Reviewerly/exHarmony huggingface dataset:
Active Learning SAR Image Classification Method Crossing Different Imaging Platforms

Synthetic aperture radar (SAR) image classification task when the training and test sets have different distributions can be initially solved using existing domain adaptation (DA) methods. However, considering that none of their classification accuracy is high, this letter proposes an active learning DA classification method to further solve this task. First, an adversarial learning-based DA pipeline is put forth, using labeled source and unlabeled target domains to conduct adversarial learning in order to narrow the domain gap. A prototype regularization process is then built, which further enhances the target domain data clusters’ ability to discriminate between them. In order to fully improve SAR image classification accuracy, we then propose a dynamic hard sample selection process to choose hard samples to supplement into the subsequent stage of training samples. This process involves moving the gradient direction of the query function closer to the gradient direction of the class margin objective function. Extensive experiments on SAR image datasets with different distributions from different imaging platforms and optical remote sensing datasets have verified the effectiveness and superiority of the proposed method.
Self-service kits to scale knowledge to autonomous teams - concept, application and limitations

In large organizations, it is not trivial to spread knowledge to all teams. Often, individual teams need to handle similar topics and re-invent the wheel. Another scenario is that a group of people with a common role (for example ?guild? in Spotify model) has to distill their practices to make them shareable. Trainings should have empower participants so to apply the learnings easily in their daily businesses. To realize this, the proposed Self-Service Kit (SSK) approach can be used in the context of a holistic methodology that fosters team autonomy while leveraging knowledge spread and sharing throughout a large organization. Such a methodology is presented and instantiated in an enterprise context in facing the mentioned challenges.
Flow-Based Reinforcement Learning

This paper presents a novel Flow-based reinforcement learning strategy to model agent systems that can adapt to complex and dynamic problem environments by incrementally mastering their skills.It is inspired by the psychological notion of Flow that describes the optimal mental state experienced by an individual when they are fully immersed in a task and find it intrinsically rewarding to engage with.The proposed model presents an algorithm to describe the Flow experience such that agents can be trained through finer distinctions to the challenges across training time to maintain them in the Flow zone.In contrast to the traditional and incremental learning approaches that suffer from limitations associated with overfitting, the Flow-based model drives agent behaviours not simply through external goals but also through intrinsic curiosity to improve their skills and thus the performance levels.Experimental evaluations are conducted across two simulation environments on a maze navigation task and a reward collection task with comparisons against a generic reinforcement learning model and an incremental reinforcement learning model.The results reveal that these two models are prone to overfit under different design decisions and loose the ability to perform in dynamic variations of the tasks in varying degrees.Conversely, the proposed Flow-based model is capable of achieving near optimal solutions with random environmental factors, appropriately utilising the previously learned knowledge to identify robust solutions to complex problems.
Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering

Outliers are data points that significantly deviate from other data points in a data set because of different mechanisms or unusual processes. Outlier detection is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for data cleansing in data science. In this study, we propose two novel outlier detection approaches using the typicality degrees which are the partitioning result of unsupervised possibilistic clustering algorithms. The proposed approaches are based on finding the atypical data points below a predefined threshold value, a possibilistic level for evaluating a point as an outlier. The experiments on the synthetic and real data sets showed that the proposed approaches can be successfully used to detect outliers without considering the structure and distribution of the features in multidimensional data sets.
SIDA-GAN: A lightweight Generative Adversarial Network for Single Image Depth Approximation

SIDA-GAN is a set of GAN based deep learning models that can be employed for predicting the depth map for a given single input RGB image. This article explores the research gap present in the state of the art which lacks GAN based depth predictors implemented using Depthwise Separable Convolution (DSC). DSC reduces the number of computations in comparison with standard convolution for the same convolution task, therefore making the model computationally efficient. The adversarial loss applied by the GAN model make the predicted depth image globally coherent while loss functions like L1, SSIM fine tunes the local feature predictions leading to enhanced prediction quality. In this proposal, the depth predictor (SIDA-GAN generator) is an encoder-decoder design featuring DSC and skip connections. SIDA-GAN has two GAN variants namely conditional GAN (cGAN), patch GAN (pGAN) and two generator implementations based on MobileNetV1, MobileNetV2 leading to four different flavours. Those are SIDA-cGAN (MobileNetV1 based generator), SIDA-cGAN (MobileNetV2 based generator), SIDA-pGAN (MobileNetV1 based generator), SIDA-pGAN (MobileNetV2 based generator). The proposed models are trained with different combinations of SSIM, L1, depth-smoothness and adversarial losses using NYU-Depth V2 (Nathan Silberman et al., 2012) dataset. SIDA-GAN has achieved reduction in trainable parameter count up to 76% and performance improvement in Average Relative Error (8%–25%), Root Mean Square Error (3%–27%), threshold accuracy (2%–6%) when compared with baseline papers.
Divergence-Based Locally Weighted Ensemble Clustering with Dictionary Learning and L2,1-Norm

