Reviewerly/exHarmony huggingface dataset:
Emotion Processing by Applying a Fuzzy-Based Vader Lexicon and a Parallel Deep Belief Network Over Massive Data

Emotion processing has been a very intense domain of investigation in data analysis and NLP during the previous few years. Currently, the algorithms of the deep neural networks have been applied for opinion mining tasks with good results. Among various neuronal models applied for opinion mining a deep belief network (DBN) model has gained more attention. In this proposal, we have developed a combined classifier based on fuzzy Vader lexicon and a parallel deep belief network for emotion analysis. We have implemented multiple pretreatment techniques to improve the quality and soundness of the data and eliminate disturbing data. Afterward, we have performed a semi-automatic dataset labeling using a combination of two different methods: Mamdani&#x2019;s fuzzy system and Vader lexicon. As well, we have applied four feature extractors, which are: GloVe, TFIDF (Trigram), TFIDF (Bigram), TFIDF (Unigram) with the aim of transforming each incoming tweet into a digital value vector. In addition, we have integrated three feature selectors, namely: The ANOVA method, the chi-square approach and the mutual information technique with the objective of selecting the most relevant features. Further, we have implemented the DBN as classifier for classifying each inputted tweet into three categories: neutral, positive or negative. At the end, we have deployed our proposed approach in parallel way employing both Hadoop and Spark framework with the purpose of overcoming the problem of long runtime of massive data. Furthermore, we have carried out a comparison between our newly suggested hybrid approach and alternative hybrid models available in the literature. From the experimental findings, it was found that our suggested vague parallel approach is more powerful than the baseline patterns in terms of false negative rate (1.33%), recall (99.75%), runtime (32.95s), convergence, stability, F1 score (99.53%), accuracy (98.96%), error rate (1.04%), kappa-Static (99.1%), complexity, false positive rate (0.25%), precision rate (97.59%) and specificity rate (98.67%). As a conclusion, our vague parallel approach outperforms baseline and deep learning models, as well as certain other approaches chosen from the literature.
New Keynesianism or smart austerity? Digital technologies and educational privatization post COVID-19

The neoliberal hegemony has found its last expression in a technoeducational agenda that, organized in policy networks with concrete ramifications in each country (technological corporations, philanthropic foundations, lobbies and think tanks), governs the subjects as in no other historical epoch. Among its most significant trends is the expansion of markets towards even more spheres of education through digital platforms, teacher’s and student’s data valorization processes, as well as dynamics of privatization or deregulation that had not been present during the last more than a decade of austerity. Therefore, the objective of the article is to analyze the displacement of public power towards the private sector and the blocking of alternative pedagogical solutions different to the market as a result of the introduction of digital technologies in Spanish educational policy. In addition to a detailed analysis of public tenders (web scraping), the methodology is based on the analytical approaches of “Network Ethnography” and “Critical Discourse Analysis”. The results verify that the European mechanisms to eradicate inequalities or digital gaps in education, or NextGenerationEU, materialize in important programmatic agendas to digitize education that end up reinforcing Spanish national capitalists.
Reliability Assessment of an Unscented Kalman Filter by Using Ellipsoidal Enclosure Techniques

The Unscented Kalman Filter (UKF) is widely used for the state, disturbance, and parameter estimation of nonlinear dynamic systems, for which both process and measurement uncertainties are represented in a probabilistic form. Although the UKF can often be shown to be more reliable for nonlinear processes than the linearization-based Extended Kalman Filter (EKF) due to the enhanced approximation capabilities of its underlying probability distribution, it is not a priori obvious whether its strategy for selecting sigma points is sufficiently accurate to handle nonlinearities in the system dynamics and output equations. Such inaccuracies may arise for sufficiently strong nonlinearities in combination with large state, disturbance, and parameter covariances. Then, computationally more demanding approaches such as particle filters or the representation of (multi-modal) probability densities with the help of (Gaussian) mixture representations are possible ways to resolve this issue. To detect cases in a systematic manner that are not reliably handled by a standard EKF or UKF, this paper proposes the computation of outer bounds for state domains that are compatible with a certain percentage of confidence under the assumption of normally distributed states with the help of a set-based ellipsoidal calculus. The practical applicability of this approach is demonstrated for the estimation of state variables and parameters for the nonlinear dynamics of an unmanned surface vessel (USV).
The emotions effect on a virtual characters design–A student perspective analysis

