Reviewerly/exHarmony huggingface dataset:
Classification Study and Prediction of Cervical Cancer

The cancer disease has created a grave threat to the life of the people over more than a decade. As time increases, the problems and cases related tend to grow at a higher scale with respect to the different lifestyles lead by people. One such cancer, which plays a dominant role in the death of many women, is cervical cancer. This study helps us in classifying the presence of cervical cancer among subjects using machine learning classification approaches, such as logistic regression, decision tree, k nearest neighbor (KNN), random forest and support vector machine by constructing effective ensemble models and overcoming the biased nature of the class distribution in the dataset in order to identify the best classifier model to identify the presence of cervical cancer using the behavioral data of the subjects. Models are constructed based on the different types of input factors, such as lifestyle, habits, medical history of the patients and sexual practice of the patient. Among all the models constructed using different machine learning classification algorithms KNN algorithm provides the best performance in classifying the patients under two categories, such as affected by cervical cancer and not affected by cervical cancer for multiple target variables, like Hinselmann test, Schiller, cytology and biopsy test and discussing the important features in the dataset responsible for the cause of cervical cancer by identifying the variable importance using random forest approach.
Research on Principle and Application of Maximum Entropy

Entropy is a measure of the degree of chaos in the system which comes from physics. Then scientists proposed information entropy form a mathematical perspective. Later, it discovered the relationship between entropy and information entropy. This broke down the barriers between disciplines and derived many related conceptual principles. Among them, the principle of maximum entropy is widely used in disciplines such as finance, computer, etc. and many applications and technologies based on it was emerged. This paper introduces the principle of entropy and maximum entropy principle and reviews the application and development of the maximum entropy principle in analysis of clustering, decision and spectrum. Drawing upon our literature survey this paper presents a new application of maximum entropy principle, called elastic net of clustering based on maximum entropy (ENCM), which applies the maximum entropy principle to elastic net to change the objective function to solve clustering. Experiments verify that this method could effectively improve the result of clustering.
Super-Resolution Reconstruction of Finger Vein Image Based on Regression Tree Model

Because the speed of clear fingerprint vein acquisition is slow, and the influence of some acquisition equipment makes the acquired vein graphics are relatively poor, this paper is based on random forest and DCT algorithm for super-resolution reconstruction of blurred images. The random forest algorithm avoids overfitting the data and solves the classification problem. DCT transform compresses the image space to further speed up the training speed of the image. The experimental result shows that the method is easy to converge during the process of training and has better image reconstruction quality.
Research on Image Feature Matching Algorithm Applied to UAV Obstacle Avoidance

The ORB, SIFT, and SURF algorithms are currently common feature matching algorithms. These three algorithms have certain differences in execution speed and matching robustness. In this paper, the data sets provided by Mikolajczyk and Schmid are used to compare the algorithms in terms of execution speed, image transformation robustness, and noise robustness. The conclusion of this paper is that the ORB algorithm is the fastest, the SURF algorithm is the most robust, and the SIFT algorithm has the most feature points.
Towards Proximity Graph Auto-configuration: An Approach Based on Meta-learning

Due to the high production of complex data, the last decades have provided a huge advance in the development of similarity search methods. Recently graph-based methods have outperformed other ones in the literature of approximate similarity search. However, a graph employed on a dataset may present different behaviors depending on its parameters. Therefore, finding a suitable graph configuration is a time-consuming task, due to the necessity to build a structure for each parameterization. Our main contribution is to save time avoiding this exhaustive process. We propose in this work an intelligent approach based on meta-learning techniques to recommend a suitable graph along with its set of parameters for a given dataset. We also present and evaluate generic and tuned instantiations of the approach using Random Forests as the meta-model. The experiments reveal that our approach is able to perform high quality recommendations based on the user preferences.
Citizen Scientists Help Detect and Classify Dynamically Triggered Seismic Activity in Alaska

In this citizen science project, we ask citizens to listen to relevant sections of seismograms that are accelerated to audible frequencies. The events we are asking citizens to help identify are local seismic events that generate much smaller signals than those associated with the seismic surface waves that might have triggered these local events. The local events include small earthquakes as well as tectonic tremor (series of deep low-frequency earthquakes). While progress has been made in understanding how these events might be triggered by the surface waves from large teleseismic earthquakes around the world, there is no consensus on its physical mechanism. The aim of our project is to receive the help of citizens to increase general knowledge of when and which triggered seismic events occur or not occur during transient strain events, such as from propagating surface waves. A better understanding of such triggered seismic events is expected to provide important clues towards a fundamental understanding of how earthquakes nucleate and how large earthquakes might interact with small ones. From the citizens’ classifications we determined that citizen scientists achieve a higher reliability in detecting earthquakes and noise than in detecting tremor or other signals and that citizen scientists more accurately identified earthquake signals than a trained machine-learning algorithm. For tremor classifications we depend entirely on the citizens as no machine has yet learned to detect tremor.
Emotion recognition of human body's posture in open environment

