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Journal articles on the topic 'Text compression'

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Published: 4 June 2021

Last updated: 9 June 2025

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1

Fenwick, Peter. "Differential Ziv-Lempel Text Compression." JUCS - Journal of Universal Computer Science 1, no. (8) (August 28, 1995): 591–602. https://doi.org/10.3217/jucs-001-08-0591.

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We describe a novel text compressor which combines Ziv-Lempel compression and arithmetic coding with a form of vector quantisation. The resulting compressor resembles an LZ-77 compressor, but with no explicit phrase lengths or coding for literans. An examination of the limitations on its performance leads to some predictions of the limits of LZ-77 compression in general, showing that the LZ-77 text compression technique is already very close to the limits of its performance.

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2

P, Srividya. "Optimization of Lossless Compression Algorithms using Multithreading." Journal of Information Technology and Sciences 9, no. 1 (March 2, 2023): 36–42. http://dx.doi.org/10.46610/joits.2022.v09i01.005.

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The process of reducing the number of bits required to characterize data is referred to as compression. The advantages of compression include a reduction in the time taken to transfer data from one point to another, and a reduction in the cost required for the storage space and network bandwidth. There are two types of compression algorithms namely lossy compression algorithm and lossless compression algorithm. Lossy algorithms find utility in compressing audio and video signals whereas lossless algorithms are used in compressing text messages. The advent of the internet and its worldwide usag

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3

P, Srividya. "Optimization of Lossless Compression Algorithms using Multithreading." Journal of Information Technology and Sciences 9, no. 1 (March 1, 2023): 36–42. http://dx.doi.org/10.46610/joits.2023.v09i01.005.

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The process of reducing the number of bits required to characterize data is referred to as compression. The advantages of compression include a reduction in the time taken to transfer data from one point to another, and a reduction in the cost required for the storage space and network bandwidth. There are two types of compression algorithms namely lossy compression algorithm and lossless compression algorithm. Lossy algorithms find utility in compressing audio and video signals whereas lossless algorithms are used in compressing text messages. The advent of the internet and its worldwide usag

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4

Murugesan, G., and Rosario Gilmary. "Compression of text files using genomic code compression algorithm." International Journal of Engineering & Technology 7, no. 2.31 (May 29, 2018): 69. http://dx.doi.org/10.14419/ijet.v7i2.31.13399.

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Text files utilize substantial amount of memory or disk space. Transmission of these files across a network depends upon a considerable amount of bandwidth. Compression procedures are explicitly advantageous in telecommunications and information technology because it facilitate devices to disseminate or reserve the equivalent amount of data in fewer bits. Text compression techniques section, the English passage by observing the patters and provide alternative symbols for larger patters of text. To diminish the depository of copious information and data storage expenditure, compression algorith

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5

Stecuła, Beniamin, Kinga Stecuła, and Adrian Kapczyński. "Compression of Text in Selected Languages—Efficiency, Volume, and Time Comparison." Sensors 22, no. 17 (August 25, 2022): 6393. http://dx.doi.org/10.3390/s22176393.

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The goal of the research was to study the possibility of using the planned language Esperanto for text compression, and to compare the results of the text compression in Esperanto with the compression in natural languages, represented by Polish and English. The authors performed text compression in the created program in Python using four compression algorithms: zlib, lzma, bz2, and zl4 in four versions of the text: in Polish, English, Esperanto, and Esperanto in x notation (without characters outside ASCII encoding). After creating the compression program, and compressing the proper texts, au

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Öztürk, Emir, and Altan Mesut. "Learning-based short text compression using BERT models." PeerJ Computer Science 10 (October 18, 2024): e2423. http://dx.doi.org/10.7717/peerj-cs.2423.

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Learning-based data compression methods have gained significant attention in recent years. Although these methods achieve higher compression ratios compared to traditional techniques, their slow processing times make them less suitable for compressing large datasets, and they are generally more effective for short texts rather than longer ones. In this study, MLMCompress, a word-based text compression method that can utilize any BERT masked language model is introduced. The performance of MLMCompress is evaluated using four BERT models: two large models and two smaller models referred to as “t

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Divakaran, Sajilal, Biji C. L., Anjali C, and Achuthsankar S. Nair. "MALAYALAM TEXT COMPRESSION." International Journal of Information Systems and Engineering 1, no. 1 (April 30, 2013): 1–11. http://dx.doi.org/10.24924/ijise/2013.04/v1.iss1/1.11.

