Reviewerly/exHarmony huggingface dataset:
Extended Object Tracking with Automotive Radar Using Learned Structural Measurement Model

This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian with structural geometry parameters (e.g., truncation bounds, their orientation, and a scaling factor) learned from the training data. The contribution is twofold. First, the learned measurement model can provide an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Second, large-scale offline training datasets can be leveraged to learn the geometry-related parameters and offload the computationally demanding model parameter estimation from the state update step. The learned structural measurement model is further incorporated into the random matrix-based EOT approach with a new state update step. The effectiveness of the proposed approach is verified on the nuScenes dataset.
Inter-University Networking in Mathematical Digital Blended Learning

The concept of blended learning assumes that the student should optimally and in the aggregate use all the opportunities provided by both traditional learning and new digital educational technologies. Blended learning combines the benefits of traditional and e-learning. The use of various models of blended learning allows you to introduce innovative educational technologies into the educational process and build individual educational trajectories for students. Networking contracts are used to effectively implement blended learning at universities. This increases the availability and quality of education, forms unique competencies for training specialists at the intersection of various areas of science and technology, makes it possible to use the material and human resources of other organizations in educational activities, reduces the costs of an educational organization and expands access to modern educational technologies. The article discusses network engineering partnership on the example of BMSTU.
TreeLSTM with tag-aware hypernetwork for sentence representation

Tree-structured neural networks, such as TreeLSTM and its variants, have proven effective for learning semantic representations of sentences, which are useful for a variety of tasks in natural language processing such as text categorisation, text semantic matching and machine translation. These neural network models take as inputs parse trees of sentences, which are generated by a language parser. However, most existing tree-structured neural network models lack the ability of distinguishing different syntactic compositions, thus the expressive power of these models is limited. Moreover, the syntactic knowledge provided by Part-of-Speech tags in a parse tree has not been fully utilised in existing tree-structured neural network models. It is expected that such syntactic knowledge should help distinguish syntactic compositions, so should result in better semantic representation. This paper proposes a novel neural network model, TagHyperTreeLSTM, which contains two components, a tag-aware hypernetwork and a sentence encoder. The tag-aware hypernetwork, which accepts tags as inputs, generates the parameters of the sentence encoder dynamically in order to distinguish different syntactic compositions. The sentence encoder, which accepts words as inputs, generates the final sentence representation. Experimental results show that the proposed model achieves superior or competitive performance in text classification and text semantic matching based on six benchmark datasets when compared against previous tree-structured models.
Towards Solving Large-scale Expensive Optimization Problems Efficiently Using Coordinate Descent Algorithm

Many real-world problems are categorized as large-scale problems, and metaheuristic algorithms as an alternative method to solve large-scale problem; they need the evaluation of many candidate solutions to tackle them prior to their convergence, which is not affordable for practical applications since the most of them are computationally expensive. In other words, these problems are not only large-scale but also computationally expensive, that makes them very difficult to solve. There is no efficient surrogate model to support large-scale expensive global optimization (LSEGO) problems. As a result, the algorithms should address LSEGO problems using a limited computational budget to be applicable in real-world applications. Coordinate Descent (CD) algorithm is an optimization strategy based on the decomposition of a n-dimensional problem into n one-dimensional problem. To the best our knowledge, there is no significant study to assess benchmark functions with various dimensions and landscape properties to investigate CD algorithm and compare with other metaheuristic algorithms. In this paper, we propose a modified Coordinate Descent algorithm (MCD) to tackle LSEGO problems with a limited computational budget. Our proposed algorithm benefits from two leading steps, namely, finding the region of interest and then shrinkage of the search space by folding it into the half with exponential speed. One of the main advantages of the proposed algorithm is being free of any control parameters, which makes it far from the intricacies of the tuning process. The proposed algorithm is compared with cooperative co-evolution with delta grouping on 20 benchmark functions with dimension 1000. Also, we conducted some experiments on CEC-2017, D = 10,30,50, and 100, to investigate the behavior of MCD algorithm in lower dimensions. The results show that MCD is beneficial not only in large-scale problems, but also in low-scale optimization problems.
Out-of-sample data visualization using bi-kernel t-SNE

