Reviewerly/exHarmony huggingface dataset:
Classification of Indian Classical Dance 3D Point Cloud Data Using Geometric Deep Learning

Indian classical dances have many unique postures that require to be identified and classified correctly. Though many classification techniques exist for two-dimensional dance images, there is a need to classify the three-dimensional images as it is still on the evolving side. Geometric deep learning is one of the growing fields in machine learning and deep learning. It enables to learn from complex type of data represented in the form of graphs and 3D objects (manifolds). Deep learning algorithms like convolution neural networks (CNN) and recurrent neural networks (RNN) have achieved higher performance on the broad range of problems. Using these algorithms, one can also classify the images. Deep learning algorithm works well for Euclidean data such as points, lines, and planes. CNN cannot be implemented on the non-Euclidean data such as graphs and 3D object (manifolds), and thus, neural network architecture that can learn from non-Euclidean data is required. In the proposed work, implementation of geometric deep learning is done on 3D image data represented as point cloud. PointNet architecture will work efficiently with point cloud data. This architecture has been used to classify Indian classical dance point cloud data into five dance forms, namely Bharatanatyam, Odissi, Kathak, Kathakali, and Yakshagana.
APEX: Unsupervised, Object-Centric Scene Segmentation and Tracking for Robot Manipulation

Recent advances in unsupervised learning for object detection, segmentation, and tracking hold significant promise for applications in robotics. A common approach is to frame these tasks as inference in probabilistic latent-variable models. In this paper, however, we show that the current state-of-the-art struggles with visually complex scenes such as typically encountered in robot manipulation tasks. We propose APEX, a new latent-variable model which is able to segment and track objects in more realistic scenes featuring objects that vary widely in size and texture, including the robot arm itself. This is achieved by a principled mask normalisation algorithm and a high-resolution scene encoder. To evaluate our approach, we present results on the real-world Sketchy dataset. This dataset, however, does not contain ground truth masks and object IDs for a quantitative evaluation. We thus introduce the Panda Pushing Dataset (P2D) which shows a Panda arm interacting with objects on a table in simulation and which includes ground-truth segmentation masks and object IDs for tracking. In both cases, APEX comprehensively outperforms the current state-of-the-art in unsupervised object segmentation and tracking. We demonstrate the efficacy of our segmentations for robot skill execution on an object arrangement task, where we also achieve the best or comparable performance among all the baselines.
Efficient spatial allocation of solar photovoltaic electric energy generation in different regions of Brazil: a portfolio approach

Several countries face the challenge of reducing the intermittency of solar photovoltaic generation due to changes in climatic conditions. Considering that Brazil has continental-scale dimensions and diverse geography, an alternative that allows the smoothing of the intermittency of solar photovoltaic generation is needed. The novelty of this study is the proposal to efficiently allocate the production of photovoltaic solar energy between the interconnected regions of the country, forming an optimal portfolio with the highest productivity and the least intermittency. Thus, the regional allocation process for solar generation is formulated through an optimization problem with two objective functions: to increase the country's productivity and simultaneously control the variability of the supply of solar energy. This study concludes that the current models of planning for the expansion of Brazil do not consider the variability of solar irradiation incident in the five regions of Brazil.
Adaptive Genetic Algorithm Based on Mutation and Crossover and Selection Probabilities

The Genetic Algorithm (GA) is an explore technique used to solve issues in many different applications. The genetic algorithm has some parameters, including crossover probability, selection mechanism, and mutation probability. In GA, parameter adaptation is an important research topic. This paper proposes a Probabilistic Adaptive Genetic Algorithm in which the mutation and crossover probabilities, as well as the selection mechanism are dynamically adapted throughout the running of the algorithm. A new set of rates is generated for the next iteration based on the differences between fitness values and individual, enhancing the searching global optimum exploitation. We have compared the proposed algorithm with some common and state-of-the-art adaptive strategies such as dynamic adaptive, dynamic deterministic, dynamic self-adaptive, and static on a set of several functions with varying degrees of complexity. Experimental results on several popular test functions have shown that the results of the proposed algorithm are significantly better than these methods on both convergence speed and the solutions' quality.The reason that the proposed method has better results than other methods is the adaptation of each parameter of the genetic algorithm.
Towards Robust Prediction on Tail Labels