This paper proposes and designs an algorithm of human pose emotion recognition based on visible light image in open environment,It mainly summarizes the emotions represented by different postures from the existing body language literature, then extracts a number of key nodes of human body parts from the induced different postures to connect and draw a simplified line map, uses CPM human body key node detection algorithm to extract the coordinates of human body key nodes in the image collected by the camera, draws the simplified line map of human body postures through clustering algorithm, and finally draws the drawn line map The simplified line map human posture map is input to CNN + softmax algorithm for classification, and the emotion information expressed by different posture is recognized. The human posture emotion recognition algorithm designed by this method can effectively monitor the attitude emotion of the target object through the visible light camera in the open environment.
Efficient parallel implementations to compute the diameter of a graph

Summary The Floyd‐Warshall algorithm is a well‐known algorithm to compute the distance of all pairs of nodes of a graph. The Blocked Floyd‐Warshall algorithm, a variant of the Floyd‐Warshall has been proposed to accelerate the Floyd‐Warshall algorithm by means of a graphics processing unit (GPU) architecture. The previously published GPU implementations for the Blocked Floyd‐Warshall algorithm perform many separated kernel calls for costly barrier synchronization. The main contribution of this article is to present efficient implementations of the Blocked Floyd‐Warshall algorithm, which performs no barrier synchronization and invokes only one kernel call. Experimental results using NVIDIA Tesla V100 show that our implementation runs 1.05‐1.31 times faster than the previously published one. Our implementation with SIMD functions also runs 1.00‐1.28 times faster than it. Second, we propose efficient GPU implementations to execute the Blocked Floyd‐Warshall algorithm for many graphs at the same time. From the experimental results, our single kernel implementation runs 1.03‐1.60 times faster than multiple kernel one. In terms of implementations with SIMD functions, our single kernel implementation runs 1.01‐1.89 times faster than it. We also propose the low‐latency implementations for many graphs. Finally, we implemented the parallel Floyd‐Warshall algorithm on the multicore processors.
Competitive Study of Various Task-Scheduling Algorithm in Cloud Computing

Cloud computing is a newly emerged field, and it develops very quickly. In cloud computing, the focus is on delivering computing resources such as hardware and software to the end-user directly, no matter when and where. These services are charged from clients on the per-use bases. There is a need for a decent scheduling mechanism that can help in the appropriate use of the assets. There are various resources available on the cloud and needs an efficient algorithm for better utilization of resources. One way is task scheduling. In cloud environment, there are numerous tasks which are running on the cloud; if two tasks try to approach a common resource, then there may be a chance of deadlock, which leads to system failure and performance degradation. For the better experience of cloud resources and to eradicate the aforementioned situations, there is a need of proper mapping between tasks and resources. In this paper, we comprehensively survey the various existing scheduling algorithms and these algorithms are compared on the basis of various performance metrics which are important to consider for the proper utilization of the resource.
Sentiment Analysis on Product Review using Support Vector Machine (SVM)

Public opinion can influence an organization or a company profile. It is important for the company to evaluate public response regarding their products. However, monitoring and organizing of public opinion are not easy. There are many published opinions in social media that is difficult to be processed manually. Therefore, a technique for automatically categorizing public reviews into positive or negative is needed. This research examined the classification of user reviews on products Windows Phone by implementing Support Vector Machine (SVM). This study used four different methods in tokenization stage: unigram, bigram, trigram, and n-gram. In the preprocessing data, there were 8 experiments whereas each group implemented 2 stemming algorithms (Snowball Stemmer and Iterated-Lovin Stemmer). The results were used as input of classification. Each input was classified 3 times with values C 0.25, 0.5, and 1.0. In conclusion, the result yielded the most appropriate model from n-gram with algorithm Iterated-Lovin Stemmer and C value 1.0. Finally, the use of SVM proves to be feasible approach of sentiment analysis regarding specific product.
Per-pixel analysis of ASTER data for porphyry copper hydrothermal alteration mapping: A case study of NE Isfahan, Iran

