Reviewerly/exHarmony huggingface dataset:
A survey on federated learning: a perspective from multi-party computation

Abstract Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw datasets. To enhance privacy in federated learning, multi-party computation can be leveraged for secure communication and computation during model training. This survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy, as well as the corresponding optimization techniques to improve model accuracy and training efficiency. We also pinpoint future directions to deploy federated learning to a wider range of applications.
Designing Interrogative Robot Theater: A Robot Who Won’t Take No for an Answer

Abstract “You are the hottest thing in the room! I couldn’t help but come over to introduce myself.” That’s how the conversation began between the human-sized female robot and an audience member in a corner of the room during Come Hither to Me! In this robot theater the robotic agent charms the audience with her seductive humor and subtly enters them into a provocative dialogue that surfaces their stereotypical biases in gendered social interactions. Come Hither to Me! exemplifies “Interrogative Robot Theater,” our performative and critical method for social robotics research with an objective of designing robotic embodiment and interactivity for theatrical performances and public interventions. We apply various design and theater-making methods to develop a socially engaging, fun, and playful interactive experience for the audience. Using humorous conversation and embodied interaction design, our feminist robot theater makes a satirical performative commentary on misogynist dating culture and stereotypical gender roles. Inspired by the male-centered pickup artist community guidelines, we designed a chatbot decision tree for our female-gendered robot actor that flirts and provokes conversation with participants of all genders, subverting the imbalanced power dynamics of sexist social interactions. This interventionist theater-making methodology builds upon social justice-oriented interaction design, interrogative design, and Theater of the Oppressed. Through the application of this approach, Come Hither to Me! interrogates and problematizes gendered intimacy and agency in social interactions.
Design of a Bespoke Web-Based 3D Virtual Venue and Video Streaming Event Platform

As technology advances, enterprises, retail stores, museums, universities, and other organizations have learned to adapt to the ever-changing challenge of drawing public attention to their offerings. This has become even more important in the current era we are living in, where not having a digital presence severely hinders their possibilities to reach a broader market. This issue has drawn even more attention in the past two years as the COVID-19 pandemic spiked the need to hold remote seminars, conferences, classes, and other events through videoconferencing platforms. As with other organizations, universities have been using their websites to promote academic programs, campus installations, events, and amenities and since have expanded to social media platforms and mobile applications to offer more features to both the students and visitors. Colleges and universities across the world have been integrating multimedia resources such as panoramic photos, 360-degree videos, interactive maps, and guided tours of their campuses either from their website or mobile app. To further expand on this idea, and in an effort to reproduce the experience of attending an event in a physical location, albeit, to a certain extent; an interdisciplinary team of digital design, interior design, graphic design, and computers science students, along with professors at Ciudad Juárez Autonomous University, developed a virtual venue that will be accessible from the university web site. Though the concept is not entirely new, the approach and developing process aims to produce a modern, configurable, and flexible platform that allows more freedom for the end users. Attendees will be able to interact with the environment, move freely from one live conference to another, or to any showroom available at that event. By leveraging the experience of the experts throughout the entire process, from interior space design, 3D modeling, texturing, illumination, signage, brand identity, and programming, the final product will provide a more engaging experience than a traditional virtual setting, without requiring any specialized viewing equipment.
Children’s Generative Play with a Combination of Analogue and Digital Resources

This chapter focuses on children’s play and making activities when involved in creative processes, in particular focusing on how different resources and materials foster their agency. The chapter will include empirical data where children (3–9 years of age) are engaged with both digital and analogue resources, (LEGO, Minecraft, and Virtual Reality). The analysis was based on data in the form of ethnographic observations, and informal conversations. The analysis focused on how children engaged with the different resources and how they unified or separated them in the design activity. We narrowed this focus into four main categories: problem scoping, idea generation, design monitoring, and design experience. The chapter contributes to further specifying dimensions of digital creation opening new avenues of play and creation with a unifying combination of analogue and digital resources. This has implications on educational and societal instances by renewing our understanding of children’s contemporary play.
Designing a Novel Approach Using a Greedy and Information-Theoretic Clustering-Based Algorithm for Anonymizing Microdata Sets