T-distributed stochastic neighbor embedding (t-SNE) is an effective visualization method. However, it is non-parametric and cannot be applied to steaming data or online scenarios. Although kernel t-SNE provides an explicit projection from a high-dimensional data space to a low-dimensional feature space, some outliers are not well projected. In this paper, bi-kernel t-SNE is proposed for out-of-sample data visualization. Gaussian kernel matrices of the input and feature spaces are used to approximate the explicit projection. Then principal component analysis is applied to reduce the dimensionality of the feature kernel matrix. Thus, the difference between inliers and outliers is revealed. And any new sample can be well mapped. The performance of the proposed method for out-of-sample projection is tested on several benchmark datasets by comparing it with other state-of-the-art algorithms.
The Value of Cloud-Based Learning Environments for Digital Education

This work-in-progress paper deals with the adoption of cloud computing in the education sector. In the past few years, the use of cloud-based teaching and learning environments has substantially increased. Many well-known applications have been enhanced with cloud features, but also completely new tools have emerged. This paper investigates the kind of educational tools that can be expected from the ongoing trend and are not feasible with traditional techniques and discusses potential contributions of cloud computing to the development and implementation of current digital education concepts.
Comparison of Deep Learning and Rule-based Method for the Sentiment Analysis Task

Sentiment analysis is a field in the Natural Language Processing. The main objective of this field is the determination of opinions expressed in the text in relation to given objects or aspects of these objects. In this paper we explored two approaches in the sentiment analysis: lexicon-based approach and machine learning. We took four Russian sentiment corpora and tested lexicon-based method and neural language model BERT on them. For lexicon-based method the intersections of ten Russian sentiment lexicons were used. Neural language model showed better performance results for all the corpora.
Irony Detection Using Transformers

With the ever-expanding social net, the use cases of irony detection and classification is also exponentially increasing. With this work, we take Task3 of SemEva1-2018 as our problem statement which further has two tasks. We intend to first determine if a given tweet is ironic or not (Task A) and then classify the tweets into four classes viz. non-ironic, verbal irony with contrast, verbal irony without contrast and situational irony (Task B). Existing papers have mainly exploited the lexical features of tweets using supervised machine learning. Here, we have proposed two NLP Transformer models viz. BERT (Bidirectional Encoder Representations from Transformers) and XLNets to classify tweets and have also compared our results to that of past papers. Using BERT, we have achieved F1 scores of 0.70 and 0.75 and using XLNets 0.74 and 0.59 for Task A and Task B respectively.
From Edge to Keypoint: An End-to-End Framework For Indoor Layout Estimation

The task of spatial layout estimation of monocular image is to segment an RGB image of indoor scenes with semantic surface labels (i.e., ceiling, floor, front wall, left wall, and right wall). Most recent methods have to produce layout hypotheses based on the estimated edge map or semantic labels, and then rank the layout hypotheses. In this paper, we present an end-to-end framework that can directly output the layout type and keypoint coordinates (defined in the LSUN challenge). The proposed method takes advantage of transfer learning via learning on the fake samples, i.e., plenty of artificial {type, keypoints, edge map} triplets are generated to learn the mapping from edge maps to keypoint coordinates. Generative adversarial network (GAN) is implemented in this work for domain adaptation of the edge maps. Experimental results show that the proposed method can achieve state-of-the-art layout estimation performance on benchmark datasets.
Speculating with glitches: keeping the future moving

This paper explores the glitch as a generative problem which is capable of introducing unanticipated possibilities and futures into situations. We understand the glitch as a sociomaterial encounter rather than merely a technical error, and argue that it calls for (re)consideration of here-and-now possible futures through practices of response and repair. Exploring the ways that people seek to respond to glitches, we consider two case studies in which unexpected problems provoke those involved to speculate playfully and practically about new possibilities. In the first case, a malfunctioning ‘Teacherbot’ incites new challenges and pedagogical opportunities in an online learning environment. In the second, Hungarian activists creatively use infrastructural and political problems to make new spaces of protest and to press the government to respond to their concerns. Considering these empirical cases allows us to observe how playful and disruptive dispositions have worked to question the terms of possible futures in the real world, and to unsettle the seemingly given terms of power-relations. Glitches are not a panacea, but they can provide an impetus to act from within situations that are uncertain, and can therefore point to new trajectories and possible futures.
GHGC: Goal-based Hierarchical Group Communication in Multi-Agent Reinforcement Learning