Accurate clustering is a challenging task with unlabeled data. Ensemble clustering aims to combine sets of base clusterings to obtain a better and more stable clustering and has shown its ability to improve clustering accuracy. Dense representation ensemble clustering (DREC) and entropy-based locally weighted ensemble clustering (ELWEC) are two typical methods for ensemble clustering. However, DREC treats each microcluster equally and hence, ignores the differences between each microcluster, while ELWEC conducts clustering on clusters rather than microclusters and ignores the sample–cluster relationship. To address these issues, a divergence-based locally weighted ensemble clustering with dictionary learning (DLWECDL) is proposed in this paper. Specifically, the DLWECDL consists of four phases. First, the clusters from the base clustering are used to generate microclusters. Second, a Kullback–Leibler divergence-based ensemble-driven cluster index is used to measure the weight of each microcluster. With these weights, an ensemble clustering algorithm with dictionary learning and the L2,1-norm is employed in the third phase. Meanwhile, the objective function is resolved by optimizing four subproblems and a similarity matrix is learned. Finally, a normalized cut (Ncut) is used to partition the similarity matrix and the ensemble clustering results are obtained. In this study, the proposed DLWECDL was validated on 20 widely used datasets and compared to some other state-of-the-art ensemble clustering methods. The experimental results demonstrated that the proposed DLWECDL is a very promising method for ensemble clustering.
Variability modules

A Software Product Line (SPL) is a family of similar programs, called variants, generated from a common artifact base. A Multi SPL (MPL) is a set of interdependent SPLs: each variant can depend on variants from other SPLs. MPLs are challenging to model and to implement efficiently, especially when different variants of the same SPL must coexist and interoperate. We address this challenge by introducing the concept of a variability module (VM), a new language construct. A VM constitutes at the same time a module and an SPL of standard (variability-free), possibly interdependent, modules. Generating a variant of a VM triggers the generation of all variants required to satisfy its dependencies. Consequentially, a set of interdependent VMs represents an MPL that can be compiled into a set of standard modules. We illustrate the VM concept with an example from an industrial modeling scenario and formalize it in a core calculus. We define family-based analyses to check that a VM satisfies certain well-formedness conditions and whether all variants can be generated. Finally, we provide an implementation of VM for the Java-like modeling language ABS, and evaluate it with case studies.
Threshold Signature for Privacy-Preserving Blockchain

Threshold signatures received renewed interest in recent years due to their practical applicability to Blockchain technology. In this article, we propose a novel (n, t)-threshold signature scheme suitable for increasing security and privacy in Blockchain technology. Our scheme allows splitting a Blockchain wallet into multiple devices so that a threshold of them is needed for signing. This increases the security of the transactions, e.g., more devices need to be compromised to recover the key and permits, and the privacy, e.g., the signing is made anonymously on behalf of the group of users sharing the Blockchain wallet. Our experimental results show that the signing algorithm requires less than 10 ms in the cases of 10 devices involved.
Application of Massive Parallel Computation Based Q-Learning in System Control

The Q-Learning algorithm is a reinforcement learning algorithm based on continuous decision-making and satisfying the Markov decision process property (MDP), and its agent operating environment also has dynamic uncertainties. Due to the existence of such a framework, the Q-Learning algorithm can be widely used in complex real-world problems, such as intelligent robots, industrial control, games, etc. However, Q-Learning is also a very slow method, which requires a long training time and computing resources to obtain a practical convergence strategy, such as obtaining a path between any two points on a map of size 21*10. The converged policy takes about 1200 seconds. Importantly, this time increases exponentially with the size of the map. This in turn limits its application in practical problems. Super computer or cloud-based computation could be a solution to this problem. However, both are too expensive to small and medium scaled entities. Other alternatives could be multithreading and GPU. In contrast to the traditional single thread Q-learning algorithm which utilize only part of hardware capacity, they can fully utilize hardware. In addition, they are far more affordable for most entities. In addition, there are enough mature support materials for this hardware. This paper attempts to improve this problem by proposing a model that uses shared Q-tables, multithreading, and GPU based massively parallel computing. This solution can give full play to the performance of hardware devices and achieve the goal of increasing the algorithm convergence speed by dozens of times without increasing hardware costs. It effectively lowers the threshold for using reinforcement learning technology.
University teachers' self-perception of digital research competencies. A qualitative study conducted in Peru

