Reviewerly/exHarmony huggingface dataset:
Evaluation of Different Decision Fusion Mechanisms for Robust Moving Object Detection

A fundamental processing step in a wide variety of video processing pipelines is detection of moving objects. Moving object detection is a challenging task due to various environmental conditions such as illumination changes, shadows, glare, background clutter; foreground complexities such as occlusion, camouflage, complex motion behavior of the foreground objects; and imaging conditions such as low resolution and/or frame rate, camera jitter etc. While many moving object detection methods have been proposed, individual approaches often fail to address all the challenges efficiently. In this paper, we propose and evaluate different decision fusion mechanisms for robust detection of moving objects. The proposed hybrid system relies on motion, change, appearance cues and utilizes a mix of classical unsupervised computer vision and supervised deep learning approaches. Motion and change cues are computed through a tensor based motion estimation and a multi-modal background subtraction modules. Appearance cue is estimated using an unsupervised deep semantic segmentation network. These complementary visual cues are fused to achieve robust performance under challenging real-world conditions. The proposed multi-cue decision fusion pipelines were tested and evaluated on the CDnet-2014 change detection dataset. Our novel, unsupervised, expert-guided, semantic rule-based fusion approach with global channel filtering (SR-Fusion+GCF) is shown to outperform the other decision fusion strategies with an F-Measure of 69%, a promising 12% increase in F-measure compared to the best single cue approach.
Integrating Subgraph-Aware Relation and Direction Reasoning for Question Answering

Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities. Although effective, most of these models solely rely on fixed relation representations to obtain answers for different question-related KB subgraphs. Hence, the rich structured information of these subgraphs may be overlooked by the relation representation vectors. Meanwhile, the direction information of reasoning, which has been proven effective for the answer prediction on graphs, has not been fully explored in existing work. To address these challenges, we propose a novel neural model, Relation-updated Direction-guided Answer Selector (RDAS), which converts relations in each subgraph to additional nodes to learn structure information. Additionally, we utilize direction information to enhance the reasoning ability. Experimental results show that our model yields substantial improvements on two widely used datasets.
Discriminant and Complementary Autoencoder Network for Multi-view Learning

In real-world applications, objects can be sampled by different equipment or collected from different angles, which appears a significant and challenging problem for classifying samples from these heterogeneous views. Although effectiveness is achieved, most of the previous methods could not learn a discriminant and complementary representation simultaneously. Differently, in this paper, we propose a novel model termed Discriminant and Complementary Autoencoder Network (DCAEN), which integrates discriminant information between views into a compact representation by the variant of the traditional LDA-like framework. The proposed method has the following merits: (1) our model incorporates supervised information into the common representation subspace, i.e., maximize the between-class distance and minimize the within-class distance from the inter-view and intra-view. (2) our model performs view-specific representation and common representation in a unified framework. The proposed model is solved by the stochastic gradient descent (SGD) algorithm and its performance is far better than many state-of-the-art methods, demonstrating its superiority.
Transferring Domain Knowledge with an Adviser in Continuous Tasks

Recent advances in Reinforcement Learning (RL) have surpassed human-level performance in many simulated environments. However, existing reinforcement learning techniques are incapable of explicitly incorporating already known domain-specific knowledge into the learning process. Therefore, the agents have to explore and learn the domain knowledge independently through a trial and error approach, which consumes both time and resources to make valid responses. Hence, we adapt the Deep Deterministic Policy Gradient (DDPG) algorithm to incorporate an adviser, which allows integrating domain knowledge in the form of pre-learned policies or pre-defined relationships to enhance the agent’s learning process. Our experiments on OpenAi Gym benchmark tasks show that integrating domain knowledge through advisers expedites the learning and improves the policy towards better optima.
Readiness of rural schoolchildrenfor delivery of norms “Ready for labor and defense”

