Reviewerly/exHarmony huggingface dataset:
Confirmatory aspect-level opinion mining processes for tourism and hospitality research: a proposal of DiSSBUS

We proposed a new rule-based text analysis method to effectively summarize and transform unstructured user-generated content (online customer reviews) into an analysable form for tourism and hospitality research. To differentiate this method, we developed the Disintegrating, Summarizing, Straining, Bagging, Upcycling, and Scoring – DiSSBUS – algorithm which can address the following problems in previous approaches: (1) false identification of irrelevant aspect terms, (2) improper handling of multiple aspects and sentiments within a text unit, and (3) data sparsity. The algorithm’s distinctive advantage is to decompose a single review into a set of bi-terms related to the aspects that are pre-specified based on domain knowledge. Therefore, this algorithm can identify customer opinions on specific aspects, which allows to extract variables of interest from online reviews. To evaluate the performance of our confirmatory aspect-level opinion-mining algorithm, we applied it to customer reviews on restaurants in Hawaii. The findings from the empirical test validated its effectiveness.
Prediction of Graduation with Naïve Bayes Algorithm and Principal Component Analysis (PCA) on Time Series Data

The percentage of students who graduated on time can be predicted with data mining methods. This research aims to provide earlier information regarding students who are at risk of not graduating on time. Thus, the study program can take appropriate action before it is too late. Several classification methods can be used for prediction. Our research combines Naïve Bayes with Principal Component Analysis (PCA). PCA is used to simplify complex academic data. The PCA result has a more straightforward structure to be processed using Naive Bayes classification. This research uses four batches of student academic performance data in Informatics Study Program, Telkom University. The dataset is partitioned by academic year to obtain time-series data of each student. The combination of PCA and Naïve Bayes algorithms obtained better results than classification using Naïve Bayes only, with 6.04% higher accuracy on average.
Towards a Modular Ontology for Cloud Consumer Review Mining

Nowadays, online consumer reviews are used to enhance the effectiveness of finding useful product information that impacts the consumers' decision-making process. Many studies have been proposed to analyze these reviews for many purposes, such as opinion-based recommendation, spam review detection, opinion leader analysis, etc. A standard model that presents the different aspects of online review (review, product/service, user) is needed to facilitate the review analysis task. This research suggests SOPA, a modular ontology for cloud Service OPinion Analysis. SOPA represents the content of a product/service and its related opinions extracted from the online reviews written in a specific context. The SOPA is evaluated and validated using cloud consumer reviews from social media and using quality metrics. The experiments revealed that the SOPA-related modules exhibit a high cohesion and a low coupling, besides their usefulness and applicability in real use case studies.
Rock mass performance monitoring with topology preserving quantised vector spaces

Abstract This work explains the development of a so-called rock mass system performance map, that provides a backdrop to assist in performance interpretation. The rock mass system performance maps presented in this paper are 2D graphical devices prepared using Sammon mappings, Learning Vector Quantisation, Self-Organising Topological Maps and combinations of these mathematical techniques. Using supervised or unsupervised learning methods, these algorithms project and partition high-dimensional vector spaces, of a dimension equal to the number of environmental and rock mass condition parameters considered, into 2D categories of rock mass condition. In providing 2D renderings, the techniques aim to preserve properties of the characterising vector space such as adjacency of states and topology. The condition of a rock mass can be ‘plotted’ on that map by identifying its k-nearest neighbours and interpreted relative to stability metric categories. Should the defining vector space include rock mass properties or environmental parameters that are repeatedly measured, or possibly continuously monitored, then the rock mass condition marker traces out a performance trajectory across the performance map. An example of rock mass performance map synthesis and use is presented.
De-noising of an Image using Fuzzy Inference System and Performance Comparison with the Conventional system

Noise prevailing in the image can diminish the physical appearance of the objects existing within the image and make them frail. Present research emphasizes a fuzzy inference system eradicating several types of noise from the images. The investigation implies the utilization of different levels of Salt &amp; Pepper noise. Followed by the pixel determination applying a mask, the disparity between the focused pixel's intensity with the minimum, average, and maximum power of the chosen window has been determined. Since two fuzzy valued outputs have been obtained to match them, the one provided by a low noise rate would demonstrate the more accurate filter for the selected window. Utilizing Matlab the Peak Signal-to-Noise ratio (PSNR) and Mean Square Error (MSE) are determined for evaluating the noise reduction performance. However, these values of PSNR and MSE obtained from this research are also compared with the conventional fuzzy filtering system.
A Multi-population Whale Optimization Algorithm Based on Orthogonal Learning

