Reviewerly/exHarmony huggingface dataset:
A Visual Target Tracking Algorithm Integrating Attention Mechanism

Aiming at the situation that the Siamese tracking algorithm is easy to track drift or loss under complex conditions such as target deformation and similar object interference, a target tracking algorithm combining DeblurGAN deblurring module and spatiotemporal attention mechanism is proposed. First, the input video frames are removed. Fuzzy preprocessing. After simple data enhancement, CIResNet, a feature extraction network embedded with the attention module, enables the model to spontaneously and adaptively perform weighting operations on different semantic target feature channels, thereby enhancing the generalization ability of the tracking model. Finally, online reorganization and independent training are used to train the modified algorithm, and the target tracking benchmark data set OTB100 is used to qualitatively and quantitatively analyze and evaluate the improved algorithm. In order to prove the effectiveness of the proposed algorithm, experiments are carried out to compare and analyze with six typical tracking algorithms in the current field. The results show that the proposed algorithm has obvious advantages in tracking indicators such as accuracy and success rate under the interference of multiple different attributes.
What Is the Expected Return on Bitcoin? Extracting the Term Structure of Returns from Options Prices

We infer the forward-looking Bitcoin risk premium from options contracts. Using data from 2018 to 2020, we show that the expected excess returns for Bitcoin are time-varying and significantly higher than in equities or gold, averaging almost 80% per annum. A temporal analysis of the term structure of Bitcoin risk premia reveals an upward sloping term structure during Bitcoin bull markets and downward sloping during Bitcoin bear markets
"Just Drive": Colour Bias Mitigation for Semantic Segmentation in the Context of Urban Driving

Biases can filter into AI technology without our knowledge. Oftentimes, seminal deep learning networks champion increased accuracy above all else. In this paper, we attempt to alleviate biases encountered by semantic segmentation models in urban driving scenes, via an iteratively trained unlearning algorithm. Convolutional neural networks have been shown to rely on colour and texture rather than geometry. This raises issues when safety-critical applications, such as self-driving cars, encounter images with covariate shift at test time - induced by variations such as lighting changes or seasonality. Conceptual proof of bias unlearning has been shown on simple datasets such as MNIST. However, the strategy has never been applied to the safety-critical domain of pixel-wise semantic segmentation of highly variable training data - such as urban scenes. Trained models for both the baseline and bias unlearning scheme have been tested for performance on colour-manipulated validation sets showing a disparity of up to 85.50% in mIoU from the original RGB images - confirming segmentation networks strongly depend on the colour information in the training data to make their classification. The bias unlearning scheme shows improvements of handling this covariate shift of up to 61% in the best observed case - and performs consistently better at classifying the "human" and "vehicle" classes compared to the baseline model.
Self-Supervised Monocular Depth Estimation With Extensive Pretraining

Although depth estimation is a key technology for three-dimensional sensing applications involving motion, active sensors such as LiDAR and depth cameras tend to be expensive and bulky.Here, we explore the potential of monocular depth estimation (MDE) using a self-supervised approach.MDE is a promising technology, but supervised learning suffers from a need for accurate ground-truth depth data.Recent studies have enabled self-supervised training on an MDE model with only monocular image sequences and image-reconstruction errors.We pretrained networks using multiple datasets, including monocular and stereo image sequences.The main challenges posed by the self-supervised MDE model were occlusions and dynamic objects.We proposed novel loss functions to handle these problems in the form of min-over-all and min-with-flow losses, both based on the per-pixel minimum reprojection error of Monodepth2 and extended to stereo images and optical flow.With extensive pretraining and novel losses, our model outperformed existing unsupervised approaches in quantitative depth estimation and the ability to distinguish small objects against a background, as evaluated by KITTI 2015.
Research on the Application of Augmented Reality Technology in College English Teaching

Augmented Reality (AR) is a technology that can innovate traditional education. This technology connects virtual objects with the real world, and it can visualize complex spatial relationships and abstract concepts, it also deepen people's perception of the surrounding environment and understanding of knowledge, and enable learners to experience impossible phenomena in the real world. AR brings new opportunities for teaching innovation. On the basis of the technical characteristics of augmented reality and the advantages of its application, and by analyzing the problems facing college English teaching, the paper discusses the advantages of augmented reality in college English teaching and the practice of teaching activities. It can promote the vigorous development of English classroom based on the deep integration of augmented reality technology and English teaching. The purpose of this paper is to provide reference model and educational theory support for the introduction of augmented reality technology into college English classroom.
A Multi-population Adaptive Genetic Algorithm for Test Paper Generation