The question of theoretical and practical readiness of rural schoolchildren to pass the norms of the All-Russian physical culture and sports complex for work and defense. The results of a questionnaire conducted among students of a rural school, as well as pedagogical testing data, are presented.The significance of the program-normative basis for the development of mass sports and the improvement of the nation is quite large. The implementation of the “Ready for Labor and Defense” complex should cover all sectors of the population, especially the younger generation. The implementation of the complex has difficulties in educational organizations, including in rural schools.The following methods were used to solve the tasks: the method of analysis of literary sources, the method of questioning (questionnaire), pedagogical testing. The study was conducted on the basis of the Andogo Secondary School Municipal budgetary educational institution (Kaduy district of the Vologda region). In total, 60 rural school students took part in the study (55 % were boys, 45 % were girls).An analysis of literary sources has shown that the problem of implementing the “TRP” program in rural schools is relevant. Identified gaps in the development of physical qualities of students. The analysis of the results of a survey of students. It has been established that the implementation of the All-Russian sports complex  Ready for work and defense program in a rural school is not sufficiently effective. Not all students are involved in the process of passing standards. There are gaps in theoretical knowledge on the main issues of this program for the development of mass sports and the improvement of the nation. Students showed low interest in passing the “Ready for work and defense” standards. Based on pedagogical testing, it was revealed that the indicators of development of students' physical qualities were reduced.The study made it possible to establish that the readiness of rural schoolchildren to pass the standards of the All-Russian physical culture and sports complex for work and defense is at a low level.
Recognition of important Korean text structure based on Reinforcement Learning and Self-attention mechanism

Aiming at the problem that the artificial tagging of Korean corpus is too time-consuming and laborious, and it is difficult for minority languages to integrate with various resources. We intend to construct an effective Korean structural representation from the perspective of representation learning to improve the effectiveness of subsequent natural language processing tasks. Combining deep reinforcement learning with Self-attention mechanism, we propose a hierarchical self-attention model (Hierarchically Structured Korean, HS-K). By using the Actor-Critic idea in reinforcement learning, the model takes the text classification effect as the label feedback of reinforcement learning, and transforms the text structure division task into the sequence decision task. The experimental results show that the model can identify the important text structure of Korean which closer the manual tagging, and it has a friendly auxiliary effect on Korean informatization and intelligentialize.
A Joint Course Knowledge Entity and Relation Extraction Method for Educational Data

Considering the current growth of data in Educational Field, especially in recent times, due to the COVID-19 epidemic, the volume of online course data has increased exponentially. How to extract useful entities and relations from mass educational data has turn into an academic hotspot. In traditional models, entity recognition and relation extraction are considered as two separate subtasks, which may cause error propagation. Therefore, the development of a more effective and accurate entity and relation extraction model in Educational Field is being widely studied. Here, we propose an entity and relation joint extraction method based on deep learning in Educational Field. The proposed model (XMMC) includes XLNet, a pretrained language model for obtaining text word vectors, Mogifier BiGRU neural network for obtaining text context information, Multi-headed Attention for focusing document key information, and CRF for extracting text entities. The obtained entity context information and entity label are further passed to the sigmoid function, which can enhance the accuracy of the subsequent relation extraction and achieve the goal of joint processing. The proposed model performs better than the existing model in terms of accuracy rates (P), recall rates (R), and F1 values (F1 scores).
Online Students’ Classification Based on the Formal Concepts Analysis and Multiple Choice Questions

Students orientation in the university is an important research area. Actually, students fill out their choice manually except some specialties in which there is a preselection. These procedures are very classics, they based on the student's marks to calculate a general average that puts the student above or below a selection bar. Several problems arise from these procedures, the major problem is that a majority of students are misguided. As the solution of this problem, we purpose, in this paper, a new approach for automatic student's orientation witch incorporate the Formal Concept Analysis (FCA) techniques. On one hand, we have students as objects and the online proposed questions are the attributes, on the other hand we have the specialties as objects and the questions as attributes. These objects and attributes help us to build a Concept Lattice Student (CLS) and Concept Lattice Specialty (CLSp). Then after we introduce two algorithms that explore the two concepts lattices and extract the pair (student, specialty) which is our classification result.
Extended Probabilistic Pseudo-Morphology for Real-World Image Denoising

Image denoising is an actively researched topic and a multitude of methods have been proposed for this task, including techniques based on mathematical morphology, which is a popular non-linear processing framework developed for binary and grayscale images, based on imposing a lattice structure on the image data. There is no universally accepted extension to the color and multivariate domain and multiple approaches have been developed. Pseudo-morphological operators do not respect all the theoretical properties of classical morphology, but can be successfully used in a variety of applications. In this paper, we present an extension to the Probabilistic Pseudo-Morphology framework by including third-order statistics in the definition of the pseudo-extrema. The approach shows improved performance in the context of a real-world image denoising application in comparison with other color morphological frameworks.
Generation and Search for the Shortest Path in Two-dimensional Labyrinths as A Model for Optimizing Routing Processes in the Packet Switching Network

