Reviewerly/exHarmony huggingface dataset:
THE EFFECT OF ARRANGEMENT PATTERNS OF DOWNLIGHT ON FEELING VALUE OF CEILING HEIGHT

In this study, we conducted two subject experiments using virtual environment technology to examine the effect of downlight arrangement patterns on the feeling value of ceiling height. The following findings were obtained. 1) When four downlights are placed on a rectangular ceiling surface, the ceiling height is evaluated higher when the downlights are distributed than when they are gathered in the center. 2) When comparing the color temperature of the downlights at 4000K and 3000K, there is no significant difference in the effect of the placement pattern on the feeling value of ceiling height.
Graph Convolutional Networks for Link Prediction in Argument Structure Extraction

Argument structure extraction is a fundamental problem in argumentation mining which aims at automatically constructing arguments from unstructured textual documents. Re-garding each argument as a node, then predicting relationships of those nodes is a key task for argument structure extraction, which is called link prediction. Graph neural networks (GNN), as an efficient approach to cope with graphical data, have shown good performances on link prediction tasks. However, they usually focus on predicting whether parts of links really exist in one argument graph, rather than predicting existence of the links between any two nodes, though it is more practical in real applications of argument mining. Thus, in this paper our goal is to predict links between any two nodes given only nodes information. This task is more difficult than the traditional link prediction tasks since the prediction scale exponentially increases with node number. We propose a GNN based model for link predic-tion which outputs the embedding of all the nodes. Then, the probability of link existence between any two nodes is calculated by their embedding results. Finally, we use a dataset of essay comments to perform evaluations and the results confirm the effectiveness of our proposed method.
SimpleDG: Simple Domain Generalization Baseline Without Bells and Whistles

We present a simple domain generalization baseline, which wins second place in both the common context generalization track and the hybrid context generalization track respectively in NICO CHALLENGE 2022. We verify the founding in recent literature, domainbed, that ERM is a strong baseline compared to recent state-of-the-art domain generalization methods and propose SimpleDG which includes several simple yet effective designs that further boost generalization performance. Code is available at https://github.com/megvii-research/ SimpleDG .
Checklist as a Working Tool for the Formation of Digital Literacy of BIM-Specialists in the Multilingual World

The article is devoted to the analysis of the problem of personnel shortage of BIM (Building Information Modeling) specialists and the search for ways to solve it. Today, the training of BIM-specialists is considered as a constant improvement of their digital literacy in order to adapt to constantly changing conditions to the needs of the enterprise. However, current educational requirements make it difficult to attract successful students to work in international companies. BIM-specialists should develop the ability to solve professional tasks in a multilingual world. The reasons for the personnel shortage are considered, such as a lack of understanding of the need for BIM-specialists, lack of places for BIM training and difficulty in training. The relevance of BIM technologies is proved by statistics and correlation analysis. The directions of training BIM-specialists at universities are given. It is proposed to use transforming checklists in training for the requests of the enterprise with instant translation into the language chosen by the student to familiarize himself with vacancies and master the program. This is a modern working tool of an effective multilingual world, which allows to increase digital literacy in professional training and training of BIM-specialists, depending on the actual production problems at different stages of planning the work of an enterprise in the construction industry and the real estate market.
Cross-Camera View-Overlap Recognition

We propose a decentralised view-overlap recognition framework that operates across freely moving cameras without the need of a reference 3D map. Each camera independently extracts, aggregates into a hierarchical structure, and shares feature-point descriptors over time. A view overlap is recognised by view-matching and geometric validation to discard wrongly matched views. The proposed framework is generic and can be used with different descriptors. We conduct the experiments on publicly available sequences as well as new sequences we collected with hand-held cameras. We show that Oriented FAST and Rotated BRIEF (ORB) features with Bags of Binary Words within the proposed framework lead to higher precision and a higher or similar accuracy compared to NetVLAD, RootSIFT, and SuperGlue.
Formation of Graduates Mathematical Culture in Multicultural Environment on the Example of Chinese Students in Russia

