Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis for Product Recommendation System Using Hybrid Algorithm

E-Commerce has been known as a rapidly growing commercial enterprise, and even though on line purchasing has no longer accompanied those identical boom patterns within the beyond, it's miles now being diagnosed for its capability. Sentiment evaluation is one of the current research subjects in the subject of textual content mining. Opinions and sentiments mining from natural language are very difficult task. Sentiment analysis is the best solution. This gives important information for decision making in various domains. Various sentiment detection methods are available which affect the quality of result. In this paper, finding the sentiments of people related to the services of E-shopping websites. The sentiments include reviews, ratings and emoticons. The main goal is to recommend the products to users which are posted in E-shopping website and analyzing which one is the best and use hybrid learning algorithm which analyze various feedbacks related to the services. Text mining algorithm is used to find scores of each word. Then sentiments are classified as negative, positive and neutral. It has been observed that the pre-processing of the data is greatly affecting the quality of detected sentiments. Finally analysis takes place based on classification. To find out fake review in the website can be analyzed. This device will discover fake critiques made via posting fake remarks about a product via figuring out the MAC deal with in conjunction with assessment posting styles. User will login to the device using his consumer identification and password and could view various merchandise and will give assessment approximately the product. To discover the evaluation is fake or authentic, system will find out the MAC address of the consumer if the machine observes fake assessment send by way of the identical MAC Address many a times it'll inform the admin to do away with that overview from the device. This gadget uses information mining technique. This machine allows the user to find out accurate overview of the product.
Phase measurement deviations in deflectometry due to properties of technical surfaces

Phase Measuring Deflectometry (PMD) is a non-coherent full-field measurement technique based on geometrical optics, usually employed for specular surfaces. The measured phase in PMD corresponds to the position on the display recorded by the camera pixels. Concerning the measurement uncertainty in PMD systems, the camera can be regarded as the best described and most thoroughly modelled part, followed by the display with its nonlinearity or flatness deviation of the monitor surface shape. However, the effect of the properties of technical surfaces on the measurement deviation still lacks detailed investigation. As the errors in phase measurement will introduce subsequent errors into the slope and shape measurement, it is important to study these error sources. This paper investigates the effects of the properties of technical surfaces on phase measurements in a PMD system. We demonstrate the dependence of the deviations of phase measurements on surface roughness using well-defined samples manufactured by high precision diamond turning.
Deep Generate Residual Similar Feature Networks for Image Super-Resolution

Abstract In this paper, Driven by advanced convolutional neural networks, we present a deep generate residual similar feature networks to improve super-resolution performance. Many researches have found that many CNN network training of SR requires skill and computing equipment. The input low-resolution features and CNN intermediate features contain rich similar feature maps. The different upscaling factor is used for needing the different model, which increase computational complexity. For the problem raised above, we proposed the generate residual feature (GRF) module to generate more high-frequency information in the residual feature. Each generated residual structure contains the short skip connection and long skip connection. Furthermore, we extract the similar feature in the residual feature by considering the interrelation among residual feature. Qualitative and quantitative assessments on benchmark datasets shows that we use different methods to achieve the same effect as best results of SR, while we make the network light weighted. Meanwhile, Our experiments shows that the pedestrian detection in the monitoring scene has achieved good results.
A Region Proposal Approach for Cells Detection and Counting from Microscopic Blood Images

In this paper, we propose a novel and efficient method for detecting and quantifying red cells from a microscopic blood image. The proposed system is based on a region proposal approach, namely the Edge Boxes, considered as the state-of-art region proposal method. Incorporating knowledge-based constraints into the detection process by Edge Boxes we can find cells proposals rapidly and efficiently. Experimental results on a well-known public dataset show both improved accuracy and increased over the state-of-art.
Gaussian noise estimation methods in images

A common and investigated problem in image processing is noise, especially Gaussian, because it affects image quality and the subsequent treatment. Therefore, different filtering techniques are created and improved every day. Some of these techniques require to know the approximate value of the noise level, so it is essential to develop noise estimation methods. Given that there are not comparatives studies of these techniques, in this work an evaluation of six relevant methods was developed. The techniques were implemented as java plugins for the free access software ImageJ. They were evaluated and compared using standard and synthetic images. Several of the methods assume certain parameter values. Here, the validity of these assertions was studied, observing that they were not valid in most of them. It was found, in most cases the evaluated techniques did not allow to obtain a good estimation of the noise level.
APP Based Teaching/Learning Mode and Quality Evaluation of MMT Course

