Reviewerly/exHarmony huggingface dataset:
An Efficient Data Preparation Strategy for Sentiment Analysis with Associative Database

Sentiment analysis is a process of categorizing and determining the expressed sentiments. It provides an explicit overview of extensive mass sentiments about particular subjects. Sentiment analysis involves various challenges because expressed opinions and sentiments contain an immense number of anomalies. Data preparation is a prerequisite assignment that can deal with those anomalies for sentiment analysis. This paper represents an efficient data preparation strategy for sentiment analysis using the associative database model. The efficient data preparation strategy involves three subtasks, such as eliminating non-sentimental sentences, eradicating unnecessary tokens, in addition to extracting vocabulary, and arranging that vocabulary uniquely through the associative database model. The experimental results show that the performance of the proposed data preparation approach is comparatively efficient. A comparison with some existing sentiment analysis approaches demonstrates that the accuracy of sentiment analysis has been comparatively improved and enhanced by integrating the proposed efficient data preparation strategy into it.
Implementation of Gamified Navigation and Location Mapping Using Augmented Reality

From hand-drawn maps, and compass to technology-based navigation systems, people have always relied on some kind of tool to help them reach their destination. At present, there are a lot of people equipped with a smartphone which has become a part of the daily life. These smartphones have applications such as Google maps which makes use of the GPS technology to facilitate navigation in the outdoor environment. It provides a great deal of accuracy to reach our destination but the same cannot be said for indoor navigation. Indoor navigation systems are still under research and development. The present indoor navigation systems use the existing technologies such as Bluetooth, Wi-Fi, RFID, and Computer Vision for navigating through the indoor environment. In this paper, we point out some of the issues of the existing technologies for indoor navigation and propose a method for indoor navigation using the Augmented Reality technology and ARCore.
A Deep Convolutional Generative Adversarial Network-Based Model to Analyze Histopathological Breast Cancer Images

Breast cancer is one of the severe cancers, and early detection is needed to remedy the severity. With the advancement of AI technology, machine learning and deep learning are performing a very important role by automatically finding the types of tumors. A machine learning model needs many data to learn the features of the dataset more precisely. Data scarcity can lead to weak accuracy and can introduce bias toward the higher class. In this study, the BreakHis dataset has been used which contains histopathological images of breast tissues of different magnification factors. The datasets have two classes, but the distribution of images is quite imbalanced. The dataset is balanced by adding generated images to the minority class by using a deep convolutional generative adversarial network (DCGAN). For the classification, four pre-trained deep convolutional neural networks (deep CNN), namely DenseNet, MobileNet, ResNetV2 and Xception, have been applied. After applying DCGAN, the performance has been improved with a maximum increase of 2.92%. As for the classification, the DenseNet model with DCGAN has given the overall top performance in this study. Moreover, the result of this study has outperformed most of the state-of-the-art works in identifying malignancy of breast tissue from histopathological images.
Nano Rover: A Multi-sensory Full-Functional Surveillance Robot with Modified Inception-Net

In this rigorous world of civilized progression, terrorism and other subsidiary security threats are undoubtedly intimidating to us. In order to fight back these mischievous activities, we focus to the technical development of our surveillance and reconnaissance systems. Military reconnaissance might be the ancient most solution for preventing the violent outcomes of terrorism but still the unfortunate chance of human life loss exists there. For this why, the solution appears to be using the unmanned surveillance vehicle or machine which would be able to transmit visual data of a certain dangerous area to the controlling headquarters of the particular law enforcement agency. Nano Rover is a significant approach of cost-efficient surveillance and reconnaissance robot which is fully functional and cost-efficient at the same time. It features the service of active reconnaissance mode with LIDAR sensor, location tracking with GPS Neo 6 M module, visual information collection, person detection, weapons detection and identification, gender and age prediction of the hostile and other artificial threat detection, etc. Remote navigation plays as the core controlling system of the robot which is also modifiable through replacement with Internet and satellite navigation system. We modified the conventional Inception-Net architecture with a better hyper-parameter tuning for the successful execution of image processing tasks with a better level of accuracy so far.
Social Listening on Budget—A Study of Sentimental Analysis and Prediction of Sentiments Using Text Analytics &amp; Predictive Algorithms

