Reviewerly/exHarmony huggingface dataset:
Grocery Product Classification and Recommendation System Based on Machine Learning and Customer Profile Identity

In today’s world number of ecommerce companies is increasing day by day. Most of the ecommerce system allow the user to rate the product and also provide flexibility to view or submit reviews. Grocery Products are also sold online by few ecommerce giants, recommendations are provided to the end user based on collaborative filtering, content based or based on text review sentiments but there is no consideration of user likeness with respect to a particular food. In this paper the product classification is performed using both sentiment score computation based on combination of support vector machine and artificial neural network along with frequency computation on specific nutrition features namely salt, sugar, protein, energy and fat Once the customer registers into application a nutrition questioner is asked for customer and data analysis is performed based on user answers in order to classify the end user into a particular likeness category. There is a relationship established between the user and the products, the products are recommended based on high positive score, low negative score and high frequency under the user likeness category.
Object Oriented Fault Prediction Analysis Using Machine Learning Algorithms

One of the important key element in the development and maintenance process of software is fault prediction, it concerns the overall success of the system. Predicting software faults at the initial phase helps the developer to build reliable software and also minimise the cost. Fault prediction model provide insights to development team about faulty behaviour and thus act accordingly. The study presented in this paper discusses the performances of various machine learning algorithms in predicting fault prone classes and also investigates the role played by different software metrics of the datasets. First we apply the correlation based feature selection technique to get set of uncorrelated metrics that are highly desirable and informative for prediction. Then we develop model for prediction with the help of some supervised machine learning techniques. These models are validated on six different versions of object oriented java project obtained from GitHub.
Improved Honey Cipher Structure Using Least Significant Bit Techniques

Honey Cipher Structure is one of such secured techniques in cryptography. There are many structures available in security, Honey Cipher structure is new model into provide security for plaintext of alphabets as well as numeric’s. I planned to implement, this structure with eight levels first level contains eight elements, from first level to last level one element is decreased, and the last level contains only one element. In this honey cipher structure first level filled by the password and repeated characters are omitted in the password, password may also contains numeric’s. After filling the password remaining levels are filled by the rest of the characters. This is reason sender can have the confident that the transferred message have secured. Honey structure is created by filling all characters and numeric’s, next step honey form is converted into Honey cipher Structure. This structure represents the three digits of numbers, this form is called as honey cipher structure, and this can be protected the message. But to provide more security, this three digit numbers are considered as ASCII decimal values. This ASCII Decimal value is converted into Binary code; by applying the Least Significant Bit (LSB) technique on this binary code we can get long distance binary value. This binary value is associated with Hexadecimal symbol. These Hexadecimal symbols are printed as a cipher text. This cipher text is transferred to receiver. Receiver decrypts this cipher text in reverse order to read the original message.
Idiom Recommendation Using POS Tagging and Sentence Parsing

In this paper, the architecture for idiom recommendation and its implementation based on the syntactical structure of phrases in sentences is proposed. Idioms are a bunch of words that have figurative meaning if taken together, which is different from the actual meaning of individual elements. The entire methodology is divided into four segments—segmentation, analysis, extraction and transformation. Emphasis on every possible syntactic structure of phrases is laid, that indicates similar implication to the structures in the dictionary and recommends idioms based on that.
Parallel PageRank Algorithm Using MapReduce

PageRank algorithm is used to determine the relevance of a web page based on the user query. It is among one of the most effective algorithms for working out on the relevance of website pages across the globe. In the world of computation, the web is a large dataset represented in the structure as a graph with billions of hubs and links that represent various features of the web page. It is difficult to find the relevance of pages and takes a large amount of time as it uses a huge number of calculations. Using basic PageRank algorithm, causes the main problem to measure the PageRank of the web page, to evaluate this problem the algorithm can be parallelized to achieve higher efficiency in terms of time, speed and accuracy. This basic algorithm of PageRank can be implemented using MapReduce in Hadoop Framework, which results in the Parallel PageRank algorithm using MapReduce works efficiently in terms of time, speed and accuracy. Our experimental results performed on different clusters deliveries higher efficiency.
Learning Shapes on Image Sampled Points with Dynamic Graph CNNs