The article considers the problem of formation of mathematical culture of a graduate of a Russian university. The concept of mathematical culture is debatable due to the non-universality of the language of mathematics, as well as due to the contradictions between the supporters of “pure” and applied mathematics. The article analyzes the components of mathematical culture and shows the transformation of this concept over the past 100 years. The problem of the dependence of mathematical culture on the ethnicity of its creators and its bearers is debatable. This problem is relevant in connection with the development of international education. The article shows that the assessment of the level of mathematical culture is subjective. Chinese students numerically prevail in the contingent of foreign citizens in Russian universities. To objectify ideas about the level of mathematical culture of Chinese graduates, it is necessary to build the mathematics teaching process of them at the university with the involvement of Russian-Chinese bilingual textbooks. The authors have developed the manual on the course of Probability Theory and Mathematical Statistics with parallel texts in Russian and Chinese. The article presents the results of the experiment comparing the academic success of Chinese students in economics before and after the introduction of the manual. The results of the experiment demonstrate the increase in academic success. Testing the corresponding hypothesis by method of confidence intervals proves this conclusion.
Domain Generalization with Global Sample Mixup

Deep models have demonstrated outstanding ability in various computer vision tasks but are also notoriously known to generalize poorly when encountering unseen domains with different statistics. To alleviate this issue, in this technical report we present a new domain generalization method based on training sample mixup. The main enabling factor of our superior performance lies in the global mixup strategy across the source domains, where the batched samples from multiple graphic devices are mixed up for a better generalization ability. Since the domain gap in NICO datasets is mainly due to the intertwined background bias, the global mix strategy decreases such gap to a great extent by producing abundant mixed backgrounds. Besides, we have conducted extensive experiments on different backbones combined with various data augmentation to study the generalization performance of different model structures. Our final ensembled model achieved 74.07% on the test set and took the 3rd place according to the image classification accuracy (Acc.) in NICO Common Context Generalization Challenge 2022.
Optimizing Text: Representation of Similarity Using Machine Learning

Strategies, which express high-dimensional information in fewer measurements while keeping a valuable measure of the data content of the first information, are helpful in information preparing and investigation. This paper investigates an impossible use of dimensionality decrease methods, applying procedures amazingly appropriate to representation of information in a few measurements to an assortment of message records in English, which contain an informational index that is not ordinarily dependent upon 2D plotting and spatial examination. The strategies utilized are Semidefinite Embedding (SDE) and Minimum Volume Embedding (MVE), which are both dependent on the overall procedure of Kernel Principal Components Analysis (KPCA). A preprocessed portrayal of text dependent on word recurrence (dismissing word request) is utilized as contribution to these calculations. This kind of portrayal has been demonstrated in other work to be viable at text order with piece strategies, and in a test with few related reports, gives great perception results with a straightforward RBF part, however very little improvement with the more remarkable SDE and MVE methods.
Depth-Based In-Bed Human Pose Estimation with Synthetic Dataset Generation and Deep Keypoint Estimation

This paper describes a method of estimating the pose of a human in bed only from a single depth image. Such estimation is useful for robotic monitoring of the elderly and the disabled, where their lying posture may indicate illness. While it can address privacy and illumination issues, depth images make the pose estimation problem more challenging. We solve this problem by generating training images with cloth simulation and deep keypoint estimation. We evaluated the effectiveness of the dataset using synthetic and real test images. We also show that adding a small number of real training data improves the results.
Matching Multiple Perspectives for Efficient Representation Learning

Representation learning approaches typically rely on images of objects captured from a single perspective that are transformed using affine transformations. Additionally, self-supervised learning, a successful paradigm of representation learning, relies on instance discrimination and self-augmentations which cannot always bridge the gap between observations of the same object viewed from a different perspective. Viewing an object from multiple perspectives aids holistic understanding of an object which is particularly important in situations where data annotations are limited. In this paper, we present an approach that combines self-supervised learning with a multi-perspective matching technique and demonstrate its effectiveness on learning higher quality representations on data captured by a robotic vacuum with an embedded camera. We show that the availability of multiple views of the same object combined with a variety of self-supervised pretraining algorithms can lead to improved object classification performance without extra labels.
Discursive Approach to Foreign Language Training via Massive Open Online Courses

This article considers some aspects of implementation a discursive approach in relation to teaching a foreign language to future professionals. The concepts of discourse and discursive competence are considered. In the course of the study, foreign-language MOOCs of the Coursera platform were selected, the components of discursive competence were identified, a methodological model for the formation of discursive competence of university students was developed, experimental training according to the model developed was carried out, pedagogical conditions for using MOOCs within the framework of a discursive approach for teaching a foreign language in a university were identified, a survey of students and teachers on the effectiveness of using MOOCs in the language classroom of a university was conducted. It is concluded that MOOCs can be an effective tool for the formation of the discursive competence of future professionals.
Russian Emotional Concepts in the Multilingual Technological Environment