Text analytics is a mining technique process applied on the text extracted from various platforms to have an understanding about the context and opinion. And this process of social listening is gaining its importance as it is the direct opinion and feedback from the social class or a consumer pool. Sentimental analysis is process of attaching sentiment to the processed text which is of people’s opinions, attitudes, and judgments on certain context. This study is attempted to understand the sentiment and predict the sentiment on the most debated democratic element of the country, i.e., budget. The data for this study have been collected from Twitter using the APIs on the day of budget. With these data, various machine learning algorithms are applied for the classification problem. For the Twitter sentiment analysis, user sentiments about—#Budget2020 are found out by various sentiment classification techniques. Later, various machine learning models such as SVM, Naïve Bayes, random forest, and XGBoost were modeled. The best model comparison has been made based on evaluation metrics such as accuracy.
Challenges of Robotics: A Quest for an Integrated Action

Because of the inherent potentials, robotics has grown increasingly popular in many workplaces throughout time. Unless malfunctioning, a robot can perform its assigned jobs non-stop, perfectly, and quickly. It can perform in extreme conditions, such as deactivating explosives; exploring mines; finding sunk shipwrecks; and rescuing survivors. These large-scale uses of robotics inescapably cause tremendous ethical, social, and legal challenges in the contemporary world, which need to be redressed. The main focus of this article is to analyze those challenges encompassing AI and robotics and shed light on the prospective solutions thereof. This paper argues that the challenges in the field cannot be solved by a single effort; rather, an integrated action is needed from all stakeholders. Hence, a joint action plan, accelerated by national–international collaboration and cooperation and led by the United Nations, might be the chosen alternative.
A combined path planning algorithm based on artificial potential field algorithm and A* algorithm

In order to ensure that mobile robots perform various tasks correctly, the research on efficient and practical path planning algorithms is of great significance. For the Artificial Potential Field algorithm, it is easy to fall into local minimums, the target points are unreachable, and the A* algorithm search time is long when planning the moving path. The speed is slow, the planned path is not smooth enough and it is not the optimal path. This paper proposes a mobile robot combined path planning algorithm based on the APF algorithm and the A* algorithm. This algorithm can solve the local oscillation problem while reducing the problem. Obstacle avoidance time, improve the efficiency of the navigation algorithm. From the results of MATLAB Simulation, it can be seen that the algorithm can complete path planning accurately and efficiently. The mobile robot can bypass the local minimum point and successfully reach the target point, achieving the expected path planning effect.
Wavelet-based multi-level generative adversarial networks for face aging

Face aging has received increasing attention from the computer vision community due to wide applications in the real world. Age accuracy and identity preserving are two important indicators for face aging. Previous works usually rely on an extra pre-trained module for identity preserving and multi-level discriminators for fine-grained features extraction. In this work, we propose a cycle-consistent loss based method for face aging with wavelet-based multi-level facial attributes extraction from both generator and discriminators. The proposed model consists of one generator with three-level encoders and three levels of discriminators with an age and a gender classifier on top of each discriminator. Experiment results on both MORPH and CACD show that the application of multi-level generator can improve the identity preserving effects in face aging and reduce the training time significantly by eliminating the rely of an identity preserving module. Our model can outperform most of the existing approaches include the state-of-the-art techniques on two benchmark aging databases in terms of both aging accuracy and identity verification confidence, demonstrating the effectiveness and superiority of our method. • Wavelet-based multi-level generator was applied to face aging for the first time. • Time reduction for model training without a pretrained module for identity preserving. • Outperforms the state of the arts in both aging accuracy and identity preserving. • Our model can generate images with a continuous and smooth increase of age.
Stacked objects count based on density map and two vanishing points

Object counting is an essential topic in computer vision due to its practical application of surveillance analysis. Most existing works focus on single-view or multi-view object counting and have achieved good performance. However, stacked objects appear frequently in industrial site and these counting is viewed as a difficulty. In this paper, a new counting method of approximates cubic stacked objects (e.g. stacked material) is proposed. The approximate cubic stacked objects count by fusing two vanishing points and single-view object counting. Two vanishing points are used to provide partial stereo information and single-view object counting is used to provide quantity information. We perform experiments of stacked object images captured in actual industrial sites. Experimental results show that our method is effective in approximate cubic stacked object counting.
The numerical solution of one-dimensional discrete asset pricing model based on the improved trigonometric extreme learning machine

