Reviewerly/exHarmony huggingface dataset:
TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport

Using the principle of imitation learning and the theory of optimal transport we propose in this paper a novel model for unsupervised domain adaptation named Teacher Imitation Domain Adaptation with Optimal Transport (TIDOT). Our model includes two cooperative agents: a teacher and a student. The former agent is trained to be an expert on labeled data in the source domain, whilst the latter one aims to work with unlabeled data in the target domain. More specifically, optimal transport is applied to quantify the total of the distance between embedded distributions of the source and target data in the joint space, and the distance between predictive distributions of both agents, thus by minimizing this quantity TIDOT could mitigate not only the data shift but also the label shift. Comprehensive empirical studies show that TIDOT outperforms existing state-of-the-art performance on benchmark datasets.
Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

Abstract With the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.
BROCCOLI: overlapping and outlier-robust biclustering through proximal stochastic gradient descent

Abstract Matrix tri-factorization subject to binary constraints is a versatile and powerful framework for the simultaneous clustering of observations and features, also known as biclustering. Applications for biclustering encompass the clustering of high-dimensional data and explorative data mining, where the selection of the most important features is relevant. Unfortunately, due to the lack of suitable methods for the optimization subject to binary constraints, the powerful framework of biclustering is typically constrained to clusterings which partition the set of observations or features. As a result, overlap between clusters cannot be modelled and every item, even outliers in the data, have to be assigned to exactly one cluster. In this paper we propose Broccoli , an optimization scheme for matrix factorization subject to binary constraints, which is based on the theoretically well-founded optimization scheme of proximal stochastic gradient descent. Thereby, we do not impose any restrictions on the obtained clusters. Our experimental evaluation, performed on both synthetic and real-world data, and against 6 competitor algorithms, show reliable and competitive performance, even in presence of a high amount of noise in the data. Moreover, a qualitative analysis of the identified clusters shows that Broccoli may provide meaningful and interpretable clustering structures.
A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors

Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index (kc) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the kc and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric.
A Hybrid Approach for Paper Recommendation

Paper recommendation has become an increasingly important yet challenging task due to the rapidly expanding volume and scope of publications in the broad research community. Due to the lack of user profiles in public digital libraries, most existing methods for paper recommendation are through paper similarity measurements based on citations or contents, and still suffer from various performance issues. In this paper, we construct a graphical form of citation relations to identify relevant papers and design a hybrid recommendation model that combines both citation- and content-based approaches to measure paper similarities. Considering that citations at different locations in one article are likely of different significance, we define a concept of citation similarity with varying weights according to the sections of citations. We evaluate the performance of our recommendation method using Spearman correlation on real publication data from public digital libraries such as CiteSeer and Wanfang. Extensive experimental results show that the proposed hybrid method exhibits better performance than state-of-the-art techniques, and achieves 40% higher recommendation accuracy in average in comparison with citation-based approaches.
Evaluation of Reinforcement and Deep Learning Algorithms in Controlling Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) are abundantly becoming a part of society, which is a trend that is expected to grow even further. The quadrotor is one of the drone technologies that is applicable in many sectors and in both military and civilian activities, with some applications requiring autonomous flight. However, stability, path planning, and control remain significant challenges in autonomous quadrotor flights. Traditional control algorithms, such as proportional-integral-derivative (PID), have deficiencies, especially in tuning. Recently, machine learning has received great attention in flying UAVs to desired positions autonomously. In this work, we configure the quadrotor to fly autonomously by using agents (the machine learning schemes being used to fly the quadrotor autonomously) to learn about the virtual physical environment. The quadrotor will fly from an initial to a desired position. When the agent brings the quadrotor closer to the desired position, it is rewarded; otherwise, it is punished. Two reinforcement learning models, Q-learning and SARSA, and a deep learning deep Q-network network are used as agents. The simulation is conducted by integrating the robot operating system (ROS) and Gazebo, which allowed for the implementation of the learning algorithms and the physical environment, respectively. The result has shown that the Deep Q-network network with Adadelta optimizer is the best setting to fly the quadrotor from the initial to desired position.
Effect of chaos on the simulation of quantum critical phenomena in analog quantum simulators

We study how chaos, introduced by a weak perturbation, affects the reliability of the output of analog quantum simulation. As a toy model, we consider the Lipkin-Meshkov-Glick model. Inspired by the semiclassical behavior of the order parameter in the thermodynamic limit, we propose a protocol to measure the quantum phase transition in the ground state and the dynamical quantum phase transition associated with quench dynamics. We show that the presence of a small time-dependent perturbation can render the dynamics of the system chaotic. We then show that the estimates of the critical points of these quantum phase transitions, obtained from the quantum simulation of its dynamics, are robust to the presence of this chaotic perturbation, while other aspects of the system, such as the mean magnetization, are fragile and therefore cannot be reliably extracted from this simulator. This can be understood in terms of the simulated quantities that depend on the global structure of phase space vs those that depend on local trajectories.
Model-based resource analysis and synthesis of service-oriented automotive software architectures

