Reviewerly/exHarmony huggingface dataset:
Error analysis and optimization for Risley-prism imaging distortion correction

The reverse ray-tracing method has become a well-known technique to correct the dynamic imaging distortion caused by the Risley-prism imaging system due to its precision and computational efficiency. However, the reverse ray-tracing method is sensitive to equipment error, which seriously degrades the quality of distortion correction when using a prism with a large wedge angle or a camera with a large field of view. We optimize the distortion correction method utilizing reverse ray tracing. In addition, we propose a distortion correction model with error parameters to investigate the influence of prism orientation error, prism tilt error, prism parameter error, and model simplification errors on the correction accuracy. The work on the optimized model clearly indicates the obvious image distortion introduced by different kinds of errors, including model error and systematic error. Furthermore, we propose an error parameter identification method to eliminate the negative results of error on the image correction. The simulation results show that the boresight pointing error and distortion correction error are reduced to about 1% of the initial value after 10 iterations, thus achieving high-precision imaging distortion correction and providing better data support for other subsequent applications.
Research on refined reconstruction method of airfoil pressure based on compressed sensing

The placement of pressure taps on the surface of the wind tunnel test model is an important means to obtain the surface pressure distribution. However, limited by space location and experimental cost, it is difficult to arrange enough pressure measuring taps on the surface of complex models to obtain complete pressure distribution information, thus it is impossible to obtain accurate lift and moment characteristics through integration. The paper proposes a refined reconstruction method of airfoil surface pressure based on compressed sensing, which can reconstruct the pressure distribution with high precision with less pressure measurement data. Tests on typical airfoil subsonic flow around flow show that the accuracy of lift and moment after the pressure integration reconstructed by 4-8 measuring points can meet the requirements of the national military standard. The algorithm is robust to noise, and provides a new idea for obtaining accurate force data from sparse surface pressure tests in engineering.
A General Framework for Feature Selection Under Orthogonal Regression With Global Redundancy Minimization

Feature selection has attracted a lot of attention in obtaining discriminative and non-redundant features from high-dimension data. Compared with traditional filter and wrapper methods, embedded methods can obtain a more informative feature subset by fully considering the importance of features in the classification tasks. However, the existing embedded methods emphasize the above importance of features and mostly ignore the correlation between the features, which leads to retain the correlated and redundant features with similar scores in the feature subset. To solve the problem, we propose a novel supervised embedded feature selection framework, called feature selection under global redundancy minimization in orthogonal regression (GRMOR). The proposed framework can effectively recognize redundant features from a global view of redundancy among the features. We also incorporate the large margin constraint into GRMOR for robust multi-class classification. Compared with the traditional embedded methods based on least square regression, the proposed framework utilizes orthogonal regression to preserve more discriminative information in the subspace, which can help accurately rank the importance of features in the classification tasks. Experimental results on twelve public datasets demonstrate that the proposed framework can obtain superior classification performance and redundancy removal performance than twelve other feature selection methods.
Task-adaptive Asymmetric Deep Cross-modal Hashing

Supervised cross-modal hashing aims to embed the semantic correlations of heterogeneous modality data into the binary hash codes with discriminative semantic labels. It can support efficient large-scale cross-modal retrieval due to the fast retrieval speed and low storage cost. However, existing methods equally handle the cross-modal retrieval tasks, and simply learn the same couple of hash functions in a symmetric way. Under such circumstances, the characteristics of different cross-modal retrieval tasks are ignored and sub-optimal performance may be brought. Motivated by this, we present a Task-adaptive Asymmetric Deep Cross-modal Hashing (TA-ADCMH) method in this paper. It can learn task-adaptive hash functions for two sub-retrieval tasks via simultaneous modality representation and asymmetric hash learning. Different from previous cross-modal hashing methods, our learning framework jointly optimizes the semantic preserving from multi-modal features to the hash codes, and the semantic regression from query modality representation to the explicit labels. With our model, the learned hash codes can effectively preserve the multi-modal semantic correlations, and meanwhile, adaptively capture the query semantics. Besides, we design an efficient discrete optimization strategy to directly learn the binary hash codes, which alleviates the relaxing quantization errors. Extensive experiments demonstrate the state-of-the-art performance of the proposed TA-ADCMH from various aspects.
An approach towards development of new linear regression prediction model for reduced energy consumption and SLA violation in the domain of green cloud computing

