Reviewerly/exHarmony huggingface dataset:
Open-source acoustics simulation in the education environment

Pachyderm Acoustic Simulation is a professional tool for geometrical and numerical simulation of acoustics in the built environment that happens to be open source under the General Public License. Over the course of the 14 year life of the open-source acoustics simulation project, Pachyderm Acoustic has had the privilege of partnering with numerous academic institutions worldwide to educate architecture and engineering students in acoustics. This has been in the form of support for student projects, lectures and workshops on acoustics simulation and other topics, and, in some cases, PHD advice. This session will explore several notable examples of successful pedagogy using the open source software as a tool for higher education. There will also be some discussion of approaches to pedagogy that have not been as successful, in an effort to further discussion regarding an appropriate approach to higher learning in acoustics.
RENEWAL OF TEACHER COMPETENCIES IN CONDITIONS OF DIGITALIZATION OF HIGHER EDUCATION (FROM THE EXPERIENCE OF CREATING THE ONLINE COURSE "FUNDAMENTALS OF ECONOMICS" FOR BACHELORIES)

The relevance of the study lies in the fact that today, when the process of development and implementation of e-learning, distance learning technologies is accelerating, as well as when developing a strategy for the development of higher education, it is necessary to take into account the readiness of the scientific and pedagogical staff with the appropriate skills to work in the electronic information and educational environment of the university, and his lack of training to successfully create online courses in particular. The use of online courses, on the one hand, is an opportunity for flexibility in content, forms of training, updating, and on the other hand, the ability to quickly prepare narrow, targeted competencies that are now in demand on the labor market. A modern trend in higher education is the use of digital technologies, including the expansion of online courses. An increasing number of universities are involved in their development and implementation. This is a new path that requires updating the competencies of the teaching staff of universities, all the more so there are no necessary educational and methodological materials for the creation and development of online courses (or they are insufficient). The success of online courses depends not so much on the latest technologies, but on the pedagogical skills of teachers, on the degree of their preparedness for the implementation of the tasks set. The requirements for the teacher of an online course are made up of both classical, traditional, and specific ones related specifically to distance teaching. The article analyzes what additional competencies, knowledge, skills a teacher will need when creating online courses. In addition, attention is focused on what existing skills and competencies need to be strengthened and expanded. The formation of an integral set of subject, technological pedagogical competencies in the field of online learning among teachers occurs both in preliminary preparation and in the process of designing an online course.
Improved Network Pruning via Similarity-Based Regularization

Network pruning has been shown as an effective technique for compressing neural networks by removing weights directly. Although the pruned network consumes less training and inference costs, it tends to suffer from accuracy loss. Some recent works have proposed several norm-based regularization terms to improve the generalization ability of pruned networks. However, their penalty weights are usually set to a small value since improper regularization hurts performance, which limits their efficacy. In this work, we design a similarity-based regularization term named focus coefficient. Differing from previous regularization methods of directly pushing network weights towards zero, the focus coefficient encourages them to be statistically similar to zero. The loss produced by our method does not increase with the number of network parameters, which allows it easy to tune and compatible with large penalty weights. We empirically investigate the effectiveness of our proposed method with experiments on CIFAR-10/100, Tiny-ImageNet, and ImageNet. Results indicate that focus coefficient can improve model generalization performance and significantly reduce the accuracy loss encountered by ultra sparse networks.
Supervised Attribute Information Removal and Reconstruction for Image Manipulation

The goal of attribute manipulation is to control specified attribute(s) in given images. Prior work approaches this problem by learning disentangled representations for each attribute that enables it to manipulate the encoded source attributes to the target attributes. However, encoded attributes are often correlated with relevant image content. Thus, the source attribute information can often be hidden in the disentangled features, leading to unwanted image editing effects. In this paper, we propose an Attribute Information Removal and Reconstruction (AIRR) network that prevents such information hiding by learning how to remove the attribute information entirely, creating attribute excluded features, and then learns to directly inject the desired attributes in a reconstructed image. We evaluate our approach on four diverse datasets with a variety of attributes including DeepFashion Synthesis, DeepFashion Fine-grained Attribute, CelebA and CelebA-HQ, where our model improves attribute manipulation accuracy and top-k retrieval rate by 10% on average over prior work. A user study also reports that AIRR manipulated images are preferred over prior work in up to 76% of cases (Code and models are available at https://github.com/NannanLi999/AIRR ).
Impact of Containerization on Low-Cost Post Moore Computing Architectures