With the development and popularization of internet and cellphone, Internet + education has become a trend for the teaching/learning mode reform. To improve teaching/learning quality and efficiency of mechanism and machine theory (MMT) course, an APP teaching/learning mode and quality evaluation approach are presented in this paper, aiming at cultivating excellent engineers in the new era. Based on APP network platform, the function of the APP teaching/learning, data tracking and analyzing are designed, by means of which an interaction and discussion teaching/learning environment for teacher-student and student-student is provided. Thus, the APP intelligent interactive teaching/learning mode with teaching strategy adapting, student-centered autonomous learning and cooperative learning, and the quality evaluation approach of learning process assessment are established. The teaching/learning of MMT course of ME major in Dalian University of Technology shows that stimulating students’ initiative in learning and improving teaching quality embodies the feasibility and practicability of application-based teaching mode and evaluation method, which facilitate teachers to master students’ learning situation and adjust teaching strategies.
Linguistic-Probabilistic and Quantum Understanding of Gene Operation

Background: Posttraumatic Stress Disorder (PTSD) is a psychiatric disorder found in individuals afflicted by a traumatic event including the natural disaster."Tsunami" occurred in Andaman coast of Thailand on December 26, 2004, in which 33.6% of survivors were diagnosed as PTSD.This study aimed to explore the single nucleotide polymorphism (SNP).rs267943 genotype is located on chromosome 5 in the intron of the death-associated protein 1 (DAP1) gene and psychosocial factors for PTSD.Methods: Participants (N = 1970) were recruited from volunteers who have complete data both of DAP1 gene and psychosocial factor.Results: Using a binary logistic regression model, significant gene-environment interactions were found for the single nucleotide polymorphism (SNP) rs267943 and psychosocial factors including depression (adj.OR = 6.0, 95% CI = 4.29 -8.39), neurotic personality (adj.OR = 2.73, 95% CI = 2.18 -3.42), planning (adj.OR = 1.52, 95% CI = 1.20 -1.93), use of emotional support (adj.OR = 1.32, 95% CI = 1.21 -1.94) with statistical significant p < 0.001 and self-distraction (adj.OR = 1.52, 95% CI = 1.15 -1.85) with statistical significant p < 0.05.Conclusion: This study dem-
User-Centered Development of a Public Transportation Vehicle Operated in a Demand-Responsive Environment

Within the scope of the research project “Reallabor Schorndorf”, funded by the state Baden Wuerttemberg (Germany), demand responsive transport and operating systems as well as a virtual vehicle concept for public transport (PT) were designed and tested in a “real-world laboratory”. The aim of the subproject Vehicle Development was to design a vehicle in an effective and repeatable manner that best meets the users requirements with respect to the demand responsive public transport system. The novel user-centered development methodology and the exemplary result are highlighted in this contribution.
Implementation of Scrum Retrospective in the Process of Improving Logistics Organization

Agile approach, and especially Srum, has gained huge importance in project management and numerous organisations acknowledge its utility for improving their processes. However, agile approach has been mainly applied to IT organisations. The main goal of this paper is to consider possible advantages of its introduction into the transport sector. It has to be underlined that the decision and the process to implement agile approach in an organization are extremely difficult. The first step of agile implementation is conducting a trial implementation in accordance with the principles of one or more of agile methods or practices in a relatively small area. In the present paper the case of a forwarding company is discussed in which the agile practice called retrospective was implemented. The short term goal of this implementation was to support process of collaborative problem identification and solving, the result of which should be improving own internal process of forwarding agency. The implementation and the advantages identified so far are presented and analysed.
Trajectory Tracking for a Commercial Quadrotor

In this paper we propose a nonlinear control law to achieve trajectory tracking in the 3D space for a commercial Bebop 2 quadrotor. The proposed control law is based on a nonlinear time varying version of the Backstepping technique, which includes an integral gain for disturbance rejection and that takes into account the dynamics of the internal controller for the orientation of the quadrotor. The stability of the control law is ensured by the design of recursive Lyapunov functions and its performance is tested by real-time experiments.
Evaluation of an Intelligent Tutoring System Prototype for the Teaching of Electrocardiogram

To develop new methodologies for electrocardiogram (ECG) teaching and considering the benefits of intelligent tutoring systems (ITSs) and the use of gamification in medical education, the exploration of the integrated use of all these technologies are justifiable. Prototypes of ITS graphic design probed ECG instructor were submitted to preliminary usability assessment by professionals and students linked to social technologies. The models were considered intuitive and with good functionalities, however, improvement in the design and aesthetic aspects is needed. Adjustments to the system requirements must be made and a new stage of evaluation of the prototypes carried out by potential users.
Efficient Adversarial Chaff Generation for Challenge-Response Authentication Over Unsecure Networks with an Application to Civilian Radio Networks