For solving the problems of low accuracy, low convergence speed and accidental premature of canonical whale optimization algorithm (WOA), a modified multi-population WOA based on orthogonal learning (MOL-WOA) is proposed. Firstly, the multi-population strategy is adopted. The diversity of the sub-populations can be improved by the generalized opposition-based learning (GOBL) strategy. Secondly, the nonlinear convergence factor and adaptive weight strategy are utilized to take account of the global search and local development. Finally, the orthogonal learning method is adopted to realize the communication among the sub-populations. At the same time, a generation jump operation is brought in for avoiding premature. Simulation experiments with 20 benchmark test functions, contrasted with the other two modified algorithms, MOL-WOA owns better performance on accuracy, convergence speed and ability to miss the local optimal.
Research of data visualization in industrial product design

In the background of data era, graphics language meets more opportunities and challenges, so it is of great significance to transform data into graphics language. This paper mainly designs a set of data-based intelligent system. Through the research of multi-dimensional data visualization, firstly, the high-dimensional data is processed to reduce the dimension, so as to get the lower dimensional data, and then the low-dimensional data is transformed into a more intuitive type of graphic markers, so that users can understand the high-dimensional data more intuitively. The research results show that this system can provide users with the most suitable types of tags, and save users more time and energy. Data visualization has different experiences for different users, so it is a very important challenge to let users feel that data perspective is effective as much as possible. At present, machine learning is a developing field. It is a very good research direction to combine the two. It can not only promote the development of big data field, but also bring more wealth to the society.
Conjecture of Quantum Entanglement as a General Mode for Information Transmission

In this paper, the author proposes a new world view in which microscopic particles exhibit quantum random selectivity and macroscopic objects exhibit classical states, and discusses how macroscopic objects behave classical states, in which quantum entanglement plays an important role. In order to better analyze the role of quantum entanglement in transmitting information, this paper shows the irreversible and chain-like transmission of quantum entanglement. In addition, whether quantum entanglement poses a threat to special relativity is discussed. As a supplement, the conjecture of inverse causality transmission and the Tumulka's theory are introduced. In the last part of this article, the misunderstanding of quantum entanglement is pointed out from the author's point of view. It is concluded that all particles in the world are connected, either through quantum entanglement, or through entanglement of particles in the form of entanglement chains.
Grid-Based Anomaly Detection of Freight Vehicle Trajectory considering Local Temporal Window

The security travel of freight vehicles is of high societal concern and is the key issue for urban managers to effectively supervise and assess the possible social security risks. With continuous improvements in motion-based technology, the trajectories of freight vehicles are readily available, whose unusual changes may indicate hidden urban risks. Moreover, the increasing high spatial and temporal resolution of trajectories provides the opportunity for the real-time recognition of the abnormal or risky vehicle motion. However, the existing researches mainly focus on the spatial anomaly detection, and there are few researches on the real-time temporal anomaly detection. In this paper, a grid-based algorithm, which combines the spatial and temporal anomaly detection, is proposed for tracing the risk of urban freight vehicles trajectory by considering local temporal window. The travel time probability distribution of vehicle historical trajectory is analyzed to meet the time complexity requirements of real-time anomaly calculation. The developed methodology is applied to a case study in Beijing to demonstrate its accuracy and effectiveness.
Fine reconstruction of underwater images for environmental feature fusion

Complicated underwater environments, such as occlusion by foreign objects and dim light, causes serious loss of underwater targets feature. Furthermore, underwater ripples cause target deformation, which greatly increases the difficulty of feature extraction. Then, existing image reconstruction models cannot effectively achieve target reconstruction due to insufficient underwater target features, and there is a blurred texture in the reconstructed area. To solve the above problems, a fine reconstruction of underwater images with the target feature missing from the environment feature was proposed. Firstly, the salient features of underwater images are obtained in terms of positive and negative sample learning. Secondly, a layered environmental attention mechanism is proposed to retrieve the relevant local and global features in the context. Finally, a coarse-to-fine image reconstruction model, with gradient penalty constraints, is constructed to obtain the fine restoration results. Contrast experiment between the proposed algorithm and the existing image reconstruction methods has been done in stereo quantitative underwater image data set, real-world underwater image enhancement data set, and underwater image data set, clearly proving that the proposed one is more effective and superior.
Performance of OFDM system receiver Based on Deep Learning

