Reviewerly/exHarmony huggingface dataset:
An Optimized Data Classifier Model Based Diagnosis and Recurrence Predictions of Gynecological Cancer for Clinical Decision Support System

Meaningful knowledge can be revealed by applying Soft Computing techniques in healthcare industry. In women-related cancers, the studies that have been done on the diagnosis and recurrence predictions of breast cancers are comparatively more to that of gynecological cancers. While cancer treatments are continuously progressing, there is still a factual risk of relapse after possibly therapeutic treatments. Few types of gynecological cancers are very dangerous and lead to lessening the lifespan of women diagnosed with such type of cancers. To prevent this, an accurate and optimized model is essential in diagnosing and predicting the relapse of gynecological cancers. Given the rising trend in the applications of Machine Learning techniques in various types of cancer predictions, this paper explores on the competent methods and techniques preferred for diagnosing and predicting recurrence in few types of gynecological cancers with the recommendation to achieve optimized results. An advanced, consistent and optimized classifier model for gynecological cancer diagnosis and recurrence predictions is expected as the outcome of this proposed work.
Dynamic ranking of cloud services for web based cloud communities: Efficient algorithm for rating based discovery and multi-level ranking of cloud services

Trust assessment in cloud depends on QoS attributes. While evaluating the trustworthiness of the CSPs, traditional methods of trust assessment do not take into account QoS attributes that keep changing dynamically. To address the issue of determining the trustworthiness of CSPs in web, rating based dynamic discovery (RBDD) of QoS attributes that keeps changing periodically and multi-layer ranking (MLR) algorithm that ranks the discovered CSPs in an efficient manner have been proposed. This approach allows us to evaluate CSPs trustworthiness from cloud auditors perception. evaluation results indicates that the RBDD is capable of sensing behavioural changes in CSPs and discovers the dynamic trustworthy service providers and MLR algorithm ranks them based on CC requirements with high accuracy and minimal time complexity compared to other approaches. The proposed system has been validated with synthetic dataset owing to absence of standardisation.
VFO controller for set-point visual servoing of unicycle-like mobile robots equipped with a camera of an uncertain depth scale factor

The paper presents derivation, control performance analysis, and numerical validation of the Vector Field Orientation (VFO) control law devised for visual servoing of the input-constrained unicycle-like robot equipped with an onboard camera of an uncertain depth scale factor. It is shown that an inherent robustness of the proposed VFO controller ensures exponential convergence of the robot pose error toward zero despite even large uncertainty of the depth scale factor. This property allows simplifying applications of visual servoing with the VFO controller by avoiding a precise calibration process relating the on-board camera and the reference plane of feature points. Simulation results show the effectiveness and robustness of the proposed approach.
Logic-Based Sequential Decision-Making

Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better. In this paper, we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can handle both high-dimensional sensory inputs and symbolic planning. The task-level interpretability is enabled by relating symbolic actions to options. This framework features a planner – controller – meta-controller architecture, which takes charge of subtask scheduling, data-driven subtask learning, and subtask evaluation, respectively. The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input. Experimental results validate the interpretability of subtasks, along with improved data efficiency compared with state-of-the-art approaches.
First results to evaluate losses and gains in solar radiation collected by solar tower plants

Accurate computation of Solar Tower Plants (STPs) accounting for the atmospheric scattering gain and loss is nowadays possible with new technologies (computation performance growth). Radiative Transfer (RT) codes can be improved to perform directly and complete optical simulation of incident solar radiation in STPs. In this work, the RT SMART-G code, enabling fast computation thanks to GPU technology, have been improved to enables interactions between solar radiation and objects. Considering all the possible optical paths, a classification of the solar beams received by STPs is presented. The preliminary results obtained with a simplified STP, show that the gains due to atmospheric contribution at STPs receiver with an average horizontal heliostat-receiver distance from 200 to 900 meters is between 0.7% to more than 5%, depending on the aerosol optical thickness. The studied system is currently a simplified STP of maximum eight heliostats, but it is a promising way for other studies with more realistic STP and different illumination conditions.
Analyzing the Effect of Museum Practice by Using a Multi-Mouse Quiz among Children from Different Grades — A Reflection Perspective

