Reviewerly/exHarmony huggingface dataset:
Analysis on the Practical Operation Mode and Innovative Application of 3DMAX in Interior Space Design

In terms of range of application, 3DMax is widely used in the advertising, film and television, industrial design, architectural design, 3D animation, multimedia production, games, auxiliary teaching, visualization and other fields. With the continuous advancement of computer technology, the drawing software of interior space design, such as 3DMax, is also gradually updated, becoming more perfect and mature. A modern designer should not only improve the professional ability but also master various interior design drawing software, skillfully use its various functions, continuously improve the functions of all kinds of drawing software in the field of interior design, so as to complete more excellent interior design works.
Parameter extraction with reservoir computing: Nonlinear time series analysis and application to industrial maintenance

We study the task of determining parameters of dynamical systems from their time series using variations of reservoir computing. Averages of reservoir activations yield a static set of random features that allows us to separate different parameter values. We study such random feature models in the time and frequency domain. For the Lorenz and Rössler systems throughout stable and chaotic regimes, we achieve accurate and robust parameter extraction. For vibration data of centrifugal pumps, we find a significant ability to recover the operating regime. While the time domain models achieve higher performance for the numerical systems, the frequency domain models are superior in the application context.
Computational Model for Rating Mobile Applications based on Feature Extraction

Google Play Store and App Store allow users to share their opinions and helps to measure users satisfaction level about the app through user comments. However, it's highly time-consuming to process all reviews manually. The usefulness of star ratings is limited for development teams since a rating represents an average of both positive and negative evaluations. Therefore, an automated solution is needed to systematically analyze reviews and other textual forms of data. The main objective of this research is to build a platform that rate apps by feature extraction and sentiment analysis to calculate the functionality index of apps based on metrics obtained by surveying 204 mobile phone users. The 5 topmost metrics obtained from them among the 16 metrics obtained from the literature review are usability, price, and frequency of updates, ad-freeness and battery consuming level. This research focuses on selected apps in music and audio category. To perform app rating indexes calculation of the overall app's reviews; data extraction, data cleaning, POS tagging, feature extraction, feature/feature values pairing, weighted feature rating, overall apps' rating and feature-wise app rating is done on textual data. The accuracy of the created model is measured by the level of satisfaction from users.
WITHDRAWN: Robust Continuous Kannada speech recognition using Kaldi toolkit

ASR system is developed for the real time Continuous Speech Recognition of Kannada sententences. The Hidden Markov Model based acoustic models are constructed using Kaldi Tool. The Bigram language model is used for decoding the kannada sentences. The 100 native kannada male and female speakers participated in creation of Kannada speech database and English speech database (South Indian accent). The developed ASR system is evaluated based on the metric Word Error Rate (WER), Wavelet Packet Analysis along with Mel filter bank is utilized to accomplish feature extraction. The proposed features perform slightly better than the existing features like MFCC and PLP under uncontrolled conditions. The proposed features shows slight improvement in performance over baseline features.
Training and implementation of google applications for online learning in the pandemic covid-19

Abstract Knowledge of the concepts of online learning and teacher skills in the use of information technology, is an important factor of online learning in the pandemic covid-19. Facts show that teachers’ knowledge and skills in online learning and the use of information and communication technology are still low. On the other hand students are prohibited from bringing mobile devices to school, with the reason to interfere with the lesson. Teachers and schools need to get help and guidance, so they can maximize learning even in the pandemic. The problem raised is how to improve the skills of teachers in online learning during the co-19 pandemic? The purpose of this activity is to provide skills as well as an understanding of how to use information technology for effective, efficient and safe learning from negative stigma that has been feared by schools and teachers. This activity using the guided training method and was carried out in four stages namely observation, training, mentoring and evaluation. The results of the activities showed that the training and technical guidance of online learning based on google application improve the skills of teachers in online learning
Monocular Instance Level 3D Object Reconstruction based on Mesh R-CNN

In recent years we have witnessed the rapid improvement of algorithms and technologies in object detection, instance segmentation and 3d reconstruction. Since the development of the R-CNN model and various improvements that follows, it is now an easy task to separate objects from the environment. For 3d reconstruction, Mesh R-CNN and PiFUHD render objects close to their original geometry, and this leads us to develop a 3d object reconstruction system that can integrate and improve its performance based on two models. We find that Mesh R-CNN can be improved with the newest PointRend model that generates more accurate shapes than Mask R-CNN on which Mesh R-CNN is based, and we reach the conclusion that a Monocular Instance Level 3D Object Reconstruction is fully feasible. document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.
Pinhole Paper: Making Static Objects Perceptually Dynamic with Rear Projection on Perforated Paper