In order to explore the performance of deep learning in wireless communication receivers, a neural network-based method is proposed to realize the functions of OFDM receivers. This method is to directly reduce the noise and restore the transmitted symbols, and does not add the existing channel estimation function network module. Specifically, the mathematical calculation function of Discrete Fourier Transform (DFT) and the training of OFDM signal samples are used to reduce the ser error rate (SER) through the Convolutional Neural Network(CNN). In addition, The robustness of this method is proved by data sets under different modulation modes and different SNR conditions. This article introduces the preliminary research results of OFDM receivers. The experimental results of the simulation in this article show that the method we propose is better than the SER of OFDM receiver based on traditional linear least squares (LS) channel estimation, which drop by 1.6% ~ 6.7% in the conditions of different SNRs and modulation modes.
Solving Nonlinear Wave Equation Based on Topology

A method of solving nonlinear wave equation based on topology is proposed. Firstly, the characteristics of stochastic graph and Scaleless network are compared, and their topological characteristics are analyzed. Because of the existence of a few axis nodes, Scaleless networks have higher average aggregation than those with the same number of airport nodes and connected stochastic graphs. According to the topological structure of nonlinear wave equation, the first-order integral method is used to solve the nonlinear wave equation. According to the first integration, the threshold range is set, and the solution flow is designed in line with the division theorem. The topology of the network is analyzed according to the node degree, aggregation coefficient and reciprocity of the network, so as to verify and analyze. The experimental results show that the application of this method is 98%, which is still effective for the hyperbolic development equation of the same type.
Monocular Depth Estimation using Integrated Model with Multi-task Learning and Multi-stream

Scene understanding is a central problem in a field of computer vision. Depth estimation, in particular, is one of the important applications in scene understanding, robotics, and 3-D reconstruction. Estimating a dense depth map from a single image is receiving increased attention because a monocular camera is popular, small and suitable for a wide range of environments. In addition, both multi-task learning and multi-stream, which use unlabeled information, improve the monocular depth estimation efficiently. However, there are only a few networks optimized for both of them. Therefore, in this paper, we propose a monocular depth estimation task with a multi-task and multistream network architecture. Furthermore, the integrated network which we develop makes use of depth gradient information and can be applied to both supervised and unsupervised learning. In our experiments, we confirmed that our supervised learning architecture improves the accuracy of depth estimation by 0.13 m on average. Additionally, the experimental result on unsupervised learning found that it improved structure-from-motion performance.
Computational intelligence techniques for cancer diagnosis

Computational intelligence (CI) is a group of techniques for building machine intelligence. These techniques follow a pragmatic approach to learning and decision making rather than a hard approach as in expert systems or rule-based systems. These techniques use soft computing, fuzzy logic, and evolutionary methods to enable a machine to learn and make a decision under different situations. This is different from artificial intelligence which is based on crisp logic rules. In this chapter, we discuss the applications of important techniques under CI that have been applied to the computational diagnosis of cancers. These important techniques include fuzzy logic, artificial neural networks, evolutionary computation based on principles of natural selection, learning theory which is the study of learning mechanism of natural organisms, probabilistic or random methods that inherently account for uncertainty in input or events. Fuzzy logic is applied in the identification of tumors in medical imaging reports where approximate reasoning is helpful. Neural networks like convolutional neural networks have been demonstrated to accurately identify and classify the various type of tumors. An evolutionary computation or natural computation methods apply the principle of natural selection to solve a multiobjective optimization problem. These methods, including swarm intelligence, etc., have wide applications in the areas of genomic data analysis. CI heavily uses the principles of learning theory to understand the cognitive processes of various natural organisms. Learning theory involves the study of how the processes of cognition, emotion regulation, accounting for environmental influences, and experience aid in gathering, improving, or changing knowledge. It also examines how these are processed and used for the prediction of future events based on experience. The application of learning theory to the prediction of the incidence of a disease is an interesting area of research. Probabilistic methods are characterized by randomness. Many probabilistic methods like Bayesian networks are used in the diagnosis of cancers. The mathematical basis is mainly machine learning methods like regression, support vector machines, decision trees, etc. In this chapter, we have analyzed commonly used CI techniques as applied to research into the classification and diagnosis of cancerous tumors and compiled a list of the most promising techniques that have improved the accuracy of diagnosis or have led to better outcomes for cancer patients.
Vehicle Detection, Tracking, and Speed Estimation Using Deep Learning and Computer Vision: An Application Perspective