Multi-Mouse Quiz (MMQ) is a quiz application based on the Single Display Groupware (SDG)[1] concept through which several users can answer quizzes by sharing a computer to take the quiz in a classroom or any other learning environment. We conducted a practice, where we used the MMQ to support collaborative learning, which was combined with a museum visit. In the previous research, we found that the 3rd-grade children were able to operate the MMQ without any special assistance from the researchers, and that their use of the MMQ was characterized by high engagement[2]. In this study, we also conducted qualitative evaluation in the form of observation data and a free description of the questionnaire; we found that, compared to previous studies, which used MMQ with 6th-grade children, the 3rd-grade were more willing to use body language to express their emotions, and this tendency made the whole class more active. Furthermore, MMQ quiz learning inspired children with reflection perspectives to participate in the museum activity and activities in the computer room.
Object Proposals Based on Variance Measure

Object proposals have recently become an important part of the object recognition process. Current object proposals are mostly based on hierarchical grouping which generates too many proposals and consumes too much processing time. This paper presents a framework utilizing variance measure to produce object proposals. We divide complete image into small patches. Our algorithm identifies possible object patches and merged them together to form object proposals. We evaluate our algorithm on UT interaction dataset. Experimental results show that our method generates fewer but quality proposals. Our method also performs reasonably fast than the state-of-the-art approaches.
Semantic Segmentation of Historical Documents via Fully-Convolutional Neural Network

This paper presents a method for character semantic segmentation in full-text documents from post World War II Czechoslovakia. Unfortunately, standard optical character recognition algorithms have problems to accurately read these documents due to their noisy nature. Therefore we were looking for some ways to improve these unsatisfactory results. Our approach is based on fully-convolutional neural network inspired by U-Net architecture. We are utilizing a synthetic image generator for obtaining a training set for our method. We reached 99.53% recognition accuracy for synthetic data. For real data, we are providing qualitative results.
Comparison of video shot detection methods using higher order local descriptor

Video Shot Detection plays a vital role in the analysis of the contents in Video. The algorithms and methodologies learnt from Video Shot Detection has a wide range of applications starting from Video Browsing, Content-based Video Retrieval and Storage, Surveillance and many more. Video Shot Detection is the temporal segmentation of Video stream to determine the transitions of Video. Out of the two categories of transition viz., Hard Cut and Soft Transition, in this paper we propose methods to determine Hard Cut. Three Video Shot Detection Methods have been used in this work, they are Mutual Information, Weighted Variance, Likelihood Ratio and Edge Change Ratio. A comparative study has been conducted to find out the best Video Shot Detection method out of the three. The essential factor that drives the above three methods is extraction of appropriate features from the frames of video stream which would be used for the temporal segmentation of video to determine the transition.
Protecting big data mining association rules using fuzzy system

Recently, big data is granted to be the solution to opening the subsequent large fluctuations of increase in fertility. Along with the growth, it is facing some of the challenges. One of the significant problems is data security. While people use data mining methods to identify valuable information following massive database, people further hold the necessary to maintain any knowledge so while not to be worked out, like delicate common itemsets, practices, taxonomy tree and the like Association rule mining can make a possible warning approaching the secrecy of information. So, association rule hiding methods are applied to evade the hazard of delicate information misuse. Various kinds of investigation already prepared on association rule protecting. However, maximum of them concentrate on introducing methods with a limited view outcome for inactive databases (with only existing information), while presently the researchers facing the problem with continuous information. Moreover, in the era of big data, this is essential to optimize current systems to be suited concerning the big data. This paper proposes the framework is achieving the data anonymization by using fuzzy logic by supporting big data mining. The fuzzy logic grouping the sensitivity of the association rules with a suitable association level. Moreover, parallelization methods which are inserted in the present framework will support fast data mining process.
Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems

We have recently presented SAFE-Solution And Fitness Evolution-a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate solutions and a population of candidate objective functions. We showed that SAFE was successful at evolving solutions within a robotic maze domain. Herein we present an investigation of SAFE's adaptation and application to multiobjective problems, wherein candidate objective functions explore different weightings of each objective. Though preliminary, the results suggest that SAFE, and the concept of coevolving solutions and objective functions, can identify a similar set of optimal multiobjective solutions without explicitly employing a Pareto front for fitness calculation and parent selection. These findings support our hypothesis that the SAFE algorithm concept can not only solve complex problems, but can adapt to the challenge of problems with multiple objectives.
Research on the Negative Effect and Countermeasure of Multimedia Technology Teaching Application