We explored the paper augmentation technique where a tablet computer is placed under the paper to modify its content appearance and create the illusion of a static object appearing perceptually dynamic. Our approach is based on the Deformation Lamps (DL) technique commonly used with front projection. However, we applied it to rear projection, which removes the need for a fixed hardware setup or handling and wearing dedicated hardware. The key challenge was to ensure the appropriate mixing of light reflecting from the paper (to see what is printed on the paper) and the light passing though the paper (to see what the tablet screen is projecting) since (i) a tablet screen is a relatively weak light source when being used in rear projection and (ii) commonly, paper has high opacity so the content from one side is not visible on the other side. In order to achieve the appropriate mixing of light, we reduced the opacity of the paper by perforating it (thus allowing more light to pass through), and modified the algorithm for generating dynamic luminance animation by adding gamma and brightness correction. The outcomes provide a guide on how to modify the paper and the algorithm in order to achieve the DL effect with rear projection using a tablet computer.
A Comprehensive Study on Ensemble-Based Imbalanced Data Classification Methods for Bankruptcy Data

In many real world classification problems the data is imbalanced where the distribution of classes is skewed. When the classification data are not approximately equivalent then the classification dataset is imbalanced. For example one class may be extremely low (minority class) and the other class may be extremely high (majority class). This imbalanced nature of data leads the prediction algorithm to be biased towards majority class. The poor representation of minority class affects the performance of the classification algorithm, which is evident through various assessment metrics. In this paper it is suggested to use ensemble techniques for imbalanced datasets, which focuses on binary class problems. The Ensemble bagging and boosting technique is applied on bankruptcy imbalanced data to improve the performance. Experimental study shows that better performance is achieved when SMOTEBOOST and SMOTEBAGGING are used with decision tree, which is a combination of SMOTE and Ensemble bagging and Ensemble boosting algorithm respectively and it outperforms other ensemble techniques.
Development of the information system for the Kazakh language preprocessing

The aim of this work is the design and development of linguistic resources and preprocessing tools for the Kazakh language. The media-corpus of the Kazakh language is presented as a linguistic resource, which is available on Al-Farabi Kazakh National University platform. The media-corpus of the Kazakh language consists of texts of news content and is implemented as an information system. The general architecture of an information system for the automatic and reliable collection, storage and analysis of texts in the Kazakh language is described. Three automatic text preprocessing tools for the Kazakh language – word forms generator, morphological analyzer, and morphological disambiguation tool – are presented in the article. The proposed tools can also be applied in the systems of automatic analysis of texts, in creation of other linguistic resources such as thesauri and ontologies.
An Energy-segmented Moth-flame Optimization Algorithm for Function Optimization and Performance Measures Analysis

The moth-flame optimization algorithm (MFO) is a novel metaheuristic algorithm for simulating the lateral positioning and navigation mechanism of moths in nature, and it has been successfully applied to various optimization problems. This paper segments the flame energy of MFO by introducing the energy factor from the Harris hawks optimization algorithm, and different updating methods are adopted for moths with different flame-detection abilities to enhance the exploration ability of MFO. A new energy-segmented moth-flame optimization algorithm (ESMFO) is proposed and is applied on 21 benchmark functions and an engineering design problem. The experimental results show that the ESMFO yields very promising results due to its enhanced exploration, exploitation, and convergence capabilities, as well as its effective avoidance of local optima, and achieves better performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.
WITHDRAWN: Soft biometric based keystroke classification using PSO optimized neural network

In this work, variable length login-id and password belonging to the user were analyzed for bringing forth a more secure verification system. Soft biometrics such as age group and gender are estimated from keystroke dynamics patterns when he/she types a given password or login id on a keyboard. Experiments were carried on GREYC a web-based keystroke dataset by exploiting the features from DWT of keystroke dynamics and provides classification results using PSO optimized neural network. Experiments done using PSO-NN resulted in 94% accuracy which clearly out performs the BPNN and GA-NN classifiers.
Hybridization of Fuzzy C-Means and Fuzzy Social Spider Optimization for Clustering

