Reviewerly/exHarmony huggingface dataset:
Automatic Aesthetics Evaluation of Robotic Dance Poses Based on Hierarchical Processing Network

Vision plays an important role in the aesthetic cognition of human beings. When creating dance choreography, human dancers, who always observe their own dance poses in a mirror, understand the aesthetics of those poses and aim to improve their dancing performance. In order to develop artificial intelligence, a robot should establish a similar mechanism to imitate the above human dance behaviour. Inspired by this, this paper designs a way for a robot to visually perceive its own dance poses and constructs a novel dataset of dance poses based on real NAO robots. On this basis, this paper proposes a hierarchical processing network-based approach to automatic aesthetics evaluation of robotic dance poses. The hierarchical processing network first extracts the primary visual features by using three parallel CNNs, then uses a synthesis CNN to achieve high-level association and comprehensive processing on the basis of multi-modal feature fusion, and finally makes an automatic aesthetics decision. Notably, the design of this hierarchical processing network is inspired by the research findings in neuroaesthetics. Experimental results show that our approach can achieve a high correct ratio of aesthetic evaluation at 82.3%, which is superior to the existing methods.
Estimation of area under the ROC curve in the framework of gamma mixtures

Receiver operating characteristic (ROC) curve is one of the well-known classification tools. There are several bi-distributional ROC models in the literature, which can be applied only when there is a prior knowledge on the class/status of the subject. If the predefined status of the subject is not known, then we need to administer a statistical methodology to identify the homogeneous components within it. Once this is done, modeling of ROC can be made, and here it is assumed that the data underlie non-normal distribution. In this paper, the need for handling non-normal data in the framework of mixture model is discussed and demonstrated using a real data set and simulation studies. It is shown that, the proposed mixGamma ROC model replaces the existing ROC models when the data is of non-normal and multi-mode.
Shift-curvature, SGD, and generalization

Abstract A longstanding debate surrounds the related hypotheses that low-curvature minima generalize better, and that stochastic gradient descent (SGD) discourages curvature. We offer a more complete and nuanced view in support of both hypotheses. First, we show that curvature harms test performance through two new mechanisms, the shift-curvature and bias-curvature, in addition to a known parameter-covariance mechanism. The shift refers to the difference between train and test local minima, and the bias and covariance are those of the parameter distribution. These three curvature-mediated contributions to test performance are reparametrization-invariant even though curvature itself is not. Although the shift is unknown at training time, the shift-curvature as well as the other mechanisms can still be mitigated by minimizing overall curvature. Second, we derive a new, explicit SGD steady-state distribution showing that SGD optimizes an effective potential related to but different from train loss, and that SGD noise mediates a trade-off between low-loss versus low-curvature regions of this effective potential. Third, combining our test performance analysis with the SGD steady state shows that for small SGD noise, the shift-curvature is the dominant of the three mechanisms. Our experiments demonstrate the significant impact of shift-curvature on test loss, and further explore the relationship between SGD noise and curvature.
Towards Building Live Open Scientific Knowledge Graphs

Due to the large number and heterogeneity of data sources, it becomes increasingly difficult to follow the research output and the scientific discourse. For example, a publication listed on DBLP may be discussed on Twitter and its underlying data set may be used in a different paper published on arXiv. The scientific discourse this publication is involved in is divided among not integrated systems, and for researchers it might be very hard to follow all discourses a publication or data set may be involved in. Also, many of these data sources—DBLP, arXiv, or Twitter, to name a few—are often updated in real-time. These systems are not integrated (silos), and there is no system for users to query the content/data actively or, what would be even more beneficial, in a publish/subscribe fashion, i.e., a system would actively notify researchers of work interesting to them when such work or discussions become available.
Prediction of Solar Radiation Using Data Driven Models

Abstract The local climatic conditions play a significant role in the amount of Solar Radiation that reaches the Earth’s surface. In India, most of the locations, receive ample Solar Radiation. The exact evaluation of SR is most essential for engineering studies on energy and climate. For the present investigations, the meteorological data for Hyderabad city in Telangana state for the period 2009 - 2020 is adopted. Five climatological variables viz., Max. Temp. (T max ), Min. Temp. (T min ) and Mean Air Temp. (T mean ), Sunshine hours (n), and Relative Humidity (RH) were collected from the weather forecast. In the present investigations, two conventional empirical equations viz., Abdalla, and Angstrom are adopted for analytical estimation of Solar Radiation (SR). Furthermore, two data-driven models via., Artificial Neural Networks (ANN) and Multi Gene-genetic Programming (MGGP) are used to develop an equation for estimating Solar Radiation (R s ). The model equations were developed based on the parameters adopted for each conventional equation. These results are compared with conventional equation results. The present investigation exhibited that, the expected results of the Solar radiation of the arrangement formed by the data are favourable and also simplify the testing data. The corresponding R 2 values for random data are 0.976 for the training dataset, while the value for the testing dataset is 0.964. Based on the performance analysis for each equation, the efficiency and reliability of the proposed MGGP and ANN model are validated and predicted for the year 2021. The predicted results were found to be in good agreement with the analytical results evaluated from three properly used equations.
FaceTopoNet: Facial Expression Recognition Using Face Topology Learning