The algorithms for generating and finding the shortest path in two-dimensional labyrinths and their characteristics are considered. The aim of the study is to determine the dependence of the working time of the shortest path search algorithms on the characteristics of mazes. This may allow these algorithms to be used to optimise routing processes in packet switching networks. All of the characteristics of the algorithms under consideration are described in detail. These search algorithms also allow for paralleling, so they can be used when creating games or any special applications. Such generation algorithms have been selected, the resulting mazes of which have different characteristics, thus making it possible to determine the required dependence. The peculiarity of applying the result of this research is that the fastest search algorithm can be selected for the generation algorithm or for specific characteristics of an already generated maze. The search algorithms can be improved to find paths in mazes in N-dimensional spaces. This search extension possibility can be widely used not only for working with mazes, but also with any other structure of a given coordinate system.
Survey on the application of blockchain in Digital Rights Protection

The current Digital Rights Management (DRM) is a centralized management system, that the private and sensitive information may be leaked, and data barriers exist between various platforms. The decentralization and traceability of blockchain provide new ideas and solutions for the standardization of digital rights protection. The consensus algorithm is the core of blockchain technology, but it cannot be directly applied to digital rights protection because of their drawbacks. Researchers have optimized and improved the consensus algorithm to meet the needs of digital rights protection. This paper reviews the application of blockchain in digital rights protection, and analyses their improvements of the consensus algorithm.
A Meta-path Based Graph Convolutional Network with Multi-scale Semantic Extractions for Heterogeneous Event Classification

Heterogeneous social events modeling in large and noisy data sources is an important task for applications such as international situation assessment and disaster relief. Accurate and interpretable classification can help human analysts better understand global social dynamics and make quick and accurate decisions. However, achieving these goals is challenging due to several factors: (i) it is not easy to model different types of objects and relations in heterogeneous events in an unified manner, (ii) it is difficult to extract different semantic dependences at different scales among word sequences, and (iii) it is hard to accurately learn the subtle difference between events. Recently, graph neural networks have demonstrated advantages in learning complex and heterogeneous data. In this paper, we design a social event modeling method based on a Heterogeneous Information Network (HIN) and meta-path to calculate the similarity of events. In order to extract different semantic dependence, we propose a multi-scales semantic feature extraction framework. We present a Local Extrema Graph convolution Network (LEGCN) to expand the difference of various types events achieving accurate classification. We conduct extensive experiments on multiple real-world datasets and show that the proposed method exhibits comparable or even superior performance and also provides convincing interpretation capabilities.
Time- and Query-optimal Quantum Algorithms Based on Decision Trees

It has recently been shown that starting with a classical query algorithm (decision tree) and a guessing algorithm that tries to predict the query answers, we can design a quantum algorithm with query complexity O( √ GT where T is the query complexity of the classical algorithm (depth of the decision tree) and G is the maximum number of wrong answers by the guessing algorithm [ 3 , 14 ]. In this article, we show that, given some constraints on the classical algorithms, this quantum algorithm can be implemented in time Õ (√ GT ). Our algorithm is based on non-binary span programs and their efficient implementation. We conclude that various graph-theoretic problems including bipartiteness, cycle detection, and topological sort can be solved in time O(n 3/2 log 2 n ) and with O(n 3/2 ) quantum queries. Moreover, finding a maximal matching can be solved with O(n 3/2 ) quantum queries in time O(n 3/2 log 2 n ), and maximum bipartite matching can be solved in time O(n 2 log 2 n ).
Minimizing Weighted Concave Impurity Partition Under Constraints

Set partitioning is a key component of many algorithms in machine learning, signal processing and communications. In general, the problem of finding a partition that minimizes a given impurity (loss function) is NP-hard. As such, there exists a wealth of literature on approximate algorithms and theoretical analysis for the partitioning problem under different settings. In this paper, we formulate and solve a variant of the partition problem called the minimum weighted concave impurity partition under constraint (MIPUC). MIPUC finds an optimal partition that minimizes a given weighted concave loss function under a given concave constraint. MIPUC generalizes the recently proposed Deterministic Information Bottleneck problem which finds an optimal partition that maximizes the mutual information between the input and partitioned output while minimizing the partitioned output entropy. Our proposed algorithm is based on an optimality condition, which allows us to find a locally optimal solution efficiently. We also show that the optimal partitions are separated by some hyperplanes in the space of posterior probability mass functions.
Cognitive Attention Network (CAN) for Text and Image Multimodal Visual Dialog Systems

