Reviewerly/exHarmony huggingface dataset:
Gradient COBRA: A kernel-based consensual aggregation for regression

In this article, we introduce a kernel-based consensual aggregation method for regression problems. We aim to flexibly combine individual regression estimators r1, ..., rM using a weighted average where the weights are defined based on predicted features given by all the basic estimators and some kernel function. This work extends the context of Biau et al. (2016) to a more general kernel-based framework. We show that this more general configuration also inherits the consistency of the basic consistent estimators, and the same convergence rate as in the classical method is achieved. Moreover, an optimization method based on gradient descent algorithm is proposed to efficiently and rapidly estimate the key parameter of the strategy. Various of numerical experiments carried out on several simulated and real datasets are also provided to illustrate the efficiency and accuracy of the method with the introduction of gradient descent algorithm and smoother kernel functions respectively. Moreover, a domain adaptation-like property of the method is also illustrated on a physics data provided by Commissariat à l'Énergie Atomique (CEA).
Real-time Traffic Monitoring System Based on Deep Learning and YOLOv8

Computer vision applications are important nowadays because they provide solutions to critical problems that relate to traffic in a cost-effective manner to reduce accidents and preserve lives. This paper proposes a system for real-time traffic monitoring based on cutting-edge deep learning techniques through the state-of-the-art you-only-look-once v8 algorithm, benefiting from its functionalities to provide vehicle detection, classification, and segmentation. The proposed work provides various important traffic information, including vehicle counting, classification, speed estimation, and size estimation. This information helps enforce traffic laws. The proposed system consists of five stages: The preprocessing stage, which includes camera calibration, ROI calculation, and preparing the source video input; the vehicle detection stage, which uses the convolutional neural network model to localize vehicles in the video frames; the tracking stage, which uses the ByteTrack algorithm to track the detected vehicles; the speed estimation stage, which estimates the speed for the tracked vehicles; and the size estimation stage, which estimates the vehicle size. The results of the proposed system running on the Nvidia GTX 1070 GPU show that the detection and tracking stages have an average accuracy of 96.58% with an average error of 3.42%, the vehicle counting stage has an average accuracy of 97.54% with a 2.46% average error, the speed estimation stage has an average accuracy of 96.75% with a 3.25% average error, and the size estimation stage has an average accuracy of 87.28% with a 12.72% average error.
GTRL: An Entity Group-Aware Temporal Knowledge Graph Representation Learning Method

Temporal Knowledge Graph (TKG) representation learning embeds entities and event types into a continuous low-dimensional vector space by integrating the temporal information, which is essential for downstream tasks, e.g., event prediction and question answering. Existing methods stack multiple graph convolution layers to model the influence of distant entities, leading to the over-smoothing problem. To alleviate the problem, recent studies infuse reinforcement learning to obtain paths that contribute to modeling the influence of distant entities. However, due to the limited number of hops, these studies fail to capture the correlation between entities that are far apart and even unreachable. To this end, we propose GTRL, an entity Group-aware Temporal knowledge graph Representation Learning method. GTRL is the first work that incorporates the entity group modeling to capture the correlation between entities by stacking only a finite number of layers. Specifically, the entity group mapper is proposed to generate entity groups from entities in a learning way. Based on entity groups, the implicit correlation encoder is introduced to capture implicit correlations between any pairwise entity groups. In addition, the hierarchical GCNs are exploited to accomplish the message aggregation and representation updating on the entity group graph and the entity graph. Finally, GRUs are employed to capture the temporal dependency in TKGs. Extensive experiments on six real-world datasets demonstrate that GTRL achieves the state-of-the-art performances on the event prediction task, outperforming the best baseline by an average of 7.35%, 6.09%, 8.31%, and 11.21% in MRR, Hits@1, Hits@3, and Hits@10, respectively.
An Intersection Attack on the CirclePIN Smartwatch Authentication Mechanism