Prior work has shown that the order in which different components of the face are learned using a sequential learner can play an important role in the performance of facial expression recognition systems. We propose FaceTopoNet, an end- to-end deep model for facial expression recognition, which is capable of learning an effective tree topology of the face. Our model then traverses the learned tree to generate a sequence, which is then used to form an embedding to feed a sequential learner. The devised model adopts one stream for learning structure and one stream for learning texture. The structure stream focuses on the positions of the facial landmarks, while the main focus of the texture stream is on the patches around the landmarks to learn textural information. We then fuse the outputs of the two streams by utilizing an effective attention-based fusion strategy. We perform extensive experiments on four large- scale in-the-wild facial expression datasets - namely AffectNet, FER2013, ExpW, and RAF-DB - and one lab-controlled dataset (CK+) to evaluate our approach. FaceTopoNet achieves state-of- the-art performance on three of the five datasets and obtains competitive results on the other two datasets. We also perform rigorous ablation and sensitivity experiments to evaluate the impact of different components and parameters in our model. Lastly, we perform robustness experiments and demonstrate that FaceTopoNet is more robust against occlusions in comparison to other leading methods in the area.
Spectral Analysis With a Piecewise Separation Occlusion Model for Image-Based Rendering

Occlusion is ubiquitous in the real world, and neglecting it will certainly lead to quality degeneration in a reconstructed light field (LF). In this paper, we derive a piecewise separation occlusion model (PSOM) for LF reconstruction that is optimized for occlusion in the continuous Fourier and integral domains. The PSOM is first obtained by determining the definite integral intervals between the occlusion and nonocclusion boundaries in the spatial domain along the camera direction. We then use these definite integral intervals to perform spectral analysis of the plenoptic function (POF) to derive the occluded and nonoccluded plenoptic spectra. Finally, we show that using the two sampling rates derived from the occluded and nonoccluded spectra is more effective than using a uniform sampling rate for reconstructing a single scene unless the scene is free of occlusion. Through numerical experiments on simulated signals and real-world inward-looking and outward-looking scenes, we demonstrate the effectiveness of our proposed approach.
An Indicator-Based Algorithm for Task Scheduling in Multi-Cloud Environments

Cloud computing is the ability to scale various resources and services that can be dynamically configured by the cloud service provider (CSP) and delivered on demand by the customers. The objective of most of the task scheduling algorithms is to ensure that the overall processing time of all the tasks (i.e., makespan) is minimized. Here, minimization of makespan in no way guarantees the minimization of execution cost. In indicator-based (IBTS) task scheduling algorithm for the multi-cloud environment, we can outline the significant contributions as the following: (1) IBTS achieves multi-objective solutions while considering parameters, makespan, and execution cost. (2) IBTS proposes a normalization framework with time and cost length indicators for efficient task scheduling. (3) The efficacy of the IBTS algorithm is demonstrated using both the benchmark and synthetic datasets. (4) The simulation outcomes of the IBTS algorithm in comparison with three existing task scheduling algorithms, namely ETBTS, MOTS, and PBTS, clearly exhibit superiority, which proves acceptance of IBTS algorithm.
Holistic Prescriptive Analytics for Continuous and Constrained Optimization Problems

We present a holistic framework for prescriptive analytics. Given side data x, decisions z, and uncertain quantities y that are functions of x and z, we propose a framework that simultaneously predicts y and prescribes the “should be” optimal decisions [Formula: see text]. The algorithm can accommodate a large number of predictive machine learning models as well as continuous and discrete decisions of high cardinality. It also allows for constraints on these decision variables. We show wide applicability and strong computational performances on synthetic experiments and on two real-world case studies.
Federated Learning for Rotating Machinery Fault Diagnosis with Knowledge Distillation

Fault diagnosis and condition-based monitoring of rotating machine leveraging labeled data are of great significance in modern industries, which ensures operation safety, avoid major system disaster, and reduces economic losses [1–3]. In recent years, with the rapid development of artificial intelligence algorithms and computer science, deep learning techniques have been applied to fault diagnosis, benefiting rotating machinery fault identification owing to the ability to automatically extract features and process large amounts of data [4–6].
A Study on Affine Transformations and a Novel Universal Prediction Theory