With the increase of mega-cities, the demand for Smart Cities is overgrowing. The mega-cities can be smarter through the Cloud of Things (CoT). Efficient energy consumption in Smart Cities has a massive impact on the environment. But, computational power is increasing rapidly in the cloud computing environment. Enormous energy consumption (EC) and Service Level Agreement violation (SLAV) becomes a key concern. The Virtual Machine (VM) consolidation approach can significantly reduce EC, SLA violation (SLAV), and increase resource utilization. However, dynamic VM consolidation may produce performance degradation of Physical Machines (PMs) and SLAV. Therefore, it becomes essential to find a trade-off between EC and SLAV. Herein, a novel New Linear Regression(NLR) prediction model, host overload/underload, and VM placement policy have been proposed to reduce EC and SLAV. The NLR model’s primary intention is to take that the model goes through a straight line and a mean point. Future CPU utilization is predicted based on the proposed NLR model. Evaluation of proposed algorithms has been accomplished by extending CloudSim Simulator. The experiment shows that proposed algorithms reduced EC and SLAV in cloud data centers and can be used to construct a smart and sustainable environment for Smart Cities.
BONIK: A Blockchain Empowered Chatbot for Financial Transactions

A Chatbot is a popular platform to enable users to interact with a software or website to gather information or execute actions in an automated fashion. In recent years, chatbots are being used for executing financial transactions, however, there are a number of security issues, such as secure authentication, data integrity, system availability and transparency, that must be carefully handled for their wide-scale adoption. Recently, the blockchain technology, with a number of security advantages, has emerged as one of the foundational technologies with the potential to disrupt a number of application domains, particularly in the financial sector. In this paper, we forward the idea of integrating a chatbot with blockchain technology in the view to improve the security issues in financial chatbots. More specifically, we present BONIK, a blockchain empowered chatbot for financial transactions, and discuss its architecture and design choices. Furthermore, we explore the developed Proof-of-Concept (PoC), evaluate its performance, analyse how different security and privacy issues are mitigated using BONIK.
Adaptive density-based clustering algorithm with shared KNN conflict game

Recently, density-based clustering algorithms have been garnering considerable attention in the unsupervised learning field as they can identify arbitrary cluster shapes. However, classical density-based algorithms are non-backtracking such as density-based spatial clustering of applications with noise (DBSCAN) and its improved variants. Therefore, the allocation of samples cannot be optimised during the clustering process. An incorrect division can cause subsequent errors. Furthermore, when the density is unevenly distributed, the globally uniform parameters will inevitably lead to catastrophic error consequences. To address these limitations, this study proposes an adaptive density-based clustering algorithm using the shared k-nearest neighbours (SKNN) conflict game (DC-SKCG). A local density-based adaptive cut-off distance-setting method is designed for the DC-SKCG, and a conflict game method based on SKNN is proposed to optimise the clustering process. Moreover, an automatic fusion mechanism for redundant high-density core regions is presented to (1) reduce the parameter sensitivity of the algorithm and (2) improve the parameter tolerance capacity. A series of experiments are conducted using various datasets demonstrate that DC-SKCG offers higher accuracy and robustness in most cases than state-of-the-art methods.
Local–Global Attentive Adaptation for Object Detection

Adversarial adaptive methods have been proven to be useful for domain transfer in many fields such as image recognition and semantic segmentation, etc However, for object detection, since each image could have different combinations of objects, brutally aligning all the images without considering their transferability may cause the notorious phenomena named ‘negative transfer’. On the other hand, strong matching the local-level features makes sense, as it not only reduces the discrepancy between different domain distributions, but preserves the category-level semantic information. However, it is hard to markedly achieve domain invariance using a simple adversarial adaptive method. In this work, we propose an effective method termed Local–Global Attentive Adaptation for object Detection (LGAAD). Our method can alleviate the negative transfer caused by improper global alignments through leveraging an adaptively and dynamically weighted transferability to highlight the more transferable images. Furthermore, the proposed method also achieves the strong matching between two domains at local-level features to alleviate the cross-domain discrepancy by using the attention mechanism after multiple local discriminators. Additionally, we also consider the domain impacts of instance-wise features and backgrounds in images with large domain divergence, a non-negligible factor for improving the domain adaptive detection model performance. Extensive experiments of various domain shift scenarios show that our method exceeds the state-of-the-art results on several public datasets. Furthermore, qualitative visualization and ablation analyzes can demonstrate the validity of our approach for attending the interested regions and instances on domain adaptation.
Making Robots Draw A Vivid Portrait In Two Minutes