Visual question answering and visual dialog systems are the emerging research areas in natural language processing that exploits the use of image and text modalities to convey an understanding of the contexts and attributes in a conversation as humans do in online chat platforms. These multimodal dialog techniques are enabling the extended use of chatbots in many open and vertical domains. In this paper, we propose the cognitive attention network (CAN) which is a visual dialog system capable of answering multiple user questions regarding an image, and also able to identify similar images from past conversations and referring to them during an ongoing question-answering (Q&A) chat. Our model comprises of faster RCNN, pre-trained BERT, late data fusion, and a memory network serving as a knowledge base for the temporary storage of previous visio-textual dialog data representations. Training on VISDIAL v1.0 benchmark dataset, we achieve a competitive result that outperforms some of the existing state-of-the-art models.
On Computer Science and Modern Educational Technology

With the development of economy and society, computer science and technology are constantly updating and developing, which has brought a qualitative leap to various fields, and the field of education is no exception. Computer science and technology plays an important role in modern educational technology with its high efficiency and fast characteristics, and brings technological innovation and support to modern educational technology. The purpose of this paper is to introduce the internal relationship between computer science and modern educational technology, to expound the profound influence of computer science and technology on modern educational technology, and to reveal the important significance between them.
Faster Training of Edge-attention Aided 6D Pose Estimation Model using Transfer Learning and Small Customized Dataset

Computer Vision is the field of machine learning that deals with computers gaining knowledge from digital images/videos and performing tasks that human vision is capable of doing. It is widely used in the field of robotics for designing guidance systems where objects in the robot's field of view are identified and located. This research work is an application-specific project enabling a half-humanoid to find the 6D pose and bounding boxes of its hand and other objects within its field of view. We add an edge prediction head to the NOCS (Normalised Object Coordinate Space) model, which predicts the edges of each object from the predicted instance maps. An additional edge-agreement-loss found from the predicted edges is added to the total loss. This increases the attention to the edges and improves the accuracy of prediction of the instance masks. This edge-attention aided model is initialized with pre-trained weights of CAMERA and REAL dataset using transfer learning. The backbone layers of the model are frozen and the head layers alone are trained using a synthetic dataset (HAND dataset) we created using a software called blender. The model gives promising results when tested with objects kept in varying lighting conditions and at different distances from the camera. The use of transfer learning in models as large as the NOCS model allows us to train the model for a new class by only training the top few layers with a significantly small dataset.
An Efficient RRT-based Framework for Planning Short and Smooth Alige.x Robot Motion

Autonomous navigation is a crucial technology for the automatic operation of mobile robots, in which path planning is the core part. In a complicated and dynamic environment, planning efficiency is crucial. In this paper, we propose a hybrid path planning scheme for a differentially driven wheeled robot with dynamical constraints by combining global path planning and local hierarchical architectures in a complex dynamic environment. In path planning under the hybrid structure, first, we generate a global path based on the adaptive goal-directed RRT algorithm. This way can achieve Efficiency. Local path planning uses the DWA with PID controller instead of the cost function for real-time motion control. A seamless interface between the global and local path planning mechanisms is designed to contribute to the whole hybrid path planning scheme. Finally, the experimental results on the grid map prove the potency and feasibility of our method.
Reinforcement Stacked Learning with Semantic-Associated Attention for Visual Question Answering

The task of visual question answering (VQA) is to generate an answer for a question according to the content of an image being asked. In this process, the critical problems of effectively embedding the question feature and image feature as well as transforming the features to the prediction of answer are still faithfully unresolved. In this paper, depending on these problems, a semantic-associated attention method and a reinforcement stacked learning mechanism are proposed. Firstly, within the associations of high-level semantics, a visual spatial attention model (VSA) and a multi-semantic attention model (MSA) are proposed to extract the low-level image feature and high-level semantic feature, respectively. Furthermore, we develop a reinforcement stacked learning architecture, which splits the transformation process into multiple stages, to gradually approach the answers. At each stage, a new reinforcement learning (RL) method is introduced to directly criticize inappropriate answers to optimize the model. The extensive experiments on the VQA task show that our method can achieve state-of-the-art performance.