Virtualization is a well-known option to deploy execution environments on diverse platforms, but the number of additional layers makes it unattractive due to the complexity and performance cost. In recent years, containerization has taken shape and strength as a method of application deployment in High-Performance Computing (HPC) environments because it is visible the low impact on performance and the easy integration with the system, reducing complexity. Therefore, this work develops a series of tests whose workloads show the effects on the computation when applications are containerized. Workloads include complex floating-point operations, calculation of prime numbers, or matrix operations. The results present the impact of containerization on computational efficiency, performance, runtime, and power consumption on low-cost Post-Moore computing architectures.
Improving Identity-Relevant Deepfake Video Detection in Real-World with Adversarial Data Augmentation

Recently, GAN-based deepfake videos frequently appeared on video websites, which has a bad impact on the credibility of the videos. Due to the variety of forgery algorithms, the classification methods trained on large-scale datasets often have poor generalization ability. In other words, they cannot effectively detect the forged video in application scenarios. Past work has often attributed this to the fact that the video in the dataset uses a different generation algorithm for the fake video to be detected. In this paper, we demonstrate that whether the dataset contains the video of the same person as the video to be detected has the same crucial influence. And to improve identity-relevant Deepfake video detection in the real world, we propose a new method for video forgery detection of a specific person that trained without fake video in an adversarial data augmentation fashion, which shows its superior performance to other non-identity-targeted algorithms on Internet collected real-world dataset in cross dataset experiments.
A Collaborative Filtering Recommendation Method Combining Approximation Algorithms

Collaborative filtering (CF) recommendation is a classic and practical recommendation method. This paper proposes a new method to improve collaborative filtering recommendation, treating the solution of the recommendation problem as an approximate problem, and uses the greedy strategy to solve the optimization problem. In this paper, the similarity calculation method of collaborative filtering algorithm is also modified. Researchers found that this improvement greatly improved the efficiency of the algorithm. Compared with the traditional algorithm, the accuracy has also made great progress. It is a successful experiment.
LAGAN: Landmark Aided Text to Face Sketch Generation

Face sketch is a concise representation of the human face, and it has a variety of applications in criminal investigation, biometrics, and social entertainment. It is well known that facial attribute is an underlying representation of the facial description. However, generating vivid face sketches, especially sketches with rich details, from given facial attributes text is still a challenging task as the text information is limited. Existing work synthetic face sketch is not realistic, especially the facial areas are not natural enough, even distorted. We aim to relieve the situation by introducing face prior knowledge, such as landmarks. This paper proposes a method, called LAGAN, that Landmark Aided Text to Face Sketch Generation. Specifically, we design a novel scale translation-invariant similarity loss based on the facial landmarks. It can measure the mutual similarity between real sketch and synthetic sketch and also measure the self similarity based on the symmetry of face attributes. Further to counter data deficiency, we construct a novel facial attribute text to sketch dataset called TextCUFSF with CUFSF face sketch dataset. Each sketch has 4 manual annotations. Qualitative and quantitative experiments demonstrate the effectiveness of our proposed method for sketch synthesis with attribute text. The code and data are available: https://github.com/chaowentao/LAGAN .
Human Handover Classification using a Deep Learning Model

Detection and classification of handover actions is one of the new challenging tasks. Robots must be able to detect and differentiate between different ways of handover in order to make the correct move. In this paper, a combination of deep learning architectures, an LSTM network, and feature selection techniques are employed to classify human handovers from both the giver and receiver perspectives. A publicly available dataset consisting of motion tracking sensor measurements, Kinect readings for 15 joint locations, 6-axis inertial sensor readings and video recordings was employed in the evaluation. The results came to be promising as 91% accuracy and precision with 88% recall are obtained using the proposed model.
Hi-Geo-Ti: A Visual Analytic Design for Pedestrian Trajectories Using Hierarchical Geographical Timelines

With the development of computer vision technology, pedestrian trajectory tracking technology pedestrian re-recognition technology has been more and more widely used, and an explicit and accurate representation illustrating the spatial and temporal correlation of a pedestrian’s movement is the key to understanding his/her trajectory in space and time. A scalable visual analytics system called Hi-Geo-Ti is proposed that applies hierachical geographical timelines to allow its users to extract and visualize a pedestrian’s trajectory from a massive storage of images instantaneously. In particular, Hi-Geo-Ti incorporates semantic correlations produced by person re-identification with spatial and temporal attributes of the camera, based on which it creates an interactive combination of 3D trajectory map with 2D plan plot to visually resolve the spatial-temporal entanglement. Taking the transmission process of Covid-19 as case study, by combining with ReID technology, the assistance of proposed visualization system in the fight against the epidemic is demonstrated and user surveys are conducted to verify the effectiveness of proposed design.
Be Free to Communicate on Social Media, but Respect the Values of Education!

