Compressed Text Research Articles

Re2Pair: Increasing the Scalability of RePair by Decreasing Memory Usage.

The RePair compression algorithm produces a context-free grammar by iteratively substituting the most frequently occurring pair of consecutive symbols with a new symbol until all consecutive pairs of symbols appear only once in the compressed text. It is widely used in the settings of bioinformatics, machine learning, and information retrieval where random access to the original input text is needed. For example, in pangenomics, RePair is used for random access to a population of genomes. BigRePair improves the scalability of the original RePair algorithm by using Prefix-Free Parsing (PFP) to preprocess the text prior to building the RePair grammar. Despite the efficiency of PFP on repetitive text, there is a scalability issue with the size of the parse which causes a memory bottleneck in BigRePair. In this paper, we design and implement recursive RePair (denoted as ), which builds the RePair grammar using recursive PFP. Our novel algorithm faces the challenge of constructing the RePair grammar without direct access to the parse of text, relying solely on the dictionary of the text and the parse and dictionary of the parse of the text. We compare to BigRePair using SARS-CoV-2 haplotypes and haplotypes from the 1000 Genomes Project. We show that our method achieves over a 40% peak memory reduction and a speed up ranging between 12% to 79% compared to BigRePair when compressing the largest input texts in all experiments. is made publicly available under the GNU public license here: https://github.com/jkim210/Recursive-RePair.

Enumeration for MSO-Queries on Compressed Trees

We present a linear preprocessing and output-linear delay enumeration algorithm for MSO-queries over trees that are compressed in the well-established grammar-based framework. Time bounds are measured with respect to the size of the compressed representation of the tree. Our result extends previous work on the enumeration of MSO-queries over uncompressed trees and on the enumeration of document spanners over compressed text documents.

Learning on Compressed Molecular Representations

Last year, a preprint gained notoriety, proposing that a k-nearest neighbour classifier is able to outperform large-language models using compressed text as input and normalised compression distance (NCD) as a...

Homomorphic Compression: Making Text Processing on Compression Unlimited

Lossless data compression is an effective way to handle the huge transmission and storage overhead of massive text data. Its utility is even more significant today when data volumes are skyrocketing. The concept of operating on compressed data infuses new blood into efficient text management by enabling mainly access-oriented text processing tasks to be done directly on compressed data without decompression. Facing limitations of the existing compressed text processing schemes such as limited types of operations supported, low efficiency, and high space occupation, we address these problems by proposing a homomorphic compression theory. It enables the generalization and characterization of algorithms with compression processing capabilities. On this basis, we develop HOCO, an efficient text data management engine that supports a variety of processing tasks on compressed text. We select three representative compression schemes and implement them combined with homomorphism in HOCO. HOCO supports the extension of homomorphic compression schemes through a modular and object-oriented design and has convenient interfaces for text processing tasks. We evaluate HOCO on six real-world datasets. The three schemes implemented in HOCO show trade-offs in terms of compression ratio, supported operation types, and efficiency. Experiments also show that HOCO can achieve higher throughput in random access and modification operations (averagely 9.18× than the state-of-the-art) and lower latency in text analytic tasks (averagely 7.16× than processing on uncompressed text) without compromising compression efficacy.

Perancangan Aplikasi Kompresi File Teks Menggunakan Algoritma Context Tree Weighting

Currently the use of applications as information is increasingly being used. but there is a problem that is often encountered, namely the need for a large space as a storage medium. Data compression is the process of converting a set of data into a coded form to save the need for data storage. Context Tree Weighting algorithm is a data compression algorithm. To determine the results of the compression process, compression ratio and space saving media are used. The results will be known, the compression ratio and space saving. In accordance with the results of the trials conducted, it can be seen that the data that was originally large in size can be compressed very well with the implementation of compression text files.