This research focuses on the problem of shape analysis on the 2D image domain. State of the art methods like convolution neural networks typically rely on the richness of texture information in images for inference. However, when the underlying object is better described as a shape, these methods tend to suffer. Here, we aim to bridge this gap by proposing a method to analyze shapes on images. The driving idea is to learn features defined on just a few point samples extracted from image super-pixels. A dynamic graph CNN (i.e., a neural net producing a different graph at each layer) is trained and used as the learning engine in a classification task. Our first set of experiments is tested on the 10-digit class MNIST dataset benchmark where we find that the proposed method performs better than others on small datasets. This altogether shows a promising direction for the analysis of more complex shapes fundamental in classification and retrieval of documents of scientific nature.
Study of K-Means Clustering Algorithm for Identification of Dengue Fever Hotspots

In India, several dengue outbreaks were reported like 2015 dengue outbreaks, increasing the need for control and prevention of such outbreaks. Cluster analysis is one of the most extensively used statistical data analysis due to its extensive use in machine learning. Clustering techniques can be integrated to determine potential real-world areas of infectious diseases. This paper presents the application of K-means in disease surveillance. K-means is one of the least difficult unsupervised learning calculations. It groups the dataset through a specific number of clusters. The primary thought is characterized k-centroids, one for each cluster. In India, several dengue outbreaks were reported like 2015 dengue outbreaks, thereby increasing the need for control and prevention of such outbreaks. This paper presents an experimental evaluation of the effectiveness of K-means using data of reported Dengue Fever (DF) which was maintained in the Health Department of Municipal Corporation of Delhi (MCD). A total of 4713 cases were seen in years 2011, 2012 and 2013. The paper successfully detected hotspots of DF and calculated the Silhouette coefficients to validate the clusters.
The Influence of Major Efficacy Satisfaction on University Dance Students’ Intention to Continue Studying: Moderating Effectsof Major Efficacy

이 연구는 무용전공 대학생의 전공활동만족도와 전공지속의사의 관계를 확인하고, 이 두 요인 사이에서 전공효능감이 매개 작용을 하는지를 구조적으로 파악하여 대학의 무용교육 현장에 효과적인 학생 관리에 자료를 제공하기 위해 설계하였다. 앞서 제시한 과학적인 연구방법과 실증 분석의 과정으로 아래와 같이 결론을 도출하였으며, 이를 토대로 시사점을 제공하였다. 첫째, 무용전공 대학생이 전공활동에 만족할수록 전공효능감이 높아지는 것으로 나타났다. 둘째, 무용전공 대학생이 전공활동에 만족할수록 미래에도 전공을 지속하고자 하는 의사가 커지는 것으로 나타났다. 셋째, 무용 대학생이 전공에 높은 효능감을 가질수록 미래에도 전공을 지속하고자 하는 의사가 커지는 것으로 나타났다. 넷째, 무용전공 대학생의 전공활동만족도가 전공지속의사를 높여주는데 있어서 전공효능감은 매개 역할을 해주는 것으로 나타났다. 향후 관련 연구를 위한 제언으로 첫째, 무용전공대학생의 무용지속의사를 높여주기 위해서는 무용활동에 대한 만족도를 높여주는 전략과 함께 무용전공효능감을 높여주는 투트랙 전략을 활용해야 함을 시사한다. 둘째, 이 연구는 전공효능감을 매개 변수로 투입하여 무용활동 만족도와 무용지속의사 간에 의미 있는 효과가 있음을 규명하여 향후 관련 연구에 실증적 토대가 활용될 것으로 기대된다.This study aims to analyze the effect of major activity satisfaction of university dance students on their intention to continue majoring in their chosen field of study and the mediating effect of major efficacy. Data collection was conducted on students majoring in dance universities in Seoul and Gyeonggi-do, responses from 237 participants were used for the final analysis. The hypothesis was verified by analyzing the structural equation model using AMOS, and the conclusions are as follows. First, major activity satisfaction had a positive effect on major effectiveness. Second, major activity satisfaction had a positive effect on the intention to continue studying the same major. Third, major efficacy had a positive effect on students’ intention to continue pursuing their major. Fourth, major efficacy had a mediating effect between major activity satisfaction of dance students and their intention to continue studying dance.
Simultaneous Material Segmentation and 3D Reconstruction in Industrial Scenarios