What happens to specific linguocultural emotional concepts in the contemporary multilingual technological environment? This issue is discussed on the example of several Russian emotional concepts described by A.Wierzbicka in the framework of the NSM approach. The NSM descriptions are compared to representations of the same concepts in IT applications explicitly related to the theory of basic emotions, and to their representations in RuWordNet. Both NSM descriptions of emotional concepts and their representations in RuWordNet retain their linguocultural specificity, although in different ways. Both of these ways of embedding specific emotional concepts in the multilingual environment are incompatible with the theory of basic emotions, but quite consistent with the Conceptual Act Theory of Emotion, which is more promising from a methodological point of view. If these methods prevail in the future, then the global multilingual environment will become semantically richer, because it will be able to absorb the semantic specifics of individual cultures. On the other hand, the uncritical use in computer applications of universalist ideas associated with the theory of basic emotions creates the illusion that these emotions are indeed universal. That is why, in the multilingual technological environment, the theory of basic emotions can act as a self-fulfilling prophecy. If this scenario dominates in the future, then the global multilingual culture will become semantically more primitive compared to the diversity of cultures that exists today.
Bag of Tricks for Out-of-Distribution Generalization

Recently, out-of-distribution (OOD) generalization has attracted attention to the robustness and generalization ability of deep learning based models, and accordingly, many strategies have been made to address different aspects related to this issue. However, most existing algorithms for OOD generalization are complicated and specifically designed for certain dataset. To alleviate this problem, nicochallenge-2022 provides NICO++, a large-scale dataset with diverse context information. In this paper, based on systematic analysis of different schemes on NICO++ dataset, we propose a simple but effective learning framework via coupling bag of tricks, including multi-objective framework design, data augmentations, training and inference strategies. Our algorithm is memory-efficient and easily-equipped, without complicated modules and does not require for large pre-trained models. It achieves an excellent performance with Top-1 accuracy of 88.16 $$\%$$ on public test set and 75.65 $$\%$$ on private test set, and ranks $$1^{st}$$ in domain generalization task of nicochallenge-2022.
Robustness of Embodied Point Navigation Agents

We make a step towards robust embodied AI by analyzing the performance of two successful Habitat Challenge 2021 agents under different visual corruptions (low lighting, blur, noise, etc.) and robot dynamics corruptions (noisy egomotion). The agents had underperformed overall. However, one of the agents managed to handle multiple corruptions with ease, as the authors deliberately tackled robustness in their model. For specific corruptions, we concur with observations from literature that there is still a long way to go to recover the performance loss caused by corruptions, warranting more research on the robustness of embodied AI. Code available at m43.github.io/projects/embodied-ai-robustness .
Seeing Objects in Dark with Continual Contrastive Learning

Object Detection, a fundamental computer vision problem, has paramount importance in smart camera systems. However, a truly reliable camera system could be achieved if and only if the underlying object detection component is robust enough across varying imaging conditions (or domains), for instance, different times of the day, adverse weather conditions, etc. In an effort to achieving a reliable camera system, in this paper, we make an attempt to train such a robust detector. Unfortunately, to build a well-performing detector across varying imaging conditions, one would require labeled training images (often in large numbers) from a plethora of corner cases. As manually obtaining such a large labeled dataset may be infeasible, we suggest using synthetic images, to mimic different training image domains. We propose a novel, contrastive learning method to align the latent representations of a pair of real and synthetic images, to make the detector robust to the different domains. However, we found that merely contrasting the embeddings may lead to catastrophic forgetting of the information essential for object detection. Hence, we employ a continual learning based penalty, to alleviate the issue of forgetting, while contrasting the representations. We showcase that our proposed method outperforms a wide range of alternatives to address the extremely challenging, yet under-studied scenario of object detection at night-time.
Activity Monitoring Made Easier by Smart 360-degree Cameras

This paper proposes the use of smart 360-degree cameras for activity monitoring. By exploiting the geometric properties of these cameras and adopting off-the-shelf tracking algorithms adapted to equirectangular images, this paper shows how simple it becomes deploying a camera network, and detecting the presence of pedestrians in predefined regions of interest with minimal information on the camera, namely its height. The paper further shows that smart 360-degree cameras can enhance motion understanding in the environment and proposes a simple method to estimate the heatmap of the scene to highlight regions where pedestrians are more often present. Quantitative and qualitative results demonstrate the effectiveness of the proposed approach.
The Analysis of Successful Teaching University Students in a Multilingual Environment in the Context of Education Digitalization