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8

Davison, Andrew. "Vague text compression." ACM SIGACT News 24, no. 1 (January 15, 1993): 68–74. http://dx.doi.org/10.1145/152992.153009.

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9

Bol'shakov, I. A., and A. V. Smirnov. "Text compression methods." Journal of Soviet Mathematics 56, no. 1 (August 1991): 2249–62. http://dx.doi.org/10.1007/bf01099202.

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10

Priyono, Eko, and Hindayati Mustafidah. "Text Compression Using the Shannon-Fano, Huffman, and Half–Byte Algorithms." International Journal of Scientific Research and Management (IJSRM) 12, no. 09 (September 5, 2024): 1422–27. http://dx.doi.org/10.18535/ijsrm/v12i09.ec01.

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Background and Objectives: File sizes increase as technology advances. Large files require more storage memory and longer transfer times. Data compression is changing an input or original data into another data stream as output or compressed data which is smaller in size. Existing compression techniques include the Huffman, Shannon-Fano, and Half-Byte algorithms. Like algorithms in computer science, these three algorithms offer advantages and disadvantages. Therefore, testing is needed to determine which algorithm is most effective for data compression, especially text data. Methods: Applying

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11

Satria, Gunawan Zain, Nirwana, Baso Kaswar Andi, Suhartono, and Rahman Patta Abd. "Implementation of Text Compression using Adaptive Shannon-Fano Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (February 29, 2020): 3984–90. https://doi.org/10.35940/ijeat.C6383.029320.

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This study aims to implement the Shannon-fano Adaptive data compression algorithm on characters as input data. This study also investigates the data compression ratio, which is the ratio between the number of data bits before and after compression. The resulting program is tested by using black-box testing, measuring the number of character variants and the number of types of characters to the compression ratio, and testing the objective truth with the Mean Square Error (MSE) method. The description of the characteristics of the application made is done by processing data in the form of a coll

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12

Nguyen, Vu H., Hien T. Nguyen, Hieu N. Duong, and Vaclav Snasel. "n-Gram-Based Text Compression." Computational Intelligence and Neuroscience 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/9483646.

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We propose an efficient method for compressing Vietnamese text usingn-gram dictionaries. It has a significant compression ratio in comparison with those of state-of-the-art methods on the same dataset. Given a text, first, the proposed method splits it inton-grams and then encodes them based onn-gram dictionaries. In the encoding phase, we use a sliding window with a size that ranges from bigram to five grams to obtain the best encoding stream. Eachn-gram is encoded by two to four bytes accordingly based on its correspondingn-gram dictionary. We collected 2.5 GB text corpus from some Vietnames

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13

Subada, Mhd Ali. "Comparisonal Analysis Of Even-Rodeh Algorithm Code And Fibonacci Code Algorithm For Text File Compression." Journal Basic Science and Technology 11, no. 1 (February 28, 2022): 1–7. http://dx.doi.org/10.35335/jbst.v11i1.1765.

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The growing requirement towards bigger storage space is the main cause of new compression techniques being developed. By doing compression, big chunks of data will be lowered in size to save storage space. In this research we will be using the Even-Rodeh Code and Fibonacci Code Algorithm, the performance will be benchmarked with bitrate, compression ratio, space saving, compression and decompression time in text files. Compression is done by reading strings on text files, and then the Even-Rodeh Code and Fibonacci Code Algorithm makes a string code and performs compression. The compression res

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14

Suneetha, D., D. Rathna Kishore, and P. Narendra Babu. "A Compression Algorithm for DNA Palindrome Compression Technique." ITM Web of Conferences 32 (2020): 03006. http://dx.doi.org/10.1051/itmconf/20203203006.

