Reviewerly/exHarmony huggingface dataset:
Applied Virtual Reality in 3D Geometry

The development of Virtual Reality technologies has improved people's daily lives in many sections. Nowadays, it is applied in fields such as Health, Work, Telecommuting and in a large number of other sectors. Education from different perspectives is something that constantly concerns researchers and virtual reality developers, which is why we come across a series of virtual reality applications for situation simulation and student education. Mathematics and especially geometry which is interrelated with the concept of the implementation of applications in three-dimensional space, as well as VR applications, from the perspective of learning are often considered complicated as there are three-dimensional objects that are not easily perceived only through a book. In this paper we examine how through virtual reality geometric concepts can be better understood but at the same time to examine geometric shapes from different angles and perspectives.
A Non Local Multi-Fiber Network For Action Anticipation In Videos

In this paper, we investigate the fascinating capacity of 3D Deep ConvNets in action anticipation. Specifically, a Non Local Multi-Fiber Network (NL-MFN) model is proposed. The proposed model is compared against several 3D Deep ConvNets for the task of early action recognition or partially observed frames on the available UCF101 dataset. Additionally, we present the results on the EPIC kitchens challenge dataset used in action anticipation task at the CVPR conference. The experimental results demonstrated the effectiveness of the proposed NL-MFN in action anticipation.
Lightweight Semantic Segmentation Network Based on DeepLabV3+

Embedded mobile devices have limited computing power and insufficient running memory, and it is difficult to deploy high-precision, high-complexity and time-consuming semantic segmentation models. We propose a lightweight semantic segmentation model based on DeepLabV3+. This model optimizes the original DeepLabV3+ model from the perspective of reducing the amount of parameters and ensuring segmentation accuracy. The original backbone network is replaced by the MobileNetV2 network with lower parameters and computational complexity to speed up model inference. We design a 3-branch parallel structure and introduce a Semantic Embedding Module (SEB) to enhance low-level feature map semantic information and pixel point feature representation. The model adds a recurrent cross-attention mechanism module (RCCA) to capture the global correlation of all pixels and obtain dense contextual information. The model achieves 74.81% Mean IoU on the mixed dataset consisting of PASCAL VOC 2012 and Semantic Boundaries Dataset, with a parameter size of 8.27MB. The comprehensive performance of the model is better than that of networks such as SegNet, BiSeNetV2 and ENet, and a good balance is achieved between segmentation accuracy and model complexity.
Darknet Traffic Classification with Machine Learning Algorithms and SMOTE Method

The Darknet is a network that can be accessed with certain privileges and runs a non-standard communication protocol. The Darknet traffic that consists of data from several known networks such as Tor and the P2P is often used for criminal activities due to its anonymity. It is so critical to correctly classify Darknet traffic to differentiate the individual flows for security purposes. In this paper, we proposed three different machine learning (ML) based traffic classification approaches; the binary classification of Darknet and Benign traffic classes (Case 1); the quadruple classification of classes Tor, NonTor, VPN, and NonVpn (Case 2); an traffic classification of eight sub-traffic classes (Case 3). We further applied the SMOTE method for balancing the sizes of the classes in the traffic dataset and feature selection (FS) algorithms to identify the most effective attributes where the number of features in the original dataset were reduced from 63 to 8, 8 and 6 for Case 1, 2 and 3 respectively. For all three cases, classification was performed with six different machine learning algorithms with and without SMOTE, and the highest accuracy values were obtained with SMOTE method. The highest accuracy values were obtained with the Random Forest Algorithm as 97.22%, 97.16% and 85.99% for Case 1, 2 and 3, respectively.
MEG-based Machine Learning Semantic Classification of Observed Words

Machine learning methods are starting to be widely used in the analysis of neuroimaging data. Apart from playing a crucial part in the development of Brain-Computer Interface technologies, machine learning can be also used in academic context to link cognitive phenomena to their neurophysiological sources. In this study we attempted to use a SVM model to classify fragments of MEG recording according to the semantic categories of the words that were presented to the subject at the moment. The preprocessed data was clustered in spatial and temporal domains and the clusters were subject to the permutational F-tests. A three-dimensional epochs array was cropped to the time intervals of significant clusters from the selected channels and had its dimensionality reduced with Principal Component Analysis (PCA) or Uniform Manifold Approximation and Projection (UMAP). The resulting vector was used to fit the model to solve the binary classification problem.
ITER-Tag: An Adaptable Framework for Robust Uncoded Fiducial Marker Detection and Identification