We present a thorough security analysis of a recent smartwatch authentication mechanism called CirclePIN, which was considered resilient to several attacks including shoulder surfing and video recording. This mechanism avoids the direct entry of the personal identification number (PIN) by using consecutive screens of random colors that fool the attacker. We disclose a vulnerability in CirclePIN inherent to the way in which the users match the random colors to their PINs’ digits and we illustrate how to exploit it with an intersection attack. This attack uses the information extracted from multiple video recordings of legitimate authentication sessions. We prove that it has a high probability of revealing the user PIN with only three video recordings and always succeeds with five. Our proof is twofold. We formulate the theoretical probability of success for the attack as a function of the number of available video recordings. Then, we validate this formula with a simulation of a large number of attacks to compute their experimental probability of success. In our estimation, manual information extraction takes around one minute per exploitable video recording. So, a complete intersection attack is cost effective in terms of time, as it lasts five minutes or less.
Conditional Video Diffusion Network for Fine-grained Temporal Sentence Grounding

Temporal sentence grounding (TSG) aims to locate a semantically related segment of an untrimmed video guided by a sentence query. Since the untrimmed videos are too long, almost all existing TSG works first sparsely down-sample each video into a shorter video of a fixed length and then conduct multimodal interactions with the query sentence for reasoning. However, this video down-sampling process may introduce a challenging issue that confuses the latter grounding process: Due to the video down-sampling, some query-related frames may be filtered out; this process may remove the specific boundary frames of the target segment and take the adjacent irrelevant frames as new boundaries, easily leading to cross-modal misalignment and introducing both boundary-bias and reasoning-bias. Therefore, it is important to keep the grounding consistency (both temporal annotations and boundary predictions) between the original and the sampled videos. To this end, in this paper, we propose a novel Conditional Video Diffusion Network (CVDN) for TSG to learn extra visual semantics to enrich and refine the biased new boundaries, which enables soft-label boundary prediction for fine-grained frame-query reasoning. Specifically, we first construct a conditional video diffusion model which is separately trained to recover the consecutive semantics of the filtered frames between the adjacent sampled frames. Through the designed stochastic interval sampling strategies in the training process, this diffusion model is able to generate absent coherent semantics between the sparsely sampled frames and in turn enrich and refine them, benefiting the integral activity understanding for TSG. In this manner, the incorrect new boundaries will be refined to be closely correlated to the original boundary frames and contain sufficient query-related information, which is crucial for accurate segment prediction. Extensive experiments on three challenging datasets demonstrate the effectiveness of CVDN.
Smart Empowerment Promotes the High-Quality and Balanced Development of Educational Collectivization

The Lanting Primary School implements educational collectivization in which the management mechanism of each campus is different. The school faces many problems and challenges. The allocation of educational resources is uneven. The teachers are unbalanced. The level of students is uneven. The traditional face-to-face management methods, the simple lecturing teaching method of teachers, and the unbalanced development state of students’ literacy in all aspects.
Selected Practice Briefs on Regional Smart Education Construction

In order to achieve the overall goal of smart education in Dongcheng District, in view of problems such as inconsistent data standards, poor linkage, and failure to exert the value of data assets, the District Education Committee actively promotes the construction and application of “data brain” to provide data services and platform support for the high-quality development of regional education.
Constructing Regional Smart Education Ecosystem: Policy, Technology, and Practices

At present, the Internet, big data, cloud computing, artificial intelligence, blockchain, and other new generation of information technologies are having a significant and far-reaching impact on economic development and social progress, and pushing human society into an intelligent era of human–machine cooperation, cross-border integration, co-creation and sharing of knowledge.
Create National Smart Education Demonstration Zone to Promote Digital Transformation

Haidian District is a strong science and technology district in Beijing and the largest basic education district in the city. Based on the idea of “service first, application king,” Haidian District explores the construction of new infrastructure, education governance system, education teaching application model and so on based on smart education.
Reflections on Integrating Interaction to Build a New Normal of Teaching in the Post-epidemic Era

Through the questionnaire survey and research on the blended teaching of basic education teachers in Dongcheng District, it is found that the blended teaching of teachers is difficult to achieve classroom interaction and online and offline integration, and students’ online learning is monitored Based on the research and analysis, a series of strategies and suggestions to promote the blended teaching of primary and secondary school teachers in the region are proposed.
Promoting the Popularization of Artificial Intelligence Education

Qingdao takes the promotion of educational informatization 2.0 as the core, the reform and innovation as the driving force, and the deep integration of educational informatization and education and teaching as the center, and has taken a path of educational informatization in Qingdao.
Smart Teaching Assistants Empower Teachers to Do Well in Every Class

The smart teaching assistants of Onion Academy are mainly supported by powerful digital educational resources and personalized teaching applications.
Construction and Application Practice Exploration of “District-School Integration” Smart Education Cloud Platform

With the development of education informatization, it has entered 2 in stage 0, the regional smart education platform plays an important role in promoting regional education development, optimizing education governance, deepening educational reform, and helping the implementation of “double reduction”.
Artificial intelligence (AI) for neurologists: do digital neurones dream of electric sheep?