The technological revolution and the search for the “new post-pandemic normality” require a re-reading of the freedoms of communi­cation (freedom of expression and freedom of information), in order to con­ceive, in the face of the change of the paradigm of digital actors to three el­ements (individual, state and company), a new system that brings togeth­er the public and the particular which can be called collective and private, capable of forming awareness for peace in the face of the guarantee of the right to non-discrimination, centered on the balance between the legal and the civic possible through the binomial instruction-respect, that is, the indi­vidual recognition of respect for human dignity as guided by human rights in the perspective of fundamental freedoms in the posting and sharing of infor­mation on social networks, as oriented by the education values.
Development of A Novel Block Design Based Key Agreement Protocol for Cloud Environment to Improve Efficient Performance and Security

Cloud computing is one of the recent emerging technologies. Heavy data sharing among multiple users is an open issue. Data sharing in cloud computing enables multiple participants to freely share the group data, which improves the efficiency of work in cooperative environments and has widespread potential applications. However, how to ensure the security of data sharing within a group and how to efficiently share the outsourced data in a group manner are formidable challenges. In this paper we focused on security issues with the help of key agreement protocols to perform efficient data sharing in cloud environment. We proposed a novel block design based key agreement method in support of symmetric balanced incomplete block design (SBIBD). The main objective of this method is to supports multiple participants, which can flexibly extend the number of participants in a cloud environment according to the structure of the block design. Based on the proposed group data sharing model, we present general formulas for generating the common conference key K for multiple participants. In addition, the fault tolerance property of our protocol enables the group data sharing in cloud computing to withstand different key attacks, which is similar to Yi’s protocol.
Desenvolvimento de um Aplicativo utilizando Plataforma Low-Code para substituição dos Cadernos de Inspeção em uma Fábrica de Adoçantes

The present work proposes an application to replace the autonomous maintenance notebooks in a sweetener factory. The proposed application was developed through PowerApps and SharePoint platforms using low-code programming. This approach has been positively received by the industry and its adoption has been growing at great levels. The application was proposed in order to facilitate the completion of inspection checklists by operators, ensuring the reliability of the collected data, reducing the time spent to carry out the activity and the consumption of sheets of paper in the factory. The implementation of the proposed application was divided into two stages: operator training and assistance in using the application on the production line. During the period of monitoring the operators in carrying out the inspections by the proposed application, the time spent by them in completing the checklists was measured again. A 53% reduction in inspection time was achieved after implementing the proposed application, in addition to the possibility of identifying the responsible user, inspection date and time.
Copy-move image forgery detection using cuckoo search

Most people face the dilemma of accepting photographs as authentic or not, mainly in the case of forensics, where the images will influence judgment. Research communities are constantly providing methods to identify these kinds of forged images. Attention capture is specifically the cases where a region of the image is copied in the same image (copy-move forgery - CMF). To detect these forged images, a recent approach named scale-invariant features transform (SIFT) has proved its worth and robustness in various geometrical transformations. However, the framework needs to be optimised for numerous parameters involved because a wrong selection of values leads to wrong identifications. To solve this problem, a novel method has been proposed named as cuckoo search-based copy-move forgery detection (CSCMFD) for optimising the parameter values in the SIFT framework. The CSCMFD demonstrates to attain better results by automatically determining the values of different parameters as compared to state of art research work. Experimentation is performed on the Christlein et al. database and MICC-F220 dataset. The experimental results proved that CSCMFD is able to capture small-forged areas as well as regions that are difficult to identify by other methods.
Development

This chapter is about issues closer to development matters, compared with the previous chapters. The topics we will be talking about here are less tightly coupled to specific Android OS APIs – it is more our concern here to find out how technical requirements can best be accomplished using Kotlin methodologies.
Augmented reality to improve self-regulated learning and spatial ability

