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Journal articles on the topic 'Byte'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 March 2023

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1

Tyagi, Vinod, and Ambika Tyagi. "Byte protecting perfect burst code." Discrete Mathematics, Algorithms and Applications 09, no. 04 (August 2017): 1750051. http://dx.doi.org/10.1142/s1793830917500513.

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Byte correcting perfect codes are developed to correct burst errors within bytes. If a code is byte correcting code and we say that the code is [Formula: see text]-burst correcting, meaning that it corrects a single burst of length [Formula: see text] or less within a byte. A byte correcting code is such that if [Formula: see text]; [Formula: see text] denote the set of syndromes obtained from the [Formula: see text]th-byte of the parity check matrix [Formula: see text] and [Formula: see text]; [Formula: see text] denote the set of syndromes obtained from the [Formula: see text]th-byte of the parity check matrix [Formula: see text] then [Formula: see text]. In an [Formula: see text] code, if there are [Formula: see text] bytes of size [Formula: see text], then [Formula: see text]. Byte correcting codes are preferred where information stored in all bytes are equally important. But there are cases where some parts of the message are more important than other parts of the message, for example, if we have to transmit a message on a border location “ Shift Battalion (Bn) from Location A to Location B” then, we will focus more on the information shift, location [Formula: see text] and location [Formula: see text] i.e., bytes containing these information will be more important than others. In this situation, it is needed that during transmission these bytes should have no possibility of error. In other words, these bytes should be protected absolutely against any error. Keeping this in mind, we study burst error correcting capabilities of byte oriented codes in terms of byte protection level of each byte. If there is a byte error pattern of length [Formula: see text] in the transmission then all those bytes of the received pattern will be decoded correctly whose burst protection level is [Formula: see text] or more even though the code word may be decoded wrongly. Taking the code length [Formula: see text] to be divided into [Formula: see text] bytes with burst protection level of the [Formula: see text]th-byte as [Formula: see text]; [Formula: see text]; [Formula: see text], we construct linear codes that we call byte protecting burst (BPB) codes and investigate their byte protecting capabilities in this paper.

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2

Tyagi, Vinod, and Tarun Lata. "bi-Byte correcting non-binary perfect codes." Discrete Mathematics, Algorithms and Applications 11, no. 02 (April 2019): 1950018. http://dx.doi.org/10.1142/s1793830919500186.

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Etzion [T. Etzion, Perfect byte correcting codes, IEEE Trans. Inform. Theory 44 (1998) 3140–3146.] has classified byte error correcting binary codes into five different categories with respect to the size of the byte. If a code is partitioned into [Formula: see text] equal byte of size say [Formula: see text] then [Formula: see text] or the size of bytes is [Formula: see text]. Alternatively if bytes are of different size say, [Formula: see text] then [Formula: see text]. The result was further modified by Tyagi and Sethi [V. Tyagi and A. Sethi, [Formula: see text]-Byte correcting perfect codes, Asian-Eur. J. Math. 7(1) (2014) 1–8.] and the classification of bytes was given with respect to size of the byte as well as length of the burst. We call such codes as [Formula: see text]-byte correcting perfect codes. Our aim in this paper is to find the possibilities for the existence of [Formula: see text]-byte correcting non-binary perfect codes.

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3

Tyagi, Vinod, and Amita Sethi. "bi-BYTE CORRECTING PERFECT CODES." Asian-European Journal of Mathematics 07, no. 01 (March 2014): 1350045. http://dx.doi.org/10.1142/s1793557113500459.

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In this correspondence, we show how to generate bi-byte correcting perfect codes, i > 0 organized in m bytes of size β, n = mβ, and present necessary and sufficient conditions for their existence. By a bi-byte correcting perfect codes we mean a code that corrects burst of size b1 in the first byte, burst of length b2 in the second byte and so on, burst of length bm in the mth byte.

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4

JURAYEVA, Nazokat. "OBSERVATION OF THE POETIC UNITY OF THE VERSE IN THE POETRY OF MUHAMMAD RIZA OGAKHI." Art and Design: Social Science 02, no. 01 (February 1, 2022): 9–12. http://dx.doi.org/10.37547/ssa-v2-i1-4.

