Samsung Electronics and Apple, Inc.: A Study in Contrast in Competitive Analysis in 21st Century

Samsung Electronic devices are one of the biggest technological innovation company currently, provides a new paradigm on how top to bottom incorporated companies nowadays function. Technologies have been modifying how value stores and markets work, so much so that how side to side, top to bottom incorporated components are considered nowadays are modifying, too. While the Samsung controls much of their value stores, they, too, delegate some of the stores to another. This business design allows them to develop on their proficiencies and, at the same time, to reduce deal costs, which allows them to fulfill the requirements of a very powerful technological innovation market. This study contains the financial analysis of the Samsung and its competitor Apple as well as the industry in which Samsung is performing, and it also contains the unique issues that Samsung is facing in these days. This study is beneficial to the academic readers and for a lot of firms, by this study, these firms can understand the issues that are impacting financial performance and position of different firms. This study provided help in commerce field, IT field, Business field and as well as professionals and the readers attaining the benefit to understand the market trends and the current performance of the multinational leader in electronics and mobile phone industry. Keywords: Samsung proficiencies, Apple competitor, Competitive analysis, Horizontal,   Vertical, DuPont, ratios analysi

Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table

Clinical letters are infamously impenetrable for the lay patient. This work uses neural text simplification methods to automatically improve the understandability of clinical let- ters for patients. We take existing neural text simplification software and augment it with a new phrase table that links complex medi- cal terminology to simpler vocabulary by min- ing SNOMED-CT. In an evaluation task us- ing crowdsourcing, we show that the results of our new system are ranked easier to under- stand (average rank 1.93) than using the origi- nal system (2.34) without our phrase table. We also show improvement against baselines in- cluding the original text (2.79) and using the phrase table without the neural text simplifica- tion software (2.94). Our methods can easily be transferred outside of the clinical domain by using domain-appropriate resources to pro- vide effective neural text simplification for any domain without the need for costly annotation

One emoji, many meanings: A corpus for the prediction and disambiguation of emoji sense

In this work, we uncover a hidden linguistic property of emoji, namely that they are polysemous and can be used to form a semantic network of emoji meanings. Our key contributions to this direction of study are as follows: (1) We have developed a new corpus to help in the task of emoji sense prediction. This corpus contains tweets with single emojis, where each emoji has been labelled with an appropriate sense identifier from WordNet. (2) Experiments, which demonstrate that it is possible to predict the sense of an emoji using our corpus to a reasonable level of accuracy. We are able to report an average path-similarity score of 0.4146 for our best emoji sense prediction algorithm. (3) We further show that emoji sense is a useful feature in the emoji prediction task, where we report an accuracy of 58.8816 and macro-F1 score of 46.6640, beating reasonable baselines in this task. Our work demonstrates that importance of considering the meaning behind emoji, rather than ignoring them, or simply treating them as extra wordforms

Gaussian mixture model based probabilistic modeling of images for medical image segmentation

In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art. INDEX TERMS Gaussian Mixture Model, Level Sets, Active Contours, Biomedical Engineerin

HCF-CRS: A Hybrid Content based Fuzzy Conformal Recommender System for providing recommendations with confidence

A Recommender System (RS) is an intelligent system that assists users in finding the items of their interest (e.g. books, movies, music) by preventing them to go through huge piles of data available online. In an effort to overcome the data sparsity issue in recommender systems, this research incorporates a content based filtering technique with fuzzy inference system and a conformal prediction approach introducing a new framework called Hybrid Content based Fuzzy Conformal Recommender System (HCF-CRS). The proposed framework is implemented to be used in the domain of movies and it provides quality recommendations to users with a confidence level and an improved accuracy. In our proposed framework, first, a Content Based Filtering (CBF) technique is applied to create a user profile by considering the history of each user. CBF is useful in the situations like: lack of demographic information and the data sparsity problems. Second, a Fuzzy based technique is incorporated to find the similarities and differences between the user profile and the movies in the dataset using a set of fuzzy rules to get a predicted rating for each movie. Third, a Conformal prediction algorithm is implemented to calculate the non-conformity measure between the predicted ratings produced by fuzzy system and the actual ratings from the dataset. A p-value (confidence measure) is computed to give a level of confidence to each recommended item and a bound is set on the confidence level called a significance level ε, according to which the movies only above the specified significance level are recommended to user. By building a confidence centric hybrid conformal recommender system using the content based filtering approach with fuzzy logic and conformal prediction algorithm, the reliability and the accuracy of the system is considerably enhanced. The experiments are evaluated on MovieLens and Movie Tweetings datasets for recommending movies to the users and they are compared with other state-of-the-art recommender systems. Finally, the results confirm that the proposed algorithms perform better than the traditional ones

Towards event-based discourse analysis of biomedical text

Annotating biomedical text with discourse-level information is a well-studied topic. Several research efforts have annotated textual zones (e.g., sentences or clauses) with information about rhetorical status, whilst other efforts have linked and classified sets of text spans according to the type of discourse relation holding between them. A relatively new approach has involved annotating meta-knowledge (i.e., rhetorical intent and other types of information concerning interpretation) at the level of bio-events, which are structured representations of pieces of biomedical knowledge. In this paper, we report on the examination and comparison of transitions and patterns of event metaknowledge values that occur in both abstracts and full papers. Our analysis highlights a number of specific characteristics of event-level discourse patterns, as well as several noticeable differences between the types of patterns that occur in abstracts and full papers