Interaction between people and virtual characters through digital and electronic devices is a reality. In this context, the design of virtual characters must incorporate emotional expression at a nonverbal level looking for effective communication with the user. This exploratory study investigates the design features of an avatar functioning as a virtual assistant in educational contexts. From a multidisciplinary approach, the user's research was elaborated by a semi-open questionnaire of self-perception of emotional characteristics: likeability, attractiveness, and applicability of a set of six 2D and 3D characters. The results extracted from a sample of 69 university students provide a relevant information on design features and open new lines for future research. Aspects such as Ekman's basic emotion discrimination and the design of facial expression are analyzed. The incorporation of other body parts, their spatial orientation and contextual elements, seems to contribute to effective emotional communication. The results also highlight how the design of a virtual character should take into consideration the complexity involved in facial gestures and changes in relation to the vertical axis and planes of movement. Finally, this article discusses the complexity involved in expressing a given emotion in a virtual character.
Globally Optimal Centralized and Sequential Fusion Filters for Uncertain Systems With Time-Correlated Measurement Noises

This paper is concerned with the fusion filtering problem for stochastic uncertain multisensor systems with time-correlated measurement noises, where the stochastic uncertainties are described by white multiplicative noises, and the additive measurement noises are first-order Gauss-Markov Processes.By introducing the recursive measurement noise estimators, the centralized fusion filter (CFF) based on the idea of batch process and sequential fusion filter (SFF) based on the idea of sequential process are designed in the linear minimum variance (LMV) sense by an innovation analysis approach, respectively.The proposed SFF can achieve the same estimation accuracy as the CFF.It is also globally optimal.The equivalence on estimation accuracy of the SFF and CFF is strictly proved by mathematical induction method.The stability and steady-state properties of the proposed fusion filters are analyzed.Two examples show the effectiveness of the proposed fusion algorithms.
Network Slicing On Software Defined Network Using Flowvisor and POX Controller To Flowspace Isolation Enforcement

Software defined network (SDN) is becoming more and more popular due to its feature such as programming control, centralize monitoring, fine-grained control, flexibility, multitenant support and scalability. Problems with the previous architecture called traditional network like network device configuration process is done one by one, decentralize control, and on multitenant support there are still problem for enforcement tenant, the tenants cannot manage their own network without being disturbed by other tenants. This study aims to perform network slicing on SDN to enforce isolation between tenants by using flowvisor and POX Controller. For isolation in this study is flowspace is part of the flowvisor that is able to enforce network isolation. Network slicing method used to multitenant support on SDN. To achieve the research objectives, two types of tenants were used and two testing processes were carried out namely connectivity testing and functionality testing. In both tests, network quality tests were also carried out by calculating the Quality of Service (QoS). The results of the study show that the flow visor can be implemented for isolation enforcement, and results of Quality of Services is good for both testing process. The conclusion from the study is that the use of Flowvisor to enforce Flowspace can slice network to support multi-tenants because each tenant can use their own slice and is not disturbed by other slices. For future study, can be increased the number of slices and is expected to be tested in the real environment.
Experimental Face Recognition Using Applied Deep Learning Approaches to Find Missing Persons

The spike in challenges to security as well as information and resource management across the globe has equally borne the rising demand for a better system and technology to curb it. A news release from the International Committee of the Red Cross (ICRC) in 2020 revealed over 40,000 people were declared missing in Africa. A staggering percentage of that number, a little over 23,000, is documented in Nigeria alone. Despite the numerous factors surrounding missing persons globally, at more than 50% of the original figure, it is unsurprising that most of the cases in Nigeria are attributed to the insurgency and security mishap that has plagued the country for almost a decade. Some of the cases remain unsolved for years, causing the victims to remain untraceable, thereby taking up a different identity and existence, especially if they went missing. Current solutions to find missing persons in Nigeria revolve around word of mouth, media and print announcements, and more recently, social media. These solutions are inefficacious, slow, and do not adequately help find and identify missing persons, especially in situations where time is a determining factor. The use of a facial recognition system with deep learning functionality can help Nigerian law enforcement agencies, and other human rights organizations and friends and families of the missing person speed up the search and find process. Our experimental system combines facial recognition with deep learning using a convoluted neural network. In this study, the authors have used high-standard facial calibration and modeling for feature extraction. These extracted features form the face encodings that are after that compared to a given image.
Dance Movement Recognition Based on Modified GMM-Based Motion Target Detection Algorithm

Under the synergistic development of social economy and science and technology, the intelligent teaching of dance has become more and more popular. This teaching method can not only decompose dance movements more specifically, which is easy for students to understand and master, but also get rid of the time and space limitation in traditional dance teaching and provide more independent learning opportunities for students. The problem of low accuracy of dance movement recognition due to complex gesture changes in dance movements is addressed. To this end, this paper proposes a modified motion target detection algorithm based on GMM. The dance movement recognition algorithm first extracts the features of dance movements through a feature pyramid network, then uses a multi-feature fusion module to fuse multiple features to improve the algorithm’s estimation of complex postures, and finally completes the recognition of dance movements. Experiments show that our method can maintain a certain recognition rate in the case where the background and target are easily confused, and can effectively improve the dance action recognition accuracy, thus realizing the action correction function for dancers. This also verifies the effectiveness of the action recognition algorithm for dance movement recognition.
An Improved Target Detection General Framework Based on Yolov4

