulated personal quantitative data, personal photos, journaling, or asking reflective questions about experiences. This study combines photo-based journaling, reflective questions and data visualization for triggering momentary and retrospective self-reflection. We prototyped a photo-based journaling app and conducted a 5-week field study (N=13) to observe in-the-wild experiences. Following, we conducted an evaluation workshop (N=9) to explore strategies for motivating reflective question answering and retrospective self-reflection. Our approach encourages momentary and retrospective self-reflection, but the question-answering process could be burdensome and retrospective self-reflection should be incentivized. For that, we compiled design strategies for both kinds of self-reflection. We contribute to the HCI literature with strategies for unifying momentary and retrospective self-reflection in photo-based journaling.
Record or Recall? Exploring Self-Reported Dietary Assessment Methods for Office Workers during the COVID-19 Work-from-Home Period

The workplace represents an important venue to influence eating behaviors. Due to the COVID-19 pandemic, the workplace has rapidly shifted from office to home (WfH). Here, two mobile self-reported dietary assessment methods were compared (4-hour Recall vs. Food Record) to monitor food intake for WfH. A within-subject study involving 30 participants was conducted over a 4-week period. We assessed the workload and acceptance of these two methods using questionnaires and follow-up interviews. Results of questionnaires revealed that most participants presented high acceptance of Food Record related to a more flexible completion time and lower mental burden. Based on interviews, we presented a set of design insights to promote WfH healthy eating, including integrating reminders into daily routines, simplifying the tracking process, and adding gaming elements. Then, we discussed design implications, including integrating digital tools into daily routines and designing simple and playful using processes, to promote healthy eating during the WfH period.
Democratizing Mobilities Design Tools Using VR - Extended Abstract

In collaboration with Metroselskabet in Copenhagen, Denmark, this project is an exploration of how virtual reality can support urban designers in visualizing crowd flow in Nørreport Metro Station. The aim was to visualize the simulated effects of rearranging the placement of electronic ticket validators. Four elements form the core of the prototype. First, crowd simulation using Unity’s NavMesh system, with agents driven by behavior trees. Second, VR technology to support an immersive and interactive experience. Third, a virtual environment based on Nørreport Metro Station. Fourth, a logging system so that data could be extracted from the simulated crowd flow, enabling further analysis by users. The project was evaluated on five participants, consisting of an expert heuristic evaluation along with cybersickness and presence questionnaires. Results showed great promise, and through multiple iterations the project was considered to be sufficient as a design tool for urban metro design.
Age Against the Machine: A Call for Designing Ethical AI for and with Children

Child-Computer Interaction (CCI) research is focused on cultivating, nurturing, and nudging children towards technology use and design. Recently, ethical aspects related to technology have come to the forefront, including the inherent limitations of technology, particularly related to Artificial intelligence (AI). Further, AI has a known diversity problem where age-inclusion can be sometimes forgotten. While various global and national policy frameworks on Children and AI are being developed, the approaches are child-centered but not child-led, restricting children from affecting their own digital futures. Further still, there is little discussion with children on the limitations, inherent biases, and lack of diversity in current design and development of AI. As AI evolves to mimic human-like cognition, emotions, conversations, and decision-making, its impact on children and their futures should be critically examined for, with, and by children. To this end, we invite NordiCHI conference participants and their children to critically examine the challenges towards AI, where all participants consider and reimagine alternative technology presents and futures. This workshop contributes to the ongoing work on Children and AI by including children as equal partners and empowering them to consider present and future challenges as experts of their own lives, with diverse interests, backgrounds, perspectives, and experiences.
Designing for Extreme Sleepers: Rethinking the Rhythms of Sleep Technology

Sleep takes one-third of our life and is of increasing technological interest. Yet development has been focused on the traditional notion of 8 hours-per-night schedules of ‘good sleep’ – overlooking the varied nature of people’s sleeping and waking practices. Based on the experiences of Shift Workers, Gamers, and Polyphasic Sleepers, this paper exposes the sleep practices of extreme sleepers for the design of technology. This cohort follows sleep patterns outwith those encoded in current technology for work, will and well-being. Through interviews and design workshops, we present three themes emerging from these perspectives on sleep. These themes revolve around the physiological, felt, and social timings of sleep. We explore a design space around these themes through eight different critical design exemplars. In conclusion, this work encourages the design of sleep technology focusing on users’ agency over their own life-rhythms rather than pre-built concepts of ‘good’ sleep.
Curating/Fermenting Data: data workflows for semantic web applications

This one-day workshop will bring together HCI researchers, Linked Open Data (LOD) experts, curators, designers and cultural practitioners to explore applications of LOD for the creation of participatory datasets. The aim of the workshop is to work with a collection of small unstructured data while engaging with questions of ethical data use in HCI design and working with data against a “colonial impulse” [22]. Starting from the Curating Data diagram (Appendix 1) and working with data gathered during a series of workshops in the project Fermenting Data [48], we will create speculative ontologies for the Fermenting Data dataset. The practice component of the workshop will interrogate the design of data curation workflows for semantic web applications, while working with actual ferments in order to physically engage with the process at the source of this dataset.
What Aspects of Collaboration are Meaningful to You? Informing the Design of Self-Tracking Technologies for Collaboration

While the workplace collaboration software market is booming, existing tools are often tech-focused and rarely support reflection and self-regulation of collaboration practices. Building on approaches from personal informatics, we aim to understand and promote the use of data to empower employees in exploring their own work practices, with a focus on collaboration processes. This study focuses on the preparation stage and examines what aspects of collaboration are meaningful to office workers. We interviewed employees from a single work team (N= 15) after a 2-weeks reflecting period on metrics related to collaboration experience. We contribute new insights into how employees envision metrics reflecting their collaboration experience and what personal, social, and organizational considerations surround collecting and sharing this data. We discuss how these insights can inform the design of self-tracking technologies for collaboration.
ProjectAR: Rapid Prototyping of Projection Mapping with Mobile Augmented Reality

Spatial Augmented Reality (AR) in the form of Projection Mapping is an interesting technology for several scenarios. However, there is a lack of rapid prototyping tools for Projection Mapping. We introduce ProjectAR, a rapid prototyping tool that combines everyday mobile AR devices, such as tablets, with regular projectors. ProjectAR supports easy projector calibration and provides a rapid prototyping environment through the mobile device. Low-fidelity sketches, text, and shapes can be used to create virtual assets. Designers can use ProjectAR as a Wizard of Oz tool to use these assets as part of an interactive Projection Mapping experience. We show the potential of ProjectAR for rapid prototyping and discuss its current limitations as well as future directions for supporting collaborative prototyping between designers and other stakeholders.