Challenge Response is one of the cornerstones of online security. The simplest form of Challenge-Response is asking for a password. Much cryptographic work has focused on developing strong forms of encryption, however some networks require transmission over networks which might be monitored. We discuss this problem in the context of a particular kind of open network used by 30,000 users, and which is an important medium supporting emergency services. The current challenge-response implementation on this network relies upon sending information about the password. We calculate the number of observations needed to capture password using brute force attack, replay attack, and version spaces. We show that even strong passwords (completely random set of characters) are at significant risk of discovery in as few as 16 login attempts. We next present an algorithm that adds adversarial “chaff” to the password information designed to minimize relative information gain during challenge-response. We show that, with enough adversarial chaff, unambiguous password recovery from passive data capture may not be possible, although passwords can still be recovered by an attacker actively probing the system. Despite this, better protection of passwords is useful, and would be immediately helpful to people using these services.
Online Optimization of Movement Cost for Robotic Applications of PSO

Particle Swarm Optimization is an optimization algorithm that can be used as a control mechanism in robotic tasks, especially robotic search. Existing algorithms are tuned to use as little evaluations of the objective function as possible. Measuring the objective with a sensor usually has a low cost in terms of time and energy compared to moving the robot. We propose a new algorithm to optimize the particle movement in SMPSO that samples the same points in the environment with less movement cost. Our experiments show that the average movement cost can be reduced by $$50\%$$ or more in all test problems we used. The huge gain shows that there is a big potential in adapting swarm intelligence algorithms to robotic applications by optimizing them to better serve the constraints of the application.
Empirical Performance Analysis of Wavelet Transform Coding-Based Image Compression Techniques

In this chapter, the performance of wavelet transform-based EZW coding and SPIHT coding technique have been evaluated and compared in terms of CR, PSNR, and MSE by applying them to similar color images in two standard resolutions. The application of these techniques on entire color images such as passport size photograph in which the region containing the face of a person is more significant than other regions results in equal loss of information content and less compression ratio. So, to achieve the high CRs and distribute the quality of the image unevenly, this chapter proposes the ROI coding technique. Compressing ROI portion using discrete wavelet transform with Huffman coding and NROI compressed with Huffman, EZW coding, SPIHT coding suggested effective compression at nearly no loss of quality in the ROI portion of the photograph. Further, higher CR and PSNR with lower MSE have been found in high-resolution photographs, thereby permitting the reduction of storage space, faster transmission on low bandwidth channels, and faster processing.
Classification of breast abnormalities in digital mammography using phase-based features

Breast cancer is one of the principal causes of death for women in the world. Invasive breast cancer develops in about one in eight women in the United States during her lifetime. Digital mammography is a common technique for early detection of the breast cancer. However, only 84% of breast cancers are detected by interpreting radiologists. Computer Aided Detection (CAD) is a technology designed to help radiologists and to decrease observational errors. Actually, for every true-positive cancer detected by the CAD there are more false predictions, which have to be ignored by radiologists. In this work, a CAD method for detection and classification of breast abnormalities is proposed. The proposed method is based on the local energy and phase congruency approach and a supervised machine learning classifier. Experimental results are presented using digital mammography dataset and evaluated under different performance metrics.
Improving Adaptive Video Streaming through Session Classification

With internet video gaining increasing popularity and soaring to dominate network traffic, extensive studies are being carried out on how to achieve higher Quality of Experience (QoE) with the delivery of video content. Associated with the chunk-based streaming protocol, Adaptive Bitrate (ABR) algorithms have recently emerged to cope with the diverse and fluctuating network conditions by dynamically adjusting bitrates for future chunks. This inevitably involves predicting the future throughput of a video session. Some of the session features like Internet Service Provider (ISP), geographical location, and so on, could affect network conditions and contain helpful information for this throughput prediction. In this article, we consider how our knowledge about the session features can be utilized to improve ABR quality via customized parameter settings. We present our ABR-independent, QoE-driven, feature-based partition method to classify the logged video sessions so that different parameter settings could be adopted in different situations to reach better quality. A variation of Decision Tree is developed for the classification and has been applied to a sample ABR for evaluation. The experiment shows that our approach can improve the average bitrate of the sample ABR by 36.1% without causing the increase of the rebuffering ratio where 99% of the sessions can get improvement. It can also improve the rebuffering ratio by 87.7% without causing the decrease of the average bitrate, where, among those sessions involved in rebuffering, 82% receives improvement and 18% remains the same.
Art History in the Context of Other Sciences: Challenges of Modernity