Previous research warns about the limitations that some university teachers in Ibero-America have in relation to digital research competencies. The objective of this research was to analyze the research competencies from the university teacher's self-perception, contrasted with the classroom evaluation. The study had a qualitative approach. Interviews were conducted via the Zoom virtual platform, and recordings (of an average of 4 h each) of classes delivered by 10 teachers with an average age of 58 years old, in charge of scientific research courses at the postgraduate level in Peruvian universities were analyzed. The instruments used included an in-depth interview guide and a checklist. The results show that university teachers perceive themselves as specialists in the area of research, however, they present limitations when transmitting knowledge during the teaching-learning process. There is evidence of a lack of pedagogy, as well as limitations in the use of digital resources and technological tools due to their resistance to change. The conclusions reveal that it is key to make teachers aware of the paradigm shift, with a teaching that includes as digital competencies: knowing how to create and manipulate data, knowing how to use programs and information systems, knowing how to socialize and collaborate in digital environments, knowing how to exercise and respect a digital citizenship, knowing how to manage knowledge assertively, and, as we propose in this paper, knowing how to be a researcher in a digital environment.
Kolmogorov complexity metrics in assessing L2 proficiency: An information-theoretic approach

Based on 774 argumentative writings produced by Chinese English as a foreign language (EFL) learners, this study examined the extent to which Kolmogorov complexity metrics can distinguish the proficiency levels of beginner, lower-intermediate, and upper-intermediate second language (L2) English learners. Kolmogorov complexity metric is a holistic information-theoretic approach, which measures three facets of linguistic complexity, i.e., overall, syntactic, and morphological complexity simultaneously. To assess its validity in distinguishing L2 proficiency, Kolmogorov complexity metric is compared with traditional syntactic and morphological complexity metrics as well as fine-grained syntactic complexity metrics. Results showed that Kolmogorov overall and syntactic complexity could significantly distinguish any adjacent pair of L2 levels, serving as the best separators explored in the present study. Neither Kolmogorov morphological complexity nor other complexity metrics at both the syntactic and morphological levels can distinguish between all pairs of adjacent levels. Results of correlation analysis showed that Kolmogorov syntactic complexity was not or weakly correlated with all the fine-grained syntactic complexity metrics, indicating that they may address distinct linguistic features and can complement each other to better predict different proficiency levels.
The Daily Me Versus the Daily Others: How Do Recommendation Algorithms Change User Interests? Evidence from a Knowledge-Sharing Platform

Recommender systems on online platforms are often accused of polarizing user attention and consumption. The authors examine this phenomenon using a quasi-experiment conducted by Zhihu, the largest online knowledge-sharing platform (or Q&amp;A community) in China. Zhihu originally used a content-based filtering algorithm, which recommends content to users on the basis of the topics to which each user has subscribed. After more than a year, Zhihu moved to a social filtering algorithm, which recommends content with which users’ social connections are already engaged. The authors find that this algorithm change increased the creation of social ties by approximately 15% but decreased question subscriptions by 20% and answer contributions by 23%. The authors show that users’ increased social interests mainly involved following popular users, leading to a greater concentration of social interests on the platform. However, users’ topical interests became less concentrated, as popular topics received significantly fewer subscribers than unpopular topics. The authors explain these findings by exploring the underlying mechanism. They show that compared with content-based filtering algorithms, social filtering algorithms are more likely to expose general users to content consumed by their followees, who are more interested in niche topics than general users are.
Prediction and Control of Input and Output for Industry–University–Research Collaboration Network in Construction Industry