Fuzzy clustering is most interested research problem in numerous real-world applications. The fuzzy c-means (FCM) is a most interested and widely used fuzzy clustering algorithm. However, it is easily stuck in the problem of local optima. Hence, the fuzzy social spider optimization (FSSO) technique has applied to solve fuzzy clustering problem in order to solve the shortcomings of FCM algorithm. Both FCM and FSSO have own advantages and disadvantages. In this presented research paper, the FCM clustering algorithm is an integrated with FSSO as a proposed method (FCM-FSSO) for solving fuzzy clustering problem in order to enhance clustering efficiency. The experimental outcome confirmed that the proposed hybrid FCM-FSSO clustering algorithms reveal improved performance compared with FCM and FSSO.
LSTM-Gate CNN network for Aspect Sentiment Analysis

In aspect sentiment analysis tasks, when one or more aspect words appear in the text, different aspects correspond to different sentiment tendencies. Therefore, it is necessary to accurately identify the corresponding emotional tendencies of different aspects. Concerning the problem that the traditional neural network model cannot accurately construct the text aspect feature and sentiment feature information, an aspect sentiment analysis model based on the LSTM-GateCNN network is proposed. Textual contextual semantic information is modeled through the LSTM network fused with attention mechanism to obtain deep semantic information, combined with the gated convolutional neural network to simultaneously model the textual and emotional information, and finally, text sentiment classification is performed at the softmax function layer. Experiments on the AI Challenger Chinese dataset have verified the effectiveness of the model, which has a further improvement in accuracy compared with the previous method.
A Longitudinal Study to Understand Learning During Search

An important step to foster learning during search is to identify behavioural patterns that distinguish searchers gaining more vs. less knowledge during search. Previous efforts have studied searchers in the short term, typically during a single lab session. We propose a longitudinal study to analyse searching behaviour of students enrolled in a university course, over the span of a few months. Our research aims are to (i) identify how searching behaviour changes over time, as students gain new knowledge on a subject, (ii) whether the perceived-relevance of specific information objects change due to knowledge acquisition, and, (iii) identify if differences exist in search processes for previously seen vs. unseen search tasks. We posit that findings from this research study will be informative for building improved information retrieval systems which are supportive of search as learning.
A Comparative Analysis of Deep Learning based Approaches for Low-Light Image Enhancement

Images clicked under low and non-uniform light conditions are visually unpleasant and lose details. Low-light images also impact the performance and thus reduce the effectiveness of various computer vision tasks. Thus numerous methods have been put forward in the past to upgrade the quality of low-light images. The innovations in the field of deep learning have paved the way for the application of neural networks to the task of enhancing low-light images. In this paper, we offer a comparative analysis of various approaches using deep learning for enhancing low-light images. We explore retinex based methods including KinD and RDGAN, and other non-retinex based methods including LLNet, GLAD Net, and Zero-DCE. We measure the effectiveness of these methods on various datasets and provide their advantages and disadvantages.
Semi-supervised Learning Combining 2DCNNs and Video Compression for Action Recognition

Action recognition is a classification task of actions in videos. Recently, 3DCNNs are widely used for action recognition. However, a large annotated dataset is necessary for achieving high performances with 3DCNNs, and the performance is often limited when it is hard to prepare a sufficient amount of annotated data. Semi-supervised learning is an approach to overcome this problem, but a few works pay attention to semi-supervised action recognition. Semi-supervised learning is actively studied in image classification. We can apply these semi-supervised learning methods directly to train 3DCNNs for action recognition. However, Jing et al. show that this approach does not significantly improve performance.
A Survey Research on the Use of Mobile Applications in Software Project Management

For several decades, project management (PM) was carried out in a traditional way, mostly without the aid of software tools. This scenario changed so that several PM information systems have been developed to assist project managers in many fields, including software projects. Similar to other systems, such tools started operating in mobile devices, given increasing adoption and use. Therefore, a study on how mobile applications have been used to manage software projects becomes important. In this paper, we report on a survey research carried out with 75 software project managers to identify the most used mobile applications and where they are mostly applied in PM. A quantitative analysis shows that 57% of the participants do not use mobile PM applications. However, mobile applications such as Trello, Jira and Redmine are mostly used by software project managers. A qualitative analysis was carried out to understand the reasons that lead software project managers to avoid those tools, such as: lack of effectiveness, lack of resources, lack of stimulation, diversity of tools, project complexity, and lack of qualified practitioners. This study brings some improvement suggestions on the existing tools as well as demands for new ones towards a more effective project management support.
Pancreas Segmentation Based on an Optimized Coarse-to-Fine Method