Proficient and precise item recognition has always been a prominent theme in the progression related to computer vision frameworks. Added further, due to the appearance of profound learning procedures, the exactness for object recognition has expanded manifoldly. The undertaking thus intends to be a part of a cutting-edge strategy for more precise object location with the sole objective of achieving high exactness coupled with a continuous presentation. In this present undertaking, we intend to utilize a profound learning-based methodology for object location in a start to finish design, using TensorFlow API to additionally take care of the issue of almost precise assessment of speed in moving vehicles. We surveyed on the idea of VASCAR, a prominent technical strategy, that police department frequently uses for estimating the speed of moving articles making use of distance accompanied by timestamps. We also studied the involvement of a human part, which prompts mistakes, and consequently, how our technique can efficiently address human blunder’s nature. Based on all this prior study, we then aptly chose to plan our own unique computer vision framework for gathering timestamps of vehicles for quantifying speed (when the distance is known). With the option of killing the human segment, our framework would be depending on our insight into material science and our product advancement aptitudes. Our framework depends on a mix of object recognition and item following to discover vehicles in a video transfer at various waypoints.
Cross-Lingual Annotation Projection for Argument Mining in Portuguese

While Argument Mining has seen increasing success in monolingual settings, especially for the English language, other less-resourced languages are still lagging behind. In this paper, we build a Portuguese projected version of the Persuasive Essays corpus and evaluate it both intrinsically (through back-projection) and extrinsically (in a sequence tagging task). To build the corpus, we project the token-level annotations into a new Portuguese version using translations and respective alignments. Intrinsic evaluation entails rebuilding the English corpus using back alignment and back projection from the Portuguese version, comparing against the original English annotations. For extrinsic evaluation, we assess and compare the performance of machine learning models on several language variants of the corpus (including the Portuguese one), following both in-language/projection training and direct transfer. Our evaluation highlights the quality of the generated corpus. Experimental results show the effectiveness of the projection approach, while providing competitive baselines for the Portuguese version of the corpus. The corpus and code are available (https://github.com/AfonsoSalgadoSousa/argumentation_mining_pt).
Intensive Foreign Language Training Technology for Internal Affairs Personnel

The purpose of the research is to develop a technology for the internal affairs personnel intensive training based on the value attitude aimed at achieving planned training results. The article presents the technology using the example of training the internal affairs personnel who study a foreign language as a part of implementation of an advanced training educational programme. Scientific novelty of the research lies in the technological approach to educational process intensification in the context of the value attitude (readiness) aimed at mastering foreign-language communicative competence taking into account professional orientation of the internal affairs personnel training. As a result, the technology ensures algorithmicity and reproducibility of intensive training organisation, achievement of specific planned training results.
Psychological problems of distance learning part-time students at university

The article is devoted to the problem of distance learning in higher education. Distance learning is a very important component of the education system and has many advantages. These include: modularity, variability, development of independence and critical thinking, strong-willed personal qualities, as well as digital literacy. In addition, this form of education provides great opportunities for part-time students. Among the disadvantages of distance learning, there is a psychological discomfort that affects the effectiveness of training and satisfaction with its results. Its leveling is an important task for psychologists and other specialists who organise the process of distance learning. The article presents the results of a study aimed at identifying problems that arise in part-time students in the process of distance learning, conducted in Borisoglebsk branch of Voronezh State University. Using a specially designed questionnaire, 71 part-time students were interviewed. The subjects were found to have both technical and psychological difficulties. Among the latter, problems associated with the lack of "live communication", visual direct contact with teachers and other students are particularly common. The result of the study was the development of a number of psychological recommendations for optimising distance learning, which will increase its effectiveness and reduce the students' discomfort.
Human Action Recognition Based on State Detection in Low-resolution Infrared Video

This paper proposes a recognition method of human action based on the states in infrared image frames of thermopile array sensors. Two sensors are used for the top-side deployment. Each frame data collected by the thermopile array sensor is called infrared heat map with a resolution 24×32. It is sequentially processed by the quantification, time-domain filtering and background removal operations. Then those processed data are used for the training of state detection models. The state detection models detect the state of the human target in each of frame, and the state sequence is obtained. Finally, A semantic analysis method functions on the state sequence and converts it into the corresponding action. The experimental results have shown that the proposed method can recognize human actions correctly.