How to raise awareness about electronic mental health services among prospect healthcare providers: a qualitative study on information preferences

IntroductionSince fall 2020, electronic mental health services (eMHSs) like apps can be prescribed by physicians and psychotherapists in Germany. However, future healthcare providers such as medical and psychology students remain reluctant to adopt eMHSs, even though they represent a vulnerable group with respect to developing mental health problems themselves. Reasons include scepticism and lacking awareness, which can be addressed by tailored multi-component information material. However, to date little is known about the most important information attributes to educate prospect healthcare providers on eMHSs.ObjectivesThe objective of this study is to explore information preferences on eMHSs among medical and psychology students.MethodsA total of 21 semi-structured interviews were conducted (n=16 medical and n=5 psychology students) across Germany based on a topic guide. Interviews were recorded, transcribed and content-analyzed using MAXQDA.ResultsMost students reported having little knowledge about eMHSs and that the issue of digital health has never been raised in their study, even though it is perceived as important. Concerning information design preferences, students favored light, neutral colors and a combination of short, compressible texts with matching images. Regarding the content, information about data protection, the underlying evidence base and the match with personal needs were perceived as important for utilization intentions, while there was little interest in tailored information focusing exclusively on psychology or medical students.ConclusionsThis study provides first insights into eMHS information preferences among prospect healthcare providers. In a next step, a discrete-choice conjoint experiment will be conducted to test the relevant information features on eMHSs.DisclosureNo significant relationships.

PECULIARITIES OF FUNCTIONING OF GRAMMATICAL MEANS IN EXPRESSIVE COMPRESSED TEXTS

The article deals with the investigation of grammatical means of achieving expressiveness in compressed texts. In addition, the peculiarities of functioning of these means are considered in the article. Researches of linguistic expressive means are closely related to stylistics, thus, expressive means in compressed texts are, first of all, represented by stylistic devices as the expressive discourse is actualized through stylistics. Grammatical level of the language is traditionally subdivided into morphological and syntactical, correspondingly we distinguish between morphological and syntactic grammatical expressive means. Thus, at the morphological level compressed texts are usually characterized by the use of certain parts of speech with expressive meanings. Here mostly belong such categories of words as adjectives and adverbs. Other morphological means may include the use of different tenses. As to syntactic level, the expressive means in the compressed texts are represented by syntactic synonymy, inversion, the use of different syntactic structures (sentence types), the use of short sentences in answers to questions, different types of repetition, ellipse, direct speech, parallel constructions. On the whole, both grammatical morphological means and grammatical syntactic ones are rather productive in achievement of expressiveness in the compressed texts.

Using DNA Algorithm and Steganography to Secure a Compressed Text

Cryptography is a main item to generate confidence and empower services in the digital technology, while Steganography has vastly expanded in digital medias. Although developing these technologies hackers has established new methods to hack confidential information. To overcome these challenges, we proposed algorithm that encrypts a message by using DNA encryption algorithm, then multiply the output by a factor then compress the multiplying message by using GZIP algorithm, the last stage is hiding a compressed message in (LSB) image pixels. By using image measures such that PSNR, MSE, SSIM, NPCR, UACI and the ratio of data compression up to 75% where reduced the image difference, we obtained a good result when we combined these technologies. This work contributes to reducing the risk of information breach when sending and receiving confidential information between parties.

A Method to Improve Exact Matching Results in Compressed Text using Parallel Wavelet Tree

The process of searching on the World Wide Web (WWW) is increasing regularly, and users around the world also use it regularly. In WWW the size of the text corpus is constantly increasing at an exponential rate, so we need an efficient indexing algorithm that reduces both space and time during the search process. This paper proposes a new technique that utilizes Word-Based Tagging Coding compression which is implemented using Parallel Wavelet Tree, called WBTC_PWT. WBTC_PWT uses the word-based tagging coding encoding technique to reduce the space complexity of the index and uses a parallel wavelet tree which reduces the time it takes to construct indexes. This technique utilizes the features of compressed pattern matching to minimize search time complexity. In this technique, all the unique words present in the text corpus are divided into different levels according to the word frequency table and a different wavelet tree is made for each level in parallel. Compared to other existing search algorithms based on compressed text, the proposed WBTC_PWT search method is significantly faster and it reduces the chances of getting the false matching result.

