Reviewerly/exHarmony huggingface dataset:
Evaluation of Students’ Learning Engagement in Online Classes Based on Multimodal Vision Perspective

The method of evaluating student engagement in online classrooms can provide a timely alert to learners who are distracted, effectively improving classroom learning efficiency. Based on data from online classroom scenarios, a cascaded analysis network model integrating gaze estimation, facial expression recognition, and action recognition is constructed to recognize student attention and grade engagement levels, thereby assessing the level of student engagement in online classrooms. Comparative experiments with the LRCN model, C3D network model, etc., demonstrate the effectiveness of the cascaded analysis network model in evaluating engagement, with evaluations being more accurate than other models. The method of evaluating student engagement in online classrooms compensates for the shortcomings of single-method evaluation models in detecting student engagement in classrooms.
Combining Decentralized IDentifiers with Proof of Membership to Enable Trust in IoT Networks

The Self-Sovereign Identity (SSI) is a decentralized paradigm enabling full control over the data used to build and prove the identity. In Internet of Things networks with security requirements, the Self-Sovereign Identity can play a key role and bring benefits with respect to centralized identity solutions. The challenge is to make the SSI compatible with resource-constraint IoT networks. In line with this objective, the paper proposes and discusses an alternative (mutual) authentication process for IoT nodes under the same administration domain. The main idea is to combine the Decentralized IDentifier (DID)-based verification of private key ownership with the verification of a proof that the DID belongs to an evolving trusted set. The solution is built around the proof of membership notion. The paper analyzes two membership solutions, a novel solution designed by the Authors using Merkle trees as a building block and a second one based on the adaptation of Boneh, Boyen and Shacham (BBS) group signature scheme. The paper concludes with a performance estimation and a comparative analysis.
Collaborative Learning for <scp>Annotation‐Efficient</scp> Volumetric <scp>MR</scp> Image Segmentation

Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotating three-dimensional (3D) MR images is tedious and time-consuming, requiring experts with rich domain knowledge and experience.To build a deep learning method exploring sparse annotations, namely only a single two-dimensional slice label for each 3D training MR image.Retrospective.Three-dimensional MR images of 150 subjects from two publicly available datasets were included. Among them, 50 (1377 image slices) are for prostate segmentation. The other 100 (8800 image slices) are for left atrium segmentation. Five-fold cross-validation experiments were carried out utilizing the first dataset. For the second dataset, 80 subjects were used for training and 20 were used for testing.1.5 T and 3.0 T; axial T2-weighted and late gadolinium-enhanced, 3D respiratory navigated, inversion recovery prepared gradient echo pulse sequence.A collaborative learning method by integrating the strengths of semi-supervised and self-supervised learning schemes was developed. The method was trained using labeled central slices and unlabeled noncentral slices. Segmentation performance on testing set was reported quantitatively and qualitatively.Quantitative evaluation metrics including boundary intersection-over-union (B-IoU), Dice similarity coefficient, average symmetric surface distance, and relative absolute volume difference were calculated. Paired t test was performed, and P < 0.05 was considered statistically significant.Compared to fully supervised training with only the labeled central slice, mean teacher, uncertainty-aware mean teacher, deep co-training, interpolation consistency training (ICT), and ambiguity-consensus mean teacher, the proposed method achieved a substantial improvement in segmentation accuracy, increasing the mean B-IoU significantly by more than 10.0% for prostate segmentation (proposed method B-IoU: 70.3% ± 7.6% vs. ICT B-IoU: 60.3% ± 11.2%) and by more than 6.0% for left atrium segmentation (proposed method B-IoU: 66.1% ± 6.8% vs. ICT B-IoU: 60.1% ± 7.1%).A collaborative learning method trained using sparse annotations can segment prostate and left atrium with high accuracy.0 TECHNICAL EFFICACY: Stage 1.
Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training

The intrinsic complexity of deep neural networks (DNNs) makes it challenging to verify not only the networks themselves but also the hosting DNN-controlled systems. Reachability analysis of these systems faces the same challenge. Existing approaches rely on over-approximating DNNs using simpler polynomial models. However, they suffer from low efficiency and large overestimation, and are restricted to specific types of DNNs. This paper presents a novel abstraction-based approach to bypass the crux of over-approximating DNNs in reachability analysis. Specifically, we extend conventional DNNs by inserting an additional abstraction layer, which abstracts a real number to an interval for training. The inserted abstraction layer ensures that the values represented by an interval are indistinguishable to the network for both training and decision-making. Leveraging this, we devise the first black-box reachability analysis approach for DNN-controlled systems, where trained DNNs are only queried as black-box oracles for the actions on abstract states. Our approach is sound, tight, efficient, and agnostic to any DNN type and size. The experimental results on a wide range of benchmarks show that the DNNs trained by using our approach exhibit comparable performance, while the reachability analysis of the corresponding systems becomes more amenable with significant tightness and efficiency improvement over the state-of-the-art white-box approaches.
When Texts Meet Emoji: A Multi-Stage Study of Tourism Brands