Abstract Context Automotive software architectures describe distributed functionality by an interaction of software components. One drawback of today’s architectures is their strong integration into the onboard communication network based on predefined dependencies at design time. The idea is to reduce this rigid integration and technological dependencies. To this end, service-oriented architecture offers a suitable methodology since network communication is dynamically established at run-time. Aim We target to provide a methodology for analysing hardware resources and synthesising automotive service-oriented architectures based on platform-independent service models. Subsequently, we focus on transforming these models into a platform-specific architecture realisation process following AUTOSAR Adaptive. Approach For the platform-independent part, we apply the concepts of design space exploration and simulation to analyse and synthesise deployment configurations, i. e., mapping services to hardware resources at an early development stage. We refine these configurations to AUTOSAR Adaptive software architecture models representing the necessary input for a subsequent implementation process for the platform-specific part. Result We present deployment configurations that are optimal for the usage of a given set of computing resources currently under consideration for our next generation of E/E architecture. We also provide simulation results that demonstrate the ability of these configurations to meet the run time requirements. Both results helped us to decide whether a particular configuration can be implemented. As a possible software toolchain for this purpose, we finally provide a prototype. Conclusion The use of models and their analysis are proper means to get there, but the quality and speed of development must also be considered.
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification

Data augmentation aims to enrich training samples for alleviating the overfitting issue in low-resource or class-imbalanced situations.Traditional methods first devise task-specific operations such as Synonym Substitute, then preset the corresponding parameters such as the substitution rate artificially, which require a lot of prior knowledge and are prone to fall into the sub-optimum.Besides, the number of editing operations is limited in the previous methods, which decreases the diversity of the augmented data and thus restricts the performance gain.To overcome the above limitations, we propose a framework named Text AutoAugment (TAA) to establish a compositional and learnable paradigm for data augmentation.We regard a combination of various operations as an augmentation policy and utilize an efficient Bayesian Optimization algorithm to automatically search for the best policy, which substantially improves the generalization capability of models.Experiments on six benchmark datasets show that TAA boosts classification accuracy in low-resource and class-imbalanced regimes by an average of 8.8% and 9.7%, respectively, outperforming strong baselines.
Effective Hybrid Content-Based Collaborative Filtering Approach for Requirements Engineering

Requirements engineering (RE) is among the most valuable and critical processes in software development.The quality of this process significantly affects the success of a software project.An important step in RE is requirements elicitation, which involves collecting project-related requirements from different sources.Repositories of reusable requirements are typically important sources of an increasing number of reusable software requirements.However, the process of searching such repositories to collect valuable project-related requirements is time-consuming and difficult to perform accurately.Recommender systems have been widely recognized as an effective solution to such problem.Accordingly, this study proposes an effective hybrid content-based collaborative filtering recommendation approach.The proposed approach will support project stakeholders in mitigating the risk of missing requirements during requirements elicitation by identifying related requirements from software requirement repositories.The experimental results on the RALIC dataset demonstrate that the proposed approach considerably outperforms baseline collaborative filtering-based recommendation methods in terms of prediction accuracy and coverage in addition to mitigating the data sparsity and cold-start item problems.
A random encoding method for secure data communication: An extension of sequential coding

AbstractAbstractThe security of data communication is very important in all aspects. V. Shukla et al. had proposed a secure sequential coding method. They have shown the results for text secret messages only. In this paper, firstly we extend their scheme by showing the results for image secret messages. Secondly, we upgrade their scheme by proposing the similar method by using random encoding. Random encoding enhances the level of security. A separate section is provided for security analysis and associated advantages.Subject Classification: Primary 93A30Secondary 49K15Keywords: CryptographyData communicationRandomnessSecrecySteganography
Methodological Experience in the Teaching-Learning of the English Language for Students with Visual Impairment

This document evidences an innovative methodological vision in the teaching-learning process of the English language focused on the inclusion of all its students in a heterogeneous learning environment. Learning a foreign language as a second language is a challenge that all students must overcome, but this challenge is more significant in visually impaired students. Therefore, the present research is based on a historical, descriptive process that reviews high-impact scientific articles supported by recognized databases. Later on, it warns about breaking down the stages of the process, supporting the analytical method, and then joining each stage, starting from the simple to the complex using the synthetic method, thus consummating a complete analytical-synthetic process. In this way, the present work shows a description of the methodological experience from a set of good practices for visually impaired students in the classroom environment. The proposed work systematizes the methodology applied in the educational process and categorizes the stages of the methodological process developed according to the skills and abilities required for learning by using typhlo-technical tools. On the other hand, the research determines a route map that allows and facilitates the management of the method implemented in learning the English language for students with visual impairment. Consequently, this constitutes a valid alternative for educational inclusion within an innovative environment that welcomes the use of information and communication technologies to achieve the natural inclusion and autonomous participation of students with visual impairment.
Atom correlation based graph propagation for scene graph generation

