scholarly journals Performance analysis on secured data method in natural language steganography

2019 ◽  
Vol 8 (1) ◽  
pp. 298-304
Author(s):  
Roshidi Din ◽  
Rosmadi Bakar ◽  
Raihan Sabirah Sabri ◽  
Mohamad Yusof Darus ◽  
Shamsul Jamel Elias

The rapid amount of exchange information that causes the expansion of the internet during the last decade has motivated that a research in this field. Recently, steganography approaches have received an unexpected attention. Hence, the aim of this paper is to review different performance metric; covering the decoding, decrypting and extracting performance metric. The process of data decoding interprets the received hidden message into a code word. As such, data encryption is the best way to provide a secure communication. Decrypting take an encrypted text and converting it back into an original text. Data extracting is a process which is the reverse of the data embedding process. The effectiveness evaluation is mainly determined by the performance metric aspect. The intention of researchers is to improve performance metric characteristics. The evaluation success is mainly determined by the performance analysis aspect. The objective of this paper is to present a review on the study of steganography in natural language based on the criteria of the performance analysis. The findings review will clarify the preferred performance metric aspects used. This review is hoped to help future research in evaluating the performance analysis of natural language in general and the proposed secured data revealed on natural language steganography in specific.

2018 ◽  
Vol 28 (7) ◽  
pp. 2251-2257
Author(s):  
Roland Vasili ◽  
Endri Xhina ◽  
Ilia Ninka ◽  
Thomas Souliotis

In recent years, technology has developed a lot and has revolutionized our perspective of the world. Technology and more precisely digital technology has created amazing tools, giving immediate access to anyone interested to any information he may need. This digital revolution of all media like computers, smartphones, etc. has produced a huge amount of digital data to be handled. In our research we care about one aspect of this data, the text data, and the way we can efficiently handle text and produce meaningful summaries. Thus, it is only until recently that text mining has become an interesting research field due to this vast increase of text volume on the web. However, because of its size, this text volume should be summarized so as to get all the useful information efficiently and without trying to deal with all of the initial text, which could be impractical in many cases. Therefore, text summarization systems are among the most attractive research areas nowadays. Text summarization is the process of finding the main source of information, extracting the main important contents and presenting them as a concise text in the predefined template. The two main summarization techniques available are Extractive and Abstractive, with a lot of research being carried out in these areas, especially in extractive summarization. However, meaningful summaries are obtained using abstractive techniques which are more complex, due to the nature of this technique which requires the summary to be constructed in an abstract way without using sentences from the original text, while in the extractive case the summary consists of sentences from the original text. In this paper there is a theoretical approach where the widely used summarization techniques are described at a first level. Moreover, these techniques are then put into practice focusing only on the Albanian language, since the language is an important factor which might lead to different outcomes for each algorithm, due to its structure, its form and its rules. This is the first attempt in the field of summarization in Albanian language and there is a high need for future research works in this area. This paper investigates various proposed text summarization methods which are usually used in English (and possibly other widely used) languages, comparing them and concluding which method is suitable for summarizing documents in the Albanian language. We analyze various summarization algorithms and provide a formal way of verifying the correctness of our results, by using different metrics (e.g. ROUGE) to evaluate the summaries’ accuracy of each technique, by utilizing some gold standard summaries, which have been produced by linguistic experts. Finally, we will also provide the whole practical implementation of this work either by uploading it to a github repository so as to be publicly accessible by anyone or by providing our services as micro-services through a web-page.


