Reviewerly/exHarmony huggingface dataset:
A real-time personalized face modeling method for peking opera with depth vision device

Personalized modeling of Peking Opera faces is a challenging question in computer vision due to the artistry of painted faces, which is more challenging on natural human faces. To address this issue, in this paper, we propose a personalized modeling method for Peking Opera faces based on a depth camera device. Firstly, to solve the abnormal deformation of unrelated areas on 3D standard Peking Opera faces, a method that divide the Action Unit (AU) according to the face painting is designed, and the shape bases are constructed using local deformation based on the Laplacian algorithm. Secondly, to complete the linear fusion of different shape bases and model a natural and vivid personalized Peking Opera face, we design a method that calculates the weight of different shape bases based on the BlendShape algorithm to solve the conflicts between different shape bases. In addition, a real-time interactive method is proposed to calculate the weight value of each shape base based on 3D information of facial feature points captured by a depth camera. The results show that our method can achieve personalized modeling of Peking Opera faces with vivid expression in real-time, while reducing the complexity of the vectorization pattern database used in the existing personalized modeling of Peking Opera faces.
Modeling belief‐desire‐intention reasoning agents for situation‐aware formalisms

Summary A natural disaster is an inevitable situation that can occur at any time and anywhere along with varied forms such as earthquakes, floods, hurricane, wildfire, and so forth, and different level of occurrences has been recorded from mild to an intense level. Timely disaster response plays an important role in reducing its deteriorate after‐effects and can save countless lives. Over the years, people have been developing guidelines and processes to cope up with such kinds of hazardous situations. In recent years, with the advent of the pervasive computing paradigm, situation‐awareness has been considered to be the most fascinating approach for situation assessment and provides decision support accordingly. Situation‐aware systems observe/perceive dynamic changes in the environment, understand/comprehend the situation, and perform actions according to the environment. Although state‐of‐the‐art formalisms have been developed to handle such kinds of hazardous situations intelligently and rescue the victims. However, belief‐desire‐intention (BDI) reasoning mechanism with the incorporation of situation‐awareness is still the thirsty area of research to manage hazardous situations. In this article, we propose a temporal epistemic situation‐aware formalism for BDI reasoning agents to model the context‐aware decision support system. To demonstrate the work of the proposed formalism, we develop a case study based on a disaster situation, in which BDI agents are modeled and simulated to present the results in terms of agents' reasoning processes. We demonstrate the scalability in temporal aspects of the system using different levels of disasters to monitor the hazardous situations and evaluate the overall behavior of the system.
Research Productivity: Trend and Comparative Analyses by Regions and Continents

Using data for the period 2000 to 2019, the aim of this paper is to profile and compare research publications in regions and continents. The empirical evidence is based on the Poisson regression model, quantile regression for counts data and panel negative binomial regression. The findings can be summarised as follows: continuous and linear increasing trends in the production of knowledge are noted in developing regions specifically in Africa even if the contribution of the continent to global research is marginal. In countries with least production, ‘internet users’ is not significant but schooling modulates its effect on research production contrarily to countries in the upper part of the distribution. In Africa, if the number of schooling years increases by one, the number of documents or published works produced is expected to increase by a factor of 1.147.
A novel intervention method for aspect-based emotion Using Exponential Linear Unit (ELU) activation function in a Deep Neural Network

Sentimental analysis, a most opinion analyzer which takes a process of mathematical calculation for fixing the exact emotion, object or user expected reviews, etc. In the modern generation everything comes in a digital way where sales and buyers are increased in a rapid manner through the internet only. Not only commercial access but also education in this pandemic situation 2021, takes a major part in worldwide internet. In this way sentimental analysis has accounted for its NLP framework in the feedback process also. Detection through many algorithms using machine learning, computer vision and deep learning are in trend now. In such views, a Novel intervention aspect-based (NIAB) sentimental analysis is proposed to classify the emotion from twitter dataset. By using an activation function, the output of each node is identified accurately. Neural network has many input and output nodes that are interconnected through n-number of hidden layers. These nodes are determined through mapping the dependent variable. For a clear direction of slope an non-linear activation function is introduced called an Exponential Linear Unit (ELU) that has the highest scale with positive value. The exact range between the input and output value is 0.1 and 0.3 which has better output compared to Rectified linear unit (RELU).
A Comparative Analysis of Security Services in Major Cloud Service Providers