Our goal is to automatically segment the pancreas from the abdominal CT scans. Convolutional Neural Network (CNN) based deep learning method is easy to be confused when the background is larger than the target (pancreas) region. To solve this problem, a coarse-to-fine method has been proposed which used the predicted segmentation from the coarse stage to generate a smaller input for the fine stage to reduce the influence of background around the pancreas. However, In different stages of the coarse-to-fine approach, the accuracy isn't enough high for clinical purposes. In this paper, we want to achieve higher performance. Therefore, a variety of methods are adopted to improve the coarse-to-fine pancreas segmentation. Firstly, Elastic deformation is used to augment data, which increased data diversity. Secondly, we design a model called Aggregation-UNet by using a deep layer aggregation structure, which leverages spatial and semantic information of the data to reduce the false segmentation. Thirdly, to make the network pay more attention to the target area, we use a novel location method after the coarse stage to find a smaller region for the fine stage. Finally, we integrate the trained model to increase the reliability of the final result and the Dice accuracy. Our proposed method is evaluated on the NIH pancreas dataset which indicates the state-of-art result, while we only use the axial view of the CT scans (generally using three views).
Anomaly Detection in Vessel Sensors Data with Unsupervised Learning Technique

In a large ship or vessel, there are a lot of sensors forming a system that is used to indicate the engine status. It is critical for the system to be able to detect any anomaly that may cause engine failures. By detecting the anomaly of the data, maintenance for the sensors can be well-recommended and this also contributes to the reduction of maintenance costs. In this research, a collection of sensor data from vessels was analyzed using an Isolation Forest to detect the anomaly of the data. To reduce the dimensionality of the data, the t-SNE was adopted.
Hidden Enemy Visualization using Fast Panoptic Segmentation on Battlefields

Most of enemies in battlefields are not visible due to cover and concealment, and it yields fear and a weakened combat power for allies. In order to overcome it, we present a new approach for visualizing hidden enemies in battlefields to enhance cognition ability and survival for soldiers. Our method is composed of two separate sub-task networks. One is an efficient real-time panoptic segmentation network based on YOLACT [1] to find hidden enemies as well as to understand scenes from the viewpoint of soldiers. The other is an image completion network to reconstruct occluded parts of enemies which is guided by the panoptic segmentation networks. Our experiments on the Cityscapes benchmarks show that the proposed panoptic segmentation network achieves almost realtime speed without significant performance drops. We also demonstrate qualitative results of our segmentation-guided image completion method successfully on a dataset constructed from images of the Battlefield4 game.
Self-healing Fault Tolerance Technique in Cloud Datacenter

In recent times, cloud computing has emerged as a leading technology for providing reliable, secure, sustainable and scalable services. These services can be provided in both private and public clouds and also in hybrid clouds. Many useful services are available in the cloud and it has been used for wide range of purposes in many applications. The cloud computing framework still faces many issues and one of them is the occurrence of faults. In this paper, an self-healing fault tolerant technique is proposed that is used to handle the execution of user request when the allocated host or system fails. The fault injection and fault tolerant are simulated by using the CloudSim toolkit and the obtained results are compared with normal execution. The results show that the proposed fault tolerant technique improves the execution rate of the failed requests.
Flight Anomaly Detection Based on Deep Hybrid Model

In this paper, we present a deep hybrid model to detect abnormal flights. Deep hybrid models for anomaly detection use deep neural networks mainly autoencoder as feature extractors, the features learned within the hidden representations of autoencoder are then input to cluster algorithm to detect abnormal flights. The model can detect flight anomalies and associated risks without requiring predefined criteria or domain knowledge. In this paper, 2018 annual flight data of Daocheng Yading airport was taken as the experimental data which contains 981 flights. Our model performed well and 91 abnormal flights were detected.
Classifying Speech Acts using Multi-channel Deep Attention Network for Task-oriented Conversational Search Agents

Understanding human spoken dialogues in an information-seeking scenario is a significant challenge for IR researchers. Prior literature in intelligent systems suggests that by identifying speech acts in spoken dialogues, we can identify the search intent and the information needs of the user. Therefore, in this paper, we have used speech acts to address the problem of natural language understanding in conversational search systems. First, we collected human-system interaction data through a Wizard-of-Oz study. Next, we developed a gold-standard dataset where the human-system conversations are labeled with corresponding speech acts. Finally, we built the Multi-channel Deep Attention Network (MDAN) to identify the speech acts in information-seeking dialogues. The results highlight that the best performing model predicts speech acts with 90.2% accuracy. The MDAN architecture outperforms not only all traditional machine learning models but also the state-of-the-art single-channel BERT by 3.3 absolute points. We performed ablation analysis to show the impact of the three channels of MDAN individually and in combination. The results indicate that the best performance is achieved using all three channels for speech act prediction.