In large-scale multi-agent systems, the existence of a large number of agents with different target tasks and connected by complex game relationships causes great difficulty for policy learning. Therefore, simplifying the learning process is an important issue. In multi-agent systems, agents with the same target tasks or attributes often interact more with each other and exhibit behaviors more similar. That means there are stronger collaborations between these agents. Most existing multi-agent reinforcement learning (MARL) algorithms expect to learn the collaborative strategies of all agents directly in order to maximize the common rewards. This causes the difficulty of policy learning to increase exponentially as the number and types of agents increase. To address this problem, we propose a goal-based hierarchical group communication (GHGC) algorithm. This algorithm divides the agents into different groups, and maintains the group's cognitive consistency through knowledge sharing. Subsequently, we introduce a group communication and value decomposition method to ensure cooperation between the various groups. Experiments demonstrate that our model outperforms state-of-the-art MARL methods on the widely adopted StarCraft II benchmarks across different scenarios, and also possesses potential value for large-scale real-world applications.
Digital Reference: a quasi-standard for digitalization in the domain of semiconductor supply chains

This paper is about the installation of trust in the Semantic Web content of the Digital Reference (DR). The DR was developed in H2020/ECSEL/Productive4.0, which contains ontologies to model production and Cyber Physical Systems addressing the whole industrial automation and control area. It should serve as a quasi-standard for digitalization in the domain of semiconductors and corresponding supply chains to overcome the partial incompatibility problem of existing standards.Semantic Web is a success story in the B2C (Business to consumer) environment as it is understandable for both humans and machines alike. To transfer this success of easy data exchange into the complex industrial B2B (Business to business) environment, a much higher quality is required - a standardization of the classes and properties. The confidence in standards usually comes from human verification, validation and confirmation, which has the disadvantage of being slow. Machine validation could be faster but often lacks confidence. Thus, how can trust be installed to ensure the "correctness" of the used classes and properties in a speedy process?We propose a human and machine blended approach based on peer review and Blockchain technology built on decentralized peer-to-peer networks to solve this problem. The consensus process combines transparency, traceability, immutability and speed of Blockchain with the fidelity of human experts.
Feature Enhancement Based Text Sentiment Classification using Deep Learning Model

Text sentiment classification is a significant task in the recent years to understand the opinions and thoughts hidden in the text to enhance more productivity in e-commerce websites and also in the social media. Here we integrate deep learning models to analyze the text sentiments. In this paper, Convolutional Recurrent Neural Network (CRNN) method for text sentiment analysis is proposed. The proposed CRNN is a combination of different layers used to extract the features from the text dataset. During training CRNN is able to learn the features set of the text sentiment dataset. The performance of the proposed approach is evaluated on text sentiments of publically available movie review (MR) dataset. Results show that the proposed method outperforms the traditional deep learning techniques.
Recognizing Human-Object Interaction (HOI) Using Wrist-Mounted Inertial Sensors

Detecting and recognizing objects and human-object interactions have great importance in many application areas, such as security and surveillance, robotics, and health monitoring. Over the past few years, many methods have been introduced for activity and object recognition with the help of image and video processing techniques. However, these techniques are very costly and subjected to privacy challenges. Ubiquitous sensing based on wearable sensors (i.e., accelerometer and gyroscope) provides a viable solution to these challenges. Thus, in this paper, we use the wrist-mounted wearable sensors to propose a new method for recognizing human-object interactions, which entails identifying an object, object interactions, and object users. In this aspect, we chose three objects (book, mobile, and laptop) with overall fourteen different interactions performed by 21 users and recorded their data using the wrist-mounted inertial sensors. Various time-domain features are extracted from the data and classified into objects in the first stage. After that, object-dependent interaction and user recognition are carried in the later stage. Three different classifiers are used for recognition experiments (including random forest (RF), J48, and support vector machine (SVM)). The highest F1-scores of 0.872, 0.888, and 0.876 are achieved using RF classifier for recognizing book, mobile, and laptop respectively. The best-case results demonstrate the significance of our proposed method.
Enhancing Visitor Experience or Hindering Docent Roles: Attentional Issues in Augmented Reality Supported Installations