Cloud computing helps in the delivery of various services like storage, servers, and databases, over the internet. Various popular cloud service providers enable customers to access these services with a certain amount of reliability. This reliability is accounted for by the cloud service security measures opted by each cloud service provider. The main service providers under observation in this paper will be Microsoft Azure, AWS (Amazon Web Services), and GCP (Google Cloud Platform). Each of them have a unique networking and defender plan with varying costs and features to protect against different types of cyber cloud attacks. With multiple options as such, it brings about ambiguity in the eyes of people and a sense of doubt as to how secure these services are. Due to the large amount of data and its nature, this insecurity against such service providers create a huge rift between the consumers and cloud services. The objective of the paper is to perform a comparative analysis to provide customers an overview of the various security services provided by the cloud service providers and provide recommendations based on the same. In the view of these results, customers can feel more confident in the selection of their cloud service provider.
Automatic Sport Video Mining using a Novel Fusion of Handcrafted Descriptors

Recently, video mining in sports has become an attractive topic for computer vision research. The task of recognizing sports activities comprises two important phases: one focused on feature extraction and another focused on the process of classification. This paper presents a novel version of the discrete co-sine transform (DCT) known as the mean multi-block DCT (MmDCT), which introduces powerful denoising capabilities. Furthermore, we propose a local shape feature descriptor for human activity recognition that combines moment invariants (MIs), the MmDCT, and uniform local binary patterns (ULBPs). The different descriptors utilized have different advantages. For example, MIs are invariant in the presence of linear transformations (rotation, translation, and scaling) and ULBPs are robust against illumination changes. Therefore, these two descriptors provide complementary information. Experimental results on three challenging datasets (KTH, UCFII, and HMDB51) demonstrate that our method can achieve better performance than numerous state-of-the-art methods.
Outlier-Robust Filtering for Nonlinear Systems With Selective Observations Rejection

Considering a common case where measurements are obtained from independent sensors, we present a novel outlier-robust filter for nonlinear dynamical systems in this work. The proposed method is devised by modifying the measurement model and subsequently using the theory of Variational Bayes and general Gaussian filtering. We treat the measurement outliers independently for independent observations leading to selective rejection of the corrupted data during inference. By carrying out simulations for variable number of sensors we verify that an implementation of the proposed filter is computationally more efficient as compared to the proposed modifications of similar baseline methods still yielding similar estimation quality. In addition, experimentation results for various real-time indoor localization scenarios using Ultra-wide Band (UWB) sensors demonstrate the practical utility of the proposed method.
Scaling Human-Object Interaction Recognition in the Video through Zero-Shot Learning

Recognition of human activities is an essential field in computer vision. The most human activity consists of the interaction between humans and objects. Many successful works have been done on human-object interaction (HOI) recognition and achieved acceptable results in recent years. Still, they are fully supervised and need to train labeled data for all HOIs. Due to the enormous space of human-object interactions, listing and providing the training data for all possible categories is costly and impractical. We propose an approach for scaling human-object interaction recognition in video data through the zero-shot learning technique to solve this problem. Our method recognizes a verb and an object from the video and makes an HOI class. Recognition of the verbs and objects instead of HOIs allows identifying a new combination of verbs and objects. So, a new HOI class can be identified, which is not seen by the recognizer system. We introduce a neural network architecture that can understand and represent the video data. The proposed system learns verbs and objects from available training data at the training phase and can identify the verb-object pairs in a video at test time. So, the system can identify the HOI class with different combinations of objects and verbs. Also, we propose to use lateral information for combining the verbs and the objects to make valid verb-object pairs. It helps to prevent the detection of rare and probably wrong HOIs. The lateral information comes from word embedding techniques. Furthermore, we propose a new feature aggregation method for aggregating extracted high-level features from video frames before feeding them to the classifier. We illustrate that this feature aggregation method is more effective for actions that include multiple subactions. We evaluated our system by recently introduced Charades challengeable dataset, which has lots of HOI categories in videos. We show that our proposed system can detect unseen HOI classes in addition to the acceptable recognition of seen types. Therefore, the number of classes identifiable by the system is greater than the number of classes used for training.
Analysis of GHI Forecasting Using Seasonal ARIMA