Artificial neural networks (NNs) with various architectures are widely used for practical problems, including multilayer perceptron (MLP), the Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), among others. The TrumpetNets and TubeNets were also recently proposed for creating two-way deepnets for both forward and inverse problems. All these studies have demonstrated the power of NNs, but the theoretical foundation needs a more thorough study. This paper first conducts an intensive study on affine transformations and its role as a prediction function. This reveals a data-to-parameter (or [Formula: see text]) encoding mechanism and its uniqueness with proof. Then, effects of the architecture and the roles of nonlinear activation functions wrapping the affine transformations are investigated. The effects of stacking of affine transformations and chaining of the wrapped affine transformations in an NN are examined, leading finally to a novel Universal Prediction Theory (UPT).
Classification of Instagram photos: topic modelling vs transfer learning

The existence of pre-trained deep learning models for image classification, such as those trained on the well-known Resnet-50 architecture, allows for easy application of transfer learning to several domains including image retrieval. Recently, we proposed topic modelling for the retrieval of Instagram photos based on the associated hashtags. In this paper we compare content-based image classification, based on transfer learning, with the classification based on topic modelling of Instagram hashtags for a set of 24 different concepts. The comparison was performed on a set of 1944 Instagram photos, 81 per concept. Despite the excellent performance of the pre-trained deep learning models, it appears that text-based retrieval, as performed by the topic models of Instagram hashtags, stills perform better.
Time Series Prediction Using Time-Series Decomposition and Multi-reservoirs Echo State Network

Echo State Network (ESN) is effective to do time series analysis, which has a dynamic reservoir, includes: input units, internal units and output units. However, due to the randomness and non-stationarity properties of most time series, it is difficult for single reservoir to better handle them, because different scales in the time series should be dealt with in a unified structure. In order to solve this, the time-series decomposition (TSD) is employed to decompose the time series into different sub-sequences, which can be handled by different reservoirs. Now, there are many TSD methods, such as: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complementary ensemble empirical mode decomposition (CEEMD), local mean decomposition (LMD), variational mode decomposition (VMD), etc. Different TSD methods are used to decompose the time series and then the multi-reservoirs ESN are constructed. Finally, experimental results using several time series are presented and compared.
Network Traffic Classification Using Supervised Machine Learning Algorithms in Systems with NFV Architecture

Network functions virtualization architecture concept is gaining more popularity and it is used in different systems. Together with the cloudification within public, private and mixed clouds it is becoming a base for the future development of the digital world. The concepts of containers, virtual network functions, application functions are cohered within the clouds and guided with the NFV systems. Another aspect which is developing rapidly are the access technologies, especially the 5G, which is the all expected enabler of the IoT. Within such circumstances, most of the network traffic is expected to flow in the east–west direction, never leaving the cloud. Our work if focused on preparation of experimental environment that will simulate such traffic. We are analysing the traffic by making classification of the network data flows, using a selected set of six supervised machine learning (ML) algorithms. The goal of our research is to find the algorithm with the best performance within the prepared environment. We define the performance as a combination of the ML algorithm's classification precision, and the time consumption of the algorithm, which bears a great significance, especially from a point of 5G, where any packet delay introduced within the system may compromise the 5G specification calls for latency. From the research we conclude that out of the 6 explored ML algorithms, the Decision Tree algorithms is the most suitable classifier that fits within the needed precision across all classes, but also within the time consumption needs. Our approach also considers the regulatory point of view for automated data analysis within systems, and we deal only with statistical features of the network flows, while the payload data, the source and destination information, as well as the network port, are excluded as attributes used for classification, especially as we deal with VoIP and encrypted VoIP data that is used in 5G.
Identifying Digital Rhetoric in the Telemedicine User Interface

Telemedicine is an alternative healthcare delivery system whereby patients access digital technology to consult with a physician virtually. Patients first interact with telemedicine via a consumer-facing website. Telemedicine promises numerous benefits to patients, such as increased access to healthcare, yet poor usability of the telemedicine user interface (UI) may hinder patient acceptance and adoption of the service. The telemedicine UI moderates patients’ ability to utilize telemedicine, and therefore it must be usable, but it must also be rhetorical to motivate patients to perform certain actions. Digital rhetoric refers to UI elements that influence user actions and knowledge and is tied to usability because of these same human–computer interaction (HCI) factors. This study examined the usability of three telemedicine provider UIs and by identifying usability problems, reveals digital rhetoric that is significant to telemedicine UIs. The article concludes by offering heuristics of digital rhetoric that lead to optimal usability.
A Survey on Graph Neural Network Based Video Recommendation System