Significant progress has been made with artistic robots. However, existing robots fail to produce high-quality portraits in a short time. In this work, we present a drawing robot, which can automatically transfer a facial picture to a vivid portrait, and then draw it on paper within two minutes averagely. At the heart of our system is a novel portrait synthesis algorithm based on deep learning. Innovatively, we employ a self-consistency loss, which makes the algorithm capable of generating continuous and smooth brush-strokes. Besides, we propose a componential sparsity constraint to reduce the number of brush-strokes over insignificant areas. We also implement a local sketch synthesis algorithm, and several pre- and post-processing techniques to deal with the background and details. The portrait produced by our algorithm successfully captures individual characteristics by using a sparse set of continuous brush-strokes. Finally, the portrait is converted to a sequence of trajectories and reproduced by a 3-degree-of-freedom robotic arm. The whole portrait drawing robotic system is named AiSketcher. Extensive experiments show that AiSketcher can produce considerably high-quality sketches for a wide range of pictures, including faces in-the-wild and universal images of arbitrary content. To our best knowledge, AiSketcher is the first portrait drawing robot that uses neural style transfer techniques. AiSketcher has attended a quite number of exhibitions and shown remarkable performance under diverse circumstances.
Towards Efficient Secure Aggregation for Model Update in Federated Learning

Currently, a large volume of IoT devices generate huge amounts of data in edge networks, which can open up many research and applications for machine learning. However, traditional machine learning requires data to be sent to a server and centrally trained, which will cause the waste of the bandwidth and expose privacy of individuals. Federated learning allows data to be locally trained in their device and only send model updates to the central server for aggregation. But the security of model updates in the aggregation should also be carefully addressed. Existing works mainly focus on secure multiparty computation or differential privacy, which depends on heavy encryption or brings low accuracy. In this paper, we propose an efficient secure aggregation method for model updates in federated learning by pre-processing the model updates from each participant and only encrypting portion of the processed updates by functional encryption for inner product to protect the whole parameters, thus achieving efficient aggregation of model update vectors. Security analysis and experimental evaluation demonstrate that our scheme can efficiently aggregate the model updates without losing security.
Harmonic-Based Optimal Motion Planning in Constrained Workspaces Using Reinforcement Learning

In this work, we propose a novel reinforcement learning algorithm to solve the optimal motion planning problem. Particular emphasis is given on the rigorous mathematical proof of safety, convergence as well as optimality w.r.t. to an integral quadratic cost function, while reinforcement learning is adopted to enable the cost function's approximation. Both offline and online solutions are proposed, and an implementation of the offline method is compared to a state-of-the-art RRT* approach. This novel approach inherits the strong traits from both artificial potential fields, i.e., reactivity, as well as sampling-based methods, i.e., optimality, and opens up new paths to the age-old problem of motion planning, by merging modern tools and philosophies from various corners of the field.
Multi-domain few-shot image recognition with knowledge transfer

Few-shot image recognition aims to recognize novel categories with only few labeled images in each class. Existing metric-based and meta-based few-shot learning algorithms have achieved significant progress, but most methods use only visual features. And our humanity can recognize novel categories by learning from prior knowledge, such as semantic information. Based on this intuition, we propose a model that can adaptively integrate visual and semantic information to recognize novel categories. Moreover, the current few-shot learning algorithms fail to generalize to unseen domains due to the domain shift across domains. To reduce the domain shift, we use the weight imprinting strategy as it provides immediate good classification performance and initialization for any further fine-tuning in the future. And we adopt a fine-tuning strategy to simulate various feature distributions under different domains. We conduct extensive experiments to evaluate the effectiveness of the proposed model on three datasets: miniImageNet, CUB, Stanford Dogs. Experimental results demonstrate that our cross-modal scheme gets encouraging improvements in the single-domain and cross-domain few-shot classification tasks.
Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Vision

Transformer architectures have brought about fundamental changes to computational linguistic field, which had been dominated by recurrent neural networks for many years. Its success also implies drastic changes in cross-modal tasks with language and vision, and many researchers have already tackled the issue. In this paper, we review some of the most critical milestones in the field, as well as overall trends on how transformer architecture has been incorporated into visuolinguistic cross-modal tasks. Furthermore, we discuss its current limitations and speculate upon some of the prospects that we find imminent.
Lightweight Unmanned Aerial System for Time-Domain Electromagnetic Prospecting—The Next Stage in Applied UAV-Geophysics