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Data Compression in Cryptography is one of the interesting research topic. The compression process reduces the amount of transferring data as well as storage space which in turn effects the usage of bandwidth. Further, when a plain text is converted to cipher text, the length of the cipher text becomes large. This adds up to tremendous information storing. It is extremely important to address the storage capacity issue along with the security issues of exponentially developing information. This problem can be resolved by compressing the ciphertext based on a some compression algorithm. In this

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15

Omer. "Arabic Short Text Compression." Journal of Computer Science 6, no. 1 (January 1, 2010): 24–28. http://dx.doi.org/10.3844/jcssp.2010.24.28.

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16

KAUFMAN, YAIR, and SHMUEL T. KLEIN. "SEMI-LOSSLESS TEXT COMPRESSION." International Journal of Foundations of Computer Science 16, no. 06 (December 2005): 1167–78. http://dx.doi.org/10.1142/s012905410500373x.

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A new notion, that of semi-lossless text compression, is introduced, and its applicability in various settings is investigated. First results suggest that it might be hard to exploit the additional redundancy of English texts, but the new methods could be useful in applications where the correct spelling is not important, such as in short emails, and the new notion raises some interesting research problems in several different areas of Computer Science.

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17

man, Suher, and Andysah Putera Utama Siahaan. "Huffman Text Compression Technique." International Journal of Computer Science and Engineering 3, no. 8 (August 25, 2016): 103–8. http://dx.doi.org/10.14445/23488387/ijcse-v3i8p124.

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18

Schmidhuber, J., and S. Heil. "Sequential neural text compression." IEEE Transactions on Neural Networks 7, no. 1 (1996): 142–46. http://dx.doi.org/10.1109/72.478398.

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19

Bell, Timothy, Ian H. Witten, and John G. Cleary. "Modeling for text compression." ACM Computing Surveys 21, no. 4 (December 1989): 557–91. http://dx.doi.org/10.1145/76894.76896.

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20

Brisaboa, Nieves, Antonio Fariña, Gonzalo Navarro, and José Paramá. "Dynamic lightweight text compression." ACM Transactions on Information Systems 28, no. 3 (June 2010): 1–32. http://dx.doi.org/10.1145/1777432.1777433.

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21

Moffat, Alistair. "Word-based text compression." Software: Practice and Experience 19, no. 2 (February 1989): 185–98. http://dx.doi.org/10.1002/spe.4380190207.

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22

Sessa, Maria I., and Andrea Pierro. "A text compression algorithm." Information Systems 15, no. 3 (January 1990): 391. http://dx.doi.org/10.1016/0306-4379(90)90009-e.

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23

Guan, Jiawei, Feng Zhang, Siqi Ma, Kuangyu Chen, Yihua Hu, Yuxing Chen, Anqun Pan, and Xiaoyong Du. "Homomorphic Compression: Making Text Processing on Compression Unlimited." Proceedings of the ACM on Management of Data 1, no. 4 (December 8, 2023): 1–28. http://dx.doi.org/10.1145/3626765.

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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

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24

Mishra, Amit Kumar. "Versatile Video Coding (VVC) Standard: Overview and Applications." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 2 (September 10, 2019): 975–81. http://dx.doi.org/10.17762/turcomat.v10i2.13578.

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Information security includes picture and video compression and encryption since compressed data is more secure than uncompressed imagery. Another point is that handling data of smaller sizes is simple. Therefore, efficient, secure, and simple data transport methods are created through effective data compression technology. Consequently, there are two different sorts of compression algorithm techniques: lossy compressions and lossless compressions. Any type of data format, including text, audio, video, and picture files, may leverage these technologies. In this procedure, the Least Significant

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25

Yoshii, Yuichi, and Naoyuki Ochiai. "PHYSIOLOGICAL RESPONSE OF PERIPHERAL NERVE TO REPETITIVE COMPRESSION: A RABBIT EXPERIMENTAL MODEL." Journal of Musculoskeletal Research 24, no. 02 (May 6, 2021): 2150008. http://dx.doi.org/10.1142/s0218957721500081.