Are social media posts with emoji more engaging? Guided by the media richness theory, this study explores the relationship between visual (i.e., emoji) and textual content, and how they collectively impact user engagement with peer-to-peer accommodation brands. A three-stage sequential design using naturalist data, including text mining, frequent/rare itemset mining, and one-way ANOVA, was used. This study revealed that the combination of { Travel Tips and Inspiration,[Formula: see text]} and { Interaction and Motivation,[Formula: see text]} tended to result in an increased amount of likes and shares in social media posts. By theoretically revealing and empirically examining the complex relationship between verbal and visual content, this study enriches the theoretical understanding of media richness in tourism brands. Practically, this study provides actionable guidelines for tourism brands to increase user engagement by effectively using visual-verbal content.
Interact: A visual what-if analysis tool for virtual product design

Virtual prototyping is increasingly used by businesses to streamline operations, cut costs, and enhance daily operations. This often includes a variety of modeling techniques among which, complex, black-box models. The path from model development to utilization in applied contexts is yet long. Domain experts need to be convinced of the validity of the models and to trust their predictions. To be used in the field, model capabilities need to be affordable, that is, allow rapid and interactive scenario building, even for non-experts. Complex relations governed by statistical interactions must be unveiled for users to understand unexpected predictions. We propose Interact, a model-agnostic, visual what-if tool for regression problems, supporting (1) the visualization of statistical interactions between features, (2) the creation of interactive what-if scenarios using predictive models, (3) the evaluation of model quality and building trust, and (4) the externalization of knowledge through model explainability. While the approach applies in various industrial contexts, we validate the application purpose and design with a detailed case study and a qualitative user study with engineers in the tire industry. By unraveling statistical interactions between features, the INTERACT tool proves to be useful to increase the transparency of black-box machine learning models. We also reflect on lessons learned concerning the development of visual what-if tools for virtual product development and beyond.
Verification of Neural Networks’ Local Differential Classification Privacy

Neural networks are susceptible to privacy attacks. To date, no verifier can reason about the privacy of individuals participating in the training set. We propose a new privacy property, called local differential classification privacy (LDCP), extending local robustness to a differential privacy setting suitable for black-box classifiers. Given a neighborhood of inputs, a classifier is LDCP if it classifies all inputs the same regardless of whether it is trained with the full dataset or whether any single entry is omitted. A naive algorithm is highly impractical because it involves training a very large number of networks and verifying local robustness of the given neighborhood separately for every network. We propose Sphynx, an algorithm that computes an abstraction of all networks, with a high probability, from a small set of networks, and verifies LDCP directly on the abstract network. The challenge is twofold: network parameters do not adhere to a known distribution probability, making it difficult to predict an abstraction, and predicting too large abstraction harms the verification. Our key idea is to transform the parameters into a distribution given by KDE, allowing to keep the over-approximation error small. To verify LDCP, we extend a MILP verifier to analyze an abstract network. Experimental results show that by training only 7% of the networks, Sphynx predicts an abstract network obtaining $$93\%$$ verification accuracy and reducing the analysis time by $$1.7\cdot 10^4$$ x.
Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution

Developmental domains refer to different areas of a child’s growth and maturation, including physical, language, cognitive, and social–emotional skills. Understanding these domains helps parents, caregivers, and professionals track a child’s progress and identify potential areas of concern. Nevertheless, due to the high level of heterogeneity and overlap, neurodevelopmental disorders may go undiagnosed in children for a crucial period. Detecting neurodevelopmental disorders at an early stage is fundamental. Digital tools like artificial intelligence can help clinicians with the early detection process. To achieve this, a new method has been proposed that creates artificial features from the original ones derived from the SmartSpeech project, using a feature construction procedure guided by the Grammatical Evolution technique. The new features from a machine learning model are used to predict neurodevelopmental disorders. Comparative experiments demonstrated that using the feature creation method outperformed other machine learning methods for predicting neurodevelopmental disorders. In many cases, the reduction in the test error reaches up to 65% to the next better one.
On the Verification of the Correctness of a Subgraph Construction Algorithm

