Reviewerly/exHarmony huggingface dataset:
Aspect Based Opinion Mining on Mobile Product

In this era, the web technology is growing quickly. Many people express their feedback related to products, social issues and services. As e-commerce site is becoming more popular, the customer review related to the product grows quickly. Due to this growth it is very difficult for the customer to read huge amount of data and make a decision whether to buy a product or not. It is also very difficult for the manufacturer of the product in-order to manage and focus on customer opinions. In this research we focus on mobile product review which is extracted from Kaggle site. In this experiment we have focused on one particular mobile product review that is Samsung. After data collection we do pre-processing, and further we extract aspect and corresponding opinion using Natural language processing and then categorize whether the extracted opinion is positive or negative by finding polarity for each extracted opinion of words. Finally performance evaluation is done by using two machine learning algorithm i.e. Multinomial Naive Bayesian (MNB) and K-Nearest Neighbour (K-NN) algorithm. This performance evaluation is calculated based on bag of words. Out of two algorithms K-NN gave best accuracy compared to Multinomial Naive Bayesian.
Abstract LB-285: SAGA functions in development and disease

Our research is focused on defining the functions of histone modifying enzymes in normal cells, so that we can understand how alterations in these activities contribute to tumor development or progression. Our recent studies on the SAGA complex, which houses both histone acetyltransferase (HAT) and deubiquitylase (DUB) activities, revealed that the Gcn5 HAT, is an important co-activator for Myc in regulating self-renewal of mouse ES cells. Gcn5 is also important for Myc functions as a Yamanaka factor in stimulating conversion of differentiated cells towards an induced pluripotent state. These developmental functions predict that Gcn5 will also be important during Myc functions as an oncogene. In support of this idea, recent studies reveal that deletion of Gcn5 delays lymphoma onset and more than doubles survival of Eμ-Myc mice. The SAGA DUB module is also implicated in cancer, as the USP22 catalytic subunit is overexpressed in several highly aggressive human tumors. We have created USP22 knock out mice to probe the functions of this DUB. Our data indicate that USP22 drives TGF-beta and receptor tyrosine kinase signaling pathways that are critical both to normal developmental processes and to oncogenesis. Altogether, our studies indicate that both Gcn5 and USP22 may provide unique targets for development of new cancer therapies.Note: This abstract was not presented at the meeting.Citation Format: Sharon Y. Dent, Aimee Farria, Evangelia Koutelou. SAGA functions in development and disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-285.
Dynamic and Advanced Security for Data Storage in Distributed Environment

With the fast improvement of innovation and computer technology, cloud-based services administrations have form into an extremely rich research area. CB administrations give customers which accommodation, yet additionally bring different issues. In this manner, the learning of access control plan to ensure clients' confinement in cloud condition is critical. In this paper, we present an entrance control framework with advantage division dependent on security insurance. we partition the clients into individual area (PSD) and open space (PUD) sensibly. In the PSD, we put (R/W) get to authorizations for clients separately. The total key encryption (KAE) is abused to execute the read access consent that improves get to effectiveness. A high level of patient protection is all the while ensured by abusing a Firm dependent on improved characteristics (IABS) that can decide the clients compose get to. For PUD clients, encryption dependent on progressive properties (HABE) is connected to evade single-purpose of disappointment issues and confounded key circulation. The aftereffect of the activity and task tests demonstrates that the PS-ACS plan can accomplish security insurance in cloud-based administrations
Smooth Path Planning of Ackerman Chassis Robot based on Improved ant Colony Algorithm

In the domain of robotics and autonomous driving, the automatic path planning of vehicle collision-free motion is an essential task on the navigation level. It is found that the traditional path planning algorithm and the ployline path cannot fully meet the driving requirements of Ackerman chassis robot. In order to solve the autonomous navigation problem of Ackerman chassis mobile robot in structured environment, this paper presents a new improved algorithm. The method of configuration space can introduce the robot's own structural size parameters into the algorithm. Through convex polygon detection method, the local U-shaped area in the map is transformed into a closed area. The essence of these two strategies is to preprocess the map. The initial pheromone distribution is no longer globally uniform, but is distributed according to the terrain. The volatilization factor of pheromone is changed from static constant to dynamic one, which is combined with Poisson distribution law. This strategy makes the improved pheromone distribution law not only avoid the randomness and blindness in the initial stage of the algorithm, but also ensure the ant colony's exploration behavior and guiding role in the middle stage of the algorithm. Path smoothing is also a challenging task. This algorithm optimizes the path step by step by improving the evaluation function, removing redundant nodes and 2-turning algorithm. Thus, a collision free smooth path suitable for Ackerman robot is obtained. This paper combines a variety of algorithm improvement strategies, not only improving the performance of ant colony algorithm path exploration, but also planning a smooth curve path suitable rather than polyline for Ackerman mobile robot tracking. The algorithm is coded and simulated by MATLAB, and the feasibility and effectiveness of the algorithm are verified. This will provide an important basis for the subsequent algorithm migration and lay the foundation for the path tracking control of the Ackermann chassis robot.
Suprasymmetry