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Objective: The objective of this study is to clarify the differences in physiological responses following the different modality of repetitive compressions versus continuous compression (CC) of the peripheral nerve. Methods: Thirty-two rabbits were used. Under anesthesia, four conditions of compression; CC, low-frequency release compression (LFR, one second release time every 30 s), high-frequency release compression (HFR, one second release time every 10 s), and high-frequency compression (HFC, one second compression time every 10 s) were each applied with 80[Formula: see text]mmHg of pressur

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26

Elmegreen, Bruce, Binquan Luan, Rodrigo Neumann Barros Ferreira, and Breanndan O’Conchuir. "A method for near-perfect gas separation in two interconnected streams." Journal of Applied Physics 133, no. 1 (January 7, 2023): 015001. http://dx.doi.org/10.1063/5.0127983.

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Gas separation such as CO[Formula: see text] from N[Formula: see text] in flue gas is an important step in reducing greenhouse gas emission. We discuss a separation method that moves mixed gas between two tracks at different pressures with ever-increasing purity at each stage along the tracks. Designs for adsorbents driven by volumetric pumps and membranes driven by a pressure drop are illustrated. For selectivity exceeding 10 in a three-stage system, the purity of the CO[Formula: see text] output is 97.8% with 99.75% recovered. For a six-stage example, the purity of CO[Formula: see text] is 9

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27

Ganjiani, M., R. Naghdabadi, and M. Asghari. "Analysis of Concrete Pressure Vessels in the Framework of Continuum Damage Mechanics." International Journal of Damage Mechanics 21, no. 6 (October 14, 2011): 843–70. http://dx.doi.org/10.1177/1056789511419692.

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In this article, a constitutive model in the framework of continuum damage mechanics is proposed to simulate the elastic behavior of concrete in tension and compression states. We assume two parts for Gibbs potential energy function: elastic and damage parts. In order to obtain the elastic-damage constitutive relation with the internal variables, two damage thermodynamic release rates in tension and compression derived from the elastic part of Gibbs potential energy are introduced. Also, two anisotropic damage tensors (tension and compression) are defined which characterize the tensile and com

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Sharath, R., Sinha Shivani, B.I Supreeth, Hebbar B.C. Varun, and B. Santhosh. "Distributed Data Compression using Cloud Computing Approach." Journal of Optical Communication Electronics 5, no. 2 (May 1, 2019): 5–10. https://doi.org/10.5281/zenodo.2656008.

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<em>Storage and data trafficking have grown a great deal over the past decade. Therefore, there is a need to reduce the size of the data for better speed and proper utilization of the bandwidth. There are compressions that happen online as well as some happen statically. The online based compression is in greater demand, since an online platform is much easier and faster. Static methods have a requisition of the entire file to be present at the time of transmission, while online compressions don’t. Compressions make use of an algorithm to effectively shrink the data. Depending upon the s

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Wijaya, Bayu Angga, Sarwando Siboro, Mahendra Brutu, and Yelita Kristiani Lase. "Application of Huffman Algorithm and Unary Codes for Text File Compression." SinkrOn 7, no. 3 (July 30, 2022): 1000–1007. http://dx.doi.org/10.33395/sinkron.v7i3.11567.

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Technique in carrying out data compression is an important point in technological developments. With compression in data in the form of text can include many uses, including for data transfer, copying and for backing up data. From its uses, this aspect is important for data security. There are many compression techniques on the data, including using huffman algorithms and unary code. One of its applications will be implemented on a text data that is widely used by digital actors in storing important data. The data must not be known by unauthorized parties in accessing the data. Therefore, huff

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30

Baidoo, Peter Kwaku. "Comparative Analysis of the Compression of Text Data Using Huffman, Arithmetic, Run-Length, and Lempel Ziv Welch Coding Algorithms." Journal of Advances in Mathematics and Computer Science 38, no. 9 (August 16, 2023): 144–56. http://dx.doi.org/10.9734/jamcs/2023/v38i91812.

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The purpose of the study was to compare the compression ratios of file size, file complexity, and time used in compressing each text file in the four selected compression algorithms on a given modern computer running Windows 7. The researcher used the Java programming language on the NetBeans development environment to create user-friendly user interfaces that displayed both the content being compressed and the output generated by the compression. A purposive sampling technique was used to select text files with varying complexities from the Google data store, as well as other text documents d

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31

Zhang, Junxuan, Zhengxue Cheng, Yan Zhao, Shihao Wang, Dajiang Zhou, Guo Lu, and Li Song. "L3TC: Leveraging RWKV for Learned Lossless Low-Complexity Text Compression." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (April 11, 2025): 13251–59. https://doi.org/10.1609/aaai.v39i12.33446.