Artificial intelligence (AI) is routinely mentioned in journals and newspapers, and non-technical outsiders may have difficulty in distinguishing hyperbole from reality. We present a practical guide to help non-technical neurologists to understand healthcare AI. AI is being used to support clinical decisions in treating neurological disorders. We introduce basic concepts of AI, such as machine learning and natural language processing, and explain how AI is being used in healthcare, giving examples its benefits and challenges. We also cover how AI performance is measured, and its regulatory aspects in healthcare. An important theme is that AI is a general-purpose technology like medical statistics, with broad utility applicable in various scenarios, such that niche approaches are outpaced by approaches that are broadly applicable in many disease areas and specialties. By understanding AI basics and its potential applications, neurologists can make informed decisions when evaluating AI used in their clinical practice. This article was written by four humans, with generative AI helping with formatting and image generation.
About two indicators of the school digital renewal process

The indicators of the process of digital technologies introduction (school digital renewal indicators) widely used today successfully capture the innovative processes that occur at the initial stages (levels) of digital renewal. At the stages of mature informatization and digital transformation, the changes associated with the expansion of the traditional classroom system and the transition to personalized and competency-based learning organization come to the fore. The authors propose two indicators that capture changes in the chronotope of study work and the teaching individualization level. The proposed indicators allow to reflect the dynamics of changes at the final stages of digital renewal. The use of school portals, which are being introduced at the final stages of digital renewal, provides conditions for assessing changes in the proposed indicators using digital traces, without contacting (surveys, questionnaires) the educational process participants. The development of bases, methods, and tools for calculating the proposed indicators is of theoretical and practical interest for solving the problems of managing the digital renewal of education at the final stages of this process.
Model and digital platform of the educational ecosystem for preadaptation of schoolchildren to innovative activities

The article presents the results of designing and piloting the model of the educational ecosystem and digital platform for preadaptation of schoolchildren to innovative activity. The relevance of the research is determined by the necessity to switch educational strategies from adaptive to preadaptive ones and to eliminate its isolation from the surrounding reality in conditions of increasing complexity and unpredictability of the world and the processes taking place in it, as well as the formation of a new system of division of labor with an emphasis on the production of knowledge. The system inclusion of young people in real activities makes it possible to form different types of thinking, and according to A. G. Asmolov, “competencies of changing competencies in a changing world”. The education system as an active partner in the development of society, operating on the principles of anthropological turn, preadaptation, entrepreneurial thinking, the authenticity of actions, educational subjectivity, and personalization is the object of this study. The subject of the research is a model and a digital platform for the preadaptation of schoolchildren for innovative activities. The results of the project can be used in the future to organize real project activities in child-adult communities in the field of territorial development, and social and technological entrepreneurship, as well as to attract motivated personnel to the regional economy through new educational opportunities.
Self-education as a leading trajectory for training finalists of the second All-Russian Olympiad in Artificial Intelligence for schoolchildren

The article presents the analysis of survey of finalists of the second All-Russian Olympiad in Artificial Intelligence for schoolchildren, held in the fall of 2022. The hypothesis of the study was the assumption of the leading role of self-education of schoolchildren in the field of studying intellectual technologies at the present stage. Analyzing the answers of the finalists, we determined that basic knowledge in four subjects of the general education cycle (mathematics, computer science, physics, and English) forms the basis for self-education in the process of preparation for the Olympiad. When solving new tasks for oneself, along with the assimilation of concepts and generalized methods of action, the student changes, transforming into a subject of educational activity. We have formed a rating of factors that helped the finalists in preparing for the finals of the Olympiad. The finalist’s awareness of their educational preferences allows them to build cognitive strategy, which is understood as the fulfillment of a specific set of tasks and a sequence of aimed at achieving an educational goal. The success of the self-educational trajectory is ensured by the observance of three principles of its implementation: obtaining high-quality subject knowledge, transforming the Olympiad finalist into a subject of educational activity, and building an individual strategy of self-determination.
Improving student learning efficiency using an online simulator for solving unconditional optimization problems