This paper evaluates the efficiency and capability of per-pixel methods comprising spectral feature fitting (SFF), multi range spectral feature fitting (MRSFF), and spectral information divergence (SID) algorithms in mapping alteration minerals using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. The study area is located in the Saveh-Yazd belt, northeastern Isfahan, Iran, which is the central part of the Urumieh–Dokhtar Magmatic Arc (UDMA). Porphyry copper mineralization formed in association with Oligo-Miocene intrusive bodies, are located in this belt. The hydrothermal alteration zones in the study area are manifested by phyllic, argillic, propylitic, and silicic. Mineralogically, these zones are marked by sericite-illite, kaolinite, montmorillonite, chlorite, epidote, and overprint of iron oxide/hydroxides. The pixel purity index (PPI) was used for extraction of image spectra, which were subsequently used as spectral reference. The SFF algorithm could map the Al–OH bearing mineral assemblages as a mixed alteration zone (phyllic-argillic zone), whereas the MRSFF provided an improved discrimination of sericite-illite and kaolinite minerals. The SID algorithm had the best performance for identifying and mapping these alteration minerals, comparing to the SFF and MRSFF. On the other hand, the results showed that SID and MRSFF algorithms were more efficient than SFF in discriminating the propylitic alteration zone. The field surveys, X-ray diffraction, spectral measurements, and microscopic studies confirmed the occurrence of hydrothermally altered minerals containing sericite-illite, kaolinite-dickite, and chlorite– epidote, which corresponded well to the ground criteria. Considering the potential of SID algorithm in discriminating the target minerals, it could be proposed for mineral mapping as part of exploration projects.
Inclusion, Measurement and Relevance… and Covid-19

This is an accepted manuscript of an article published by Springer in Postdigital Science and Education on 17/08/2020, available online: https://doi.org/10.1007/s42438-020-00182-9 The accepted version of the publication may differ from the final published version.
The problem of personality developing technologies in the training of future specialists of the architectural and construction sphere

Abstract The article raises the problem of personality developing technologies in the training of future specialists in the fieldt of architecture and construction, reveals the essence of the development of the student’s professional and personal potential in the process of studying at a university. The implementation of this complex pedagogical problem is possible in the process of applying innovative educational technologies based on internally motivated activities. The basic methodological approaches in the paradigm of personality-oriented professional education are revealed. As the authors note, in relation to the personality-oriented paradigm of professional education, the following approaches are considered personal, individual, cultural, axiological, activity-based, competence-based and contextual. The article analyzes the educational systems used today, identifies their advantages and disadvantages. The features of the teacher’s activities when using personality-developing technologies in the process of professional training of future architectural and construction specialists are determined. Also, the conditions for providing personal-centered learning in vocational education are defined in the article.
StoryVR: A Virtual Reality App for Enhancing Reading

We present a virtual reality app specifically designed for reading/listening to short stories and poems. Empirical studies with VR narrative experiences have proven that the process of embodied simulation enhanced by the VR medium increases users’ absorption and engagement. Accordingly, this solution can be effectively used to promote reading and increase motivation for learning. We discuss the design choices adopted to facilitate its widespread adoption and maximise readers’ engagement with stories.
The Information &amp; Mutual Information Ratio for Counting Image Features and Their Matches

Feature extraction and description is an important topic of computer vision, as it is the starting point of a number of tasks such as image reconstruction, stitching, registration, and recognition among many others. In this paper, two new image features are proposed: the Information Ratio (IR) and the Mutual Information Ratio (MIR). The IR is a feature of a single image, while the MIR describes features common across two or more images. We begin by introducing the IR and the MIR and motivate these features in an information theoretical context as the ratio of the self-information of an intensity level over the information contained over the pixels of the same intensity. Notably, the relationship of the IR and MIR with the image entropy and mutual information, classic information measures, are discussed. Finally, the effectiveness of these features is tested through feature extraction over INRIA Copydays datasets and feature matching over the Oxford's Affine Covariant Regions. These numerical evaluations validate the relevance of the IR and MIR in practical computer vision tasks.
Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and structured. However, with robots gradually applied in more domains, robots often encounter working scenes which are complicated, unpredictable, and unstructured. To overcome the limitation of traditional programming method, in this paper, we apply deep reinforcement learning (DRL) method to train robot agent to obtain skill policy. As policy trained with DRL on real-world robot is time-consuming and costly, we propose a novel and simple learning paradigm with the aim of training physical robot efficiently. Firstly, our method train a virtual agent in an simulated environment to reach random target position from random initial position. Secondly, virtual agent trajectory sequence obtained with the trained policy, is transformed to real-world robot command with coordinate transformation to control robot performing reaching tasks. Experiments show that the proposed method can obtain self-adaptive reaching policy with low training cost, which is of great benefits for developing intelligent and robust robot manipulation skill system.
A Level Set Approach for Segmentation of Intensity Inhomogeneous Image Based on Region Decomposition