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Learning-based probabilistic models can be combined with an entropy coder for data compression. However, due to the high complexity of learning-based models, their practical application as text compressors has been largely overlooked. To address this issue, our work focuses on a low-complexity design while maintaining compression performance. We introduce a novel Learned Lossless Low-complexity Text Compression method (L3TC). Specifically, we conduct extensive experiments demonstrating that RWKV models achieve the fastest decoding speed with a moderate compression ratio, making it the most sui

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32

S. J, Rexline, Robert L, and Trujilla Lobo.F. "Dictionary Based Text Filter for Lossless Text Compression." International Journal of Computer Trends and Technology 49, no. 3 (July 25, 2017): 143–49. http://dx.doi.org/10.14445/22312803/ijctt-v49p122.

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33

Amir Sultan, Duha. "Better Adaptive Text Compression Scheme." JOURNAL OF EDUCATION AND SCIENCE 27, no. 2 (March 1, 2018): 48–57. http://dx.doi.org/10.33899/edusj.2018.147575.

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34

Sailunaz, Kashfia, Mohammed Rokibul Alam Kotwal, and Mohammad Nurul Huda. "Data Compression Considering Text Files." International Journal of Computer Applications 90, no. 11 (March 26, 2014): 27–32. http://dx.doi.org/10.5120/15765-4456.

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35

S. Bhadade, Umesh, and A. I. Trivedi. "Lossless Text Compression using Dictionaries." International Journal of Computer Applications 13, no. 8 (January 12, 2011): 27–34. http://dx.doi.org/10.5120/1799-1767.

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36

Sangwan, Nigam. "Text Encryption with Huffman Compression." International Journal of Computer Applications 54, no. 6 (September 25, 2012): 29–32. http://dx.doi.org/10.5120/8572-2307.

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37

Bell, T. "Better OPM/L Text Compression." IEEE Transactions on Communications 34, no. 12 (December 1986): 1176–82. http://dx.doi.org/10.1109/tcom.1986.1096485.

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38

Brisaboa, Nieves R., Antonio Fariña, Gonzalo Navarro, and José R. Paramá. "Lightweight natural language text compression." Information Retrieval 10, no. 1 (September 9, 2006): 1–33. http://dx.doi.org/10.1007/s10791-006-9001-9.

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39

Gillman, Peter. "Data handling and text compression." Journal of Information Science 18, no. 2 (April 1992): 105–10. http://dx.doi.org/10.1177/016555159201800204.

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40

Platoš, Jan, Václav Snášel, and Eyas El-Qawasmeh. "Compression of small text files." Advanced Engineering Informatics 22, no. 3 (July 2008): 410–17. http://dx.doi.org/10.1016/j.aei.2008.05.001.

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41

Vines, Phil, and Justin Zobel. "Compression techniques for Chinese text." Software: Practice and Experience 28, no. 12 (October 1998): 1299–314. http://dx.doi.org/10.1002/(sici)1097-024x(1998100)28:12<1299::aid-spe203>3.0.co;2-e.

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42

Raita, Timo, and Jukka Teuhola. "Predictive encoding in text compression." Information Processing & Management 25, no. 2 (January 1989): 151–60. http://dx.doi.org/10.1016/0306-4573(89)90003-4.

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43

Al-Fedaghi, Sabah S., and Humoud B. Al-Sadoun. "Morphological compression of Arabic text." Information Processing & Management 26, no. 2 (January 1990): 303–16. http://dx.doi.org/10.1016/0306-4573(90)90033-x.

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44

Nguyen, K. "Comment: Text compression as rule-based pattern recognition; Text compression using rule based encoder." Electronics Letters 31, no. 9 (1995): 701. http://dx.doi.org/10.1049/el:19950510.

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45

Abu Hilal, Tariq, Hasan Abu Hilal, and Ala Abu Hilal. "Multistage Arabic and Turkish Text Compression via Characters Encoding and 7-Zip." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 01 (March 1, 2021): 11–15. http://dx.doi.org/10.5383/juspn.15.01.002.