The topic of my research was problem of creating virtual environments (VE) in immersive art. I was focused on a roles of Presence, Flow, Immersion, and Interactivity. I was particularly interested in the problem of presence and flow in VE. Presence is defined as the subjective experience of being in one place or environment, even when one is physically situated in another. Presence is a normal awareness phenomenon that requires directed attention and is based in the interaction between sensory stimulation, environmental factors that encourage involvement and enable immersion. Flow is a state of experience where someone is completely absorbed and immersed in an activity. I researched relations between presence, flow, immersion and interactivity, e.g. how interactivity and sound spatialization improves experience of presence. I have developed machine learning methods that extend granular and pulsar synthesis in composing and new methods of building and transforming virtual environments.
Fine-grained Classification Using Multi-channel ResNet

At present, fine-grained classification has attracted extensive attention.The task of fine-grained classification is difficult due to the challenge of accurately locating the key regions with resolution and extracting valid features from the detected key regions for classification.In this paper, we propose a new convolutional neural network (Multi-channel ResNet).Multi-channel ResNet uses Mask R-CNN for foreground extraction to reduce the interference of image background on fine-grained classification results.In addition, the four-channel ResNet module is used to learn fine-grained features at multiple scales, and Gaussian blur processing and crop processing are used to learn details and contours, all and local features, so as to improve the accuracy of fine-grained classification.The model does not require bounding box/part annotations.We experiment with the CUB_200_2011 dataset, and the results show that Multi-channel ResNet has an improvement in fine-grained classification tasks on the baseline of no pre-trained ResNet-18.
A Framework for Estimating Dependency based on Eccentricity

Version control systems are employed for change management on the existing code base during development phase and maintenance phase. The changes may or may not affect the existing ones. If the changes affect the existing code they may lead to errors (compile time and run time) or instability of the system. Version control systems employ various strategies to cater the needs of various scenarios but it leads to delays. The agility of the system depends on the updating mechanism and the strategy it deploys while handling multiple users and dependent modules at the similar time. The capability to handle changes by version control systems depends upon the strategy to organize the current modules, update them and serve them to no of users parallely. The objective is to propose a system that is found to be an enhanced version of the current system.
Taking the measure of quantum dynamics

1. Quantum Zeno effect and the many-body entanglement transition Authors: Y. Li, X. Chen, and M.P. A. Fisher Phys. Rev. B 98, 205136 (2018); DOI: 10.1103/PhysRevB.98.205136 2. Measurement-Induced Phase Transitions in the Dynamics of Entanglement Authors: B. Skinner, J. Ruhman, and A. Nahum Phys. Rev. X 9, 031009 (2019); DOI: 10.1103/PhysRevX.9.031009 3. Unitary-projective entanglement dynamics […]
Digital Safety

Digital safety is a branch of cyber security that deals with people and the levels of online comfort, convenience, and privacy. It may be regarded as the act of staying safer online. This includes being aware of the risks associated with your online activity and avoiding being exposed to unwanted information, materials, or risks on the Internet that might harm one’s devices. The Internet can be a risky place for children and their parents should have strategies for protecting them. This paper provides an introduction to digital security.
Prediksi Pendistribusian Air di Perusahaan Daerah Air Minum (PDAM) dengan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

Water is a very important requirement for the life of living things. Clean water is a major component of human activities, both for drinking water, household needs and other supporting activities. To help make the distribution of water in the Tirta Kepri Tanjungpinang PDAM even better, the author wants to predict the distribution of water per month, so that the PDAM Tirta Kepri Tanjungpinang can estimate water use requirements based on water distribution inhibiting factors. Prediction is done using the Adaptive Neuro Fuzzy Inference System (ANFIS) method with the k-means cluster. In this study the data used for prediction of water distribution began from January 2014 - October 2018. The results of the tests carried out with 17 data using the learning rate 0.6 resulted in the RMSE value of 0.07991.
Barrett Taxonomy Reorganization To Improve Students' Intensive Reading Ability