Extreme multi-label learning (XML) works to annotate objects with relevant labels from an extremely large label set. Many previous methods treat labels uniformly such that the learned model tends to perform better on head labels, while the performance is severely deteriorated for tail labels. However, it is often desirable to predict more tail labels in many real-world applications. To alleviate this problem, in this work, we show theoretical and experimental evidence for the inferior performance of representative XML methods on tail labels. Our finding is that the norm of label classifier weights typically follows a long-tailed distribution similar to the label frequency, which results in the over-suppression of tail labels. Base on this new finding, we present two new modules: (1)ReRank works to re-rank the predicted score, which significantly improves the performance on tail labels by eliminating the effect of label-priors; (2)Taug augments tail labels via a decoupled learning scheme, which can yield more balanced classification boundary. We conduct experiments on commonly used XML benchmarks with hundreds of thousands of labels, showing that the proposed methods improve the performance of many state-of-the-art XML models by a considerable margin (6% performance gain with respect to [email protected] on average). Anonymous source code is available at https://github.com/ReRANK-XML/rerank-XML.
A Real-Time Deep Transfer Learning Model for Facial Mask Detection

With the rise of COVID-19, wearing masks in public areas has become paramount to preventing the transmission of the coronavirus, Furthermore, many public service providers may require customers to properly wear masks in order to be served. But current enforcement measures of mask mandates are mostly monitored by humans, and thus may be hard to execute effectively in densely populated and fast-moving venues like airports and public transit. An automated mask detection monitoring system could greatly reduce the required human labor. In this study, a facial mask detection software model, usable in existing surveillance applications such as airport monitoring systems, is developed using Keras, a high-level deep learning API, and TensorFlow, an end-to-end open source platform for machine learning. The model uses deep convolutional neural networks (DCNN), which were trained from a database of roughly 12,000 images of faces, with roughly a 50-50 split of masked and unmasked, with the goal of achieving binary classification to detect whether a person is wearing a mask. The DCNN mask detection software achieved a validation accuracy of 98 percent. We also used computer vision techniques from OpenCV including facial detection and contours to detect the location of the people’s faces. The DCNN was then paired with this application to achieve live feed object detection by first detecting a person, and then determine whether they are wearing a mask. Finally, we explore the potential implementation and applications of the facial detection model via the linkage of cameras in monitoring areas to signal and notify officials.As more countries are developing mask wearing regulations, automated masked face detection is a key real-world application [3]. Transportation systems are one area that have been considerably affected by the pandemic, as cities around the world had to enforce massive restrictions on public transport in order to limit transmission of the virus and ensure the safe passage of key workers during the emergency response [4]. Masks have since been required on planes, buses, trains, and other forms of public transportation traveling into, transportation hubs such as airports and stations [5].However, Machine Learning and Deep Learning can help to combat Covid-19 in many ways. Enabling researchers and clinicians to evaluate a vast amount of data to make various predictions. In particular, face mask detection may prove to be useful as a guide for government surveillance on people in public areas susceptible to transmission, to ensure that face mask regulations are upheld. To achieve this, deep learning binary classification facial mask model based on VGG19 and one-shot object detection methods will be trained.We contribute a dual face-and-mask detection system that can provide immediate feedback in live surveillance systems. The method introduced is fast and accurate and can work for live mask-checking in busy public areas, alleviating some of the human effort and error typically subject to the mask mandating task.
Bulk scanning method of a heavy metal concentration in tailings of a gold mine using SWIR hyperspectral imaging system

This work introduces a bulk data acquisition method using a hyperspectral imaging system (HIS) to measure chromium (Cr) concentration in the soil samples obtained from tailings of a gold mine considering of the spectral competition between heavy metal elements. The chemical, mineralogical, and spectroscopic analyses in a laboratory environment revealed that heavy metal elements' competitive geochemical behaviors were manifested as spectral competitions between heavy metal elements in tailings. The heavy metal elements can be classified into two groups based on their geochemical behaviors: chromium-nickel (Cr-Ni) and zinc-arsenic-cadmium-lead (Zn-As-Cd-Pb). We found an inverse relationship between the two groups in their spectral absorption depth changing patterns, possibly caused by their competition in bonding with the agent minerals. The prediction model of Cr concentrations in the tailing samples using a short-wave infrared (SWIR) HIS was developed from the sample data analysis. The imaging model of Cr concentration is statistically significant with R2 = 0.7 and NRMSE = 11% to 12%. The future use of HIS for massive data acquisition of heavy metal concentration in a natural environment is made possible with such pilot spectroscopic analyses presented in this work.
Greedy Broad Learning System