Fiducial marker-based tracking is an effective method for pose estimation in coffined environments, such as the International Thermonuclear Experimental Reactor. In this paper, we propose a novel framework for marker detection and identification that is moderately robust to occlusion, even while using a relatively small number of marks. The proposed approach (ITER-Tag) consists of a hybrid pipeline that extracts marker candidates from images using classical methods and identifies uncoded markers using a shallow feed forward neural network. The method requires minimal parameter tuning, data collection and annotation. The methods can be easily adapted to different use cases with varying number and positions of the marks. We test the robustness of our approach in three different use cases in ITER’s divertor, using either retro reflective markers or laser engravings and achieve high detectability rates. We demonstrate how the proposed approach can be used to accurately and robustly retrieve the six-degree-of-freedom pose of the targets.
Autonomus Systems Control Design Using Neuro-Evolution

This paper aims to design and implement a neuro-evolution for the purpose of an autonomous vehicle moving on the road, where its control is performed by an MLP-type feed-forward artificial neural network. The network perceives the environment and controls the angle of the vehicle's wheels as well as its speed in a 2D simulation environment. The vehicle itself consists of a four-wheeled chassis with rotatable front wheels with its own dynamics. The neural network is represented by a four-layer architecture and its parameters are optimized using a parallel genetic algorithm to minimize the chosen performance criterion for controlling the motion of the vehicle.
Solar Radiation Prediction by Machine Learning Algorithm &amp; their Techniques

Abstract: For the power grid to run smoothly or for the best control of the energy flows into the solar system, it is necessary to forecast the output power of solar systems. It is crucial to concentrate the prediction on solar irradiance before predicting the solar system's output. There are numerous ways to anticipate the global solar radiation, but the two main categories are machine learning algorithms and cloud images mixed with physical models. This paper's goal is to provide an overview of machine learning-based algorithms for solar irradiation forecasting in this setting. Despite the fact that many studies explain techniques like support vector regression or neural networks. Due to the variability of the data collection, time step, forecasting horizon, setup, and performance measures, ranking the performance of such methods is challenging. Overall, the prediction error is very similar. Some write. Global solar radiation suggested using hybrid models or an ensemble forecast technique to boost prediction accuracy
Cross Domain Data Generation for Smart Building Fault Detection and Diagnosis

Benefiting extensively from the Internet of Things (IoT) and sensor network technologies, the modern smart building achieves thermal comfort. It prevents energy wastage by performing automatic Fault Detection and Diagnosis (FDD) to maintain the good condition of its air-conditioning systems. Often, real-time multi-sensor measurements are collected, and supervised learning algorithms are adopted to exploit the data for an effective FDD. A key issue with the supervised methods is their dependence on well-labeled fault data, which is difficult to obtain in many real-world scenarios despite the abundance of unlabelled sensor data. Intuitively, the problem can be greatly alleviated if some well-labeled fault data collected under a particular setting can be re-used and transferred to other cases where labeled fault data is challenging or costly. Bearing this idea, we proposed a novel Adversarial Cross domain Data Generation (ACDG) framework to impute missing fault data for building fault detection and diagnosis where labeled data is costly. Unlike traditional Transfer Learning (TL)-related applications that adapt models or features learned in the source domain to the target domain, ACDG essentially “generates” the unknown sensor data for the target setting (target domain). This is accomplished by capturing the data patterns and common knowledge from known counterparts in the other setting (source domain), the inter-domain knowledge, and the intra-domain relations. The proposed ACDG framework is tested with the real-world Air Handling Unit (AHU) fault dataset of the ASHRAE Research Project 1312. Extensive experimental results on the cross-domain AHU fault data showed the effectiveness of ACDG in supplementing the data for a missing fault category by exploiting the underlying commonalities between different domain settings.
A pragmatic methodical framework for the user-centred development of an electronic process support for the sleep laboratory patients’ management

Limited capacities and ineffective care pathways result in long waiting times for patients and sporadic treatment controls in sleep medicine. As one objective of the 'Telesleep Medicine' project, a portal should be developed, which supports sleep specialists in an efficient and resource-saving patient management. On account of the limited project timeframe, the 'classical' user-centred design and evaluation methods could not be comprehensively implemented. Therefore, a pragmatic methodical framework was developed.For the iterative development of the portal, a combination of low-cost and quick-to-implement methods was used. In chronological order, these were: context interviews, personas, the development of an as-is model, a web search of design standards and good design aspects of similar systems, the development of a to-be model, the creation of an overarching mind map, and the iterative creation of mockups with simplified usability walkthroughs.The feasibility of the pragmatic methodological framework for the development of a prototype for the portal was demonstrated. The used method combination resulted in a prototype based on the needs and requirements of the sleep specialists, taking into account their specific workflow and the technical implementation conditions.The presented pragmatic methodological framework can be a valuable resource for developers of comparable projects. The combination of methods worked well together regarding the limited timeframe and resources for concept development. For the future, we plan to implement and test the portal in the clinical field and thus enrich our framework with additional methods.
Second International Workshop on Deep Video Understanding