Abstract With the rapid development of information age, the emergence of various new techn ologies constantly and widely used.In the field of education teaching, useing multimedia techn ology in teaching and learning is the indispensable method and approach, but through the educ ation of modern multimedia technology applied in teaching and research. In the whole process of teaching and learning people often only pay attention to its advantages, and even exaggerati ng the role of it, but neglect its negative effects on the effect of teaching and learning. This arti cle from the multimedia teaching present situation, the multimedia teaching application such as negative effects and the reasons for the in-depth study, research and analysis, put forward the s cientific application of the principles and strategies of using multimedia teaching.
Twitter User Modeling Based on Indirect Explicit Relationships for Personalized Recommendations

Information overload has increased due to social network website use in recent times. Social media has increased the popularity of websites such as Twitter. It is believed that a rich environment is provided through Twitter whereby information sharing will be able to aid in recommender system research. This paper will focus upon Twitter user modeling through the utilization of indirect explicit relationships that exist amongst users. The further aim of this paper is to ensure that personal profiles are built via the use of information that will be sourced from Twitter so as to provide recommendations that are more accurate. The proposed method adopts Twitter user’s indirect explicit relationships in order to get information which is vital in the process of building personal user profiles. The proposed method has been validated through the implementation of an offline evaluation using real data. Proposed user profiles’ performances have been compared with each other and against the baseline profile. The performance of this has been validated using real data and is both practical and effective.
Effects of VR on Intentions to Change Environmental Behavior

We present a study investigating the question whether and how people's intention to change their environmental behavior depends on the degrees of immersion and freedom of navigation when they experience a virtual coral reef. The most striking result is, perhaps, that the highest level of immersion combined with the highest level of navigation did not lead to the highest intentions to change behavior.
Dynamics that underlie concepts of entropy

Thermodynamic entropy is reconsidered from viewpoint of growth, following up on recently recovered works by G.A. Linhart (1885-1951). The main idea is to consider rate of change relative to residual “way to go”, known as an intensity function with its implicit maximum “path length”; as force of mortality, the concept is familiar from demography.
A systematic process to define expert-driven software metrics thresholds (S)

Software metrics are usually used for quantification, not giving the necessary support for decision making.To increase their usefulness, it is necessary to give them meaning through the definition of significant thresholds.Despite its importance, the state of the art on threshold derivation is mostly based on data-driven approaches.This paper presents a systematic approach to define thresholds for metrics in the absence of data and based on eliciting knowledge from experts.The proposed approach is based on identifying context factors that influence the thresholds for a given metric and is supported by fuzzy logic concepts to model the crisp value (i.e., collected data) into a linguistic variable (i.e., interpreted information).We present context factors elicited from three experts for the metrics code coverage, static code analysis warnings count and defect count.Further, we present cases on how to implement the proposed approach.As a result, we conclude that the approach is promising.
"I am from all over the world"

In "Our Friends Electric"* Rogers et al. [4] advocate for internet health by exploring preferable future scenarios for voice enabled internet technologies. The fictional designs in the film were developed from 4 position statements that reflect a core set of values by which to measure internet health. In this provocation, one of these position statements is taken and used to propose a preferable near-present scenario; a small design change to current voice assistants that acknowledges the contributions that nations and their peoples make to the production of the voice enabled internet. Finally, envisioning this design change reveals the potential for the internet to provide an openness and neutrality that the real-world cannot.
An adaptive framework for saliency detection

Abstract At present, people are inclined to use one saliency detection method to cover all the pixels in an image. However, every method has its own limitations. A single method may not yield a good performance at all image scenes. In this article, we propose a new adaptive framework to detect salient objects. For each pixel in an image, it adaptively selects an appropriate method according to the pixel context relationship. In our framework, an image is characterized by a set of binary maps, which are generated by randomly thresholding the image's initial saliency map. And then, we utilize the surroundedness cue, which are obtained by a series of operations on the binary maps, to classify all the pixels in an image. Furthermore, based on the classes, we choose methods to detect salient objects. Extensive experimental results on three benchmark datasets demonstrate that our method performs favorable against 11 state‐of‐the‐art methods.
Customized Object Recognition and Segmentation by One Shot Learning with Human Robot Interaction

