Reviewerly/exHarmony huggingface dataset:
Early Literacy Assessment among Kindergarten Teachers in Indonesia: A Phenomenological Study

&lt;p style="text-align:justify"&gt;The study aims to identify how kindergarten teachers perform early literacy assessments in the classroom and the challenges these teachers have in performing such assessments. During the study, the data were gathered through an in-depth interview in the form of a forum group discussion (FGD). Then, the phenomenological data were attained from 30 public and private Kindergarten teachers. The researchers could illustrate how these kindergarten teachers assessed their children's early literacy through these data. Furthermore, the study results show that the teachers' literacy knowledge has been sufficient and that the literacy programs for the children have been variously designed in each school. The teachers' techniques in performing the early literacy assessment are observation and documentation (portfolio), and the measurement of literacy skills itself refers to the scope of literacy. Concerning the findings, numerous obstacles and expectations that kindergarten teachers have are also discussed within the study.&lt;/p&gt;
Concept Propagation via Attentional Knowledge Graph Reasoning for Video-Text Retrieval

Due to the rapid growth of online video data, video-text retrieval techniques are in urgent need, which aim to search for the most relevant video given a natural language caption and vice versa. The major challenge of this task is how to identify the true fine-grained semantic correspondence between videos and texts, using only the document-level correspondence. To deal with this issue, we propose a simple yet effective two-stream framework which takes the concept information into account and introduces a new branch of semantic-level matching. We further propose a concept propagation mechanism for mining the latent semantics in videos and achieving enriched representations. The concept propagation is achieved by building a commonsense graph distilled from ConceptNet with concepts extracted from videos and captions. The original concepts of videos are detected by pretrained detectors as the initial concept representations. By conducting attentional graph reasoning on the commonsense graph with the guidance of external knowledge, we can extend some new concepts in a detector-free manner for further enriching the video representations. In addition, a propagated BCE loss is designed for supervising the concept propagation procedure. Common space learning is then constructed for cross-modal matching. We conduct extensive experiments on various baseline models and several benchmark datasets. Promising experimental results demonstrate the effectiveness and generalization ability of our method.
Curriculum-NAS: Curriculum Weight-Sharing Neural Architecture Search

Neural Architecture Search (NAS) is an effective way to automatically design neural architectures for various multimedia applications. Weight-sharing, as one of the most popular NAS strategies, has been widely adopted due to its search efficiency. Existing weight-sharing NAS methods overlook the influence of data distribution and treat each data sample equally. Contrastively, in this paper, we empirically discover that different data samples have different influences on architectures, e.g., some data samples are easy to fit by certain architectures but hard by others. Hence, there exist architectures with better performances on early data samples being more likely to be discovered in the whole NAS searching process, which leads to a suboptimal searching result. To tackle this problem, we propose Curriculum-NAS, a curriculum training framework on weight-sharing NAS, which dynamically changes the training data weights during the searching process. In particular, Curriculum-NAS utilizes the multiple subnets included in weight-sharing NAS to jointly assess data uncertainty, which serves as the difficulty criterion in a curriculum manner, so that the potentially optimal architectures can obtain higher probability of being fully trained and discovered. Extensive experiments on several image and text datasets demonstrate that our Curriculum-NAS can bring consistent improvement over existing weight-sharing NAS. The code is available online at https://github.com/zhouyw16/curriculum-nas.
InDiD: Instant Disorder Detection via a Principled Neural Network

For sequential data, a change point is a moment of abrupt regime switch in data streams. Such changes appear in different scenarios, including simpler data from sensors and more challenging video surveillance data. We need to detect disorders as fast as possible. Classic approaches for change point detection (CPD) might underperform for semi-structured sequential data because they cannot process its structure without a proper representation. We propose a principled loss function that balances change detection delay and time to a false alarm. It approximates classic rigorous solutions but is differentiable and allows representation learning for deep models. We consider synthetic sequences, real-world data sensors and videos with change points. We carefully labelled available video data with change point moments and released it for the first time. Experiments suggest that complex data require meaningful representations tailored for the specificity of the CPD task --- and our approach provides them outperforming considered baselines. For example, for explosion detection in video, the F1 score for our method is 0.53 compared to baseline scores of 0.31 and 0.35.
The ACM Multimedia 2022 Deep Video Understanding Grand Challenge