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This article presents scientific and analytical ideas on the definition of types in Uzbek literature in terms of the structure of the byte and its function in the structure of the poetic work. Also, in our literature on the relationship of bytes and genres, historical sources have been studied and the correct theoretical foundations have been formed. The level of application, types, and functions of genres such as chiston, the problem is analyzed. This article examines in detail the use of the poetic unity of the byte in the poetry of Muhammad Rizo Ogakhi, a unique representative of Uzbek classical poetry, in the genres created by various poets. that the development of meaning and word movement between verses (tadmin) later led to the development of poetic thinking in the byte, meaning that the bytes, although independent in meaning and lexical expression, the fact that they always have a relative logical connection with each other is shown in the example of Ogakhi’s work. It also explores the relationship between bytes and genres, historical sources, and forms the correct theoretical foundations. Such a relative interrelationship between the bytes serves to ensure the development of poetic imagery, the reaction to the existence of artistic determinism between byte verses in Eastern poetry through scientific considerations and proved by examples that it is the leading law of byte creation for all poets. based on a combination of weight, word, and rhyme. The combination of words and weights in each byte is cedar, hash, aruz in the byte; The rhyme serves not only the function of uniting the verses by creating a melody but also serves to form the content of the byte and to express the idea as a whole.

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5

Sonwalkar, Prasun. "Byte by byte." Journalism: Theory, Practice & Criticism 10, no. 3 (April 28, 2009): 378–80. http://dx.doi.org/10.1177/1464884909102580.

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6

Fox, Robert. "Culture byte by byte." OCLC Systems & Services: International digital library perspectives 24, no. 4 (October 31, 2008): 192–98. http://dx.doi.org/10.1108/10650750810914193.

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7

Xue, Linting, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, and Colin Raffel. "ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models." Transactions of the Association for Computational Linguistics 10 (2022): 291–306. http://dx.doi.org/10.1162/tacl_a_00461.

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Abstract Most widely used pre-trained language models operate on sequences of tokens corresponding to word or subword units. By comparison, token-free models that operate directly on raw text (bytes or characters) have many benefits: They can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Because byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments.1

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8

Liang, Zheng You, Yi Xuan Tang, and Shuai Chang. "Smallest Cache Utility Algorithm Based on Popularity." Advanced Materials Research 171-172 (December 2010): 358–63. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.358.

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Streaming media is an important research direction in internet applications. In this paper, A Smallest Cache Utility algorithm based on Popularity in future (SCU-PK) is proposed. It comprehensively considered the merit of media time popularity and the smallest cache utility algorithm, using the entire file byte usefulness instead of the file segment bytes usefulness, so that a more effective byte usefulness calculation method and a more rational file segment popularity calculation method was designed and implement. The experiments show that the SCU-PK algorithm has better performance in increasing cache space utilization rate and byte hit ratio than LRU, LFU and SCU-PFUT in stream media proxy cache.

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9

Koppel, Tom. "Reading Books Byte by Byte." Scientific American 264, no. 6 (June 1991): 116–17. http://dx.doi.org/10.1038/scientificamerican0691-116b.

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10

Marvin, Kerry A. "General Psychology Byte by Byte." Contemporary Psychology: A Journal of Reviews 41, no. 6 (June 1996): 569–70. http://dx.doi.org/10.1037/002957.

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Pristiwanto, Pristiwanto, and Abdul Halim Hasugian. "Steganography Formation by utilizing Enhanced Least Significant Bit Algorithm." Jurnal Info Sains : Informatika dan Sains 11, no. 1 (March 1, 2021): 19–22. http://dx.doi.org/10.54209/infosains.v11i1.38.

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Steganography is the science and art of hiding secret messages in other messages so that the existence of those messages cannot be known. The letter sent does not attract attention with steganography, and the container media does not arouse suspicion. Steganography requires two properties, namely container media and secret messages. The LSB method (least significant bit)is the simplest and easiest steganography method to implement. An example of implementing this method is to use a digital image as a cover text. Each pixel in the image is 1 (one) to 3 (three) bytes in size. Pda bit arrangement in a byte ( 1 byte = 8 bits), there are the most significant bits ( MSB) and the least significant bits (LSB). For example, on 11010010 bytes, the first bit from the right is the MSB bit, and the last bit from the right is the LSB bit. The matching bit is replaced with the message bit is the LSB bit, because the modification only changes the byte value to one higher or one lower than the previous value

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12

Wang, Changhan, Kyunghyun Cho, and Jiatao Gu. "Neural Machine Translation with Byte-Level Subwords." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9154–60. http://dx.doi.org/10.1609/aaai.v34i05.6451.