2019 ◽  
Vol 13 (1) ◽  
pp. 20-27 ◽  
Author(s):  
Srishty Jindal ◽  
Kamlesh Sharma

Background: With the tremendous increase in the use of social networking sites for sharing the emotions, views, preferences etc. a huge volume of data and text is available on the internet, there comes the need for understanding the text and analysing the data to determine the exact intent behind the same for a greater good. This process of understanding the text and data involves loads of analytical methods, several phases and multiple techniques. Efficient use of these techniques is important for an effective and relevant understanding of the text/data. This analysis can in turn be very helpful in ecommerce for targeting audience, social media monitoring for anticipating the foul elements from society and take proactive actions to avoid unethical and illegal activities, business analytics, market positioning etc. Method: The goal is to understand the basic steps involved in analysing the text data which can be helpful in determining sentiments behind them. This review provides detailed description of steps involved in sentiment analysis with the recent research done. Patents related to sentiment analysis and classification are reviewed to throw some light in the work done related to the field. Results: Sentiment analysis determines the polarity behind the text data/review. This analysis helps in increasing the business revenue, e-health, or determining the behaviour of a person. Conclusion: This study helps in understanding the basic steps involved in natural language understanding. At each step there are multiple techniques that can be applied on data. Different classifiers provide variable accuracy depending upon the data set and classification technique used.


2021 ◽  
Vol 27 (1) ◽  
pp. 146045822199486
Author(s):  
Nicholas RJ Frick ◽  
Felix Brünker ◽  
Björn Ross ◽  
Stefan Stieglitz

Within the anamnesis, medical information is frequently withheld, incomplete, or incorrect, potentially causing negative consequences for the patient. The use of conversational agents (CAs), computer-based systems using natural language to interact with humans, may mitigate this problem. The present research examines whether CAs differ from physicians in their ability to elicit truthful disclosure and discourage concealment of medical information. We conducted an online questionnaire with German participants ( N = 148) to assess their willingness to reveal medical information. The results indicate that patients would rather disclose medical information to a physician than to a CA; there was no difference in the tendency to conceal information. This research offers a frame of reference for future research on applying CAs during the anamnesis to support physicians. From a practical view, physicians might gain better understanding of how the use of CAs can facilitate the anamnesis.


Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4710
Author(s):  
Mariusz Kostrzewski ◽  
Rafał Melnik

Condition monitoring of rail transport systems has become a phenomenon of global interest over the past half a century. The approaches to condition monitoring of various rail transport systems—especially in the context of rail vehicle subsystem and track subsystem monitoring—have been evolving, and have become equally significant and challenging. The evolution of the approaches applied to rail systems’ condition monitoring has followed manual maintenance, through methods connected to the application of sensors, up to the currently discussed methods and techniques focused on the mutual use of automation, data processing, and exchange. The aim of this paper is to provide an essential overview of the academic research on the condition monitoring of rail transport systems. This paper reviews existing literature in order to present an up-to-date, content-based analysis based on a coupled methodology consisting of bibliometric performance analysis and systematic literature review. This combination of literature review approaches allows the authors to focus on the identification of the most influential contributors to the advances in research in the analyzed area of interest, and the most influential and prominent researchers, journals, and papers. These findings have led the authors to specify research trends related to the analyzed area, and additionally identify future research agendas in the investigation from engineering perspectives.


2021 ◽  
Vol 54 (2) ◽  
pp. 1-37
Author(s):  
Dhivya Chandrasekaran ◽  
Vijay Mago

Estimating the semantic similarity between text data is one of the challenging and open research problems in the field of Natural Language Processing (NLP). The versatility of natural language makes it difficult to define rule-based methods for determining semantic similarity measures. To address this issue, various semantic similarity methods have been proposed over the years. This survey article traces the evolution of such methods beginning from traditional NLP techniques such as kernel-based methods to the most recent research work on transformer-based models, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network–based methods, and hybrid methods. Discussing the strengths and weaknesses of each method, this survey provides a comprehensive view of existing systems in place for new researchers to experiment and develop innovative ideas to address the issue of semantic similarity.