Data anonymization is a technique that safeguards individuals' privacy by modifying attribute values in published data. However, increased modifications enhance privacy but diminish the utility of published data, necessitating a balance between privacy and utility levels. K-Anonymity is a crucial anonymization technique that generates k-anonymous clusters, where the probability of disclosing a record is 1/k. However, k-anonymity fails to protect against attribute disclosure when the diversity of sensitive values within the anonymous cluster is insufficient. Several techniques have been proposed to address this issue, among which t-closeness is considered one of the most robust privacy techniques. In this paper, we propose a novel approach employing a greedy and information-theoretic clustering-based algorithm to achieve strict privacy protection. The proposed anonymization algorithm commences by clustering the data based on both the similarity of quasi-identifier values and the diversity of sensitive attribute values. In the subsequent adjustment phase, the algorithm splits and merges the clusters to ensure that they each possess at least k members and adhere to the t-closeness requirements. Finally, the algorithm replaces the quasi-identifier values of the records in each cluster with the values of the cluster center to attain k-anonymity and t-closeness. Experimental results on three microdata sets from Facebook, Twitter, and Google+ demonstrate the proposed algorithm's ability to preserve the utility of released data by minimizing the modifications of attribute values while satisfying the k-anonymity and t-closeness constraints.
Online Optimization Method of Learning Process for Meta-Learning

Abstract Meta-learning is a pivotal and potentially influential machine learning approach to solve challenging problems in reinforcement learning. However, the costly hyper-parameter tuning for training stability of meta-learning is a known shortcoming and currently a hotspot of research. This paper addresses this shortcoming by introducing an online and easily trainable hyper-parameter optimization approach, called Meta Parameters Learning via Meta-Learning (MPML), to combine online hyper-parameter adjustment scheme into meta-learning algorithm, which reduces the need to tune hyper-parameters. Specifically, a basic learning rate for each training task is put forward. Besides, the proposed algorithm dynamically adapts multiple basic learning rate and a shared meta-learning rate through conducting gradient descent alongside the initial optimization steps. In addition, the sensitivity with respect to hyper-parameter choices in the proposed approach are also discussed compared with model-agnostic meta-learning method. The experimental results on reinforcement learning problems demonstrate MPML algorithm is easy to implement and delivers more highly competitive performance than existing meta-learning methods on a diverse set of challenging control tasks.
On the Possibility of Collaborative ESD Activities between Schools and Local Communities with Emphasis on “Local Sustainability”

This study discusses the collaborative process between schools and local communities through a case study of the “Toyama-go ESD Promotion Project” in Iida City, Nagano Prefecture. In this project, schools facing the consolidation crisis implemented curriculum restructuring that aligned with sustainable development goals (SDGs). Through this initiative, the schools actively engaged with the local community and schools outside of the region. Consequently, community residents undertook independent activities to revitalize the community, advocated for inward migration and settlement policies, started new businesses leveraging the local environment, and initiated courses to relearn the community's history. These transformations were achieved through collaborative efforts between the schools and the community residents based around the community centers, but with lateral support from government agencies and research institutions also playing an essential role.
The influence of conversational agents’ role and communication style on user experience

Conversational Agents (CAs) are characterized by their roles within a narrative and the communication style they adopt during conversations. Within computer games, users' evaluation of the narrative is influenced by their estimation of CAs' intelligence and believability. However, the impact of CAs' roles and communication styles on users' experience remains unclear. This research investigates such influence of CAs' roles and communication styles through a crime-solving textual game. Four different CAs were developed and each of them was assigned to a role of either witness or suspect and to a communication style than can be either aggressive or cooperative. Communication styles were simulated through a Wizard of Oz method. Users' task was to interact, through real-time written exchanges, with the four CAs and then to identify the culprit, assess the certainty of their judgments, and rank the CAs based on their conversational preferences. In addition, users' experience was evaluated using perceptual measures (perceived intelligence and believability scales) and behavioral measures (including analysis of users' input length, input delay, and conversation length). The results revealed that users' evaluation of CAs' intelligence and believability was primarily influenced by CAs' roles. On the other hand, users' conversational behaviors were mainly influenced by CAs' communication styles. CAs' communication styles also significantly determined users' choice of the culprit and conversational preferences.
Development and practice of learning materials for an engineering oriented circuit design workshop for elementary school student