Reliable and scalable Kafka-based framework for optical network telemetry

Telemetry data acquisition is becoming crucial for efficient detection and timely reaction in the case of network status changes, such as failures. Streaming telemetry data to many collectors might be hindered by scalability issues, causing delay in localization and detection procedures. Providing efficient mechanisms for managing the massive telemetry traffic coming from network devices can pave the way to novel procedures, speeding up failure detection and thus minimizing response time. This paper proposes a novel Kafka-based monitoring framework leveraging the telemetry service. The proposed framework exploits the built-in scalability and reliability of Kafka to go beyond traditional monitoring systems. The framework allows a continuous monitoring of optical system data and their distribution through simple compressed text messages to a large number of consumers. Moreover, the proposed framework keeps a limited history of the monitored data, easing, for example, root cause failure analysis. The implemented monitoring platform is experimentally validated, considering the disaggregated paradigm, in terms of functional assessment, scalability, resiliency, and end-to-end message latency. Obtained results show that the framework is highly scalable, supporting up to around 4000 messages per second (and potentially more) with low CPU load, and is capable of achieving an end-to-end (i.e., producer–consumer) latency of about 50 ms. Moreover, the considered architecture is capable of overcoming the failure of a monitoring framework core component without losing any message.

An encryption based on DNA and AES algorithms for hiding a compressed text in colored Image

The vast serious security challenges has created new secured ways of transmitting information and reduce the chance of hackers to use their methods to analyse the confidential information between two parties. The research combines between several stages of encryption techniques: DNA encryption algorithm, GZIP algorithm, AES cryptography and image steganography. We proposed multiplying by a factor along the last stage of DNA encryption, and the outputs of this process are compressed using the GZIP algorithm, where the message is converted to a new form and its size reduced to (75%), then after the message is encrypted using the AES encryption algorithm to increase the level of security. Furthermore, LSB image Steganography technology is utilized to hide the encrypted message in a high-quality image. By combining these four security technologies we obtained good results for image measures such that Lina sample provided (PSNR is equal 67.589, MSE is equal 0.0116, SSIM is equal 1, NPCR score is 0.011630262741374 and UACI score is 4.5608873495583733e-05) and we ensure transferring data without gaps & make it difficult and more complex for the hacker.

An Experimental Appraisal of the Acquisition of Creative Literary Compression versus Descriptive Texts

ABSTRACT This paper argues that compression is a hallmark of creativity, demonstrating the effect of the process of compression and decompression on the cultivation of creative potentials. This study also suggests some cognitive strategies by which creative literary compression can be encoded and decoded in the light of some relevant theories. In order to raise the creative, cognitive potentials of compression and decompression, this research puts a compressed text in an experimental contrast with a detailed, descriptive text and examines how both address the minds and creative thinking of the research subjects. The research tests the differences between the impact of the two types of literary texts by measuring both the conceptual and linguistic outputs of the participants. The results support the hypothesis that tackling compressed texts deliberately through the cognitive process of decompression generates higher divergent thinking and encourages participants to translate their ideational processes into more creative writing.

К вопросу о дискурсивной теории ономастики

The article deals with the relevance of a new discursive approach to onomastic vocabulary. The onym is considered as a unit that represents a compressed text, which can be expanded under certain conditions. In addition, reverse processes are possible - the compression of discourse to a minimum set of lexical units (a single word or a phrase). There are four important components in the discursive field of an onym: the nominator (the author of the name - N), an onym (the name of the object - O), the recipient (the perceiving subject - P) and the discourse-text (D) connected with an onym. We explain the discursive nature of the onym through the concept of the discursive field, which is a combination of all texts, existing in discours, included in the nominative situation both initially (in the process of nominating an object) and involved in communication during the functioning of the onym in the discursive practices of native speakers.