Recently, online examination has well developped. As an essential task of online examination, test paper generation plays a critical role. Genetic algorithm has been used in test paper generation systems. However, traditional genetic algorithm is easy to be stuck at local optimal solutions and can not adapt well to the student's mastery of knowledge concepts. In this paper, we propose a novel Multi-population Adaptive Genetic Algorithm (MAGA) for test paper generation. Our algorithm introduces the Longest Common Subsequence(LCS) and Hamming distance to judge genetic similarity and comprehensively considers the influence of evolutionary generation, fitness and genetic similarity on parameters of the proposed algorithm. MAGA constructs the initial population which is then fed into the crossover and mutation operators to be optimized and produce the optimal solution. We conducted experiments for the sequential test paper generation on the datasets. Experimental results demonstrate that our algorithm achieves significant improvement over traditional genetic algorithms with faster convergence speed and higher fitness.
Design and Implementation of Comprehensive Decision Support System in the Lower Yellow River

The lower Yellow River has been restricted by many factors such as nature, economy, society and environment for a long time. It is a complex and arduous task to manage the channel and beach area of the lower Yellow River. The comprehensive decision support system provides a good way to solve the above problems. In this paper, the architecture of the system is studied to improve the versatility and portability. Secondly, GIS, network communication and database technology are used to integrate hydrological model, hydrodynamic model, sediment transport model, optimal operation of reservoir group, functional planning of beach area and virtual reality technology. The system, model and hydrological data are closely integrated to build the decision support system of the lower Yellow River. The system provides technical support and application platform for river channel and beach area treatment in the lower Yellow River.
Feral data visualization: A manifesto for cultivating livable worlds with visual sensemaking practices

Feral data visualization is a posture offering theories and practices for designing data visualizations for collaborative survival in the midst of ecological and social turmoil. Feral data visualization demands an embrace of situated, embodied, vegetized, and feral forms of visual pattern making that might allow us to take up the task of building livability in our surrounding ecologies and communities. We demonstrate how these imperatives might unfold in practice through Tsuga Convictio, a research through design project. Feral data visualization calls designers to action, outlining three paths for crafting data visualizations for the feminine, vegetal, and embodied.
Conceptual foundations for the development of developing software systems using multivariate synthesis of design solutions

Abstract Considering software systems as developing, changing their structure and characteristics during the entire process of development, implementation and operation is an important task. The difficulties that arise in this case, associated with the large dimension of combinatorial problems that arise in the process of studying software systems, taking into account the dynamics of their development, it is advisable to overcome on the basis of using methods of multivariate synthesis, with the help of which a multi-stage approach to solving emerging problems is formed, including evaluating the effectiveness of the solution at each stage.
Lane-Level Three-Dimensional Semantic Mapping Based on Stereo Vision

Autonomous vehicles need to clarify their position and recognize objects in the urban scene, making it increasingly rely on maps to provide them with prior semantics for advanced tasks such as positioning and navigation or planning control. Semantic segmentation and geometric reconstruction techniques required for mapping have gradually developed. They have been combined to a certain extent for applications such as generating semantic maps. Still, they are rarely refined to the lane level, even if the lane can be used as a necessary constraint for vehicles to maneuver on the road. Important clues. This paper proposes a three-dimensional lane-level semantic mapping system that utilizes stereo vision and use the scrolling grid representation to save computing time and memory. We use the deep neural network to obtain the stereo disparities and lane-level semantics and then combined pose and depth to transmit the semantics into the three-dimensional space. Experiments on the KITTI data sequence show that our system can continuously identify and reconstruct objects on the road, even if their strong structure prior or few appearance clues.
A study on untact online class operation －Focusing on 〈Inference and Argumentation〉 classes at University B