Abstract This study aimed to develop teaching materials for circuit design and fabrication for elementary school students, and conduct workshops oriented to engineering. First, the content of the circuit design workshop was discussed and it was determined that a connection‐focused design would be appropriate. Second, we examined the issue of existing teaching materials. Many of the existing kit materials were not suitable for learning circuit design as it was already completed. In addition, many of them required soldering work, which was too difficult for elementary school students. Hands‐on materials that allow students to construct circuits through trial and error were suitable for learning circuit design. Therefore, we developed design and fabrication support materials using a 3D printer and conductive tape. Next, we provided workshop opportunities using developed material, with the theme of “making light‐up cards,” wherein elementary school students worked on designing the layout of the components and fabrication. Consequently, students’ conceptual understanding of the connection was improved. All students were able to complete the circuit, with high self‐evaluations of satisfaction and understanding suggesting that the activity was engineering‐oriented.
Complex Representation Learning with Graph Convolutional Networks for Knowledge Graph Alignment

The task of discovering equivalent entities in knowledge graphs (KGs), so-called KG entity alignment, has drawn much attention to overcome the incompleteness problem of KGs. The majority of existing techniques learns the pointwise representations of entities in the Euclidean space with translation assumption and graph neural network approaches. However, real vectors inherently neglect the complex relation structures and lack the expressiveness of embeddings; hence, they may guide the embeddings to be falsely generated which results in alignment performance degradation. To overcome these problems, we propose a novel KG alignment framework, ComplexGCN, which learns the embeddings of both entities and relations in complex spaces while capturing both semantic and neighborhood information simultaneously. The proposed model ensures richer expressiveness and more accurate embeddings by successfully capturing various relation structures in complex spaces with high-level computation. The model further incorporates relation label and direction information with a low degree of freedom. To compare our proposal against the state-of-the-art baseline techniques, we conducted extensive experiments on real-world datasets. The empirical results show the efficiency and effectiveness of the proposed method.
Map Matching Based on Seq2Seq with Topology Information

Most existing road network matching algorithms are designed based on previous rules and do not fully utilize the potential of big data and historical tracks. To solve this problem, we introduce a new road network matching algorithm based on deep learning and using the topology information of the road network. Taking inspiration from the sequence-to-sequence (seq2seq) model popular in natural language processing, our algorithm builds multiple grid-dependent dictionaries based on the topology of road networks. Then the Byte Pair Encoding (BPE) algorithm is used to compress the grid dictionary, which effectively restricts the output range. A Bidirectional gated loop unit (Bi-GRU) with attention mechanisms is used as a recurrent neural network to capture information from a sequence of trajectory points. The model output feedback obtained by training the road network on Yibin City and the empirical evidence of the comparison in this experiment prove the effectiveness of the algorithm. When juxtaposed with similar algorithms, it shows superior accuracy and faster training speeds in road networks matching different scenarios.
Gesture Recognition for Human-Robot Interaction Through Virtual Characters

Human-Robot Interaction (HRI) has grown increasingly important with the integration of various types of robots into industrial and daily life aspects. Non-verbal communication plays a vital role in HRI and heavily relies on gesture recognition. The ability of robot’s interpretation of human gestures is essential for enhancing the overall user experience. To achieve precision and effectiveness of gesture recognition in HRI, the incorporation of Virtual Reality (VR) technology offers promising avenues. In this article, we describe the development of a gesture recognition pipeline tailored to HRI in Precision Agriculture scenarios. Its key feature is the use of Virtual characters and Machine learning techniques to allow agricultural robots to recognize gestures performed by both digital agents and humans. We address challenges by presenting a set of gesture definitions, data generation, and system evaluation using varies combinations of real and virtual simulated data. Our results demonstrate how the adoption of virtual simulations can significantly increase the system’s accuracy and efficiency.
Social Perception and Scene Awareness in Human-Robot Interaction