Nowadays in solving geological problems, the technologies of UAV-geophysics, primarily magnetic and gamma surveys, are being increasingly used. However, for the formation of the classical triad of airborne geophysics methods in the UAV version, there was not enough technology for UAV-electromagnetic sounding, which would allow studying the geological environment at depths of tens and hundreds of meters with high detail. This article describes apparently the first technology of UAV-electromagnetic sounding in the time domain (TDEM, TEM), implemented as an unmanned system based on a light multi-rotor UAV. A measuring system with an inductive sensor—an analogue of a 20 × 20 or 50 × 50 m receiving loop is towed by a UAV, and a galvanically grounded power transmitter is on the ground and connected to a pulse generator. The survey is carried out along a network of parallel lines at low altitude with a terrain draping at a speed of 7–8 m/s, the maximum distance of the UAV’s departure from the transmitter line can reach several kilometers, thus the created technology is optimal for performing detailed areal electromagnetic soundings in areas of several square kilometers. The results of the use of the unmanned system (UAS) in real conditions of the mountainous regions of Eastern Siberia are presented. Based on the obtained data, the sensitivity of the system was simulated and it was shown that the developed technology allows one to collect informative data and create geophysical sections and maps of electrical resistivity in various geological situations. According to the authors, the emergence of UAV-TEM systems in the near future will significantly affect the practice of geophysical work, as it was earlier with UAV-magnetic prospecting and gamma-ray survey.
Information System for Monitoring and Managing the Quality of Educational Programs

The article explores the issues of the objective approach to managing the educational process allowing to effectively update and monitor it.The study is aimed at organizing the processes of control and management of the quality of educational programs with reduced time costs.The authors have researched the processes of methodological support of the educational process and the corresponding documents of an educational organization.An information system for monitoring and managing the quality of educational programs has been developed.The authors have developed models of the problem area, allowing one to determine the bottlenecks in organizing education quality control.The results of the study are meant for the internal audit of educational services and carrying out the procedures of public control, accreditation, or licensing of an educational organization.They allow for the quantitative evaluation of the educational program content and unambiguous interpretation of the obtained result.For this, the authors have developed an objective system for assessing the quality of educational programs.The assessment includes a set of objective indicators with a certain significance: for example, compliance with certain standards, the use of relevant peer-reviewed literature, agreed forms of education, a point-rating system, etc.All criteria are divided into blocks, each of which has a corresponding conformity assessment system.The developed assessment scale unifies the quality monitoring procedure, reduces subjective evaluations, and ensures less time-consuming monitoring of the quality of an educational program at the different stages of its life cycle.
The Influence of Digital Tools and Social Networks on the Digital Competence of University Students during COVID-19 Pandemic

The pandemic caused by COVID-19 has generated a transformation in students’ competences and university education, especially in the use of digital tools. This study aims to analyze the use of digital tools and social networks of university students during the COVID-19 pandemic. For the collection of information, a validated Likert questionnaire (10-point scale) was adopted. The instrument consisted of a total of 66 items comprising a total of seven dimensions. The sample contained 581 students pursuing degrees in Childhood Education and Primary Education. The analysis of the available information was carried out in two different stages. First, we started by performing an exploratory factorial analysis (EFA) to determine the underlying structure of the Digital Competence of Higher Education Students (DCHES) scale factor. In the second phase, we used SEM (structural equation modeling), a statistical approach to test the relationships between observed and latent variables. More specifically, we estimated a multiple indicators multiple causes (MIMIC) model. The results showed the importance of two of the considered covariates in explaining the variability of the different dimensions of the scale analyzed (DCHES) considering the use of social networks and digital tools of university students. In this sense, both the degree to which virtual tools are used to develop teamwork and the degree of use of YouTube when communicating most fully explained the level of digital skills among the university students studied.
Dota 2 Bot Competition

Multiplayer Online Battle Area (MOBA) games are a recent huge success both in the video game industry and the international eSports scene. These games encourage team coordination and cooperation, short and long-term planning, within a real-time combined action and strategy gameplay. Artificial Intelligence and Computational Intelligence in Games research competitions offer a wide variety of challenges regarding the study and application of AI techniques to different game genres. These events are widely accepted by the AI/CI community as a sort of AI benchmarking that strongly influences many other research areas in the field. This paper presents and describes in detail the Dota 2 Bot competition and the Dota 2 AI framework that supports it. This challenge aims to join both, MOBAs and AI/CI game competitions, inviting participants to submit AI controllers for the successful MOBA \textit{Defense of the Ancients 2} (Dota 2) to play in 1v1 matches, which aims for fostering research on AI techniques for real-time games. The Dota 2 AI framework makes use of the actual Dota 2 game modding capabilities to enable to connect external AI controllers to actual Dota 2 game matches using the original Free-to-Play game.se of the actual Dota 2 game modding capabilities to enable to connect external AI controllers to actual Dota 2 game matches using the original Free-to-Play game.