In modern historical conditions, there is a unique opportunity for the mass use of remote forms of learning in a virtual environment. Natural, digital languages and languages of signs and technologies simultaneously participate in digital communication. This gives an opportunity to take a fresh look and rethink the potential possibilities of interaction between participants of multilingual communication. The authors, using statistical data and the author’s questionnaire, conducted a comparative study of the success of teaching higher school students in a traditional and multilingual environment using remote digital technologies. The results of the study revealed significant multi-level problems: cognitive competence; personal effectiveness; behavioral infantilism. Thus, the multilingual context revealed hidden problems of learning quality. These negative trends as a key task give rise to the need for further research and the formation of multilingual thinking.
A Simulation-Based Model for Detecting Fall of Elders Using Proteus

The People’s body undergoes a variety of physical changes as they become older. We know that majority of fall occur at home. The elderly, disabled, as well as ordinary people will fall to the ground due to poor balance, fainting, and other causes. In the proposed system, piezoelectric sensors are integrated under the tiles of the bathroom floor. The values of the piezoelectric sensor output voltage and the pre-set threshold voltage are compared by the Arduino microcontroller. A recue signal will be sent to the guardian. In case of emergency, the guardian can activate the solenoid lock system, which is already built into the doors, to unlock them. A simulation model is being formulated here to check how the hardware system might work.
Review on Optimizing Text-Video Retrieval Using CLIP (Contrastive Language-Image Pre-training)

Text-to-video retrieval in the multi-modal research is one of the important and vastly utilized concept in many of the real-world applications. However, it is very expensive process to construct and maintain the datasets in terms of time and thus difficult to get on an outsized scale. The existing methods which are being used for problems related to text-to-video retrieval do not fully utilize the cross-modal cues existing within the video. The contrastive language-image pre-training (CLIP), an image-language pre-training model, has successfully explained the facility of learning from the visual concepts with the help of image-text datasets collected from various sources mainly the Web. During this work, we proposed to find out any such embedding from the input video data which is available with the tongue annotations within the sort of automatically transcribed narrations. We observed models on large scale using How To100M: text dataset for 2 tasks: Firstly, we have done is the standard ranking for the video annotation and the retrieval. Secondly, it was observed that the optimization of text-to-video trained using the sample data results in the higher level of results and the presence of an object in the various datasets such as YT-UGC, HowTo100, and more. In last, it was shown that the video transfers very well to the other datasets too like MSR-VTT and LSMDC. This basic idea helps us in determining the higher level of results for the retrieving of video through the datasets.
Developing Digital Identity Management Skills Among University Students

Digital literacy is an umbrella term that covers a lot of competences or skills to be taught to those who are to function successfully in the multilingual digital world of the 21st century. Some competences such as information and data literacy, safety or problem solving have been paid a lot of attention to by educators. Other competences such as communication and collaboration have frequently been left outside the educational framework. The paper focuses on the analysis of formation of digital identity management skills as part of the communication and collaboration competence among university and college students. It introduces the concept of digital reputation, considers factors that improve or impede digital communication as compared to real life communication both in a monolingual and a multilingual society and elaborates on the idea that digital perception of communication partners by each other depends on their use of registration names, e-mail addresses and avatars. After analyzing students’ use of those and carrying out experiments on the impact of names, addresses and avatars on the choice of communication partners, the author comes to the conclusion that identity management skills are not sufficiently developed and suggests teaching them in educational institutions in line with other digital literacy skills.
Real-Time Facial Detection Using MATLAB (Utilizing VJ Method)

In PC writing, face recognition has been quite possibly the most contemplated themes. Given a subjective picture, the aim of the proposed work is the decision of any appearances in the picture along with the detection of eyes and the upper body. While this seems to be a trifling assignment for the individuals, yet it seems a testing job of PCs. The trouble related in recognition of face could be ascribed with numerous varieties of scale, area, light, impediments, and so on. Though many reports reveal approaches for face identification, the Viola and Jones (V&J) face discovery approach is the most important method, which helps to quickly prepare pictures and achieve high detection rate. In the proposed experiment, we intend to examine and comprehend the Viola–Jones calculation by carrying out the discovery outline work, perform execution and analysis to improve the presentation.