In order to overcome the extremely time-consuming drawback of deep learning (DL), broad learning system (BLS) was proposed as an alternative method. This model is simple, fast, and easy to update. To ensure the fitting and generalization ability of BLS, the hidden layer neurons are often set too many, in fact, a lot of neurons are not needed. Greedy BLS (GBLS) is proposed in this paper to deal with the redundancy of the hidden layer in BLS from another perspective. Different from BLS, the structure of GBLS can be seen as a combination of unsupervised multi-layer feature representation and supervised classification or regression. It trains with a greedy learning scheme, performs principal component analysis (PCA) on the previous hidden layer to form a set of compressed nodes, which are transformed into enhancement nodes and then activated by nonlinear functions. The new hidden layer is composed of all newly generated compressed nodes and enhancement nodes, and so on. The last hidden layer of the network contains the higher-order and abstract essential features of the original data, which is connected to the output layer. Each time a new layer is added to the model, and there is no need to retrain from the beginning, only the previous layer is trained. Experimental results demonstrate that the proposed GBLS model outperforms BLS both in classification and regression.
EXACTA: Explainable Column Annotation

Column annotation, the process of annotating tabular columns with labels, plays a fundamental role in digital marketing data governance. It has a direct impact on how customers manage their data and facilitates compliance with regulations, restrictions, and policies applicable to data use. Despite substantial gains in accuracy brought by recent deep learning-driven column annotation methods, their incapability of explaining why columns are matched with particular target labels has drawn concern, due to the black-box nature of deep neural networks. Such explainability is of particular importance in industrial marketing scenarios, where data stewards need to quickly verify and calibrate the annotation results to ascertain the correctness of downstream applications. This work sheds new light on the explainable column annotation problem, the first of its kind column annotation task. To achieve this, we propose a new approach called EXACTA, which conducts multi-hop knowledge graph reasoning using inverse reinforcement learning to find a path from a column to a potential target label while ensuring both annotation performance and explainability. We experiment on four benchmarks, both publicly available and real-world ones, and undertake a comprehensive analysis on the explainability. The results suggest that our method not only provides competitive annotation performance compared with existing deep learning-based models, but more importantly, produces faithfully explainable paths for annotated columns to facilitate human examination.
Good conversations: Grounding, convergence, and richness

We apply three communication theories (the collaborative theory of language use, communication accommodation theory, and media richness theory) to aspects of conversational structure (openings, closings) and communicative setting (audiovisual, written) in order to make predictions about how people will feel about conversational interactions. We distinguish between two sources of information that interlocutors use to evaluate conversations: conversational outcomes and subjective conversational experience. We propose that positive subjective conversational experience comes about when interlocutors reach common ground with one another and do so with the least required effort possible (collaborative theory of language use), converge on their language use and communicative behaviors (communication accommodation theory), and select the appropriate medium for their conversation to take place (media richness theory). This positive subjective conversational experience is an important component of good conversations.
Modeling and Optimization of Semantic Segmentation for Track Bed Foreign Object Based on Attention Mechanism

The problem of foreign object intrusion onto the track bed often occurs in the actual operation process of high-speed railways.To solve the problem, we propose an anomaly detection method for the ballastless track bed, which is based on semantic segmentation.Firstly, we put forward the RFODLab semantic segmentation network according to the randomness of foreign objects distribution, and a small proportion of target pixels in the track image.The segmentation results of track image obtained through this model can be used to obtain the accurate pixel information of foreign objects.To further improve the recall and precision, the channel attention mechanism is introduced for the backbone network of the model to aggregate the context information of images, which achieves the weighted constraints of the model on the area to be recognized.Furthermore, to improve the model performance affected by unbalanced sample category distribution during the anomaly detection, we modify the loss function by balancing distribution of each category.The experimental results show that our proposed method can effectively segment various types of anomalies on the ballastless track bed including broken elastic strips, animal carcasses, and fallen pieces.The precision of anomaly detection on the test set can reach 90% while the recall can be maintained at more than 95%.The anomaly detection results on actual lines also verify the effectiveness of the method.
DF-RBAC: Dynamic and Fine-grained Role-Based Access Control Scheme with Smart Contract

Access control is a technology that can guarantee the security of information in network transmission, in which role-based access control is to separate the subject from the permission, and to assign the permission by distributing the corresponding role of the user. However, the traditional role-based access control scheme is generally centralized, the allocation of user’s role lacks fine granularity, and there is static in the allocation of the role and permission, which is not consistent with the distributed and dynamic network architecture nowadays. Hence, we propose a dynamic and fine-grained role-based access control model DF-RBAC, which can realize the flexible assignment of roles by resource owners and security verification of assigned roles. Further, we take advantage of blockchain technology and cryptography technology to combine with the DF-RBAC framework to achieve access to the activity log security audit function, which can ensure the security of the overall architecture. By safety and experimental analysis, our framework has proved to be feasible.
Mobile Robot Control Based on 2D Visual Servoing: A New Approach Combining Neural Network With Variable Structure and Flatness Theory