The COVID-19 pandemic has forced the majority of universities in Korea to switch to untact classes. However, we now expect that untact classes will expand even into the post-coronavirus era. Online learning has advantages when it comes to convenience, connectivity, and accessibility. In addition, many of the students who have experienced untact online classes expect to expand online classes if the qual-ity of the classes is guaranteed. Considering these points, it is necessary for us to seek qualitative improvements in the operation of untact classes.This study examines practical educational strategies and their effects in untact situations using the &lt;Inference and Argumentation&gt; course at University B in Bus-an as an example. The course consisted of pre-recorded video and real-time video classes in parallel every other week. In the pre-video class, students take in theo-retical explanations and conduct example solving activities, and assignments are required. The real-time class consists of the students solving the assignments tog-ether, so that prior learning and the real-time classes are organically linked. In the real-time class, students' problem solving was checked through chatting, enab-ling learners to understand and reinforce their understanding of learning. The eff-ectiveness of this untact class operation is checked through a questionnaire. In the questionnaire, not only the level of improvement in critical and logical thinking skills before and after learning were measured, but also their understanding of pre-recorded video classes. Although this is subjective survey, it is possible for us to gauge how much the learners' academic achievement has improved, as well as the educational efficacy they feel through the untact operation of the course.
Data Exchange Module Implementation for NPP APCS Gateway between Upper Unit Level System and Lower Level Systems

This document describes structure and algorithms of the data exchange module of NPP APCS gateway, based on example of “gate” program for gateway interface software of upper unit level control system, developed in ICS of RAS. This “gate” program is designed to communicate with SICAM devices by Siemens AG, using IEC 60870-5-104 protocol.
THE PROBLEM OF IMPLICIT «SUBSTITUTION» OF GROUNDS AND DEFERRED EVIDENCE IN THE SYSTEM OF PEDAGOGIC KNOWLEGE

Aim.To present the problem of implicit «substitution» of grounds and deferred evidence, showing on specific examples their manifestation and impact on education.Methodology.In the article, using specific examples, the problem of choosing the grounds for evidencebased argumentation of pedagogical research is considered, the source of which is the coexistence of three versions of scientific character at the current stage of science development: classical, nonclassical and post-non-classical.In the process of scientific research, the theoretical research methods have been used such as comparative analysis, generalization, interpretation, scientific theorization in the context of the ideas of post-non-classical science and on its methodological basis.Results.The article presents the classical and post-nonclassical versions of the ontology of education.It is stated that for the modern pedagogical research, the presence of an ontological conflict between the ontological basis of setting research tasks and the ontological basis of methods for understanding and solving them is typical, the conflict takes the form of an implicit «substitution» of the ontological foundations of research.Its indicator is the structural identity of the educational innovations and the traditional education system, which is concretized by the examples in the article.The «substitution» of the ontological foundations of research entails the paradox of the evidencebased argumentation.The problem under consideration forms a barrier to the development of education, acting as a deep factor in the stagnation of its crisis state, which determines the strategic significance of its solution.Research implications.The research results contribute to the development of the theoretical foundations of post-non-classical education.
Adversarial Deep Reinforcement Learning Enabled Threat Analytics Framework for Constrained Spatio-Temporal Movement Intelligence Data

Intelligence, surveillance, and reconnaissance (ISR) systems assist the defense and military in their tactical operations by gathering movement intelligence data for tracking adversaries and their activities in an area-of-interest. However, there are significant spatio-temporal gaps in the collected data due to short track durations and discontinuous coverage. As a result, the ISR operators or analysts are unable to connect the incomplete set of movements to detect threats in the form of salient activities of the adversaries. Our proposed approach aims to fill this gap by developing a novel threat analytics framework that consists of an adversarial agent, powered by deep reinforcement learning, and a machine learning-based threat detector to help analysts identify salient adversarial activities in the wake of incomplete observations. The experiment results on simulated data show that the proposed framework is able to correctly identify, on an average, 99% of the threats.
Specific Identification of Radar Emitters Based on Manifold Learning Method

Specific emitter identification is one of the important part of radar countermeasure signal processing. Current specific radar emitter recognition algorithm highly relies on prior knowledge and labeled training samples. In this paper, a radar emitter identification method based on manifold learning is proposed to solve such problem. Based on the characteristics of radar emitters, this method extracts representative slices of ambiguity function as signal features, and proposes a limited looseness geodesic Gaussian Locality Preserving Projection (LLGGLPP) algorithm to reduce the dimension of signal feature samples, which has the advantage of unsupervised learning and easy sample expansion. Simulation results show that the proposed method achieves fine sample clustering characteristics, and provides a new method for specific identification of radar emitters.
Meta-feature Extraction Strategies for Active Anomaly Detection

Active Learning is a machine learning scenario in which methods are trained by iteratively submitting a query to a human expert and then taking into account his feedback for the following computations. The application of such paradigm to the anomaly detection task takes the name of Active Anomaly Detection (AAD). Reinforcement Learning describes a family of algorithms that aim to teach an agent to determine a policy to deal with external factors, and are based on the maximization of a reward function. Recently some AAD methods, based on the training of a meta-policy with Deep Reinforcement Learning have been very successful because, after the training, the methods simply work on a small number of meta-features that can be directly applied to any new dataset without further tuning.
Technology 5.0 in architecture based on the understanding of environmentalism