The article describes the process of developing an online simulator for solving uconditional optimization problems using the Python programming language and the Flask framework. The functional requirements for the simulator as an information system are considered. In the course of the research models describing the system were created and algorithms of its operation were built. The simulator reproduces the process of step-by-step training of two methods for finding the extremum of a function of two variables — the method of finding a stationary point and the Newton’s method. The article considers the process of developing and testing an online simulator. Testing of the online simulator was carried out on the basis of Murom Institute (branch) of the Vladimir State University within the discipline “Decision Theory”. The test confirmed that the simulator allows to consolidate the studied material, to acquire skills in solving problems on finding the extremum of a function of two variables, to track errors in the course of solving and to see the results of checking the solution of problems. The simulator generates equations by templates, which leads to a high variability of tasks for students. At the same time, the program can automatically step by step check the solution of the problem by the student, which leads to a decrease in the routine workload on the teacher in terms of checking solutions.
The TikTok of Teaching: The Pedagogical Possibilities of Collaborative Digital Ethnography

Working collaboratively with students, lecturers, and others, to conduct digital ethnography enriches the ethnographies produced, expands pedagogical possibilities, and allows us to rethink how we teach and do research in anthropology. This Special Issue is an output of such a collaborative attempt. In this Editorial we introduce the TikTok Ethnography Collective and the collaborative mode of research and learning we established in September 2020. The articles we have collated demonstrate that collaborative ethnographic methods are the ideal tool for researching algorithmically shaped digital spaces. But more than this, by sharing our collective experience, we make the case for incorporating collaborative methods into anthropological teaching and learning in order to disrupt traditional, hierarchical models of education and research. We propose that inclusion of students in the research process is imperative for facilitating a safe, creative sandbox environment that allows staff and students to explore and formulate theories and reflections somewhat liberated from the expectations around who should and should not be the expert. We invite readers to join us in considering the broader implications of embracing collaborative research and teaching methods.
Multi-view deep subspace clustering with multilevel self-representation matrix fusion

Currently, multi-view deep subspace clustering algorithms have achieved remarkable success. However, existing methods have not yet focused on comprehensive information mining within the deep encoder for clustering performance improvement. To this end, this paper presents a novel multi-view deep subspace clustering algorithm with multilevel self-representation matrix fusion. Specifically, the valid clustering information contained within the deep encoder is deeply mined by a multilevel self-representation matrix extraction module. At the same time, diversity and consistency constraints are used to differentiate the clustering information contained in the self-representation matrix (deep and shallow) for each view. Further, through the multilevel self-representation matrix fusion module, the various types of self-representation matrices are effectively fused: the deep and shallow self-representation matrices of each view and the consistency representation matrix to obtain a higher quality and more comprehensive self-representation coefficient matrix, which effectively improves the clustering performance. Experimental and visual analyses of four datasets show that the clustering performance of the proposed method is significantly better than some existing state-of-the-art methods.
SIRR: Semantically Infused Recipe Recommendation Model Using Ontology Focused Machine Intelligence

In the current era, recipe recommendation is of extreme significance as there is no expert intelligence system specifically for recommendation. In the era of Web 3.0, semantically driven frameworks are mostly absent. As a result, semantically infused recipe recommendation model is highly important. This study proposes a semantically infused intelligent approach to recommend recipes using Ontology Focused Machine Intelligence to classify the documents. The semantic similaritySemantic Similarity was computed by hybridizing Normalized Compression Distance (NCDNormalized Compression Distance (NCD)) and KL divergenceKL Divergence under a cultural algorithmCultural Algorithm. The model uses both Recurrent Neural NetworkRecurrent Neural Network (RNN) and XGBoostXGBoost at different locations to sort the data and compute semantic similaritySemantic Similarity. The model yielded an FDR of 0.04, and an accuracy of 97.09%.