A precise understanding of solar energy generation is important for many reasons like storage, delivery, and integration. Global Horizontal Irradiance (GHI) is the strongest predictor of actual generation. Hence, the solar energy prediction problem can be attempted by predicting GHI. Auto-Regressive Integrated Moving Average (ARIMA) is one of the fundamental models for time series prediction. India is a country with significant solar energy possibilities and with extremely high weather variability across climatic zones. However, rigorous study over different climatic zones seems to be lacking from the literature study. In this paper, 90 solar stations have been considered from the 5 different climatic zones of India and an ARIMA model has been used for prediction for the month of August, the month with most variability in GHI. The prediction of the models has also been analyzed in terms of Root Mean Square Error. The components of the AR models have also been investigated critically for all climatic zones. In this study, some issues were observed for the ARIMA model where the model is not being able to predict the seasonality that is present in the data. Hence, a Seasonal ARIMA (SARIMA) model has also been used as it is more capable in case of seasonal data and the GHI data exhibits a strong seasonality pattern due to its availability only in the day time. Lastly, a comparison has also been done between the two models in terms of RMSE and 7 days Ahead Prediction.
One-Shot Summary Prototypical Network Toward Accurate Unpaved Road Semantic Segmentation

Recent studies of driving scene understanding based on image semantic segmentation have achieved dramatic advances in speed and accuracy. Large-scale public datasets for semantic segmentation of paved road driving scenes have led the advances, but there is no large-scale public dataset for unpaved road environments. Building a large-scale image semantic segmentation dataset for unpaved roads is very expensive, and domain gaps between geographically distributed locations and those of seasonal changes hinder building a training dataset that is adequate to train a convolutional neural network model. In this paper, to resolve the data insufficiency problem, we use an one-shot learning setting in unpaved road driving scene understanding. Our One-shot Summary Prototypical Network (OSPNet) is trained with paved road driving scenes, and it identifies drivable regions in unpaved roads given only a single support image and unpaved road mask data. The OSPNet improves previous two branch few-shot segmentation approaches by introducing the summary branch which enables channel-wise weighting for important features in the feature map of support and query branches. Our experiments show that our model quantitatively and qualitatively outperforms recent supervised and few-shot segmentation models.
Personalization of News for a Logistics Organisation by Finding Relevancy Using NLP

Artificial Intelligence-driven applications have already stepped in to streamline logistics on a global scale. News impact and relevancy helps in taking the right decision at the right time in the logistics industry. This paper attempts to provide a state of art in finding relevancy in news headlines. We present the research done on logistics data using natural language processing. In this paper, we will explain the different algorithms we have used as well as the various embedding strategies we have tried to find news relevancy. We have used statistical and deep learning models to extract information from the corpora. The proposed methods are compared based on relevancy score and results are significantly acceptable.
Context Prior based Semantic-Spatial Graph Network for Human Parsing

Recently, many methods enlarged the kernel size or fused multi-scale features to capture contextual information for parsing the hard samples, such as heavy occlusions, limb absence, and complex poses. Nevertheless, those required higher computational and cannot explore the effective global context to achieve more precise parsing results. In this paper, we propose an end-to-end network for human parsing, named Context Prior based Semantic-Spatial Graph Network (CP-SSGNet), which achieves higher precision and consistency by encoding the context prior in the graph model. CP-SSGNet consists of three modules, Semantic Constraint Module (SCM), Spatial Perceiving Module (SPM), and Intra-class Attention Module (IAM). SCM captures richer global dependencies by encoding semantic structure context prior in a semantic graph, which can reduce semantic structure errors. SPM learns enhanced local features by encoding the spatial consistency context prior in the spatial graph, which can optimize boundaries and reduce the local consistency errors. IAM utilizes the spatial graph with strong–weak connections for intra-class features aggregation to distinguish the inter-class features clearly. Extensive experiments are conducted on two challenging datasets, PASCAL-Person-Part and LIP, effectively achieving state-of-the-arts performance.
One-Class Support Vector Machine and LDA Topic Model Integration—Evidence for AI Patents

The present contribution suggests a two-step classification rule for unsupervised document classification, using one-class Support Vector Machines and Latent Dirichlet Allocation Topic Modeling. The integration of both algorithms allows the usage of labelled, but independent training data, not stemming from the data set to be classified. The manual labelling when trying to classify a specific class from an unlabelled data set can thus be circumvented. By choosing appropriate document representations and parameters in the one-class Support Vector Machine, the differences between the independent training class and the data set to be classified become negligible. The method is applied to a large data set on patents for the European Union.
DIS: A Data-centred Knowledge Representation Formalism