There are two difficulties to utilize state-of-the-art object recognition/detection/segmentation methods to robotic applications. First, most of the deep learning models heavily depend on large amounts of labeled training data, which are expensive to obtain for each individual application. Second, the object categories must be pre-defined in the dataset, thus not practical to scenarios with varying object categories. To alleviate the reliance on pre-defined big data, this paper proposes a customized object recognition and segmentation method. It aims to recognize and segment any object defined by the user, given only one annotation. There are three steps in the proposed method. First, the user takes an exemplar video of the target object with the robot, defines its name, and mask its boundary on only one frame. Then the robot automatically propagates the annotation through the exemplar video based on a proposed data generation method. In the meantime, a segmentation model continuously updates itself on the generated data. Finally, only a lightweight segmentation net is required at testing stage, to recognize and segment the user-defined object in any scenes.
ENTORNOS DE APRENDIZAJE EN LA ESCUELA SUPERIOR POLITECNICA DE CHIMBORAZO TRANSFORMACION MEDIANTE MOODLE Y GOOGLE ANALYTICS

One of the challenges in Higher Education is the continuous improvement of educational quality through follow-up, monitoring and feedback of the teaching-learning processes. ICTs and in particular Moodle and Google Analytics through monitoring functions, generation of early warnings and the possibility of developing trends; they offer the opportunity to improve learning processes and transform the current educational reality. The number of worldwide users of Moodle is more than 79 million; In Ecuador, 902 sites are registered and the majority of Ecuadorian Higher Education Institutions use Moodle to support teaching-learning processes; making this research replicable in most Ecuadorian Universities. The research methodology used was quantitative, descriptive, cross-sectional, non-experimental; The interview and direct observation were used as instruments. The following questions were answered: What Information and Communication Technology tools support the learning process at ESPOCH? And how could learning at ESPOCH be improved with these tools? Finally, it concludes by indicating the possibilities for improving learning at ESPOCH, based on the use of Moodle and Google Analytics.
Arithmetic Circuits Using Reversible Logic: A Survey Report

In this paper, a survey has been made on the design of arithmeticBanerjee, Arindam circuits like adder, subtractor, multiplier, and squarer. There are many design schemes for those arithmetic circuits some of which have beenDas, Debesh Kumar studied and described in this paper.
Optimasi Parameter Artificial Neural Network Menggunakan Algoritma Genetika Untuk Prediksi Kelulusan Mahasiswa

It is difficult to predict student graduation status in a college. Higher education needs to predict student behavior from active students so that it can be seen the failure factor of students who do not graduate on time. Data mining classification techniques used to predict students are using artificial neural networks. Artificial neural network is one method to predict student graduation. This researcher tries to apply artificial neural network methods using genetic algorithms to predict student graduation. In this study using the learning rate parameter 0.1 with optimization using genetic algorithms then evaluating to get accuracy. The results of this study get an accuracy value for artificial neural network models of 71.48% and accuracy for artificial neural network models based on genetic algorithms by 99.33% with an accuracy difference of 27.85%.
Remote Pedestrian Detection Algorithm Based on Edge Information Input CNN

In order to solve remote pedestrian detection problem, the target need to be detected in the absence of information, a new pedestrian detection algorithm based on Convolution Neural Network (CNN) is proposed. The algorithm uses shallow layer edge features combined with grayscale images to replace the RGB color information of the original image, as an input to the Convolutional Neural Network to increase the amount of effective information. Then, in deep learning training process, the cross entropy is combined with the learning rate to optimize the cross entropy function. Finally, the improved Convolutional Neural Network is trained on four common pedestrian hybrid datasets to apply it to the remote pedestrian intrusion detection of the railway industry using transfer learning. The experimental results show that compared with the existing Convolutional Neural Network remote pedestrian detection algorithm, the new method can effectively improve the accuracy of detection 2% and has a good universality.
Cross Projects Defect Prediction Modeling

Software defect prediction has been much studied in the field of research in Software Engineering. Within project Software defect prediction works well as there is sufficient amount of data available to train any model. But rarely local training data of the projects is available for predictions. There are many public defect data repositories available from various organizations. This availability leads to the motivation for Cross projects defect prediction. This chapter cites on defect prediction using cross projects defect data. We proposed two experiments with cross projects homogeneous metric set data and within projects data on open source software projects with class level information. The machine learning models including the ensemble approaches are used for prediction. The class imbalance problem is addressed using oversampling techniques. An empirical analysis is carried out to validate the performance of the models. The results indicate that cross projects defect prediction with homogeneous metric sets are comparable to within project defect prediction with statistical significance.