Comparison Analysis with Huffman Algorithm and Goldbach Codes Algorithm in File Compression Text Using the Method Exponential Comparison

With the development of technology at this time many people know about compression. In simple compression is a process to shrink the file from its original size. At this time compression applications that are often used are WinZip, WinRar, and 7-Zip, namely with the aim of compressing documents and saving space on memory or data transmission. Compressed data can be in the form of images, audio, video and text. The use of the Huffman algorithm and the Goldbach Codes algorithm in compressing text files is intended to provide enormous benefits in the sending and storage process and requires less memory space compared to uncompressed text. The algorithm starts by providing a string of inputs as input, how to produce an algorithm output in the form of a binary string or code that translates each input string, so that the string has a small number of bits compared to strings that are not compressed. Thus, the problem is how to obtain the code with sorted characters and frequency tables as input and shorter binary code as output. In applying the Huffman algorithm and the Goldbach Codes algorithm in compressing text files is very good, the results were not reduced from the original file or there was no reduction

Strengthening mechanism and thermal deformation behavior of Al-12Si/Fe piston composite

The present paper aims the investigation on the influence of Fe based amorphous on strength of aluminum alloy (Al-12Si), and the thermal deformation behavior of new Al-12Si/Fe base amorphous composites. On this foundation, a heat flow stress model of the sample was proposed by a Zener-Hollomon parameter and microstructural studies were carried out on Al-12Si/Fe base amorphous composite by many means. Microstructural study reveals dynamic recovery and dynamic recrystallization of Al-12Si/Fe base amorphous composite at thermal compression text. In addition, the deformation activation energy of Al-12Si/Fe-based amorphous composites is as high as 211.29 kJ mol−1, compared with Al-12Si alloy (170.51 kJ mol−1). Importantly, the flow stress equation of the new composite is = 4.42×1014 [sinh(0.0166σ)]6.13 exp(−211290/RT), and the linear regression coefficient is 0.99. This study constitutes a significant advance in strengthening the Al-12Si engine piston and controlling the thermal deformation behavior and opens new directions for the development of piston material.

To index or not to index: Time–space trade-offs for positional ranking functions in search engines

Positional ranking functions, widely used in web search engines and related search systems, improve result quality by exploiting the positions of the query terms within documents. However, it is well known that positional indexes demand large amounts of extra space, typically about three times the space of a basic nonpositional index. Textual data, on the other hand, is needed to produce text snippets. In this paper, we study time–space trade-offs for search engines with positional ranking functions and text snippet generation. We consider both index-based and non-index based alternatives for positional data. We aim to answer the question of whether positional data should be indexed, and how.We show that there is a wide range of practical time–space trade-offs. Moreover, we show that using about 1.30 times the space of positional data, we can store everything needed for efficient query processing, with a minor increase in query time. This yields considerable space savings and outperforms, both in space and time, recent alternatives from literature. We also propose several efficient compressed text representations for snippet generation, which are able to use about half of the space of current state-of-the-art alternatives with little impact in query processing time.

GRAPHIC MEANS IN THE ENGLISH-LANGUAGE EXPRESSIVE COMPRESSED TEXTS

This article deals with the consideration of functioning of graphic means on the basis of the English-language expressive compressed texts. The types of abbreviations as well as other graphic peculiarities of the information presentation in the text have been investigated, using examples of English advertising texts, anecdotes, aphorisms, and contemporary American literature. English-language compressed texts have both leading and subordinate features. Their leading feature is increased informational saturation, while the subordinate signs usually include abbreviations, lack of auxiliary and emotionally coloured words. Among the main graphic means used in the English-language compressed texts, various cuts of words in the form of abbreviations, traditional or specific reductions should be noted. One can also mention a special system of the location of the material, which allows to highlight the main information and omit the secondary, significantly reducing the volume. The use of footnotes also helps to save a certain amount of text array. Punctuation marks in compressed text not only make it possible to reduce a certain part of the information, but may also carry additional information load. In general, the use of the mentioned graphic means in English-language compressed texts have been analyzed, the peculiarities of their functioning have been revealed in the article. As the perspectives of the research, it was offered to identify and investigate the existing graphic means in other types of the English-language compressed texts.