Abstract Technology that developed rapidly in the late 19 th century was the beginning of modern times and brought significant changes in every aspect of human life, including architecture. Starting from technology 1.0 until 5.0, technology still has a role in the field of architecture. On one side, technology offers convenience for humans to monitor buildings through the operating system and increases the value of life through its architectural style which is represented by the construction system and materials. On the other hand, technological developments also present problems. The problem that occurs due to technological development that are experienced globally is environmental damage. The purpose of this study is to determine the appropriate technology for use in buildings based on an understanding of environmental science. This study uses a qualitative method regarding the primary literature from books and journals (national and international). The results of this study concluded that through understanding the science of environmentalism, the basis for creating a design must begin with an understanding of the natural conditions of the design area. Henceforth, technology in architectural buildings is not in the form of mechanical technology that potentially increases the amount of building heat, but rather in technology that supports the natural performance inside the building in the form of wall material, new approach in terms of building openings, development of solar panel, windmills, and chimneys technology.
Regional Clustering for Developing Electricity Systems in Archipelagic Area: A Case Study of Maluku and Papua Islands

Maluku and Papua islands have complex geographical conditions, thousands of small islands, uneven population densities, and lower income distribution compared to other part of Indonesia. Therefore, the development of electricity system cannot be performed by assuming that every area has the same growth characteristics. In this study, clustering is performed to group the areas with the same characteristics. Hence, the development of electricity system can be based on this clustering. This study used k-means clustering algorithm to perform the clustering method. In this study there are six clusters created based on the data such as, population, average growth of population, Gross Regional Domestic Product (GRDP), and average growth of GRDP. These six clusters can be used as a reference for the development of electricity system in Maluku and Papua Islands.
Unsupervised Lexicon-Based Stemming by Dual Dictionary Models

In this paper, we present an unsupervised statistical method for detecting stems given a large set of words. The idea is to find the segmentation point within a word that yields the most likely prefix-suffix pair using dynamic dual dictionaries. We initialize prefix and suffix dictionaries through a greedy heuristic based on two-way lookup, and then iteratively apply expectation maximization to update them with corresponding probabilities of all prefix-suffix pairs. We also provide a nonparametric technique for scaling suffix probabilities with respect to that of prefixes by minimizing the sum of squared word reconstruction errors. Evaluation of the method is conducted on manually labeled test sets from a large collection of words obtained from Turkish Wikipedia along with another unsupervised morphological segmentation model, namely, Morfessor. As a result, the proposed model has been found to have higher accuracy in detecting correct stems with lower time complexity.
Stacked hourglass networks based on polarized self-attention for human pose estimation

The human body pose estimation mainly locates the position of the key points of the human body in the image. The stacked hourglass network uses top-down and bottom-up feature extraction methods to obtain better results in the task of human pose estimation. However, in the process of feature extraction, the resolution of the image will be lost, and it will have a greater impact on the positioning of the key points of the human body. Therefore, this paper incorporates a polarized self-attention mechanism into the stacked hourglass network. A polarized self-attention (PSA) module is added before the second convolution of the basic residual block and added before the max pool down-sampling and the nearest neighbor up-sampling for each stage of the hourglass module. The space and channel of the PSA are used to maintain a high feature resolution, thereby improving the accuracy of the model's positioning of the key points of the human body posture. Finally, experiments on the human body pose estimation data set (MPII) show that the improved network PCKh@0.5 reaches 92.6%, which is 1.5% higher than the original model, which further illustrates the correctness and effectiveness of the network.
Hate speech and autonomy of religious Communities

The autonomy of religious communities is not a valid basis for them to be freed from their obligation to observe the law prohibiting hate speech. At the same tenor, hate speech by religious ministers is equally prohibited as coming from any other public official or politician capable of influencing a large amount of people who are believers or followers. On the other hand, hate speech against religious communities should not be equal to blasphemy or defamation. The former should always be directed against persons, whereas the latter is directed against ideas, beliefs and practices. It is argued in this paper that this differentiation is possible to be done and should be done, even if in many cases it may appear to be very difficult to be done. In this sense, there is no hate speech against communities, but there can be hate speech against all members of a religious community. While prohibitions of hate speech are compatible with equal dignity as a prerequisite of free speech, blasphemy and defamation of religions are not.