The asset pricing model plays an essential role in finance and has extended to all areas of finance and investment. This paper focuses on the numerical solutions of one-dimensional discrete asset pricing models, which can be translated into integral equations. To solve integral equations, an improved trigonometric extreme learning machine is developed, where the Fourier series is selected as the basis function. In particular, the sampling frequency of the trigonometric function varies with the property of data distribution, which helps to improve the accuracy of numerical solutions. The interior-point method is introduced to optimize the sampling frequency. Then, the integral equation is converted into a linear system which is solved by the extreme learning machine. This paper gives the mathematical derivation of solving the asset pricing model using the improved trigonometric extreme learning machine, which can be adapted to solve various types of integral equations. Subsequently, the improved trigonometric extreme learning machine is employed to solve the one-dimensional discrete-time asset pricing model. Simulation results demonstrate the high precision of the proposed method, which leads to less error in the approximate equity price and illustrates the feasibility of the improved trigonometric extreme learning machine. Furthermore, when the improved trigonometric extreme learning machine is compared with other related methods, the numerical simulations verify the efficiency and superiority of the proposed algorithm in solving the asset pricing model.
Partial arithmetical data types of rational numbers and their equational specification

Upon adding division to the operations of a field we obtain a meadow. It is conventional to view division in a field as a partial function, which complicates considerably its algebra and logic. But partiality is one out of a plurality of possible design decisions regarding division. Upon adding a partial division function ÷ to a field Q of rational numbers we obtain a partial meadow Q(÷) of rational numbers that qualifies as a data type. Partial data types bring problems for specifying and programming that have led to complicated algebraic and logical theories – unlike total data types. We discuss four different ways of providing an algebraic specification of this important arithmetical partial data type Q(÷) via the algebraic specification of a closely related total data type. We argue that the specification method that uses a common meadow of rational numbers as the total algebra is the most attractive and useful among these four options. We then analyse the problem of equality between expressions in partial data types by examining seven notions of equality that arise from our methods alone. Finally, based on the laws of common meadows, we present an equational calculus for working with fracterms that is of general interest outside programming theory.
An effective behavior recognition method in the video session using convolutional neural network

In order to further improve the accuracy of the video-based behavior recognition method, an effective behavior recognition method in the video session using convolutional neural network is proposed. Specifically, by adding the target detection phase before the behavior recognition algorithm, the body region in the video can be accurately extracted to reduce the interference of redundant and unnecessary background noises, and at the same time, the inappropriate images can be replaced, which has reached the role of balance background trade-off, and finally, the neural network can learn the human behavior information with emphasis. By adding fragmentation and stochastic sampling, the long-time time-domain modeling of the whole video session can be established, so that the model can obtain video-level expression ability. Finally, the improved loss function is used for behavior recognition to solve the problem of classification difficulty and possible sample imbalance. In addition, we conducted the hyperparametric experiment, the ablation experiment and the contrast experiment on different open source and benchmark datasets. Compared with other commonly used behavior recognition algorithms, the experimental results verify the effectiveness of the proposed method. In addition, the related deep learning-based methods used in behavior recognition are reviewed at the beginning of this paper, and the challenges in behavior recognition and future research directions are prospected at the end of this paper, which will undoubtedly play a double role in the work of later researchers.
Unlabeled data driven cost-sensitive inverse projection sparse representation-based classification with 1/2 regularization

Sparse representation-based classification (SRC) has been widely used because it just relies on simple linear regression ideas to do classification, and it does not need learning. However, the performance of SRC is limited by needing sufficient labeled samples per class and the sensitivity to class imbalance. For tackling these problems, an improved SRC model is constructed in this paper. For alleviating the problem of insufficient labeled samples, an unlabeled data-driven inverse projection sparse representation-based classification model is constructed to achieve effective and stable representation and recognition results. The L1/2 and S1/2 regularizations are introduced to capture the sparsity of 1-D and 2-D, and to make the model have good statistical properties. Moreover, the cost-sensitive strategy is integrated into the model’s classification criteria to improve the imbalance of class distribution adaptively, especially for multiclass imbalanced data. A solver utilizing the mixed Gauss-Seidel and Jacobian ADMM algorithm is developed to obtain the approximate solution. Experiments on common public test databases show that the proposed model achieves competitive results compared with the latest published results and some deep-learning algorithms.
Procedural Maze Generation Considering Difficulty from Human Players’ Perspectives