This is the introduction paper to the 2nd International Workshop on Deep Video Understanding, organized at the 24th ACM International Conference on Multimodal Interaction. 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 workshop is to foster innovative research in this new direction and to provide benchmarking evaluations to advance technologies in the deep video understanding community.
REVIEW OF THE BOOKOF B.G. AYAGAN: HISTORY OF THE ULUG ULUS -THE GOLDEN HORDE: A COURSE OF LECTURES. ALMATY, LLP "LITERA-M", 2020. 224 P.

The review provides a summary of B.G. Ayagan`s book –Doctor of Historical Sciences, Professor, Deputy Director of the Institute of State History of the CS MES RK “History of Ulug Ulus –the Golden Horde: lecture course”. The book raises the most controversial issues on the history of formation, functioning and collapse of the Golden Horde state. The work is a textbook of generalizing nature, where the style of presenting historical information is freed from an excessive load of scientific terms, and the form of presentation of the material is presented in a fairly accessible and easy to read language. At the same time, the necessary data on the dating of the formation of the Ulug Ulus, the activities of prominent rulers, military figures, and the chronology of the main events are given fully with the presence of color illustrations, inserts from sources and a methodological complex.
Secure Parallel Computation on Privately Partitioned Data and Applications

Parallel computation is an important aspect of multi-party computation, not only in terms of improving efficiency, but also in terms of providing privacy for computation involving conditional branching based on private data. While applying multi-party computation in parallel over several sets of input data is straightforward if the partitioning of the input data into sets is publicly known, the problem becomes much more challenging when this partitioning is private. This setting is relevant to broad class of secure computations, in particular to secure graph and database analysis in which the underlying data (graph or database) is private. In this paper, we consider a general class of functions which can be expressed via the iterative evaluation of a binary associative operation, and propose efficient protocols for evaluating such functions in parallel over privately partitioned input data. Our protocols are optimal in terms of the required number of evaluations of the underlying binary operation (i.e.\ N-1 evaluations for total input size N), while simultaneously achieving a round complexity which is only logarithmic in the total size of the input data (i.e.\ O(łog N)).
Situating Network Infrastructure with People, Practices, and Beyond: A Community Building Workshop

Abstract: Our world is now connected and even entangled in unprecedented ways through networked technologies. Yet pockets of unequal connectivity persist, and technical infrastructures for connectivity remain difficult to design and build even for experts. In this workshop we aim to bring together a global community of multi- and inter-disciplinary researchers and implementers working on infrastructure development and connectivity to explore the existing design challenges and opportunities for bringing technical dimensions of networked infrastructures in conversation with human-computer interaction (HCI) and the social science of infrastructure. We will share, assess and define research problems and resources for rethinking networked infrastructures from human-, community-, and society-centered perspectives, understanding them to be embedded with human values and biases. We particularly intend our collaborative work to support real-world connectivity initiatives, which have grown in critical importance over the pandemic years—especially projects in support of Global South communities. Concrete deliverables from the workshop will include: (1) an initial shared bibliography to help formalize the state of knowledge in our area, (2) an agenda of shared goals, challenges, and intentions in our field, (3) a compilation of resources to support future work, and (4) social and organizing infrastructures for continued communication and academic collaboration.
Some aspects of instrumentalization Procedures for assessing educational achievements In the format of competencies

The article discusses one of the key issues of the organization and the implementation of the educational process is a question of the effectiveness of the control system of educational achievements of students. Its consideration is proposed in the context of the competence approach. One of the problems raised by the author is that, often, the teacher checks only knowledge without taking into account the elements of practical assimilation of the material by students. This is, in principle, understandable, since traditional assessment does not provide a complete picture for analyzing the development of integral personal characteristics, which include "competence" and which the modern education system should operate with. To solve this problem, the article clarifies and systematizes by levels and types the primary indicators for assessing the quality of educational achievements of students, a set of evaluation scales is proposed. The conclusion is made about the inadmissibility of poor-quality assessment of educational achievements as a key element of the specialist training system.
Radial Basis Approximations Based BEMD for Enhancement of Non-Uniform Illumination Images