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Almost all existing machine translation models are built on top of character-based vocabularies: characters, subwords or words. Rare characters from noisy text or character-rich languages such as Japanese and Chinese however can unnecessarily take up vocabulary slots and limit its compactness. Representing text at the level of bytes and using the 256 byte set as vocabulary is a potential solution to this issue. High computational cost has however prevented it from being widely deployed or used in practice. In this paper, we investigate byte-level subwords, specifically byte-level BPE (BBPE), which is compacter than character vocabulary and has no out-of-vocabulary tokens, but is more efficient than using pure bytes only is. We claim that contextualizing BBPE embeddings is necessary, which can be implemented by a convolutional or recurrent layer. Our experiments show that BBPE has comparable performance to BPE while its size is only 1/8 of that for BPE. In the multilingual setting, BBPE maximizes vocabulary sharing across many languages and achieves better translation quality. Moreover, we show that BBPE enables transferring models between languages with non-overlapping character sets.

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Zavadskyi, I. O. "Pattern matching by the terms of cache memory limitations." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 3 (2019): 56–59. http://dx.doi.org/10.17721/1812-5409.2019/3.8.

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A few known techniques of exact pattern matching, such as 2-byte read, skip loop, and sliding search windows, are improved and applied to pattern matching algorithms, performing over 256-ary alphabets. Instead of 2-byte read, we offer “1.5-byte read”, i.e. reading more than 8 but less than 16 bits of two sequential bytes of a text at each iteration of a search loop. This allows us to fit the search table into L1 cache memory, which significantly improves the algorithm performance. Also, we introduce the so-called double skip loop instead of single one, resolve problems caused by endianness of a machine, and adopt the sliding windows technique to our algorithms. The experimental results averaged over 500 runs of algorithms on 40 different computers show that our algorithms outperform all other tested methods for all tested pattern lengths.

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14

Morris, R. J. "Back Byte." History and Computing 6, no. 3 (October 1994): 192. http://dx.doi.org/10.3366/hac.1994.6.3.192.

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Burt, Janet. "Back Byte." History and Computing 7, no. 2 (June 1995): 123–24. http://dx.doi.org/10.3366/hac.1995.7.2.123.

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Nicol, Alexandra. "Back Byte." History and Computing 8, no. 3 (October 1996): 187–90. http://dx.doi.org/10.3366/hac.1996.8.3.187.

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17

Yu, P. P. "Byte Back." Journal of Oncology Practice 2, no. 5 (September 1, 2006): 246. http://dx.doi.org/10.1200/jop.2.5.246.

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18

Yu, Peter Paul. "Byte Back." Journal of Oncology Practice 2, no. 5 (September 2006): 246. http://dx.doi.org/10.1200/jop.2006.2.5.246.

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19

Pollitt, Mark M. "Byte/Counterbyte." Journal of Digital Forensic Practice 1, no. 4 (June 22, 2007): 283–84. http://dx.doi.org/10.1080/15567280701418015.

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20

Hong, Feng, Jianquan Zhang, Shigui Qi, and Zheng Li. "PCM-2R: Accelerating MLC PCM Writes via Data Reshaping and Remapping." Mobile Information Systems 2022 (July 16, 2022): 1–19. http://dx.doi.org/10.1155/2022/9552517.

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Multilevel cell (MLC) phase change memory (PCM) shows great potential in terms of capacity and cost compared with single-level cell (SLC) PCM by storing multiple bits in one physical PCM cell. However, poor write performance is a huge challenge for MLC PCM. In general, write latency of MLC PCM is 10 to 100X longer compared with DRAM technology. Considerable write latency greatly degrades the overall system performance and restricts the application of MLC PCM. Actually, several chips compose a memory DIMM to match the wide interface of data bus. The data of a write request, i.e., a cache line block, are distributed to multiple PCM chips. As a result, the write service time is determined by the chips with the most data amount. Conventional PCM write schemes do not care for the modified-byte distribution among PCM chips and it just waits for the completion of the chip with the most amount of data. However, it is observed that (1) the conventional PCM write scheme suffers from unbalanced modified-byte distribution that some PCM chips bear too many modified bytes while some chips are kept idle for long times. (2) The modified-byte distribution shows some unique patterns that some bytes are changed more frequently compared with others. (3) MLC PCM shows significant asymmetry considering only MSB or LSB transitions. Based on these observations, in order to solve the poor write problem of PCM, this article presents a novel PCM write scheme called PCM-2R. The key ideas behind our proposed scheme are to reshape the data to evenly distribute the cache line blocks among all chips based on their modified-byte distribution pattern to avoid unbalanced distribution and then remap modified bytes to fast region after decoupling MLC PCM cells considering the state transition asymmetries. The evaluation results show that PCM-2R achieves 51% read latency reduction, 37% write latency reduction, 1.9X IPC improvement, 41% running time reduction, 2.2X throughout improvement, and 52% energy reduction compared with the baseline. Moreover, compared with the state-of-the-art write schemes, PCM-2R achieves 0.2X more IPC improvement and 0.2X throughout improvement.