Author(s):  
Hamza Sajjad Ahmad ◽  
Muhammad Junaid Arshad ◽  
Muhammad Sohail Akram

To send data over the network, devices need to authenticate themselves within the network. After authentication, the device will be able to send the data in-network. After authentication, secure communication of devices is an important task that is done with an encryption method. IoT network devices have a very small circuit with low resources and low computation power. By considering low power, less memory, low computation, and all the aspect of IoT devices, an encryption technique is needed that is suitable for this type of device. As IoT networks are heterogeneous, each device has different hardware properties, and all the devices are not on one scale. To make IoT networks secure, this paper starts with the secure authentication mechanism to verify the device that wants to be a part of the network. After that, an encryption algorithm is presented that will make the communication secure. This encryption algorithm is designed by considering all the important aspects of IoT devices (low computation, low memory, and cost).


As the world is getting digitalized, the rush for need of secured data communication is overtop. Provoked by the vulnerability of human visual system to understand the progressive changes in the scenes, a new steganography method is proposed. The paper represents a double protection methodology for secured transmission of data. The original data is hidden inside a cover image using LSB substitution algorithm. The image obtained is inserted inside a frame of the video producing a stego-video. Stego-video attained is less vulnerable to attacks. After decryption phase, the original text is obtained which is error-free and the output image obtained is similar as the cover image. The quality of stego-video is high and there is no need for additional bandwidth for transmission. The hardware implement is required in order to calculate the corresponding analytical results. The proposed algorithm is examined and realized for various encryption standards using Raspberry Pi3 embedded hardware. The results obtained focuses on the attributes of the proposed model. On comparing with other conventional algorithms, the proposed scheme exhibits high performance in both encryption and decryption process with increase in efficiency of secured data communication.


2020 ◽  
Vol 34 (05) ◽  
pp. 9733-9740 ◽  
Author(s):  
Xuhui Zhou ◽  
Yue Zhang ◽  
Leyang Cui ◽  
Dandan Huang

Contextualized representations trained over large raw text data have given remarkable improvements for NLP tasks including question answering and reading comprehension. There have been works showing that syntactic, semantic and word sense knowledge are contained in such representations, which explains why they benefit such tasks. However, relatively little work has been done investigating commonsense knowledge contained in contextualized representations, which is crucial for human question answering and reading comprehension. We study the commonsense ability of GPT, BERT, XLNet, and RoBERTa by testing them on seven challenging benchmarks, finding that language modeling and its variants are effective objectives for promoting models' commonsense ability while bi-directional context and larger training set are bonuses. We additionally find that current models do poorly on tasks require more necessary inference steps. Finally, we test the robustness of models by making dual test cases, which are correlated so that the correct prediction of one sample should lead to correct prediction of the other. Interestingly, the models show confusion on these test cases, which suggests that they learn commonsense at the surface rather than the deep level. We release a test set, named CATs publicly, for future research.


2020 ◽  
Author(s):  
David DeFranza ◽  
Himanshu Mishra ◽  
Arul Mishra

Language provides an ever-present context for our cognitions and has the ability to shape them. Languages across the world can be gendered (language in which the form of noun, verb, or pronoun is presented as female or male) versus genderless. In an ongoing debate, one stream of research suggests that gendered languages are more likely to display gender prejudice than genderless languages. However, another stream of research suggests that language does not have the ability to shape gender prejudice. In this research, we contribute to the debate by using a Natural Language Processing (NLP) method which captures the meaning of a word from the context in which it occurs. Using text data from Wikipedia and the Common Crawl project (which contains text from billions of publicly facing websites) across 45 world languages, covering the majority of the world’s population, we test for gender prejudice in gendered and genderless languages. We find that gender prejudice occurs more in gendered rather than genderless languages. Moreover, we examine whether genderedness of language influences the stereotypic dimensions of warmth and competence utilizing the same NLP method.


Vector representations for language have been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Sentiment Analysis. In particular, we target three sub-tasks namely sentiment words extraction, polarity of sentiment words detection, and text sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. Vector representations has been used to compute various vector-based features and conduct systematically experiments to demonstrate their effectiveness. Using simple vector based features can achieve better results for text sentiment analysis of APP.


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