An unreasonable allocation of resources has led to a low rate of output in the industry–university–research collaboration network. A solution to this problem is to control and predict the input and output. However, the network has the characteristics of strong nonlinearity and insufficient samples. It is difficult for the existing control methods to migrate to collaboration networks because the traditional control methods, including Proportional–Integral–Derivative (PID) control and Model Predictive Control (MPC), are usually not applied to the system with strong nonlinearity and the controlled system needs to have specific parameters, while the modern control methods, including feedforward control and feedback control, have their limitations in both parameters and other aspects. In addition, there is a lack of research on the control and output prediction of collaboration networks, and there is no effective and applicable scheme for the control and prediction. Considering the nonlinearity and insufficient samples of the collaboration network, a Feedforward Control–Feedback Control Model based on the Multi-Layer Perceptron (FCFCM-MLP) is proposed in this paper. Adopting the controller structure of the Grid Search-Multilayer Perceptron (GS-MLP), a control block diagram, a feedforward controller, a feedback controller, and prediction methods such as Harris Hawk Optimization-Support Vector Regression (HHO-SVR) are designed for the FCFCM-MLP, which effectively realizes the feedforward control, feedback control, and prediction of inputs and outputs. In this paper, simulation tests on output-feedback tracking control are conducted with real statistics of papers jointly produced by the industry–university–research collaboration network in the construction industry. The results show that the proposed model has obvious effectiveness. Specifically, compared with the model composed of other controller structures and prediction methods, the optimal model Particle Dynamic Multiple Perturbation_Butterfly Optimization Algorithm-Support Vector Regression_Grid Search-Multi-Layer Perceptron (PDM_BOA-SVR_GS-MLP) obtained in this paper can minimize the predictive control error and effectively improve the control accuracy.
Infrared thermal monitoring

This chapter describes remote sensing methods capable of measuring the temperatures of surface phenomena, and considers ground-based techniques somewhat more briefly. It is especially convenient to do this if the remote sensing data that we intend to use for thermal monitoring themselves contain the basis for such a correction. The chapter introduces some of the extra physics necessary for a proper understanding of the information that longer wavelength thermal infrared data can reveal about low-temperature thermal anomalies, and considers the effects of the atmosphere. Volcanic activity is almost always accompanied by heat output, and so at their most fundamental level we might expect thermal measurements to tell us about the amount of power being liberated. Weather satellite data useful for measuring low-temperature thermal anomalies are cheap and easily available. Landsat is probably the most commonly used satellite for geological applications.
Interoperable AI: Evolutionary Race Toward Sustainable Knowledge Sharing

The advancement and deployment of artificial intelligent agents brought numerous benefits in knowledge and data gathering and processing. However, one of the key challenges in deploying such agents in an open environment like the Web is their interoperability as they currently mostly run in silos. In this article, we report on a simulation and evaluation based on evolutionary agent-based modeling to empirically test how sustainable different strategies are for knowledge sharing in open multiagent systems. Our results show the importance of translation-based approaches and the need for incentives to support these.
Proposed Detection Face Model by MobileNetV2 Using Asian Data Set

In 2019, the infectious coronavirus disease 2019 (COVID-19) was first reported in Wuhan, China. It has then become a public health problem in the world. This pandemic is having a heavy impact on the lives of people in our country. All countries are trying to control the spread of this disease. To solve the problem, each person needs to wear masks in a public place. Therefore, we propose a model capable of distinguishing between masked and nonmasked faces using a convolutional neural network (CNN) based on deep learning (DL)—MobileNetV2 in this paper. The model can detect people who are not wearing masks. It has an accuracy of up to 99.37%. The model will be applied in places such as schools, offices, and so on to monitor the wearing masks.
Word Cards Design as Learning Media for Dyslexia Students in Elementary School

Reading is one of the most important activities in elementary school to get necessary knowledge and skills. Each students have different reading capability, especially dyslexic students whom known for their hardship in terms of reading capability. This will certainly have an impact on the growth of those students. One of the solutions for the problems are the usage of learning tools, to help them navigate throughout their school life. Media that are used should be easy and fun to use to keep them engaged, but still provide necessary knowledge for dyslexic students understand and remember each letter in alphabetical sequence. This research wants to provide solution about learning media in the form of word cards. The method used is research and development with observation techniques, interviews and documentation are conducted to discover problems and learning media needed for dyslexic students in the field, as well as to develop learning media for expert validation. It is expected that this word cards design as a learning media can be developed according to the needs of dyslexic students in elementary schools to assist in the reading process.