Reviewerly/exHarmony huggingface dataset:
Research on the Construction of the Teaching Resource Database of “Computer Application Fundamentals” Based on the Wisdom of Vocational Education Cloud Higher Vocational Preschool Education Major

Based on the wisdom of vocational education cloud higher vocational preschool education major “Computer Application Fundamentals” teaching resource library construction work should be targeted and focused, in this process, schools and teachers need to clarify the specific content and specific direction of the relevant resource library construction. Regard online education as an auxiliary part of offline education, focusing on realizing students’ independent and efficient learning. Based on the development perspective of the Smart Vocational Education Cloud, this paper studies and discusses the direction of the construction of the “Computer Application Fundamentals” teaching resource library for the preschool education major of higher vocational education.
Narrativas de la ciudad bambuquera de américa: proyecto de aula desde la virtualidad

This writing presents the experience carried out through remote access at the Colom-bus American School educational institution with fourth grade students, in the development of the project entitled Narratives of the Bambuquera City of America. Under the Teaching for Understanding (EPC) framework, students converged knowledge and know-how in their learning process.This experience highlights the importance of the student being the protagonist of the class; With this, autonomy, collaborative work, problem solving, decision-making, among others, are promoted. Likewise, the project was of an interdisciplinary nature in which the areas of Natural Sciences, Social Sciences, Spanish language, foreign languages and technology and information technology, which allowed students to get to know and analyze the city of Neiva from different dimensions.
ePose: Let's Make EfficientPose More Generally Applicable.

EfficientPose is an impressive 3D object detection model. It has been demonstrated to be quick, scalable, and accurate, especially when considering that it uses only RGB inputs. In this paper we try to improve on EfficientPose by giving it the ability to infer an object's size, and by simplifying both the data collection and loss calculations. We evaluated ePose using the Linemod dataset and a new subset of it called Occlusion 1-class. We also outline our current progress and thoughts about using ePose with the NuScenes and the 2017 KITTI 3D Object Detection datasets. The source code is available at https://github.com/tbd-clip/EfficientPose.
An Alternative Approach for Computing Discrete Logarithms in Compressed SIDH

Currently, public-key compression of supersingular isogeny Diffie-Hellman (SIDH) and its variant, supersingular isogeny key encapsulation (SIKE) involve pairing computation and discrete logarithm computation. In this paper, we propose novel methods to compute only 3 discrete logarithms instead of 4, in exchange for computing a lookup table efficiently. The algorithms also allow us to make a trade-off between memory and efficiency. Our implementation shows that the efficiency of our algorithms is close to that of the previous work, and our algorithms perform better in some special cases.
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers.

As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem. We introduce MAUVE, a comparison measure for open-ended text generation, which directly compares the learnt distribution from a text generation model to the distribution of human-written text using divergence frontiers. MAUVE scales up to modern text generation models by computing information divergences in a quantized embedding space. Through an extensive empirical study on three open-ended generation tasks, we find that MAUVE identifies known properties of generated text, scales naturally with model size, and correlates with human judgments, with fewer restrictions than existing distributional evaluation metrics.
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set.

We introduce a video compression algorithm based on instance-adaptive learning. On each video sequence to be transmitted, we finetune a pretrained compression model. The optimal parameters are transmitted to the receiver along with the latent code. By entropy-coding the parameter updates under a suitable mixture model prior, we ensure that the network parameters can be encoded efficiently. This instance-adaptive compression algorithm is agnostic about the choice of base model and has the potential to improve any neural video codec. On UVG, HEVC, and Xiph datasets, our codec improves the performance of a low-latency scale-space flow model by between 21% and 26% BD-rate savings, and that of a state-of-the-art B-frame model by 17 to 20% BD-rate savings. We also demonstrate that instance-adaptive finetuning improves the robustness to domain shift. Finally, our approach reduces the capacity requirements on compression models. We show that it enables a state-of-the-art performance even after reducing the network size by 72%.
KEEPING STUDENTS ENGAGED USING THE SOCIAL NETWORK TIK TOK IN PRACTICAL CLASSES OF PHYSICAL THERAPY

E. Falcó, F.B. Pérez Domínguez, J. Casaña Granell, J. C. Benítez MartínezUniversidad de Valencia (UV) (SPAIN)
ONLINE TEACHING AT A SELECTED UNIVERSITY IN THE CZECH REPUBLIC AND ITS TECHNOLOGICAL CLAIM

Education around the world has been affected by the COVID-19 pandemic. The affected institutions who were not prepared for the arrival of such a pandemic were also universities. These universities were forced to teach online on a day-to-day basis. This move to online was challenging not only for teachers but also for students. The aim of the presented article is to evaluate the satisfaction with online teaching and the use of technology at a selected university in the ongoing COVID-19 pandemic. The data was obtained by quantitative research using a questionnaire among respondents (n = 306). The data was collected in 2020 among full-time bachelor’s and master’s degree students at selected university in the Czech Republic. The results demonstrate that 71% of the respondents are satisfied with various aspects of online teaching. More than 80% of respondents use a laptop for online teaching. Over 85% of respondents use WIFI connections and the most used platform for online learning is MSTeams (more than 95% of respondents).
Thought Experiment : Artificial Intelligence Time Travel!