This study aims to determine the effect of the Barrett Taxonomy Reorganization Method on the intensive reading ability of class VIII students of SMP Negeri 2 Duampanua, Pinrang Regency, 2019/2020 academic year. This type of research is a quasi-experimental design with True Experimental Design. The variables in this study were the Barrett Taxonomy Reorganization Method and intensive reading skills. The experimental group was class VIII.1 students and the control group was grade VIII.3 students of SMP Negeri 2 Duampanua, Pinrang Regency. Research data collection techniques in the form of tests. The results showed that the intensive reading ability using the Barrett Taxonomy Reorganization method was higher than the intensive reading ability using the conventional method for eighth grade students of SMP Negeri 2 Duampanua, Pinrang Regency. This is evidenced by the results of the t-test with a significance level of 5% (95% confidence level) obtained t count (3.061)&gt; t table (0.278). The value of t count&gt; t table shows that the intensive reading skills of the two groups differ significantly. Therefore, it can be concluded that the effect of the Barrett Taxonomy Reorganization method can not only improve students' understanding but also their reading ability.
14 The Conversion of a Science Institute to a Faculty Library in Ghent, Belgium

The Faculteit Letteren en Wijsbegeerte/Faculty of Arts and Philosophy of
Time-Series Motion Segmentation of a Lathe System in Virtual Reality Environment during Cutting Operations

In recent years, application of virtual reality (VR) for work-related training at production sites has progressed rapidly, but there is a problem of insufficient training owing to the lack of educators. The aim of this study is to verify automatic motion analysis using a VR-based training system such that trainees can recognize and learn critical information for self-improvement at work. In this study, we used video cameras to capture an operator’s motions and actions during the outer rounding and end-face cutting operations on a lathe. For the motion analysis, we used a learning model for time-series segmentation, which extracts features from video images and predicts the class of each frame. We prepared six labels, including four labels for advanced and penetration-type movements as well as two labels for static and transporting empty. A model was created and trained with 90 video data samples of several minutes each, and the training results showed more than 80% success with several metrics, such as accuracy, edit distance, and segmentation F1 score with an overlapping threshold of 50%. These results suggest the feasibility of application of the proposed scheme to training systems.
Face Detection and Gender Classification using Facial Features

Face Recognition is the most important part to identifying people in biometric system. It is the most usable biometric system. This paper focuses on human face recognition by calculating the facial features present in the image and recognizing the person using features. In every face recognition system follows the preprocessing, face detection techniques. In this paper mainly focused on Face detection and gender classification. They are performed in two stages, the first stage is face detection using an enhanced viola jones algorithm and the next stage is gender classification. Input to the video or surveillance that video converted into frames. Select few best frames from the video for detecting the face, before the particular image preprocessed using PSNR. After preprocessing face detection performed, and gender classification comparative analysis done by using a neural network classifier and LBP based classifier.
Implementation of a novel reactive navigation algorithm

In this project a reactive navigation algorithm is applied to a non-holonomic differential drive robot. The algorithm uses a stochastic process to navigate a robot through terrain while lacking a priori information. A graph is made from a random array of points that is used to connect the current location of the robot to its destination. Dijkstra's algorithm is used to select the shortest route that leads to the destination. The robot attempts to traverse this route until it detects that it is being blocked by an obstacle. The graph is then recreated with different random points, an a new route is calculated. This procedure is repeated until the robot arrives at its destination. This is tested by making a simulated robot with perfect localization travel through two kinds of environments. Processing speed is maintained by hashing location information according to its coordinates.
Interactive Computer Mediated Technology: Social Media and Indian Politics

Nowadays social media become the battleground for political parties in India. It becomes an essential part of campaigning. The political parties form their own social media teams and they generate propagating content such as cartoons, memes etc. This field has also become a tool for common people to voice their concern. With the increasing usage of smartphones use of social media are also growing rapidly. Media plays the most important role in a Democracy and it keeps the Government active. With the advent of social media including Twitter, Facebook, WhatsApp, YouTube etc. dramatically changed the way campaigns are run in Politics. Indians are rated as one of the most active social media users in the world. In India the 16th Lok Sabha election held in 2014 we saw the new trend in Indian politics. Social media played a proactive role in deciding which party wins the most seats. The Internet in India is offering opportunities for people to participate in interactive political social media campaigns and become political activists. Sometimes offensive online behavior on social media pages poses potential threats to its users. There are sometimes misunderstandings in comments. It will not be very effective using it as a mere campaign tool. But if used as a platform to share the latest decisions made by the Government and used effectively for active citizen engagement, social media will show positive results for future Indian elections. Social media have also become one of the important tools for nearly all of the world’s political movements. It can be a potent weapon to spread one's ideology, or at least get it to reach more ears. Social media made democracy more inclusive from which nothing can be hidden.
Vector spaces