This paper focuses on the 2D visual servo-control of a mobile robot using a neural network (NN) with variable structure. The interaction matrix relating camera movement and changes in visual characteristics requires an estimation phase to determine its parameters as well as a camera calibration phase. It is common in applications related to mobile robotics that the robot model contains uncertainties generated by the sliding phenomenon. We suggest online identification, using NN to avoid this problem. The RBF NN is used to estimate the block formed by the interaction matrix and the reverse robot. Since the number of variables to be estimated is large, this can lead to the use of an excessive number of RBFs. We propose to use a single point of the scene which is sufficient to solve the problem. This problem reduction is possible thanks to flatness theory which allows to reduce the number of NN inputs from 8 inputs (4 image points) -generally used in the literature- to 2 (one image point) only. In order to further reduce the complexity of the proposed algorithm, the number of neurons for each layer and for each iteration is optimized. We use a neural network with variable structure to reach this objective. The very encouraging results obtained validate the proposed approach.
Deep Learning for Solar Irradiance Nowcasting: A Comparison of a Recurrent Neural Network and Two Traditional Methods

This paper aims to improve short-term forecasting of clouds to accelerate the usability of solar energy. It compares the Convolutional Gated Recurrent Unit (ConvGRU) model to an optical flow baseline and the Numerical Weather Prediction (NWP) Weather Research and Forecast (WRF) model. The models are evaluated over 75 days in the summer of 2019 for an area covering the Netherlands, and it is studied under what circumstance the models perform best. The ConvGRU model proved to outperform both extrapolation-based methods and an operational NWP system in the precipitation domain. For our study, the model trains on sequences containing irradiance data from the Meteosat Second Generation Cloud Physical Properties (MSG-CPP) dataset. Additionally, we design an extension to the model, enabling the model also to exploit geographical data. The experimental results show that the ConvGRU outperforms the other methods in all weather conditions and improves the optical flow benchmark by \(9\%\) in terms of Mean Absolute Error (MAE). However, the ConvGRU prediction samples demonstrate that the model suffers from a blurry image problem, which causes cloud structures to smooth out over time. The optical flow model is better at representing cloud fields throughout the forecast. The WRF model performs best on clear days in terms of the Structural Similarity Index Metric (SSIM) but suffers from the simulation’s short-range.
Hate Speech as an Indicator for the State of the Society

Abstract. Previous research indicates that user comments serve as exemplars and thus have an effect on perceived public opinion. Moreover, they also shape the attitudes of their readers. However, studies almost exclusively focus on controversial issues if they explore the consequences of user comments for attitudes and perceived public opinion. The current study wants to find out if hate speech attacking social groups due to characteristics such as religion or sexual orientation also has an effect on the way people think about these groups and how they think society perceives them. Moreover, we also investigated the effects of hate speech on prejudiced attitudes. To explore the hypotheses and research questions, we preregistered and conducted a 3 × 2 experimental study varying the amount of hate speech (none/few/many hateful comments) and the group that was attacked (Muslims/homosexuals). Results show no effects of the amount of hate speech on perceived public opinion for both groups. However, if homosexuals are attacked, hate speech negatively affects perceived social cohesion. Moreover, for both groups, we find interaction effects between preexisting attitudes and hate speech for discriminating demands. This indicates that hate speech can increase polarization in society.
A Knowledge-Based Adaptive Discrete Water Wave Optimization for Solving Cloud Workflow Scheduling

Workflow scheduling in cloud environments has become a significant topic in both commercial and industrial applications. However, it is still an extraordinarily challenge to generate effective and economical scheduling schemes under the deadline constraint especially for the large scale workflow applications. To address the issue, this article investigates the cloud workflow scheduling problem with the aim of minimizing the whole cost of workflow execution whereas maintaining its execution time under a predetermined deadline. A novel knowledge-based adaptive discrete water wave optimization (KADWWO) algorithm is developed based on the problem-specific knowledge of cloud workflow scheduling. In the proposed KADWWO, a discrete propagation operator is designed based on the idle time knowledge of hourly-based cost model to adaptively explore the huge search space. The adaptive refraction operator is employed to avoid stagnation and expand the available resource pool. Meanwhile, the dynamic grouping based breaking operator is designed to exploit the excellent block structure knowledge of task allocation scheme and corresponding resource to intensify the local region and accelerate convergence. Extensive simulation experiments on the well-known scientific workflow demonstrate that the KADWWO approach outperforms several recent state-of-the-art algorithms.