This is the overview paper for the Deep Video Understanding (DVU) Grand Challenge. In recent years, a growing trend towards working on understanding videos (in particular movies) to a deeper level started to motivate researchers working in multimedia and computer vision to present new approaches and datasets to tackle this problem. This is a challenging research area which aims to develop a deep understanding of the relations which exist between different individuals and entities in movies using all available modalities such as video, audio, text and metadata. The aim of this grand challenge is to foster innovative research in this new direction and to provide benchmarking evaluations to advance technologies in the deep video understanding community.
Scheduled Line of Symmetry Solar Tracker with MPT and IoT

The enormous ability to generate the sustainable and eco-friendly energy has increased because, there have been energy shortages in small and developing countries due to the shortage and depletion of non-renewable energy resources in the nature, and hence there is a need for the usage for the renewable energy which will help to reduce the emission of greenhouse gases, global warming and help in the climate change which are the major threats in the future. Among all renewable energies, the solar energy is one of the biggest and eco-friendly energy generators to the world. However, since many years solar energy is being used but not in a complete effective way. The Photovoltaic Solar Systems (PSS) suffer from insufficient sunrays due to the orientation angle i.e., the sunrays are not able to be acquired by the solar panel after a certain period of time because, the solar panel is fixed in one direction and it cannot be moved as the sun rotates, due to which maximum utilization of energy does not occur. To overcome this challenge, a solar tracker has been designed which tracks the exact polar co-ordinates using the existing data and instructs the solar panel to rotate in the direction of sunlight so that the maximum energy can generate the electricity. Moreover, the Maximum Power Tracking (MPT) controller has been used to achieve the maximum energy storage without wasting the generated energy from the solar panel.
Continual Referring Expression Comprehension via Dual Modular Memorization

Referring Expression Comprehension (REC) aims to localize an image region of a given object described by a natural-language expression. While promising performance has been demonstrated, existing REC algorithms make a strong assumption that training data feeding into a model are given upfront, which degrades its practicality for real-world scenarios. In this paper, we propose Continual Referring Expression Comprehension (CREC), a new setting for REC, where a model is learning on a stream of incoming tasks. In order to continuously improve the model on sequential tasks without forgetting prior learned knowledge and without repeatedly re-training from a scratch, we propose an effective baseline method named Dual Modular Memorization (DMM), which alleviates the problem of catastrophic forgetting by two memorization modules: Implicit-Memory and Explicit-Memory. Specifically, the former module aims to constrain drastic changes to important parameters learned on old tasks when learning a new task; while the latter module maintains a buffer pool to dynamically select and store representative samples of each seen task for future rehearsal. We create three benchmarks for the new CREC setting, by respectively re-splitting three widely-used REC datasets RefCOCO, RefCOCO+ and RefCOCOg into sequential tasks. Extensive experiments on the constructed benchmarks demonstrate that our DMM method significantly outperforms other alternatives, based on two popular REC backbones. We make the source code and benchmarks publicly available to foster future progress in this field: https://github.com/zackschen/DMM.
DIGITAL LITERACY OF TEACHERS

Отмечается, что переход на дистанционный формат обучения в период пандемии выявил низкий уровень цифровой грамотности педагогов, что препятствует эффективной организации учебного процесса инструментами онлайн. Акцентируется внимание на недостаточности разработки критериев и индикаторов цифровой грамотности среди педагогов, отсутствии ее мониторинга в организациях общего образования. Анализируются подходы зарубежных и российских исследователей к определению содержания понятия цифровой грамотности как ориентира цифровой трансформации системы образования. На основе самооценки педагогов определяются дефициты в их цифровой подготовке в условиях организации дистанционного обучения в системе общего образования на базе анализа ситуации в Сибирском федеральном округе. Предлагается предварительная сравнительная оценка структуры и уровня цифровой грамотности учителей, полученная на основе самоанализа адаптационных образовательных практик педагогов в период пандемического кризиса. It is noted that the transition to a distance learning format during the pandemic revealed a low level of digital literacy of teachers, which hinders the effective organization of the educational process with online tools. Attention is focused on the lack of development of criteria and indicators of digital literacy among teachers, the lack of its monitoring in general education organizations. The approaches of foreign and Russian researchers to the definition of the content of the concept of digital literacy as a benchmark for the digital transformation of the education system are analyzed. The authors adhered to an expanded interpretation of digital literacy as an element of mastering the culture of the digital age, which implies the presence of broad competencies in the field of search, production, communication and distribution of digital content and information, as well as the conversion of digital skills into pedagogical technologies. In order to identify the understanding of the structure of digital literacy by teachers in the context of limited remote interaction using digital means, the authors used the focus group method in an online format to conduct the study. Based on self-assessment of teachers, deficits in digital pedagogical training of teachers are determined in the context of organizing distance learning in the system of general education based on an analysis of the situation in the Siberian Federal District. A preliminary comparative assessment of the structure and level of digital literacy with students, obtained on the basis of self-analysis of the adaptive educational practices of teachers during the pandemic crisis, is proposed.
A non-intrusive and adaptive speaker de-identification scheme using adversarial examples