Recognising material categories is one of the core challenges in robotic nuclear waste decommissioning. All nuclear waste should be sorted and segregated and according to their materials, then different disposal post-process can be applied. In this paper, we proposed a novel transfer learning approach to learn boundary-aware material segmentation from meta-dataset and weakly annotated data. The proposed method is data-efficient which leverages publically available dataset for general computer vision tasks and coarsely labelled material recognition data. And the limited number of fine pixel-wise annotations are required. Importantly, our approach is integrated with a Simultaneously Localisation and Mapping (SLAM) system to fuse the per-frame understating delicately into a 3D global semantic map to facilitate robot manipulation with self-occluded object heap or robot navigation in the disaster zones. We evaluate the proposed method on Materials in Context dataset over 23 categories and our integrated system delivers a quasi-real-time 3D semantic mapping with high-resolution images. The trained model is also verified in an industrial environment as part of the EU RoMaNs project and promising qualitative results are presented.
Jerk-limited Holonomic Robot Motion Planning Using Barrier Functions

This paper proposes a jerk-limited motion restriction algorithm to adapt a higher level single-integrator-based path planner to a constrained triple integrator robot. The algorithm guarantees that the robot never deviates from the nominal path represented by a single-integrator virtual agent by more than a predefined lateral distance. This is achieved by using an online discrete-time modification of the barrier function approach. A tracking controller's inputs are bounded based on the barrier function formulation, which ensures that dynamic constraints are met. The nominal position of the virtual agent is then advanced according to the current robot position and the barrier function value. The effectiveness of this method is demonstrated with a simulation of a quadrotor model. The quadrotor was able to track the required inputs well and keep within the predefined displacement from the nominal path with some acceptably small errors.
Random Indexing and Centroid Based Technique for Multi Document Summarization

In the era of presence of multiple textual information about a particular topic, it becomes of immense importance to process these multiple documents and summarize them. This is the process of Multi-Document Summarization (MDS). One of the very famous technique for Multiple Document Summarization is Centroid based method which is used in a popular tool for MDS viz. MEAD. This research aims to fill the gap of developments taken in recent years and the technique of Centroid based summarization. Primarily, the centroid based technique works on vector space model of representation of sentence vectors. Recently, Random Indexing was proposed for efficient Single Document Summarization. This research aims to build a hybrid technique for MDS which utilizes the best of both techniques in one. The experimental results confirm not only a decrease in temporal requirement but also in efficiency measured in terms of ROUGE scores. The proposed work has been validated for generic multi document summarization using DUC2005 dataset.
A systematic literature review on dynamic cognitive augmentation through immersive reality: challenges and perspectives