The interview reveals modern art history’s main trends identified within the framework of the conference “Art History in the Context of Other Sciences in the Modern World. Parallels and Interactions”. The Russian State Library and the scienti­fic journal “Observatory of Culture” were partners in organizing the conference in 2019. The method of aca­demic interviewing used in this publication provides an opportunity to reveal the personal vision of the conference project’s author and co-chairman of the Organizing Committee, chairman of the Program Committee, head of the Department of Humanities and Social Sciences of the Institute of Contemporary Art, chief researcher of the GITR Film &amp; Television School, expert of the Times Higher Education World University Rankings, member of the Russian Expert Council (ASEP/Scopus), D.Sc. (Art History), professor Grigoriy R. Konson. In fact, the interview is a quintessence of the author’s policy document on the development of culture, science and education in modern society. The academic forum was a socially significant event of international scale, characterized by the latest scientific and educational trends in Russia and fo­reign countries, as well as by art studies integration into the context of interdisciplinary research loca­ted at the intersection of art history, philology, linguistics, philosophy, cultural studies and psychology. As a result, there are prospects for reaching the level of cross-sectoral conceptualization of research ge­neralizations. The interview reveals the topical issues of science functioning in the modern internatio­nal society. There is concluded that the scientific integration characteri­zing the conference “Art History in the Context of Other Sciences in the Modern World. Parallels and Interactions” is a progressive method in understanding the essence of art, permeated by multi-vector trends in the global humanita­rian process. Therefore, the joint efforts of scientists here contribute to the development of an antidote to destructive trends in the socio-cultu­ral life of mo­dern society.
Shadower

Shadows are ubiquitous in our life. Through the phenomena of shadows, we focus on a novel way which can change people's perspectives of observing surroundings and create a natural outdoor interaction for children in their daily life. In this paper, we present a mobile AR game which uses shadows as clues with a treasure hunting game mechanism to make a connection with children and outdoor surroundings. In the field experiment at a kindergarten, children(n=6) participated in the outdoor interaction experience with Shadower. We conducted a preliminary user study with Smileyometer strategy to evaluate children's reaction to our prototype. Qualitative results indicate that the use of shadows from the outdoor environment as AR makers have the potential to expand the approach to facilitate children's engagement through their outdoor interaction.
Semi-Automatic Generation of Cognitive Science Theories

Developing computational models constitutes an important aspect of research in science in general and cognitive science in particular. It is a notoriously difficult enterprise and methods for (semi-)automatically developing models are desirable, possibly using evolutionary mechanisms. Although the evolutionary properties of scientific knowledge have received some attention in the philosophy of science, the actual use of computationally based Darwinian selection processes to develop theories has been limited to research by the present authors. The optimisation capacities of genetic programming are employed as a basis for the semi-automatic generation and testing of cognitive science models. A population of models is subjected to Darwinian selection processes and evolved using a genetic programming algorithm. Good models make predictions that are consistent with the data. A number of philosophical issues raised by this approach are explored, including the role of Lakatos research programmes in the development of cognitive science theories, simulation as a kind of fictional modelling, the nature of explanations produced by models generated by genetic programming, problems of theory confirmation, and the extent to which theory development can be regarded as an exclusively human activity.
Seeking affinity structure: Strategies for improving m-best graph matching

State-of-the-art methods for finding the m-best solutions to graph matching (QAP) rely on exclusion strategies. The k-th best solution is found by excluding all better ones from the search space. This provides diversity, a natural requirement for transforming a MAP problem into a m-best one. Since diversity enforces mode hopping, it is usually combined with a mode-approximation strategy such as marginalisation. However, these methods are generic insofar they do not incorporate the detailed structure of the problem at hand, i.e. the properties of the global affinity matrix which characterise the search space. Without this knowledge, it is thus hard to devise a practical criterion for choosing the next variable to clamp. In this paper, we propose several strategies to select the next variable to clamp, spanning the whole range between depth-first and breadth-first search, and we contribute with a unifying view for characterising the search space on the fly. Our strategies are: a) Number of factors in which the variables participate, b) centrality measures associated with the affinity matrix, and c) discrete pooling. Our experiments show that max number of factors and centrality provide a trade-off between efficiency and accuracy, whereas discrete pooling leads to an improvement of the state-of-the-art