Spatial ability and self-regulated learning are important aspects in human life. Besides, Augmented Reality (AR) nowadays becomes technology which steals the world’s attention to be developed in many fields, including in education. This study aims to explain "can Augmented reality improve spatial abilities and self-regulated learning?". The method used is a literature review. Data was obtained from scientific journals and books that have been published in a place that is reputable. The results showed that AR can improve spatial ability and possibly improve self-regulated learning of student. AR can improve spatial abilities based on a lot of scientific research and from the aspect of spatial abilities which basically can be improved by using AR. Meanwhile, AR is said to have the potential to improve SRL based on many researchers developing AR to be a mobile application and using it for mobile learning. Which many researchers state that mobile learning can improve SRL.
ЦИФРОВАЯ ТРАНСФОРМАЦИЯ ОБРАЗОВАНИЯ В СОВРЕМЕННОМ ОБРАЗОВАТЕЛЬНОМ ПРОСТРАНСТВЕ

The purpose of this work is to identify and study the conditions for using digital technologies in the educational space, which act as drivers for changing the model of the domestic education system in the global, social and economic aspect. The article analyzes the phenomena denoted by the concepts of “digitalization of education”, “digital transformation of education”, which have become markers of sociocultural changes in the life of Russian society and are widely discussed in scientific communities. The trends of the digital transformation of domestic education in the world, information, economic aspects are presented: engineering and technological, subject-environmental, organizational and methodological, etc. The relevance of the study is due to the fact that the content components of changes in the educational space are considered as strategic factors for the modernization of the domestic education system. The authors believe that the formation of the education system at all levels goes through a difficult path from the integration of digital tools to the formation of a complex worldview of the modern generation. Today, digital technologies are most actively implemented in additional and additional professional education: digital verified content, personalization of the educational route based on the student's digital footprint. The formation of a digital competence profile in the system of continuous education based on individual characteristics and activity experience reflected in the digital footprint already has successful examples of implementation in the form of practical solutions in domestic and foreign practice. As an effective practice, the achieved results of the digitalization of the educational process in the “Quantorium” technopark (Voronezh) are considered. The subject of scientific interest is the factors and conditions for the formation of the ecosystem of the educational space in the context of the digital transformation of the leading sectors of the economy and the professional self-determination of students.
Hermeneutical practice of moral education in a small town

The authors of the article investigate the upbringing role of a real pedagogical situation, since a human act carries a possible expression of understanding a particular text, its interpretation. When students, together with a teacher, reconstruct a particular cultural norm, a text, assuming all their experience in them. This is where the variability of interpretations is manifested, thanks to which the hermeneutic approach to the educational situation is realized. Within the framework of this approach, the activities of the educator are carried out on the basis of deep understanding, penetration into the specifics of thinking, feelings, experiences of the child (pupil). The decisive role here is played by the appeal not so much to pedagogical facts and phenomena as to what stands behind them: subjective meanings, unconscious motives of behavior. Hermeneutic educational practice is built as work with the experiences of children, their memories, expectations, fantasies. Children's poems, amateur songs, essays, autobiographical notes, diaries, letters are becoming important attributes of the educational process. These products of children's verbal creativity should be accepted by the educator: not analyzed and evaluated, but recognized as a personal value of a growing up person who is allowed to be himself. Hermeneutic acceptance of a child is impossible without reflection of the educator himself of his own childhood experience, his childhood experiences, “living” memories from childhood. Such a teacher will not lead the child, instruct him, but will be able to cooperate with him as a senior comrade.
PipeDevice

Containers have become prevalent in public clouds due to the performance, portability, and deployment benefits compared to virtual machines [1, 8, 14]. They support a wide variety of workloads, from microservices to data analytics and machine learning. Containerized applications often entail extensive bulky data transfers to exchange intermediate results of data processing among peers. Examples include the shuffle stage in MapReduce jobs [3, 15, 16, 23] and the model update process with parameter server and allreduce in distributed machine learning [5, 19, 32, 35].
In search of a living middle

The article analyzes the main category of Zedlmayr's theoretical works "the middle" (Mitte). Zedlmayr is a classic of the Vienna School of Art Studies. The relevance of Zedlmayr's scientific views in the era of digitalization is due to his desire to understand the whole. For the first time this category is used in antiquity. The "middle" is an ontological and epistemological category denoting the integrity and relevance of a work of art. Sedelmaier understands a work of art as a living whole, residing here and now. A work of art for Zedlmayr is being, presence (dasein). According to Zedtlmayr, the "middle" is both the beginning, the middle (center), and the end (goal) of the work. The "middle" is exactly what makes the work acquire integrity, self, being, and individuality. The category "middle" describes the principle of the existence of the whole, implying the unity of the whole and the part. The middle expresses the structure and life of a work of art. It is also the main means of learning art. The middle also denotes the person himself and his connection with God.