The column focusses on the question: Does the fact that we are co-molded by our digital twins - our quantified self, our abstract existence created by BigTech for advertisers, government agencies, etc. - also mean that some of our individuality and uniqueness is being taken away from us? The answer is yes!
Distance learning of the Ukrainian language: features, benefits and prospects

The article highlights the problem of distance learning of the Ukrainian language in general secondary education. Emphasis is placed on its features and advantages, prospects are described. It is noted that distance learning is a form of learning based on the use of a wide range of traditional and new information and telecommunications technologies, which at different stages of the educational process provide interactive interaction between teachers and students, students and independent work of each other with information network materials. It is emphasized that the obligatory part of the educational process in the conditions of learning with the use of distance technologies is the communication of the teacher with each student. It is clarified that with students who for some reason can not communicate simultaneously, the teacher must agree on a different format of communication. It is emphasized that in addition to providing communication, the use of remote technologies solves the problem of individual approach to students, and also helps to avoid information overload. A system of multilevel exercises and tasks aimed at implementing the competency approach in terms of distance learning is presented. It is noted that the current direction of further research is the analysis of the state of distance education in Ukraine, theoretical substantiation of psychological and pedagogical principles of development and use of elements of distance technologies, in particular in the study of the Ukrainian language
The Application of Collaborative Writing Strategy in Teaching Composition in a Public Junior High School

The qualitative descriptive method was used by the researcher in this study. The term "qualitative descriptive research" refers to a survey study that determined and described the use of a collaborative writing strategy in teaching composition. The study's objectives are to learn how the teacher uses the collaborative writing strategy in teaching composition in a public junior high school, as well as the students' reactions to the strategies used by the teacher in teaching writing in a public junior high school. The teachers and students of UPT SMP Negeri 6 Makassar's ninth grade were the study's subjects. As a result, the researcher chose one of the two teachers at this school who could use Microsoft 365 and two classes, BI.2 and 9. A, as the subjects of the study. According to the findings, the English teacher at UPT SMP Negeri 6 Makassar frequently employs three of five strategies, including horizontal division parallel writing, stratified division parallel writing, and reactive writing strategy. Furthermore, the researcher discovered that students' responses to the strategies that the teacher implemented in teaching writing of collaborative writing gave a positive response toward the collaborative writing strategy that the teacher used, and students' responses to the strategies that the teacher implemented in teaching writing tend to prefer groups in the form of group discussions rather than writing alone because they cannot exchange information and understanding. Because the students' background knowledge is still insufficient, the researcher discovered that group single author writing and sequential single writing are rarely used by the teacher during the research process.
Unsupervised Domain Adaptation with Adapter

Unsupervised domain adaptation (UDA) with pre-trained language models (PrLM) has achieved promising results since these pre-trained models embed generic knowledge learned from various domains. However, fine-tuning all the parameters of the PrLM on a small domain-specific corpus distort the learned generic knowledge, and it is also expensive to deployment a whole fine-tuned PrLM for each domain. This paper explores an adapter-based fine-tuning approach for unsupervised domain adaptation. Specifically, several trainable adapter modules are inserted in a PrLM, and the embedded generic knowledge is preserved by fixing the parameters of the original PrLM at fine-tuning. A domain-fusion scheme is introduced to train these adapters using a mix-domain corpus to better capture transferable features. Elaborated experiments on two benchmark datasets are carried out, and the results demonstrate that our approach is effective with different tasks, dataset sizes, and domain similarities.
TEACHING METHODS AND TECHNIQUES USED DURING ONLINE LITERATURE LESSONS IN THE SLOVAK REPUBLIC

The paper analyses the repertoire of teaching methods and techniques that are used during online literature lessons at secondary level of education in the Slovak Republic. Selection of teaching methods and techniques, which depends on the teacher's teaching style as well as the learning style of students, but also subsequent use of the teaching techniques affect the educational process, the activity of the teacher and the students as well as their interaction. Methods and techniques that seem to be simple and practical in traditional education can be problematic in case of online learning. The effectiveness of e-Learning is determined by insufficient feedback, caused by switched off cameras and microphones of students, but also by the inability to monitor work of students and progress in completing assigned tasks. In e-Learning, face-to-face communication must be substituted with other methods and teaching techniques. The paper is focused on the research of teaching techniques that are used during online literature lessons in the Slovak Republic. Research methods can be characterised as direct structured observation and a questionnaire. That is the reason research data are not only unrivalled but also valid and reliable.
Research Progress on the Relationship between Vascular Aging and Hypertension