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Turkish lossless text compression was proposed by converting the character’s from UTF-8 to ANSI system for space-preserving. Likewise, we present a decoding method that transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, some of the Turkish alphabets are being stored in 2 bytes size. All that space comes at a price. The developed sequential encoding technique will reduce the size of the text file up to 9%. Moreover, the Turkish encoded text will retain its original form after decoding. According to our proposal, it is considered as a lossless te

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46

Fenwick, Peter. "Symbol Ranking Text Compression with Shannon Recodings." JUCS - Journal of Universal Computer Science 3, no. (2) (February 28, 1997): 70–85. https://doi.org/10.3217/jucs-003-02-0070.

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In his work on the information content of English text in 1951, Shannon described a method of recoding the input text, a technique which has apparently lain dormant for the ensuing 45 years. Whereas traditional compressors exploit symbol frequencies and symbol contexts, Shannon's method adds the concept of "symbol ranking", as in `the next symbol is the one third most likely in the present context'. While some other recent compressors can be explained in terms of symbol ranking, few make explicit reference to the concept. This report describes an implementation of Shannon's method and shows th

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47

Kadhim, Doaa J., Mahmood F. Mosleh, and Faeza A. Abed. "Exploring Text Data Compression: A Comparative Study of Adaptive Huffman and LZW Approaches." BIO Web of Conferences 97 (2024): 00035. http://dx.doi.org/10.1051/bioconf/20249700035.

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Data compression is a critical procedure in computer science that aims to minimize the size of data files while maintaining their vital information. It is extensively utilized in Numerous applications, including communication, data storage, and multimedia transmission. In this work, we investigated the results of compressing four different text files with Lempel-Ziv-Welch compression techniques and Adaptive Huffman coding. The experiment used four text files: Arabic and English paragraphs and repeated Arabic and English characters. We measured Bit-rate, Compression Time, and Decompression Time

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48

Perepelitsyn, Artem, Alona Chepelevych та Andrii Litvinov. "Аналіз форматів архівів та їх програмної реалізації для стиснення текстових файлів". Aerospace Technic and Technology, № 6 (21 листопада 2024): 103–11. https://doi.org/10.32620/aktt.2024.6.10.

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The subject of study in this article and research is widely used archive formats for file compression, features of their implementation, text compression rates, and the time required for existing programs for different platforms. The goal of the work is to simplify the process of choosing an archive format and program solutions for working with it for compressing text files, with taking into account time requirements, compression ratio, and opensource. The task is to perform an analysis of existing technologies and tools involved in the data archiving process, to analyze archive formats that a

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49

Ignatoski, Matea, Jonatan Lerga, Ljubiša Stanković, and Miloš Daković. "Comparison of Entropy and Dictionary Based Text Compression in English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian." Mathematics 8, no. 7 (July 1, 2020): 1059. http://dx.doi.org/10.3390/math8071059.

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The rapid growth in the amount of data in the digital world leads to the need for data compression, and so forth, reducing the number of bits needed to represent a text file, an image, audio, or video content. Compressing data saves storage capacity and speeds up data transmission. In this paper, we focus on the text compression and provide a comparison of algorithms (in particular, entropy-based arithmetic and dictionary-based Lempel–Ziv–Welch (LZW) methods) for text compression in different languages (Croatian, Finnish, Hungarian, Czech, Italian, French, German, and English). The main goal i

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50

Maharjan, Shyam, Sujan Poudel, and Dipesh Tandukar. "A Comparative Study of Text-Based Lossless Compression." American Journal of Smart Technology and Solutions 3, no. 2 (September 24, 2024): 34–39. http://dx.doi.org/10.54536/ajsts.v3i2.3566.

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Lossless data compression is a critical technique used to reduce file sizes without any loss of information during the encoding and decoding processes. This study presents a comparative analysis of two widely-used lossless compression algorithms: Huffman Encoding and Lempel-Ziv-Welch (LZW). The primary objective is to evaluate the performance of these algorithms in terms of compression ratio, compression time, decompression time, and space savings. The analysis was conducted on 100 files of varying sizes. The results demonstrate that the LZW algorithm outperforms Huffman Encoding, offering sup

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