This textbook is a comprehensive introduction to the key disciplines of mathematics - linear algebra, calculus, and geometry - needed in the undergraduate physics curriculum. Its leitmotiv is that success in learning these subjects depends on a good balance between theory and practice. Reflecting this belief, mathematical foundations are explained in pedagogical depth, and computational methods are introduced from a physicist's perspective and in a timely manner. This original approach presents concepts and methods as inseparable entities, facilitating in-depth understanding and making even advanced mathematics tangible. The book guides the reader from high-school level to advanced subjects such as tensor algebra, complex functions, and differential geometry. It contains numerous worked examples, info sections providing context, biographical boxes, several detailed case studies, over 300 problems, and fully worked solutions for all odd-numbered problems. An online solutions manual for all even-numbered problems will be made available to instructors.
A Design on Bank Customer Complaints Analysis using Natural Language Processing

The banking sector has undergone a major revolution with the advent of digital transformation. The entry of Fintech and tech giants such as Google, Amazon, and Facebook have introduced convenient banking that is easy to understand and use. In this focused condition, banks are understanding the significance of client care and fulfillment and need to give close consideration to the Voice of Customer to improve client experience. By dissecting and getting bits of knowledge from client input, banks will have better data to settle on key choices. In their quest to better understand their customers, banks are seeking artificial intelligence (AI) solutions in the form the of sentiment analysis. What is sentiment analysis? In simple words, sentiment analysis is the process of detecting a customer’s reaction to a product, brand, situation or event through texts, posts, reviews, and other digital content. Using sentiment analysis, business leaders can gain deep insight into how their customers think and feel. The analysis can help in tracking customer opinions over a period of time, determine customer segmentation, plan product improvements, prioritize customer service issues, and many more business use cases
A Method of Fault Fix Priority Identification for Open Source Project

Open source software are adopted as embedded systems, server usage because of quick delivery, cost reduction and standardization of systems. Many open source software are developed under the peculiar development style known as bazaar method. According to this method, faults are detected and fixed by developers around the world, and the fixed result will be reflected in the next release. Also, the fix time of faults tends to be shorter as the development of open source software progresses. However, several large-scale open source projects have a problem that faults fixing takes a lot of time because the faults corrector cannot handle many faults reports quickly. In this paper, we aim to identify the fix priority of newly registered faults in the bug tracking system by using random forest, and we make an index to detect the faults that require high fix priority and long fault fixing time when faults are reported in specific version of open source project. The index is derived and identified by using open source project data obtained from bug tracking system. In addition, we try to improve the detection accuracy of the proposed index by learning not only the specific version but also the fault report data of the past version by using random forest considering the characteristic similarities of faults fix among different versions. As a result, the detection accuracy has highly improved comparing with using only specific version data and using logistic regression.
Big Data Processing-Beyond Batch Processing

This paper mainly focus on analysis of large sets of students data with one of the batch processing analysis techniques Beyond batch process, analysis of data streaming is done based on program of word counting program which executes data with HDFS along with dynamic created data. To compute similar coherent strategies one can implement a schema named batch and streaming process which dynamically creates data. The architecture is reduced to serve as X-Platform which uses ample number of tools for batch and stream analysis on this proposed frame work. Here we use spark-sql, a query language which acts as interface for interactive process to have iterative processes. Real time streaming data processing involves spark streaming works. Here we focus on preliminary evaluation of results and analysis report which compares data sets performance and also achieve low latency rate with usage of RDD.
Dynamic background modelling using multi-swarm optimisation

Background modelling is a fundamental task in video analytics. This paper presents an adaptive background modelling for real-time indoor and outdoor videos. This proposed method treats background modelling as an optimisation problem and, it fetches multiple peaks from the histogram of the video frame and optimises them using a multi-swarm technique. The background is successfully adapted whenever there is a change in the environment as well as a number of objects in the background. The experimental result of foreground extraction confirms the effectiveness and robustness of the proposed background modelling technique against the various background modelling approaches.
Advanced Topics in Power Query

When you build queries using the Power Query designer, the designer creates the query using the M query language. Although you can create robust queries using just the visual interface, there is a lot of useful processing that you can only complete by writing M code. This chapter goes beyond the basics and explores some of the advanced functionality in Power Query, including the M query language, parameters, and functions.