Rapid advances in immersive reality technologies have resulted in a vast quantity of research papers which generally include or attempt to apply it to human cognitive augmentation. Taking advantage of traditional psychological and physiological metrics for studying human cognition and behavior, researchers are applying various near-real time analysis techniques to modulate immersive experiences and influence the mental state of the user. Because of the variety of contributing sub-domains, there is little consensus as to any rigid paradigm for the knowledge being synthesized. In this paper, we conduct a systematic literature review to determine the state of the art of dynamic cognitive augmentation in immersive environments. Following a structured query of academic publications, we conduct an in-depth analysis of 104 papers from a sample of 538. We observed that roughly 66% of papers among this frontier apply methods best suited for exploratory purposes, limiting the overall extent to which conclusions can be drawn about immersive reality technology’s capability to augment human cognition. We further identify a pressing gap in the knowledge necessary for the effective application of immersive reality towards dynamic cognitive augmentation in practical industrial scenarios. We hope this work will influence academia, industry, and standards development organizations to extend the use of XR technology networked with biosensor-enabled intelligent cognitive assistants to enhance the effectiveness of hybrid human-machine systems.
ConvAE-Advanced: Adaptive Transmission Across Multiple Timeslots for Error Resilient Operation

Recent advancements in machine learning for communications show that channel autoencoders could revolutionize conventional communication systems through end-to-end optimization. For channel autoencoders to reliably transmit over the air, a scheme to enable adaptive use of resources is needed. Thus, we propose ConvAE-Advanced, an improved channel autoencoder structure that can adaptively transmit across multiple timeslots. ConvAE-Advanced utilizes an unexploited input dimension in ConvAE by the use of the resource-aware residual block and whole resource power normalization. This enabled ConvAE-Advanced to adaptively transmit information according to channel conditions. Simulations for a 2-by-2 multiple-input multiple-output system under the WINNER2 A1 scenario shows that ConvAE-Advanced outperforms ConvAE across all SNR ranges. Most importantly, ConvAE-Advanced can achieve a better BER and achievable rate performance without additional wireless resource usage compared to ConvAE.
Left ventricle segmentation using a Bayesian approach with distance dependent shape priors

We propose a method for segmentation of the left ventricle in magnetic resonance cardiac images. The framework consists of an initial Bayesian segmentation of the central slice of the volume. This segmentation is used to locate a shape prior for the LV myocardial tissue. This shape prior is determined using the fact that the myocardium is approximately annular as seen in the short-axis. Then a second Bayesian segmentation is performed to obtain the final result. This procedure is repeated for the rest of the slices. An extrapolation of the area of the LV is used to determine a stopping criterion. The method was evaluated on the databases of the Cardiac Atlas project. Our results demonstrate a suitable accuracy for myocardial segmentation (≈0.8 Dice's coefficient). For the endocardium and the epicardium the Dice's coefficients are 0.94 and 0.9 respectively. The accuracy was also evaluated in terms of the Hausdorff distance and the average distance. For the myocardium we obtain 8 mm and 2 mm respectively. Our results demonstrate the capability and merits of the proposed method to estimate the structure of the LV. The method requires minimal user input and generates results with quality comparable to more complex approaches. This paper suggests a new efficient approach for automatic LV quantification based on a Bayesian technique with shape priors with errors comparable to state-of-the-art techniques.
A Deep Learning Sentiment Primarily Based Intelligent Product Recommendation System

In recent years, technological enhancements in computing have semiconductor to the event of delicate call support systems to produce support to the purchasers United Nations agency ar victimization social networks for obtaining services. At intervals the past, sure researchers classified product and building reviews into positive and negative slots, that were accustomed build picks to settle on out applicable hotels, product and services for patrons and to produce tips to the business personalities concerned in hotels. Today, folks kind on-line teams and overtly discuss not solely the professionals—of, as associate example, hotels—however in addition air complaints. If feedback isn’t addressed properly by building service suppliers, it’s about to possibly increase then the hotel’s quality downsized. Food served to customers depends on the preparation still as results of the worth, location and times at that it’s served. Further, the angle of the sales folks and building workers, in general, plays a key role in customers’ picks. Thus, on-line shopper feedback through social media is beneficial for shopper behavior analysis, crucial for the success of business. A recommendation system that addresses of these problems will give customers higher picks in their alternative of hotels and services. Throughout this proposal, a try of recent classification algorithms unit of measurement projected. One depends on a modern kind of support vector machines spoken as cluster support vector machines to perform major, and sub classification, of sentiments, still as kind teams supported people’s sentiments with connectedness changes in times and locations. The intelligent cluster support vector machine rule projected throughout this thesis improves classification accuracy to produce correct recommendations. The foremost advantage of the projected work is that it helps make sure folks with similar interests, supported sentiments well-known from tweets, and type interested teams for animated discussions on fascinating topics. A modern clump rule is projected throughout this analysis work that is helpful in forming teams supported clusters. Throughout this work, a modern genetic weighted K-means clump rule is projected to notice correct cluster structures from a try of datasets, Twitter and Face book. The genetic rule chosen here to perform clump is associate economical technique that improves classification accuracy.
A Learning Frequency-Aware Feature Siamese Network for Real-Time Visual Tracking