An image can be degraded due to many environmental factors like foggy or hazy weather, low light conditions, extra light conditions etc. Image captured under the poor light conditions is generally known as non-uniform illumination image. Non-uniform illumination hides some important information present in an image during the image capture Also, it degrades the visual quality of image which generates the need for enhancement of such images. Various techniques have been present in literature for the enhancement of such type of images. In this paper, a novel architecture has been proposed for enhancement of poor illumination images which uses radial basis approximations based BEMD (Bi-dimensional Empirical Mode Decomposition). The enhancement algorithm is applied on intensity and saturation components of image. Firstly, intensity component has been decomposed into various bi-dimensional intrinsic mode function and residue by using sifting algorithm. Secondly, some linear transformations techniques have been applied on various bidimensional intrinsic modes obtained and residue and further on joining the transformed modes with residue, enhanced intensity component is obtained. Saturation part of an image is then enhanced in accordance to the enhanced intensity component. Final enhanced image can be obtained by joining the hue, enhanced intensity and enhanced saturation parts of the given image. The proposed algorithm will not only give the visual pleasant image but maintains the naturalness of image also.
Podcast Audio Visual Sebagai Media Komunikasi Pendidikan

Communication in education is a very important element in its position, even a very large role in determining the success of education. Successful and interesting learning requires a good and appropriate communication model. Good communication should be supported by educational communication media. Communication media provides more value for benefits so that education has a positive use-value for life and life. This study aims to determine the effectiveness of audio-visual podcasts as a medium of educational communication in terms of student learning outcomes. This type of research is quantitative research with a quasi-experimental research method with a pretest-posttest control group design with one experimental class and one control class from class VII Muhammadiyah 6 Krian Junior High School (SMP). The research instrument was a question of students' cognitive abilities. Based on the results of the pretest value of the control class of 54.8 and the experimental class of 62.8, the post-test value of the control class was 65.9 and the experimental class was 67.2. Data analysis was carried out using the N-gain method. The control class's N-Gain score was 13% and the experimental class was 20% so the average N-gain of the two classes was 16%, which can be categorized that audio-visual podcasts are not effective for junior high school students' learning outcomes.
Post-Authentic Engagement with Alternative Political Commentary on YouTube and Twitch

The authors examine authenticity in relation to Alternative Political Commentators (APCs) on YouTube and Twitch. Drawing on Owens (2019) provocative claim that contemporary (online) culture may be ‘post-authentic’, the authors use the term post-authentic engagement to explore in/out-group dynamics between influencers and their audiences. This view is evidenced through an examination of the usage of emojis and emotes by audiences to engage in the fast-paced chats that accompanied the livestream coverage of two APCs, HasanAbi and The Young Turks, during the 2020 US Presidential Election.
Stereo matching algorithm for autonomous vehicle navigation using integrated matching cost and non-local aggregation

Stereo matching algorithm plays an important role in an autonomous vehicle navigation system to ensure accurate three-dimensional (3D) information is provided. The disparity map produced by the stereo matching algorithm directly impacts the quality of the 3D information provided to the navigation system. However, the accuracy of the matching algorithm is a challenging part to be solved since it is directly affected by the surrounding environment such as different brightness, less texture surface, and different image pair exposure. In this paper, a new framework of stereo matching algorithm that used the integration of census transform (CT) and sum of absolute difference (SAD) at the matching cost computation step, non-local cost aggregation at the second step, winner take all strategy at the third step, and a median filter at the final step to minimize disparity map error. The results show that the accuracy of the disparity map is improved using the proposed methods after some parameter adjustment. Based on the standard Middlebury and KITTI benchmarking dataset, it shows that the proposed framework produced accurate results compared with other established methods.
Automatic keyphrases extraction: an overview of deep learning approaches

Automatic keyphrases extraction (AKE) is a principal task in natural language processing (NLP). Several techniques have been exploited to improve the process of extracting keyphrases from documents. Deep learning (DL) algorithms are the latest techniques used in prediction and extraction of keyphrases. DL is one of the most complex types of machine learning, relying on the use of artificial neural networks to make the machine follow the same decision-making path as the human brain. In this paper, we present a review of deep learning-based methods for AKE from documents, to highlight their contribution to improving keyphrase extraction performance. This review will also provide researchers with a collection of data and information on the mechanisms of deep learning algorithms in the AKE domain. This will allow them to solve problems encountered by AKE approaches and propose new methods for improving key-extraction performance.