We automatically verify the crucial steps in the original proof of correctness of an algorithm which, given a geometric graph satisfying certain additional properties removes edges in a systematic way for producing a connected graph in which edges do not (geometrically) intersect. The challenge in this case is representing and reasoning about geometric properties of graphs in the Euclidean plane, about their vertices and edges, and about connectivity. For modelling the geometric aspects, we use an axiomatization of plane geometry; for representing the graph structure we use additional predicates; for representing certain classes of paths in geometric graphs we use linked lists.
Evaluating Recursive Blind Forecast Against API and Baseline: A Puerto Rican Case Study on Solar Irradiance for Normal and Extreme Weather

This paper leverages ongoing work in a community microgrid in Adjuntas, Puerto Rico to forecast global horizontal irradiance (GHI) and compare performance in normal and extreme weather. Given a positive correlation of 0.98 between GHI and PV power, forecasting GHI can be an effective, indirect forecast of photovoltaic (PV) power, especially in microgrids where the end-users, owners, operators, or other stakeholders are reluctant to share data for training or validation due to privacy and security concerns. A recursive one-shot (termed as "blind") forecast is, hence, formulated, wherein a gradient-boosted regression tree (GBR) is built to forecast GHI for a 7-day horizon in normal weather, and a 2-day horizon in extreme weather. To demonstrate its resilience, the architecture is trained on normal and hurricane weather GHI from 2002-2022. It is generalized on February 9-16, 2023, and on the landfall of Hurricane Nicole (Nov 4-5, 2022), respectively. Forecasts from GBR are compared against that from a satellite-based API resource and three baselines: persistence, averaging, and exponential smoothing. Results show GBR and persistence outperform sophisticated API in both types of weather for this case study.
Use of Augmented Reality to Integrate Knowledge

Augmented reality (AR) has been shown to have a positive effect on motivation and learning. Yet, reviews of such findings continue show that AR research is being conducted within a narrow scope of learning processes. The current qualitative, exploratory case study took place in a primary school, where pupils aged 10–11 years used AR technology to summarise a learning session. The study applies a socio-constructive perspective focusing on active and collaborative learning to investigate the process. Detailed interaction analysis of talk and activities was used to investigate the extent to which and how the pupils used an AR application to integrate knowledge from the learning session. The findings show that the pupils expanded issues, made relations between them and put them into a larger context in a way that can be characterised as a process of integrating knowledge.
Hybrid Educational Environments – Using IoT to detect emotion changes during student interactions

The use of hybrid educational environments, especially after COVID-19 pandemic, has intensified leading to new pedagogic challenges as the integration of biometric sensors into learning processes. Instructors must adapt their methods so that teaching and its quality are not negatively affected. The aim of this study is to enhance the understanding of students’ learning experiences by analysing biometric data during different learning activities. This paper focuses on the use of an Internet of Things (IoT) device, to collect Galvanic Skin Response (GSR) and Heart rate (HR) levels, in addition to biometric data. The quantitative analysis of the collected data shows a correlation between the data extracted that allow us to detect changes on students’ emotions. Subsequently the data analysis is used by instructors to provide formative feedback to enhance student learning, benefiting learners in terms of self-directed learning and motivation which can help them to improve their performance. The paper illustrates a case study of a hybrid learning university learning activity adopted in undergraduate programmes.
Teaching Design of Technologies for Collaborative Interaction in Physical-Digital Environments

There is an increasing awareness of the importance of considering and mediating human collaboration in the design of technology. There are several research approaches focused on this, such as e.g., computer supported cooperative work (CSCW) and computer supported cooperative learning (CSCL), however, less attention has been given to developing educational materials for the role that technology play in mediating human collaboration in human-computer interaction (HCI) in physical, digital and hybrid spaces, why hands-on teaching activities are insufficient. In this paper, we present an emergent pedagogical framework on teaching for collaborative interaction in HCI, consisting of a collection of eleven pedagogical patterns. The paper is based on results from applying a modified pedagogical design pattern approach in the iterative development of an open educational resource containing teaching patterns, and from pilot testing of patterns with students. Preliminary results from pilot testing indicate that teachers experience an increased capacity to teach design of technologies that mediate human collaborative interaction in relevant and innovative ways. As part of “Design Education for Hybrid Environments” special issue, we hope this emerging pedagogical framework can provide teachers with inspiration to sensitize their students and make them better prepared to become responsible designers of technologies for collaborative interaction and contribute crafting next-generation physical, digital and hybrid spaces.
Division of Regulatory Power: Collaborative Regulation for Privacy-Preserving Blockchains