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21

Tyagi, Vinod, and Tarun Lata. "Restricted bi-byte correcting non-binary optimal codes." Discrete Mathematics, Algorithms and Applications 11, no. 02 (April 2019): 1950019. http://dx.doi.org/10.1142/s1793830919500198.

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In this paper, we have introduced the idea of restricted burst, which is the modification of the classical definition of burst. We show the existence of [Formula: see text]-restricted burst correcting non-binary optimal codes for byte-oriented memories with different possibilities of size of the bytes [Formula: see text] and length of burst [Formula: see text] for [Formula: see text].

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Gulmuradovna, Juraeva Nazokat. "The Relationship of Rubai and Bytes in Classical Literature." International Journal of Multicultural and Multireligious Understanding 7, no. 7 (August 8, 2020): 344. http://dx.doi.org/10.18415/ijmmu.v7i7.1857.

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No matter how much the phenomena of artistic creation are studied, there are still so many unsolved mysteries and strange problems that can be seen only in the example of the poetics of poetic unity, which is called a byte in classical poetry. Because often when we know the name of each poetic event and its general properties, we think in all places within the framework of these general concepts, views. However, the study of issues such as the formation of a small poetic unit, its requirements, its peculiarities, historical development and destiny requires a great deal of strength, knowledge and effort. One of the genres in Uzbek classical poetry, which consists of four lines based on bytes, is rubai. This genre consists of two bytes. The rubai tarona, that is, the rubai with four rhymes, has a structure of two musarra bytes, while the ordinary rubai consists of one musarra and one Aryan byte. the second criterion is that the Hajj sea is written in the ahram and akhrab trees, each consisting of twelve branches. Hence, in classical poetry, the rubai must be written in twenty-four weights.

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Sun, Shaofei, Hongxin Zhang, Liang Dong, Xiaotong Cui, Weijun Cheng, and Muhammad Saad Khan. "A Novel Multi-Objective Electromagnetic Analysis Based on Genetic Algorithm." Sensors 19, no. 24 (December 15, 2019): 5542. http://dx.doi.org/10.3390/s19245542.

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Correlation electromagnetic analysis (CEMA) is a method prevalent in side-channel analysis of cryptographic devices. Its success mostly depends on the quality of electromagnetic signals acquired from the devices. In the past, only one byte of the key was analyzed and other bytes were regarded as noise. Apparently, other bytes’ useful information was wasted, which may increase the difficulty of recovering the key. Multi-objective optimization is a good way to solve the problem of a single byte of the key. In this work, we applied multi-objective optimization to correlation electromagnetic analysis taking all bytes of the key into consideration. Combining the advantages of multi-objective optimization and genetic algorithm, we put forward a novel multi-objective electromagnetic analysis based on a genetic algorithm to take full advantage of information when recovering the key. Experiments with an Advanced Encryption Standard (AES) cryptographic algorithm on a Sakura-G board demonstrate the efficiency of our method in practice. The experimental results show that our method reduces the number of traces required in correlation electromagnetic analysis. It achieved approximately 42.72% improvement for the corresponding case compared with CEMA.

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24

Zhou, Hai Yan. "A Pattern Matching Algorithm for Double-Type Characters." Applied Mechanics and Materials 571-572 (June 2014): 461–64. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.461.

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A fast and efficient matching algorithm is proposed to address the issue on multi-pattern matching of double-byte string, for example Chinese characters, which has major difference with single-byte string matching algorithm. The algorithm capitalizes on double cross link data list and two finite prefix automata to match a double-byte character, so as to solve the storage expansion problems in which the double-byte cross data link table results. The method requires less storage in comparison with double-byte cross data link table, and has the same order of magnitude in efficiency as a single-byte cross-link table approach.

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25

Satman, Mehmet Hakan, and Emre Akadal. "Machine-coded genetic operators and their performances in floating-point genetic algorithms." International Journal of Advanced Mathematical Sciences 5, no. 1 (January 25, 2017): 8. http://dx.doi.org/10.14419/ijams.v5i1.7128.