In video game development, creating maps, enemies, and many other elements of game levels is one of the important processes. In order to improve players’ game experiences, game designers need to understand players’ behavioral tendencies and create levels accordingly. Among various components of levels, this paper targets mazes and presents an automatic maze generation method considering difficulty based on human players’ tendencies. We first investigate the tendencies using supervised learning and then create a test player considering human-likeness by exploiting the tendencies. The test player simulates human players’ behaviors when playing mazes and judges difficulty according to the simulation results. Maze evaluation results from subject experiments show that our method succeeds in generating mazes where the difficulty estimated by the test player matches human players’.
Opponent Model Selection Using Deep Learning

It is observed that the strength of many game programs varies a lot in a tournament when facing opponents of different styles. In order to adapt one’s playing strategy when playing against different programs to obtain the best possible outcome, it is important to estimate the strength of the opponent. We use a neural network to predict the strength or style of an opponent via the first 20 plies of a game in Chinese Dark Chess. According to the prediction result, a contempt factor is used when playing against the target opponent after the first 20 plies. Both the experiment result and the tournament results show that this approach can improve the rank of our program in a competitive tournament.
Technological Resources in a Blended Learning Environment for the Teaching of Ornithology

Ornithology courses have traditionally been developed through face-to-face lectures complemented with field trips, but currently technologies offer new possibilities to the training process. The present study aims to provide useful guidance for the application of technological resources in teaching the fundamental principles of ornithology and bird watching in a blended learning environment. The course was aimed at students and professionals of the biological sciences through the combined modality of synchronous and asynchronous classes. In the synchronous classes, lectures were held and the asynchronous ones through the use of a virtual learning environment with practical and autonomous activities. The result of the anterior and posterior knowledge test had variations, although without significant differences. Regarding the development of the activities, the undergraduate students were those who carried out the content creation tasks using different technological resources, while the postgraduate professionals showed tendencies towards the development of jobs with greater cognitive demand. Among the most popular resources, EDpuzzle, Padlet and GoConqr were highlighted. Regarding the virtual environment, the participants attended mostly in the evening and at night, with the most frequent sessions being the introduction, the anatomical study and the systematics of mangrove birds. In conclusion, the students have used the different technological resources provided by the teacher to design their own learning experiences, but they were not enough for them to fully master the different topics that were addressed in the course.
Hearing Health Virtual Assessment Through Association Rules Mining Inside a College Community

This study quantified possible cases of hearing loss and determined possible causes through association rules mining. The data was collected through an online survey that evaluated the medical background of the participants, and a free online hearing test provided by the American company Phonak. The study sample consisted of 226 entries, among students, faculty, and staff members of the Escuela Politécnica Nacional, a public university from Quito, Ecuador. For the initial data treatment and conversion to binary tables of the audiometry results, a Python optical character recognition algorithm was used. Finally, the association rules were obtained using the Apriori algorithm, which was implemented through the arules package for R. The results showed that 66.36% of the sample presents hearing loss in at least one ear. Furthermore, hearing loss causes in males are primarily related to prolonged use of headphones and loud noises, and these cases are mostly seen in younger individuals; while hearing loss causes in females are primarily related to family history of deafness.
Analysis of Variables that Influence University Student’s Perception About Virtual Education Adopted Due to COVID-19

The changes in higher education due to the COVID-19 pandemic have forced the maintenance of a virtual education system, transforming face-to-face classes into online classes through different platforms available for this purpose. However, these classes, which can be synchronous or asynchronous, are new to many students, many universities, and several professors, so there is little information about their adaptation and which variables can affect students’ better acceptance of this system. This study seeks to determine how the students’ perception of the new educational system is altered by the access to technological tools to attend classes and, at the same time, how the adaptation of the university and professors to the new system influences said student perception. This study investigated the effects of these variables on students’ perception by utilizing a questionnaire that obtained 617 complete responses, and the obtained data were analyzed using the linear regression model. The results found show that the student’s perception of the new system improves as the capacity of the university and professors to adapt to the recent education model increases, and it also improves when students have access to technological tools. Furthermore, the variable of adaptation of the university and professors shows a greater incidence in improving student perception. These results lead to the search to implement strategies that will allow the capacity of these two variables to increase and achieve a better acceptance of the new system.