Studies using augmented reality (AR) technology have suggested that users focus excessively on the virtual content in the AR environment at the expense of the physical world around them. This has implications related to the design of installations that aim to incorporate the user's physical environment as part of the AR experience. To better understand how user attention is managed in an AR environment, we present an observational study of Rewild Our Planet, a multi-modal installation that combined video, audio, a human docent and mobile AR to promote awareness about environmental issues. We found that, while AR was successful in engaging visitors, it drew attention away from other modalities within the installation. This impacts the work of the human docent and affects how visitors absorb information presented in the installation. Based on these observations, we present guidelines to inform the design of future AR-supported installations with the aim of minimizing or taking advantage of the observed attentional issues.
Handwritten Bangla Character Recognition Using Convolutional Neural Network and Bidirectional Long Short-Term Memory

Handwritten character recognition (HCR) is presently gaining much popularity due to its wide range of applications. Bangla language comprises many unique characters of complex shapes, cursive nature, and resemblance among different characters. This nature of the script makes Bangla Handwritten character recognition (BHCR) a very challenging task. Very few HCR models can classify all types of Bangla characters with respectable accuracy. The current work proposes a hybrid BHCR model combining the Convolutional Neural Network (CNN) with stacked Bidirectional Long Short-Term Memory (Bi-LSTM). The experimental model used CNN for feature extraction and stacked Bidirectional LSTM for classification purposes. An augmented version of the CMATER dB dataset was implemented for training and evaluating the model. The model could recognize 243 individual classes of Bangla handwritten characters with 96.07% accuracy. No other proposed models are found to classify such a high number of classes with high accuracy.
Combining immersion and interaction in XR training with 360-degree video and 3D virtual objects

Research shows that XR experiences activate portions of the brain that facilitate learning. Case studies in industry bear this out, showing enormous benefits of XR when used to train employees, including reduced time to learn and complete a task, increased ability to perform the task correctly the first time, and better knowledge retention than with traditional training methods. The emerging technologies that fall under the umbrella term XR—virtual reality, augmented reality, mixed reality, and 360-degree video—can facilitate or enhance learning through different affordances. The degree of immersion and the ability to exercise agency through interaction also enhances learning. 360-degree video is highly immersive. But it is not inherently interactive. It is theorized that by inserting interactive 3D objects into a 360-degree video environment, both immersion and interactivity can be achieved, with the result of enhanced learning. The solution presented here also has the advantage of being more easily produced and cheaper than virtual reality. The objectives of this preliminary study were to create a simple and relatively inexpensive prototype virtual environment very close to reality and test the technical effectiveness of learning and situational awareness using 360-degree videos together with 3D objects. An iterative human-centered design (HCD) process that integrates airplane pilots and training personnel as end users and participants in the cocreation of an aviation training system; actively involves users; provides a clear understanding of tasks; facilitates good interaction between user and technology; and includes well-designed interactions was used. A few scripts were created in the prototype to improve the user experience (UX) when interacting with the user interface (UI). The prototype app was presented to flight instructors and flight pilots using a Samsung S7 smartphone with a Samsung Gear VR by Oculus headset. The prototype also allows the experience to be viewed on a mobile device without a VR headset. The experience is based on a 360-degree video taken within a Cessna 150, with a view of the flight panel. 3D objects were inserted and were made interactive so the user can control them. A survey that asked for suggestions to improve the next version of this prototype was given to the test subjects. Preliminary findings suggest system user acceptance, improved pilot confidence before the actual flight, decreased errors, and better situational awareness. In conclusion, the combination of 360-degree video and 3D virtual objects can help train pilots better, more safely, in less time, and with more confidence in the use of the aircraft checklist. The technology provides an alternative to costly and time-consuming VR solutions for training those without access to aircraft, and permits learning with the use of their smartphone.
An intelligent vehicle image segmentation and quality assessment model

Image quality assessment (IQA) plays a significant role in computer vision. The performances of traditional approaches are prone to be affected by noise or complicated background. Image quality assessment towards vehicle is important for vehicle recognition and classification, which is widely applied in video surveillance. In this paper, we propose a novel full-reference image quality assessment (FR-IQA) towards vehicle scene. More specifically, we first leverage background subtraction to obtain foreground pixels, which represents the vehicle silhouette. Subsequently, we train a classifier based on Histograms of Orientation Gradient (HOG) feature to further select candidate objects from the foreground pixels. Afterwards, we design a two-stage CNN architecture to extract deep representation of reference and test images, and gradient information is also considered as the second feature. Finally, we design FR-IQA based on feature similarity index, which is utilized to recognize the similarity between reference vehicle image and test image. Extensive experiment has demonstrated that our method is effect and robust.