This paper introduces various aspects of social perception skills and scene awareness for interactive robots. The low-level audio-visual perceptual cues e.g., interruption events, eye contact, speech energy, etc. have been fused to make high-level interpretations e.g., a person’s willingness to maintain human-robot interaction in social settings. The detection of objects in the scene helps generate context-aware queries or responses for the robot. The overall behavior of the interactive robot, Ameca, has been generated in the form of speech and body language. In this work, a fusion of rule-based and caption-based speech generation has been incorporated. The experimental results in human-robot interaction scenarios show promising outcomes as far as the robot’s ability to perceive and react to social and environmental cues.
Empowering Collaboration: A Pipeline for Human-Robot Spoken Interaction in Collaborative Scenarios

In the context of collaborative robotics, robots share the working space with humans and communication between the two parties is of utmost importance. While different modalities can be employed, speech represents a natural way of interaction for people. In this paper, we introduce a speech-based pipeline for collaborative robotics, specifically designed to operate in the context of precision agriculture. The system exploits frame semantics as a modality-independent way of representing information, which allows for easier management of the dialogue between the robot and the human. One of the key features of this pipeline is the utilization of various techniques from Natural Language Processing (NLP) to extract and manage frames.
The Influence of a Robot’s Personality on Real-Time Explanations of Its Navigation

Navigational decisions of autonomous robots in social environments impact humans and their expectations of robots’ actions. Explanations can help mitigate adverse effects on humans and enhance their understanding of robots’ decisions. We present real-time multi-modal explanations of a robot’s navigation and model the influence of a robot’s personality on it. Our approach considers the contextual background and spatial aspect of the robot environment. Our results show that the more extroverted the robot is, its explanations are less detailed and more often generated. Finally, we discuss potential avenues and constraints for future research. Our work promotes social and safe robot navigation.
KULF-TT53: A Display-Specific Turntable-Based Light Field Dataset for Subjective Quality Assessment

Light field datasets enable researchers to conduct both objective and subjective quality assessments, which are particularly useful when acquisition equipment or resources are not available. Such datasets may vary in terms of capture technology and methodology, content, quality characteristics (e.g., resolution), and the availability of subjective ratings. When contents of a light field dataset are visualized on a light field display, the display system matches the received input to its output capabilities through various processes, such as interpolation. Therefore, one of the most straightforward methods to create light field contents for a specific display is to consider its visualization parameters during acquisition. In this paper, we introduce a novel display-specific light field dataset, captured using a DSLR camera and a turntable rig. The visual data of the seven static scenes were recorded twice by using two settings of angular resolution. While both were acquired uniformly within a 53-degree angle, which matches the viewing cone of the display they were captured for, one dataset consists of 70 views per content, while the other of 140. Capturing the contents twice was a more straightforward solution than downsampling, as the latter approach could either degrade the quality or make the FOV size inaccurate. The paper provides a detailed characterization of the captured contents, as well as compressed variations of the contents with various codecs, together with the calculated values of commonly-used quality metrics for the compressed light field contents. We expect that this dataset will be useful for the research community working on light field compression, processing, and quality assessment, for instance to perform subjective quality assessment tests on a display with a 53-degree display cone and to test new interpolation methods and objective quality metrics. In future work, we will also focus on subjective tests and provide relevant results. This dataset is made free to access for the research community.
Say No to Speculation in Crypto market during NFT trades: Technical and Financial Guidelines

Saying no to speculation in the crypto market during NFT trades is a call for prudence, diligence, and a commitment to responsible investing. Embracing these principles will contribute to a more sustainable and credible ecosystem, allowing the underlying blockchain technology to fulfill its potential beyond short-term market fluctuations. Speculation in the cryptocurrency market, particularly during NFT (Non-Fungible Token) trades, demands a cautious and informed approach. The crypto market is renowned for its inherent volatility, a characteristic accentuated by the NFT space. Prices can experience sudden and unpredictable fluctuations, making it imperative for investors to exercise prudence and avoid succumbing to the lure of speculation. NFTs derive their value from factors such as perceived scarcity, demand, and cultural significance rather than traditional fundamentals. This lack of intrinsic value makes it challenging for investors to make well-informed decisions based on solid analysis. Furthermore, the rapid evolution of the crypto space means that NFTs may lack a substantial history of price data, adding an additional layer of complexity to the assessment of their value.