Faced with the threat of identity leakage during voice data publishing, users are engaged in a privacy-utility dilemma while enjoying convenient voice services. Existing studies employ direct modification or text-based re-synthesis to de-identify users' voices, but resulting in inconsistent audibility for human participants and not adaptive to informed attacks. In this poster, we propose a non-intrusive and adaptive speaker de-identification scheme to balance the privacy and utility of voice services. We generate adversarial examples to conceal user identity from exposure by Automatic Speaker Identification (ASI). By learning a compact distribution with a conditional variational auto-encoder, our system enables on-demand target sampling and diverse identity transformation. We also introduce the acoustic masking effect to construct inaudible perturbations, thus preserving the speech content and perceptual quality. Experiments on 50 speakers show our system could achieve 98.2% successful de-identification on 4 mainstream ASIs with an objective perceptual quality of 4.38 and a subjective mean opinion score of 4.56.
GBERT

Due to the prevalence of group activities on social networks, group recommendations have received an increasing number of attentions. Most group recommendation methods concentrated on dealing with persistent groups, while little attention has paid to ephemeral groups. Ephemeral groups are formed ad-hoc for one-time activities, and therefore they suffer severely from data sparsity and cold-start problems. To deal with such problems, we propose a pre-training and fine-tuning method called GBERT for improved group recommendations, which employs BERT to enhance the expressivity and capture group-specific preferences of members. In the pre-training stage, GBERT employs three pre-training tasks to alleviate data sparsity and cold-start problem, and learn better user representations. In the fine-tuning stage, an influence-based regulation objective is designed to regulate user and group representations by allocating weights according to each member's influence. Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for ephemeral group recommendations.
A study on the correlation between seat selection and interaction preference in virtual-reality fusion simulation experiment

In order to explore the correlation between students’ seat choice and interaction preference in the open gamification scenario, an experiment has been carried out on the platform of provincial virtual simulation experiment teaching center of a university, and tested the relationship between absolute distance, seat type, workstation type, and students’ interaction preference. The results show that in the virtual-reality fusion gamification scenario where students can move freely: (1) The inner circle students can stimulate the outer circle students’ willingness to invest in learning. (2) The task attribute and the seat distribution of the group may lead to the difference of students’ interaction preference. (3) Students are more likely to learn knowledge and skills by interacting with “people” rather than “object.” (4) Gender and major influence students’ experience of participating in gamified teaching. The results confirm that the interactive engagement effect of location does exist in immersive virtual-reality fusion gamification teaching scenario, and suggestions are put forward to adjust the effect of location through instructional design and teacher intervention.
Automatically Temporal Labeled Data Generation Using Positional Lexicon Expansion for Focus Time Estimation of News Articles

Many facts change over time, which is a fundamental aspect of our physical environment. In the case of pandemic articles, the user is not interested in the creation date of the document, but in the facts and the cause of the last pandemic. Fake news can be better combated by having a document with a temporal focus. Currently, neither the sequence of events nor the temporal focus is considered when obtaining news documents. Despite the limited number of temporal aspects in the available datasets, it is difficult to test and evaluate the temporal conclusions of the model. The goal of this work is to develop a temporal focus news article retrieval model based on co-training to advance research in semi-supervised learning. A mapping of the dataset is performed using 1) the evolving focus time of news articles, 2) the semi-supervised method based on coincidence contexts for learning low-dimensional continuous vectors for learning neural contrast embedding models generating focus time-based query in sequential news articles to facilitate temporal understanding by learning low-dimensional continuous vectors. A diverse dataset of news articles is used to evaluate the effectiveness of the proposed method. With semi-supervised learning and lexicon expansion, the result of the developed model can achieve 89%. The method performed better than previous baselines and traditional machine learning models with improvements of 12.65% and 4.7%, respectively.
Silicon-correlated Simulation Methodology of EM Side-channel Leakage Analysis