Recommender systems helps the user to recommend something based on their interest. As online data is growing extensively, we require recommender systems more, and there has a lot of work going on in this field. Recommender system in online video streaming platforms plays a huge role. 80% of videos watched online are from recommendations. There are many techniques on which this system works. As most of the information is in graph structure and graph neural networks (GNNs) have a specialty in representation learning, the field of utilizing GNN in recommender systems is expanding. This chapter provides knowledge of GNN-based recommender systems and state-of-the-art models in developing this domain.
Towards Detecting Fake Spammers Groups in Social Media: An Unsupervised Deep Learning Approach

In recent years, the acceptance of social media has increased, where multiple users use various platforms to share different kinds of information. The current estimated number of online users is almost a billion. Today's web traffic has increased by more than a half within a year because of the increase in online activities. More data can be mined with more users performing online activities. Therefore, advertising and marketing administrations need to know how people express their views and their response can improve their business efficiency. Since social media are rooted at diverse scales in various data sources, they are often quite large. Nowadays, people review products online, which plays a vital role in making buying choices by most consumers in digital consumer markets. Spammers often write fake reviews to increase/decrease the value of specific products by taking advantage of online reviews. Past studies have focused on spotting distinct fake reviewer-ids of fake reviews. However, to target and control a specific product's sentiment, fake reviewers create multiple fake ids and work together in groups to write reviews. In this chapter, we address the problem of detecting such groups of fake reviewers. Explicitly, we propose a deep learning-based framework for fake reviewer groups detection data of reviewers using various unsupervised deep learning algorithms such as Self Organizing Maps and Restricted Boltzmann Machine. We perform the hyperparameter optimization of these algorithms on this reviewer graph data using advanced deep learning frameworks such as TensorFlow and popular machine learning frameworks such as scikit-learn. In the end, we discussed the practical implementation of grouping the fake reviewers on the real-world Google Play Store app review dataset, which has fractional ground-truth data on about 2207 fake reviewer-ids out of all 38,123 reviewer-ids in the original dataset. We validate with the experimental results that the projected method can detect the group of fake reviewers with reasonable accuracy. It can also be extended to perceive opinion spammers groups in social media with semantic features, sequential affinity, and emotion analysis.
Deep Learning for Code-Mixed Text Mining in Social Media: A Brief Review

The advent of social media in day-to-day life has made communications between people more often and easier than ever before. Analyzing the content in social media has opened up a massive amount of research and commercial opportunities. However, the content in social media is noisy and multi-lingual, which postures computational challenges ahead. Especially, the non-native English speakers and writers tend to mix their native language with English while generating social media content. Thus it requires a comprehensive prepossessing of text, including the identification of language for many language processing applications. In the area of language processing, deep learning has shown to be very successful, and the latest research works have witnessed the adoption of deep learning solutions to cater to the challenges in analyzing code-mixed text. Here, we highlight a comprehensive study of deep learning techniques used for analyzing the code-mix text of social media to understand the state-of-the-art and existing research challenges. We will discuss several applications of code-mixed text analysis and future directions.
Deep Digging of Anomalous Transactions in Financial Networks with Imbalanced Data

Anomaly (user or transaction) detection is a major concern and has intrigued a wide spectrum of disciplines, for example, finance, security, etc. Anomalous instances in a dataset are very rare in general, which leads to imbalanced distribution of classes in the dataset, and poses a great challenge to anomaly detection. In the case of credit card transactions, it is of utmost importance to identify fraudulent transactions in order to save financial organizations from giving credit to users that are not likely to be repaid in the future. Most of the prevailing techniques to address the class imbalance problem aim to balance the dataset by either producing synthetic data samples of a minority class or eliminating some samples of the majority class. In this chapter, we provide a brief overview of the challenges faced due to class imbalance while learning deep learning-based prediction models and discuss the sampling strategies that deal with the class imbalance data problem, for example, oversampling, SMOTE, and its variants, and data ensembles. Data ensembles divide the dataset into small subsets that are in itself balanced. Then, corresponding to each balanced sub-dataset, a classifier is trained, and voting is used for the first level of prediction. As deep learning approaches have the ability to map complex non-linear relations within high-dimensional data, they can be leveraged to anomaly detection problems. To improve the classification task’s performance and avoid overfitting, Deep Multilayer Perceptron (DMLP) and Deep Convolutional Neural Network (DCNN) are trained for each balanced sub-dataset, and second level voting is performed. Also, a detailed numerical analysis of the proposed architecture is provided.