Vascular aging refers to the structural and functional changes of the arterial wall with age. Vascular aging plays an important role in elderly diseases, such as hypertension. Therefore, the relationship between vascular aging and hypertension has attracted extensive attention. This article mainly reviews the mechanism of vascular aging and its influence on hypertension, so as to provide new ideas and directions for the research and prevention of hypertension.
THE TREND AND PATH OF THE CONSTRUCTION OF BLENDED TOP-RANKING CURRICULUM IN COLLEGES AND UNIVERSITIES

High-quality blended curriculum is the top priority for course construction in the post-epidemic era, and is also the core of the current work of cultivating first-class talents in institution of higher learning. Researchers use NVivo11.0 to code and analyze the lecture texts of 19 institution of higher learning with nearly 60,000 words blended top-ranking curriculum, and investigate the design and construction of online and offline blended curriculum in colleges and universities. The research analyzes the impact of online and offline blended curriculum construction in colleges and universities from the six dimensions of curriculum type, curriculum teaching design, curriculum teaching philosophy, curriculum teaching time arrangement, curriculum teaching conditions, and curriculum reform effectiveness. The construction path of updating the teaching concept, reconstructing the organic composition of the curriculum and adding Curriculum Supervision is obtained, which provides effective suggestions for the development and construction of online and offline blended curriculum in Colleges and universities in the future.
STRATEGIES OF THE UNIVERSITY STUDENTS DURING THE STATE OF ALARM AND SITUATION OF CONFINEMENT TO FACE PROPERLY THEIR PROCESS OF UNIVERSITY TRAINING

STRATEGIES OF THE UNIVERSITY STUDENTS DURING THE STATE OF ALARM AND SITUATION OF CONFINEMENT TO FACE PROPERLY THEIR PROCESS OF UNIVERSITY TRAINING
SM3D: Simultaneous Monocular Mapping and 3D Detection

Mapping and 3D detection are two major issues in vision-based robotics, and self-driving. While previous works only focus on each task separately, we present an innovative and efficient multi-task deep learning framework (SM3D) for Simultaneous Mapping and 3D Detection by bridging the gap with robust depth estimation and "Pseudo-LiDAR" point cloud for the first time. The Mapping module takes consecutive monocular frames to generate depth and pose estimation. In 3D Detection module, the depth estimation is projected into 3D space to generate "Pseudo-LiDAR" point cloud, where LiDAR-based 3D detector can be leveraged on point cloud for vehicular 3D detection and localization. By end-to-end training of both modules, the proposed mapping and 3D detection method outperforms the state-of-the-art baseline by 10.0% and 13.2% in accuracy, respectively. While achieving better accuracy, our monocular multi-task SM3D is more than 2 times faster than pure stereo 3D detector, and 18.3% faster than using two modules separately.
Proposal of Notation of Rough Set Theory Based Decision Rule Condition Part Applying Formal Concept Analysis Based Inclusion Relation

In recent years, rough set theory has attracted attention as one of the data mining techniques. However, when there are tens or hundreds of decision rules extracted as a result of analysis, it is difficult to identify the combination of attribute values with high frequency of occurrence (core) and the inclusion relations among decision rules and attribute values. Therefore, the purpose of this study was to develop a notation that reflects the extraction of cores in decision rules and the inclusion relations between attributes obtained by formal concept analysis in the decision rule condition part. This enabled us to discover inclusion relations between decision rules and affinity between cores and attribute values, which were difficult to discover in rough set theory alone. We also verified the usefulness of the proposed notation by comparing the proposed notation with the conventional decision rule notation using two example problems as case studies.
Training quantum measurement devices to discriminate unknown non-orthogonal quantum states

Here, we study the problem of decoding information transmitted through unknown quantum states. We assume that Alice encodes an alphabet into a set of orthogonal quantum states, which are then transmitted to Bob. However, the quantum channel that mediates the transmission maps the orthogonal states into non-orthogonal states, possibly mixed. If an accurate model of the channel is unavailable, then the states received by Bob are unknown. In order to decode the transmitted information we propose to train a measurement device to achieve the smallest possible error in the discrimination process. This is achieved by supplementing the quantum channel with a classical one, which allows the transmission of information required for the training, and resorting to a noise-tolerant optimization algorithm. We demonstrate the training method in the case of minimum-error discrimination and show that it achieves error probabilities very close to the optimal one. In particular, in the case of two unknown pure states our proposal approaches the Helstrom bound. A similar result holds for a larger number of states in higher dimensions. We also show that a reduction of the search space, which is used in the training process, leads to a considerable reduction in the required resources. Finally, we apply our proposal to the case of the dephasing channel reaching an accurate value of the optimal error probability.
Application of IAC Yokohama system for breast cytology – The experience at a tertiary care hospital