Decentralized anonymous payment schemes may be exploited for illicit activities, such as money laundering, bribery and blackmail. To address this issue, several regulatory-friendly decentralized anonymous payment schemes have been proposed. However, most of these solutions lack restrictions on the regulator’s authority, which could potentially result in power abuse and privacy breaches. In this paper, we present a decentralized anonymous payment scheme with collaborative regulation (DAPCR). Unlike existing solutions, DAPCR reduces the risk of power abuse by distributing regulatory authority to two entities: Filter and Supervisor, neither of which can decode transactions to access transaction privacy without the assistance of the other one. Our scheme enjoys three major advantages over others: ① Universality, achieved by using zk-SNARK to extend privacy-preserving transactions for regulation. ② Collaborative regulation, attained by adding the ring signature with controllable linkability to the transaction. ③ Efficient aggregation of payment amounts, achieved through amount tags. As a key technology for realizing collaborative regulation in DAPCR, the ring signature with controllable linkability (CLRS) is proposed, where a user needs to specify a linker and an opener to generate a signature. The linker can extract pseudonyms from signatures and link signatures submitted by the same signer based on pseudonyms, without leaking the signer’s identity. The opener can recover the signer’s identity from a given pseudonym. The experimental results reflect the efficiency of DAPCR. The time overhead for transaction generation is 1231.2 ms, representing an increase of less than 50% compared to ZETH. Additionally, the time overhead for transaction verification is only 1.2 ms.
Blockchain-Based Governance Models Supporting Corruption-Transparency: A Systematic Literature Review

Corruption and lack of transparency remain critical challenges in governance systems around the world. These issues are often perpetuated by centralized systems and their manipulation by system administrators. Furthermore, the lack of data ownership and the monetization of user data by tech companies further increase concerns about transparency. In light of these concerns, this study aims to review existing blockchain-based governance models and identify best-practice governance models focusing on corruption transparency, their characteristics, and components. The research will also examine the role of a token economy in addressing trusted third-party issues related to asset ownership management. Furthermore, we discuss the effect of smart contracts, blockchain, decentralized autonomous organizations (DAO), Web 3.0, and multifactor challenge set self-sovereign identity authentication (MFSSIA) as modern technologies to combat corruption and achieve transparency in the public sector. To achieve this, we conduct a systematic literature review (SLR) comprising peer-reviewed journals, proceedings, and book chapters published between 2012 and 2023. Using the SLR methodology, 45 primary and supporting studies have been selected for result extraction and analysis. Finally, we discovered seven blockchain-based governance models with their characteristics and primary components.
Development of Management Information System Based on Android Application at Al-Anwari High School

This study aims to portray the development of information systems at SMA al-Anwari Bangkalan based on android applications. The research method uses a descriptive qualitative approach. The results show that the Development of Management Information System (SIM) Based on Android Application at Al-Anwari High School reflects the progressive transformation in the world of education towards the digital era. This article investigates the needs, benefits, and main functions of this application, as well as discussing system integration, security and privacy aspects, and the importance of training and mentoring. The implementation of this app at Al-Anwari High School brings significant benefits to teachers, students, parents and administrative staff by providing easy, quick and integrated access to academic and school management information. The main functions of the application involve schedule management, attendance recording, academic information, parent-teacher communication, and extracurricular management.
VISIONE for newbies: an easier-to-use video retrieval system

This paper presents a revised version of the VISIONE video retrieval system, which offers a wide range of search functionalities, including free text search, spatial color and object search, visual and semantic similarity search, and temporal search. The system is designed to ensure scalability using advanced indexing techniques and effectiveness using cutting-edge Artificial Intelligence technology for visual content analysis. VISIONE was the runner-up in the 2023 Video Browser Showdown competition, demonstrating its comprehensive video retrieval capabilities. In this paper, we detail the improvements made to the search and browsing interface to enhance its usability for non-expert users. A demonstration video of our system with the restyled interface, showcasing its capabilities on over 2,300 hours of diverse video content, is available online at https://youtu.be/srD3TCUkMSg.
User Relevance Feedback and Novices: Anecdotes from Exquisitor's Participation in Interactive Retrieval Competitions

User relevance feedback is an important component of the multimedia analytics toolbox. In the Exquisitor project, we have primarily focused on the scalability of user relevance feedback over media collections. We have also, however, developed a proof-of-concept user interface and applied the system in interactive retrieval competitions. In this short paper, we describe the main user interface components of the Exquisitor system, along with anecdotal evidence about their effectiveness, or lack thereof.
Novice-Friendly Text-based Video Search with vitrivr

Video retrieval still offers many challenges which can so far only be effectively mediated through interactive, human-in-the-loop retrieval approaches. The vitrivr multimedia retrieval stack offers a broad range of query mechanisms to enable users to perform such interactive retrieval. While these multiple mechanisms offer various options to experienced users, they can be difficult to use for novices. In this paper, we present a minimal user interface geared towards novice users that only exposes a subset of vitrivr’s functionality but simplifies user interaction.