A New Chaotic Image Steganography Technique Based on Huffman Compression of Turkish Texts and Fractal Encryption With Post-Quantum Security

As the digital age is becoming an unavoidable part of our daily life people are becoming more concern about their privacy and security of their digital communications. Steganography and cryptography are the two most frequently used merits of digital life to fulfill these needs. In this study, a new spatial domain chaotic steganography scheme is presented utilizing also new fractal stream encryption algorithm to hide Huffman encoded and compressed Turkish texts. The study composes of four main phases. Firstly, a sample of Turkish newspaper columnist corpus is gathered to obtain not only the frequencies of letters including special Turkish characters but also punctuations, spaces, newlines, quotations, etc. As a result of the first phase, a static Huffman encoding dictionary is obtained for 102 encountered characters. Secondly, super Mandelbrot sets are used with the Logistic map to obtain one-time-pad stream keys to encrypt compressed texts. Thirdly, the LSB (Least Significant Bit) plain of the cover image is analyzed morphologically to find low entropy pixel locations so that in steganography phase spotted locations can be avoided and scheme can become more resistant against stego analysis. Lastly, the chaotic steganography phase is the final phase in which data hiding pixel is selected according to logistic map chaotic function then LSB of the selected pixel is updated. Many tests are carried out on many images to show the image quality namely PSNR, SNR, SSMI, UIQI, Entropy, MSE, and histogram. Results indicate that proposed schemes are successful for encryption and offer robust steganography.

Failure Prediction of Two Types of Rocks Based on Acoustic Emission Characteristics

The stability analysis of rock is an important basis to ensure the safe exploitation of underground resources and the reliable operation of space engineering. Uniaxial compression and acoustic emission (AE) tests were carried out on two common rock samples with strong rock burst tendency. The relationship between mechanical characteristics, AE characteristics, and rock burst tendency in the failure process of rock with different body types and the evolution of fractal characteristics of AE parameters were discussed. Based on the cusp mutation theory, the catastrophe model of AE characteristic parameters was established to quantify the instability mechanism of rock mass. The results show that the AE mutation rate (AEMR) of the cubic specimens increase from a low level to a high level gradually in the stable fracture stage, while that of the cylindrical specimens increase sharply to the maximum when the specimens are near failure. The AE cumulative energy curves of cubic specimens show a “step” rise, while that of cylindrical specimens show a “gradual” rise, and the rock burst process of cubic specimens is faster. The fractal dimension evolution mode of AE characteristic parameters of cubic specimens during uniaxial compression text is decline‐rise‐decline, while that of cylindrical specimen is decline‐rise‐decline‐steeply rise. According to the periodic change of AE cumulative energy curve, combined with the rock failure cusp mutation model, the occurrence of rock burst can be well predicted, providing certain theoretical guidance for the stability analysis of underground engineering rock mass.

Extraction of Hidden Text from Images using DWT

Extraction of hidden text in web images, computer screen images, news, games and e-learning is a very important task in image processing. Compression of digital images leads to poor visual quality of background and text images. Digital images are significantly considered and segmented using DWT into text and background blocks. Huffman coding is used to perform the lossless compression process in the compressed text pixels and the SPIHT algorithm in employed to the compress the background pixels. The result of DWT segmentation shows fringes in the segmented text image. The proposed method uses connected region and edge detection approach which provides a segmented text from digital video stills. The segmented text is converted to binary image using luminance thresholding which leads to fine quality of extracted text.