The Domain Information System (DIS) is a novel data-centered knowledge representation formalism that offers a modular structure, which separates the domain knowledge (i.e., the ontology) from the domain data view. The formalism is designed to take advantage of a Cartesian perspective on information, using part Of as its fundamental ontological relation. A DIS consists of a domain ontology, to model the domain knowledge, a cylindric algebra, to model the domain data view, and an operator which couples the two. The core component of the ontology is a Boolean lattice built from atomic concepts taken from a data schema. It is enriched with relevant concepts from the domain of application. A detailed case study is presented to highlight the features of this formalism. The integration of several datasets and their respective ontologies for reasoning tasks requiring data-grounded and domain-related answers to user queries is illustrated. The ability to handle information evolution is demonstrated. Two open issues in ontologies, namely, the lack of clear and explicit guidelines for ontology construction, and the prohibitive cost of adapting and reusing existing ontologies, are addressed.
Managerial competencies and business performance: innovation as a mediator in Rwandan SMEs

Purpose The purpose of this study is to examine the mediating effect of innovation on the relationship between managerial competencies and business performance of small and medium enterprises (SMEs) in Rwanda. Design/methodology/approach A cross-sectional survey and quantitative methodological approach were used to collect the data. The bootstrap method was used to test mediation effects. Findings The findings revealed that innovation is a significant mediator in the association between managerial competencies and business performance. This study, therefore, adds new knowledge by stating that innovation toward business performance is a partial mediator in the relationship between managerial competencies and business performance of SMEs in Rwanda. Research limitations/implications Only a single research methodological approach was used. Qualitative studies through interviews could be undertaken to triangulate. Furthermore, the findings from the present study are cross-sectional. Longitudinal studies ought to be undertaken to examine the mediation effects studied to investigate any possibility of variations in the results. Practical implications The results may help owners-managers of SMEs to develop policies and strategies that could enable them to take advantage of new opportunities in relation to updated technology and cope with changes that may take place in the business environment to boost their business performance level. Originality/value To the authors’ knowledge, no research has ever been carried out on the mediating role of innovation in the relationship between managerial competencies and business performance of SMEs in Rwanda.
Person Retrieval with Conv-Transformer

Part-level features obtained by uniformly partitioning have attracted much attention in person re-identification. However, standard uniform part partitions may lead to within-part inconsistency across different samples, as shown in Figure 1. Attention mechanisms (e.g., refined part pooling) have been proposed to refine part division with enhanced consistency. Unfortunately, such mechanisms adopt single-headed convolutional structures, fail to fuse fine-grained part information. Besides, convolution-based schemes can maintain local positional information but cannot effectively pre-serve relative positions between parts. This paper proposes a new CNN-Transformer hyper architecture called the Person Retrieval with Conv-Transformer (PRCT). We integrate the multi-head self-attention and positional embedding module, which are the core ingredients of non-convolutional Transformer, with a CNN-based part-feature extractor to maintain more precise within-part consistency in feature aggregation. With PRCT, we can effectively eliminate the part mis-alignments when matching different samples. We conduct extensive evaluations on the MSMT17, DukeMTMC-ReID, and Market-1501 datasets and obtain state-of-the-art performance.
A novel semi-supervised framework for call center agent malpractice detection via neural feature learning

This work presents a practical solution to the problem of call center agent malpractice. A semi-supervised framework comprising nonlinear power transformation, neural feature learning, and clustering is outlined. We put these building blocks together and tuned the parameters to obtain the best performance. The data used in the experiments is obtained from our in-house call center. It is made up of recorded agent–customer conversations, which have been annotated using a convolutional neural network (CNN) based segmenter. The methods provided a means of tuning the neural network parameters to achieve a desirable result. Using our proposed framework, we show that it is possible to significantly reduce the malpractice classification error of k-means and agglomerative clustering models, which would serve the same purpose. By presenting the amount of silence per call as a key performance indicator, we show that the proposed system has increased the efficiency of quality control managers, thus enhancing agents’ performance at our call center since deployment.
Self-Supervised Mutual Learning for Video Representation Learning

This work tackles the problem of self-supervised learning of video representation tasks. The related works construct different surrogate supervision signals from data itself. Instead of proposing novel signal, our main insight is that the field of self-supervised learning can be benefited from mutual learning, that is, these supervision signals can learn from others and the combination between them leads to better representation. Unifying these two approaches, we present a frame-work called Self-supervised Mutual (SSM) Learning: a simple framework for mutual learning of video representation under the content of self-supervised. In order to understand what enables the task to learn useful representation, we systematically study the major components of our framework. We show that (1) surrogate supervision signal can learn from others effectively under the framework of mutual learning; (2) introducing a learnable align unit between the deep features supervised by multiple supervision signals in hidden space improves the quality of the learned representation. By combining these findings, we are able to considerably outperform previous methods for self-supervised learning on HMDB51 and UCF101 when applied to action recognition tasks.