Visual object tracking by Siamese networks has achieved favorable performance in accuracy and speed. However, the features used in Siamese networks have spatially redundant information, which increases computation and limits the discriminative ability of Siamese networks. Addressing this issue, we present a novel frequency-aware feature (FAF) method for robust visual object tracking in complex scenes. Unlike previous works, which select features from different channels or layers, the proposed method factorizes the feature map into multi-frequency and reduces the low-frequency information that is spatially redundant. By reducing the low-frequency map’s resolution, the computation is saved and the receptive field of the layer is also increased to obtain more discriminative information. To further improve the performance of the FAF, we design an innovative data-independent augmentation for object tracking to improve the discriminative ability of tracker, which enhanced linear representation among training samples by convex combinations of the images and tags. Finally, a joint judgment strategy is proposed to adjust the bounding box result that combines intersection-over-union (IoU) and classification scores to improve tracking accuracy. Extensive experiments on 5 challenging benchmarks demonstrate that our FAF method performs favorably against SOTA tracking methods while running around 45 frames per second.
Study on Development of Humor Discriminator for Dialogue System

Studies on automatic dialogue systems, which allow people and computers to communicate with each other using natural language, have been attracting attention. In particular, the main objective of a non-task-oriented dialogue system is not to achieve a specific task but to amuse users through chat and free dialogue. For this type of dialogue system, continuity of the dialogue is important because users can easily get tired if the dialogue is monotonous. On the other hand, preceding studies have shown that speech with humorous expressions is effective in improving the continuity of a dialogue. In this study, we developed a computer-based humor discriminator to perform user- or situation-independent objective discrimination of humor. Using the humor discriminator, we also developed an automatic humor generation system and conducted an evaluation experiment with human subjects to test the generated jokes. A t-test on the evaluation scores revealed a significant difference (P value: 3.5×10 -5 ) between the proposed and existing methods of joke generation.
An Application of Differential Evolution Algorithm-based Restricted Boltzmann Machine to Recommendation Systems

Due to growth of electronic commerce, currently, many customers prefer to buying products from the internet. Thus, recommendation system, like restricted Boltzmann machine (RBM), has become a good technique to recommend the right product to the potential customer. This can dramatically increase the customer loyalty. However, it is necessary to determine some parameters for RBM and enhance its computation performance. Therefore, this study intends to propose a hybrid algorithm which combines the cluster-based restricted Boltzmann machine (CRBM) with differential evolution (DE) algorithm to optimize the RBM’s parameters for collaborative filtering. The CRBM applies a clustering algorithm to determine the size and elements for each mini-batch gradient descent method for the RBM. The proposed DE-based CRBM algorithm is validated using four benchmark datasets. The results are compared with those of batch RBM, mini-batch RBM, clustering RBM, PSO-based and GA-based clustering RBM. The experimental results reveal that optimizing the RBM’s parameters using metaheuristic can obtain the better result. It also show that the proposed DE-based CRBM algorithm performs better than GA-based and PSO-based CRBM algorithms.