Long-tailed distribution in the dataset is one of the major problems of the scene graph generation task. Previous methods attempt to alleviate this by introducing human commonsense knowledge in the form of statistical correlations between object pairs. However, the reasoning path they used is usually composable and the prior knowledge they employed is generally image-specific, making the knowledge learning less flexible, stable and holistic. In this paper, we propose Atom Correlation Based Graph Propagation (AC-GP) for the scene graph generation task. Specifically, diverse atom correlations between objects and their relationships are explored by separating relationships to form new semantic nodes and decomposing the compound reasoning paths. Based on these atom correlations, the knowledge graphs are introduced for the feature enhancement by information propagating in the global category space. By exploiting atom correlations, the introduced prior knowledge can be more common and easy to learn. Moreover, propagating the knowledge in the global category space enables the model aware of more comprehensive and holistic knowledge. As a result, the model capacity and stability can be effectively improved to mine infrequent and missed relationships. Experimental results on two benchmark datasets: Visual Relation Detection (VRD) and Visual Genome (VG) show the superiority of the proposed AC-GP over strong baseline methods.
Rain Streaks Removal in digital images by Dictionary based sparsity process with MCA Estimation

During the rainy season, many public outdoor crimes have been caught through video surveillance, and they do not have complete feature information to identify the image features. Rain streak removal techniques are ideal for indexing and obtaining additional information from such images. Furthermore, the rain substantially changes the intensity of images and videos, lowering the overall image quality of vision systems in outdoor recording situations. To be successful, the elimination of rain streaks in the film will require an advanced trial and error method. Different methods have been utilized to identify and eliminate the rainy effects by using the data on photon numbers, chromaticity, and probability of rain streaks present in digital images. This research work includes sparse coding process for removing rain streak by incorporating morphological component analyses (MCA) based algorithm. Based on the MCA algorithm, the coarse estimation becomes very simple to handle the rain streak or impulsive noisy images. The sparse decomposition of coarse is possible by estimating and eliminating all redundancies from the sources. This novel MCA approach is combined with sparsity coding process to provide better PSNR and less MSE results from the reconstructed images. This method is compared with of the existing research works on rain streak removal process. Besides, the obtained the results are illustrated and tabulated.
Semi-Supervised Domain Adaptation Using Explicit Class-Wise Matching for Domain-Invariant and Class-Discriminative Feature Learning

Semi-supervised domain adaptation (SSDA) is a promising technique for various applications. It can transfer knowledge learned from a source domain having high-density labeled samples to a target domain having limited labeled samples. Several previous works have attempted to reduce the distribution discrepancy between source domain and target domain by using adversarial-based or entropy-based methods. These works have improved the performance of SSDA. However, there are still lacunae in producing class-wise domain-invariant features, which impair the improvement of the classification accuracy in the target domain. We propose a novel mapping function using explicit class-wise matching that can make a better decision boundary in the embedding space for superior classification accuracy in the target domain. In general, in a target domain with low-density label samples, it is more challenging to create a well-organized distribution for the classification than in a source domain where rich label information is available. In our mapping function, a representative vector of each class in the embedding spaces of the source and target domains is derived and aligned by using class-wise matching. It is observed that the distribution in the embedding space of the source domain can be effectively reproduced in the target domain. Our method achieves outstanding accuracy of classification in the target domain compared with previous works on the Office-31, Office-Home, Visda2017 and DomainNet datasets.
Weighted stochastic block model

We propose a weighted stochastic block model (WSBM) which extends the stochastic block model to the important case in which edges are weighted. We address the parameter estimation of the WSBM by use of maximum likelihood and variational approaches, and establish the consistency of these estimators. The problem of choosing the number of classes in a WSBM is addressed. The proposed model is applied to simulated data and an illustrative data set.
Accelerating privacy-preserving momentum federated learning for industrial cyber-physical systems

Abstract Federated learning (FL) is a distributed learning approach, which allows the distributed computing nodes to collaboratively develop a global model while keeping their data locally. However, the issues of privacy-preserving and performance improvement hinder the applications of the FL in the industrial cyber-physical systems (ICPSs). In this work, we propose a privacy-preserving momentum FL approach, named PMFL, which uses the momentum term to accelerate the model convergence rate during the training process. Furthermore, a fully homomorphic encryption scheme CKKS is adopted to encrypt the gradient parameters of the industrial agents’ models for preserving their local privacy information. In particular, the cloud server calculates the global encrypted momentum term by utilizing the encrypted gradients based on the momentum gradient descent optimization algorithm (MGD). The performance of the proposed PMFL is evaluated on two common deep learning datasets, i.e., MNIST and Fashion-MNIST. Theoretical analysis and experiment results confirm that the proposed approach can improve the convergence rate while preserving the privacy information of the industrial agents.
Hybrid feature selection approach to identify optimal features of profile metadata to detect social bots in Twitter

Abstract The last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we propose a hybrid feature selection (FS) method to evaluate profile metadata features to find these optimal features, which are evaluated using random forest, naïve Bayes, support vector machines, and neural networks. We found that the cross-validation attribute evaluation performance was the best when compared to other FS methods. Our results show that the random forest classifier with six optimal features achieved the best score of 94.3% for the area under the curve. The results maintained overall 89% accuracy, 83.8% precision, and 83.3% recall for the bot class. We found that using four features: favorites_count , verified , statuses_count , and average_tweets_per_day, achieves good performance metrics for bot detection (84.1% precision, 81.2% recall).