The speed and precision of the target detection algorithm have received wide attention in the application. This paper proposes an improved network framework for the yolov4 algorithm, which reducing the parameters of the model without lowering too much accuracy. Firstly, the up-sampling and down-sampling links of the PANet are strengthened with an increased CBAM attention mechanism, which improve the algorithm ability to deal with the object occlusion. The depth separable convolution is introduced to reduce the amount of model parameters and improves the algorithm speed. Secondly, the Se-Net attention mechanism is added to the residual module of CSPDarknet53 to pay more attention to the channel. Thirdly, Soft-NMS is used to optimize the screening of the detection frame during the detection process. Experiments show that the comparison of VOC2007 dataset is 84.26%, and in the VOC2007+VOC2012 dataset is 91.2%. The detection speed of FPS has increased by 2.21.
Bidirectional quantum operation teleportation with a six-qubit composite Bell-GHZ state

Abstract A bidirectional quantum operation teleportation scheme is proposed by utilizing a six-qubit composite Bell-GHZ state. In which, two users Anne and Benson can perform their respective concerned single-qubit operations on each other’s target states. Compared with the other existing schemes of quantum operation teleportation (QOT), the present scheme can realize the remote implement of the positive and negative two-way quantum operations, which not only improves the communication capacity, but also expands the application scenarios of QOT. Additionally, it is deterministic and feasible with the present experimental techniques.
Metric Temporal Answer Set Programming over Timed Traces

In temporal extensions of Answer Set Programming (ASP) based on linear-time, the behavior of dynamic systems is captured by sequences of states. While this representation reflects their relative order, it abstracts away the specific times associated with each state. In many applications, however, timing constraints are important like, for instance, when planning and scheduling go hand in hand. We address this by developing a metric extension of linear-time temporal equilibrium logic, in which temporal operators are constrained by intervals over natural numbers. The resulting Metric Equilibrium Logic provides the foundation of an ASP-based approach for specifying qualitative and quantitative dynamic constraints. To this end, we define a translation of metric formulas into monadic first-order formulas and give a correspondence between their models in Metric Equilibrium Logic and Monadic Quantified Equilibrium Logic, respectively. Interestingly, our translation provides a blue print for implementation in terms of ASP modulo difference constraints.
Aspect-Based Sentiment Analysis for Hotel Review Using LDA, Semantic Similarity, and BERT

Hotel review is frequently used as a main input in sentiment analysis process.It aims at helping travellers easily find more accurate information about hotel aspects in selecting hotels for their journeys.Based on the review datasets, hotel service organizers may evaluate guests' responses towards the services provided by the hotels.Hotel organizers, in turn, may also know the hotel aspects which need improvement for the next experiences.The common problems are because the processed data do not focus on small scale so that wrong selection of terms from review document frequently appears.The problem that often arises is that the amount of data that is processed is not limited to a small scale, so there are often errors in taking terms from a review document.Meanwhile, these terms are the main input source used for the assessment of aspect categorization and aspect-based sentiment analysis.So, we need aspect categorization and aspect-based sentiment analysis methods that can work automatically on a large scale with good accuracy results.In this study, first, the results of the pre-processing were processed using TF-ICF to obtain terms from reviews based on aspect keyword variables in each hotel aspect category.Next, LDA was used to get the hidden topic of each term.The aim was to obtain better terms accuracy results.Then, the aspect categorization process was carried out using BERT embedding and semantic similarity with the aim of obtaining more significant differences in similarity results in each aspect category so that the determination of aspect categories from a review could be more accurate.The results of the aspect extraction obtained an evaluation of the aspect categorization for each precision 0.86, recall 0.92, and f1-measure 0.89.Furthermore, BERT sentiment analysis method is used in the aspect-based sentiment analysis process.Finally, the evaluation result of aspect-based sentiment analysis obtained for each precision, recall, and f-1 measure are 0.96, 0.98, and 0.97.
Theoretical approaches to the study of the problem of self-development of future specialists