Cryptography hardware is vulnerable to side-channel (SC) attacks on power supply current flow and electromagnetic (EM) emission. This article proposes simulation-based power and EM side-channel leakage analysis (SCLA) techniques on a cryptographic integrated circuit (IC) chip in system level assembly. SCLA measures SC leakage metrics including T-score, SC leakage score, and the number of measurement traces to disclosure, leveraged by a secure system-on-chip design flow toward SC attack resiliency and SC leakage sign off. Power SCLA features the tracking of security sensitive registers within cryptographic logic paths and the automatic assignments of probe points on associated physical power nets. Power supply current traces are efficiently simulated for the large set of input payloads, with direct vector-based and vector-less random switching controls. EM SCLA evaluates magnetic fields created by every piece of metal wiring in metal stacks where power supply current of cryptographic processing flows. The EM emission and EM SCLA from the backside Si surface of an IC chip in flip-chip packaging are experimentally examined with a 0.13 μm test chip. The proposed simulation-based SCLA exhibits the SC leakage metrics of on-chip location and direction dependency as accurately as in the measurements.
FocalMatch: Mitigating Class Imbalance of Pseudo Labels in Semi-Supervised Learning

Semi-supervised learning (SSL) is a popular research area in machine learning which utilizes both labeled and unlabeled data. As an important method for the generation of artificial hard labels for unlabeled data, the pseudo-labeling method is introduced by applying a high and fixed threshold in most state-of-the-art SSL models. However, early models prefer certain classes that are easy to learn, which results in a high-skewed class imbalance in the generated hard labels. The class imbalance will lead to less effective learning of other minority classes and slower convergence for the training model. The aim of this paper is to mitigate the performance degradation caused by class imbalance and gradually reduce the class imbalance in the unsupervised part. To achieve this objective, we propose FocalMatch, a novel SSL method that combines FixMatch and focal loss. Our contribution of FocalMatch adjusts the loss weight of various data depending on how well their predictions match up with their pseudo labels, which can accelerate system learning and model convergence and achieve state-of-the-art performance on several semi-supervised learning benchmarks. Particularly, its effectiveness is demonstrated with the dataset that has extremely limited labeled data.
Internet of Things — From Ubiquitous Computing to Ubiquitous Intelligence Applications

ubiquitous ID (uID) technology, automatically identifies physical objects and/or locations by the uIDs assigned to them in order to provide services associated with those physical objects and/or locations. uID architecture is for the realization of “Context Awareness” in Ubiquitous Computing.
Regional metal pollution risk assessment based on a big data framework: A case study of the eastern Tianshan mining area, China

With the increasing maturity of big data technology, its application in the field of environmental assessment has become an important issue. The mining of mineral resource can affect the balance in the ecological environment surrounding mining areas and the normal life activities of humans through groundwater. To solve this problem, big data-based methods were used to evaluate the pollution risk in mining areas in this article. Based on a spatial big data management framework, in this paper, high-risk areas in eastern Tianshan were quantitatively analyzed and delineated via a new comprehensive heavy metal pollution assessment system. A distributed storage environment, unstructured management method and spatial index coding were used to uniformly manage spatial data in vector and raster formats retrieved from different sources. A system involving 18 geological environment indicators was used to evaluate the risk level in the study area and in delineated areas with a high risk of heavy metal pollution. The results indicated that the proposed framework could efficiently store and process spatial data and realize environmental pollution risk assessment in a big data environment. In addition, some of the high-pollution risk areas identified based on the assessment results were consistent with actual mine locations. Overall, the results show that integrating multi-source geological characteristics through a comprehensive evaluation system based on big data can have a positive effect on improving the accuracy of heavy metal pollution risk assessment. Heavy metal pollution assessment can provide a reference for environmental monitoring and governance of the eastern Tianshan region and offer new solutions to large-scale heavy metal pollution assessment.
An Ergonomic Holographic Procedural Monitor for Thyroid Radiofrequency Ablation Using a Mixed-Reality Headset

Thyroid radiofrequency (RF) ablation is an effective treatment option for benign symptomatic thyroid nodules (1). The procedure is performed under ultrasound (US) guidance using the moving-shot technique. RF ablation of large solid nodules exceeding 4 cm in maximal dimension can take an extended time, over 45 minutes, to ablate most of the nodule. Continuous US monitoring is performed throughout the procedure to assess the progress of ablation. Moreover, the impedance from the RF ablation generator is monitored for roll-off and charring of tissue.