Breast malignancies are one of the commonest malignancies in Indian women. Increasing urbanization is attributed to raise of breast cancers and have surpassed cervix cancer in recent times and are now ranked top in some metros. FNA is used as an important diagnostic tool as a part of triple assessment. The application of the IAC Yokohama system in breast cytology improves the reporting and also allows the calculation of risk of malignancy (ROM). The main aim of the study is to; 1. Apply the IAC Yokohama system for the diagnostic; 2. assessment and subsequent categorization of FNA samples and correlate them with histopathology examination (HPE); 3. calculate ROM; 4. Calculate p-value. A total of 305 samples received were diagnosed and classified according to the IAC Yokohama system. The study was conducted over a period of 2 yrs retrospectively from June 2019 to May 2021. For the 305 FNA samples received, HPE correlation was made and statistical parameters, ROM , p-value were calculated. 305 FNA samples were obtained and categorized according to the IAC Yokohama system. They were categorized and the percentage of incidence noted as 1. Insufficient - 6.89%, 2.Benign - 72.46%, 3.Atypical probably benign - 3.28%, 4. Suspicious - 3.28%, 5. Malignant - 14.09%. ROM was also calculated. The distribution of cases in percentage as per categories are: category 1 – 0%, category 2 - 2%, category 3 – 0%, category 4- , 42.86% category 5- 100%. For 305 FNA samples reported 95 cases received for HPE and subsequently cyto – histopathological correlation was made and statistical parameters were calculated. Compiled statistics showed sensitivity of 100%, specificity of 93.54%, NPV of 100%, PPV of 89.19% and diagnostic accuracy at 95.78%. The p-value for the present study is 0.0001 which is very significant. Application of the IAC Yokohama system of reporting breast cytopathology helps in better categorization of FNA samples and it improves the efficacy of cytopathologist. It gives better clarity to the clinicians in the management of the patient and can reduce unnecessary surgeries.
Understanding the Dynamics of DNNs Using Graph Modularity

There are good arguments to support the claim that deep neural networks (DNNs) capture better feature representations than the previous hand-crafted feature engineering, which leads to a significant performance improvement. In this paper, we move a tiny step towards understanding the dynamics of feature representations over layers. Specifically, we model the process of class separation of intermediate representations in pre-trained DNNs as the evolution of communities in dynamic graphs. Then, we introduce modularity, a generic metric in graph theory, to quantify the evolution of communities. In the preliminary experiment, we find that modularity roughly tends to increase as the layer goes deeper and the degradation and plateau arise when the model complexity is great relative to the dataset. Through an asymptotic analysis, we prove that modularity can be broadly used for different applications. For example, modularity provides new insights to quantify the difference between feature representations. More crucially, we demonstrate that the degradation and plateau in modularity curves represent redundant layers in DNNs and can be pruned with minimal impact on performance, which provides theoretical guidance for layer pruning. Our code is available at https://github.com/yaolu-zjut/Dynamic-Graphs-Construction.
Graph-Based Similarity of Neural Network Representations

Understanding the black-box representations in Deep Neural Networks (DNN) is an essential problem in deep learning. In this work, we propose Graph-Based Similarity (GBS) to measure the similarity of layer features. Contrary to previous works that compute the similarity directly on the feature maps, GBS measures the correlation based on the graph constructed with hidden layer outputs. By treating each input sample as a node and the corresponding layer output similarity as edges, we construct the graph of DNN representations for each layer. The similarity between graphs of layers identifies the correspondences between representations of models trained in different datasets and initializations. We demonstrate and prove the invariance property of GBS, including invariance to orthogonal transformation and invariance to isotropic scaling, and compare GBS with CKA. GBS shows state-of-the-art performance in reflecting the similarity and provides insights on explaining the adversarial sample behavior on the hidden layer space.
The present and future of confidential microdata access: Post-workshop report

In the summer of 2021, the University of the West of England, in collaboration with UN Economic Commission for Europe, Eurostat, INXEDA and statistical organisations across the world, hosted a workshop to review lessons learnt in microdata access over the past 15 years, and future directions. The workshop themes were technology, statistical disclosure control, organisation management and societal context, and within each theme the conference considered best practices, solutions for sustainability, affordability, international data sharing, and LMICs. This report presents the findings from this workshop alongside an introduction to current literature and practices within the areas.