The problem of a person's creative self-realization, in the context of professional self-development, becomes more and more urgent every year, due to which the study of the concept of self-development, professional self-development is given great importance at the present time. This topic is represented by a wide range of multi-vector research, both fundamental and applied. Fundamental research is aimed at revealing the theoretical provisions and features of the process of professional self-development, at the same time, applied provisions provide specific practical tools that help a person realize their inner potential. In this regard, the problem of professional self-development is actualized, which manifests itself, on the one hand, in the integration of the individual into the system of professional relations, and on the other hand, in the implementation of professional functions, the formation of an individual professional style, and the search for ways of professional growth.
Effect of internal corporate social responsibility activities on tourism and hospitality employees ‘normative commitment during COVID-19

The purpose of this research is to explore the extent to which internal corporate social responsibility activities affect employees’ normative organizational commitment during the COVID-19 crisis and to survey the role of the administrative level as a moderator between the two of them. A survey in the form of a self-completed questionnaire was conducted on a sample of employees in hospitality and tourism establishments in Hail city, Kingdom of Saudi Arabia. 213 employees voluntarily participated in the current study. Their responses were examined by using linear regression and exploratory factor analyses. The findings revealed that internal corporate social responsibility is a positive predictor of normative organizational commitment. The total effect of internal corporate social responsibility on normative organizational commitment could not be enhanced through the moderation of the administrative position. This study has important implications for both academics and professionals. Theoretical and managerial implications, contributions, limitations, and future study objectives were reviewed in light of the findings.
Unsupervised Learning of Depth Estimation and Camera Pose With Multi-Scale GANs

Unsupervised learning methods have achieved remarkable performance in monocular depth estimation and camera pose, which mostly solve the multi-task learning problem by using their inner geometry consistency as the self-supervision signal. While most existing approaches mostly adopt the generative model to obtain the depth map prediction, so in the resolution of depth map there is room for improvement. To this end, we present our unsupervised learning architecture based on adversarial learning model, which is used for unsupervised learning of high-resolution single view depth and camera pose. Specifically, we present a multi-scale deep convolutional Generative Adversarial Network (GAN) based learning system, which consists of three networks (pose estimation network PCNN, Generator-D and Discriminator-D for depth map prediction). Furthermore, in order to generate high-resolution depth map, we propose a multi-scale GAN model (MSGAN) to decompose the hard high-quality image generation problem into more manageable sub-problems through a coarse-to-fine process. Then, we modify the overall generation architecture of GAN model by changing the down-sampling and up-sampling components to improve the quality and accuracy of the depth map prediction. Finally, in order to improve the rate of convergence, we use the Least Square Error to increase the penalty for outliers. Detailed quantitative and qualitative evaluations of the proposed framework on the KITTI dataset show that the proposed method provides better results for both pose estimation and depth recovery.
Limit theorems for distributions invariant under groups of transformations

A distributional symmetry is invariance of a distribution under a group of transformations. Exchangeability and stationarity are examples. We explain that a result of ergodic theory implies a law of large numbers for such invariant distributions: If the group satisfies suitable conditions, expectations can be estimated by averaging over subsets of transformations, and these estimators are strongly consistent. We show that, if a mixing condition holds, the averages also satisfy a central limit theorem, a Berry–Esseen bound, and concentration. These are extended further to apply to triangular arrays, to randomly subsampled averages, and to a generalization of U-statistics. As applications, we obtain a general limit theorem for exchangeable random structures, and new results on stationary random fields, network models, and a class of marked point processes. We also establish asymptotic normality of the empirical entropy for a large class of processes. Some known results are recovered as special cases, and can hence be interpreted as an outcome of symmetry. The proofs adapt Stein’s method.
PSSTRNet: Progressive Segmentation-Guided Scene Text Removal Network

Scene text removal (STR) is a challenging task due to the complex text fonts, colors, sizes, and background textures in scene images. However, most previous methods learn both text location and background inpainting implicitly within a single network, which weakens the text localization mechanism and makes a lossy background. To tackle these problems, we propose a simple Progressive Segmentation-guided Scene Text Removal Network(PSSTRNet) to remove the text in the image iteratively. It contains two decoder branches, a text segmentation branch, and a text removal branch, with a shared encoder. The text segmentation branch generates text mask maps as the guidance for the regional removal branch. In each iteration, the original image, previous text removal result, and text mask are input to the network to extract the rest part of the text segments and cleaner text removal result. To get a more accurate text mask map, an update module is developed to merge the mask map in the current and previous stages. The final text removal result is obtained by adaptive fusion of results from all previous stages. A sufficient number of experiments and ablation studies conducted on the real and synthetic public datasets demonstrate our proposed method achieves state-of-the-art performance. The source code of our work is available at: \href{https://github.com/GuangtaoLyu/PSSTRNet}{https://github.com/GuangtaoLyu/PSSTRNet.}