Medical images like magnetic resonance angiogram (MRA) images have inhomogeneous intensities. When such images are segmented using active contour models, they do not produce desired results. Hence, segmenting an image with inhomogeneous intensities is a challenge. In this paper, the problem of segmenting medical images with intensity inhomogeneity has been addressed. Here, we propose a method which uses the concept of region decomposition along with combination of region-based segmentation and edge-detector function. In this process, the input image is divided into four quadrants and checked for segmentation. If the segmentation of any sub-image is not as desired, it is further decomposed into four more sub-regions. The process continues until each sub-image is properly segmented. In the end, all the segmented parts are merged to obtain the desired segmented image. The proposed method has been implemented and desired results are obtained by the same and compared with existing results.
Pattern Forming Acceleration for Dancing UAVs Using Ant Colony Optimization

A dancing UAV show is a performance where a number of UAVs fly and form certain patterns. The dancing UAV performance is very limited by the show time and also the flying time of each UAV. Beside the time problem, determining the trajectory of each UAV to form certain patterns in a three-dimensional area is a problem. To overcome the time problem and determine the trajectory, it is necessary to determine the best waypoint and trajectory. In this paper, we use Ant Colony Optimization (ACO) as a method to figure out the best waypoint and trajectory. Experiments using simulations were carried out to see the magnitude of the effect of selecting the best waypoint and trajectory using the ACO. The results of the experiment show that method can shorten the distance, especially for the pattern that formed with the large number of UAVs.
DOP-Tacotron: a Fast Chinese TTS System with Local-based Attention

As used in human-robot interaction, text-to-speech(TTS) systems can generate human-like speech from written text input to mimic human speakers. End-to-end TTS systems are widely explored in recent years. In this paper, we propose a fast trained end-to-end Chinese TTS system DOP-Tacotron. We propose the DOP module for the encoder and the post-processing network. DOP has almost similar effects with CBHG module while using 35.5% fewer parameters. We use local-based attention mechanism, which always follows the previous attention state. DOP-Tacotron achieves a 3.683 subjective 5-scale mean opinion score of naturalness on Chinese Mandarin, outperforming Tacotron in terms of naturalness. In addition, DOP-Tacotron adds stop-talk-loss to loss for spectrogram, and uses sample-length-batch for mini batch and accurate Chinese pinyin with punctuation as input. Our proposed TTS system can be easily trained since training time of DOP-Tacotron is only 2.5 hours.
Linking QoE and Performance Models for DASH-based Video Streaming

HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. Splitting the video clip into small segments and providing multiple quality levels per segment allows the client to dynamically adapt the quality to current network conditions. The performance of HAS, and as a consequence the user Quality of Experience (QoE), is influenced by a multitude of parameters. This includes adjustable settings like quality switching thresholds, the initial buffer level, or the maximum buffer, as well as video characteristics like segment duration or the variation of segment sizes along the video. Recently, a couple of analytical models for video streaming have been proposed, allowing to compare these input parameters and derive their impact on QoE-relevant metrics for HAS-based video delivery. The outcome of these models are typically asymptotic probabilities, distribution functions, or centralized and standardized moments. For instance, these models do not yield any temporal information in terms of stalling events or requested video quality. This contradicts to QoE prediction models like P.1203, which compute the QoE based on the chronological sequence of a specific video playback. So far, it is unclear how and to which extent the generalized results of analytical models can be utilized to derive sequence-based QoE values or the QoE distribution for a set of sequences for similar input parameters with stochastic variations. To address this problem, we compare testbed measurements with the output of a GI/GI/1 model with pq-policy and buffer-based quality switching capability to conclude to which extent the results still allow to approximate the video QoE.
Coh-metrix readability formulas for an academic text analysis

Abstract Readability formulas play a significant role in the process of text design for academic purposes. Nowadays formulas are digitalized that makes their implementation easy and convenient. Formulas are designed for different languages and are aimed at dealing with texts of different genres. In the article, we evaluate readability of academic texts, which are in the textbook “English for Engineers”. This book is recommended by the Ministry of Science and Higher Education of the Russian Federation for all technical Universities. Readability indices evaluation is performed with online tool Coh-Metrix. The tool suggests three readability indices obtained by means of three formulas: Flesch-Kincaid Grade Score, Flesch Reading Ease and Coh-Metrix L2 Reading Index. The Pearson correlation clearly demonstrates the absence of readability indices decrease from the first text in the book, which is considered to be the most readable to the last one that should be the less readable. By the way, Pearson correlation illustrates strong correlation between Flesch-Kincaid and Coh-Metrix L2 readability indices.