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Machine-coded genetic algorithms (MCGAs) use the byte representation of floating-point numbers which are encoded in the computer memory. Use of the byte alphabet makes classical crossover operators directly applicable in the floating-point genetic algorithms. Since effect of the byte-based mutation operator depends on the location of the mutated byte, the byte-based mutation operator mimics the functionality of its binary counterpart. In this paper, we extend the MCGA by developing new type of byte-based genetic operators including a random mutation and a random dynamic mutation operator. We perform a simulation study to compare the performances of the byte-based operators with the classical FPGA operators using a set of test functions. The prepared software package, which is freely available for downloading, is used for the simulations. It is shown that the byte-based genetic search obtains precise results by carrying out the both exploration and exploitation tasks by discovering new fields of the search space and performing a local fine-tuning. It is also shown that the introduced byte-based operators improve the search capabilities of FPGAs by means of convergence rate and precision even if the decision variables are in larger domains.

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26

Kurnianto, Danny, Eka Wahyudi, and Muntaqo Alfin Amanaf. "Kinerja Jaringan ZigBee pada Topologi Star dan Tree dengan Interferensi Sinyal Wi-Fi Pada Sistem Presensi Mahasiswa." Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) 6, no. 1 (February 26, 2021): 19. http://dx.doi.org/10.33772/jfe.v6i1.15879.

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Sistem presensi mahasiswa memiliki arti yang penting bagi sebuah perguruan tinggi. Pemakaian jaringan wireline untuk sistem presensi mahasiswa memiliki kelemahan yang bisa diatasi dengan menggunakan jaringan wireless. ZigBee adalah salah satu standar komunikasi wireless yang dapat digunakan pada sistem presensi tersebut. Adanya jaringan Wi-Fi dapat menyebabkan gangguan kinerja pada jaringan ZigBee akibat interferensi sinyal. Pada penelitian ini akan dibahas unjuk kerja jaringan ZigBee dengan topologi star dan tree yang berjalan di dalam lingkungan yang memiliki jaringan Wi-Fi. Parameter kinerja yang diamati adalah nilai daya sinyal RSSI, throughput, packet loss dan waktu delay. Besarnya data yang dikirimkan dari end device ke coordinator sebesar 15 Byte dan 20 Byte. Hasil penelitian menunjukkan bahwa pada topologi star memiliki unjuk kerja yang lebih baik untuk nilai daya sinyal RSSI, throughput dan waktu delay. Topologi tree memiliki unjuk kerja yang lebih baik hanya pada nilai packet loss. Rerata nilai daya sinyal sebesar -69,19 dBm, rerata nilai throughput sebesar 10,22 kbps untuk data 15 byte dan 14,28 kbps untuk data 20 byte, rerata nilai waktu delay sebesar 28,9 ms untuk data 15 byte dan 38,6 ms untuk data 20 byte. Nilai packet loss sebesar 27% untuk data 15 byte dan 43% untuk data 20 byte.

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27

Ruzhentsev, Victor. "The conditions of provable security of block ciphers against truncated differential attack." Studia Scientiarum Mathematicarum Hungarica 52, no. 2 (June 2015): 176–84. http://dx.doi.org/10.1556/012.2015.52.2.1307.

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The modified method of estimation of the resistance of block ciphers to truncated byte differential attack is proposed. The previously known method estimate the truncated byte differential probability for Rijndael-like ciphers. In this paper we spread the sphere of application of that method on wider class of ciphers. The proposed method based on searching the most probable truncated byte differential characteristics and verification of sufficient conditions of effective byte differentials absence.

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Barman, Ludovic, Alexandre Dumur, Apostolos Pyrgelis, and Jean-Pierre Hubaux. "Every Byte Matters." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 2 (June 23, 2021): 1–45. http://dx.doi.org/10.1145/3463512.

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Wearable devices such as smartwatches, fitness trackers, and blood-pressure monitors process, store, and communicate sensitive and personal information related to the health, life-style, habits and interests of the wearer. This data is typically synchronized with a companion app running on a smartphone over a Bluetooth (Classic or Low Energy) connection. In this work, we investigate what can be inferred from the metadata (such as the packet timings and sizes) of encrypted Bluetooth communications between a wearable device and its connected smartphone. We show that a passive eavesdropper can use traffic-analysis attacks to accurately recognize (a) communicating devices, even without having access to the MAC address, (b) human actions (e.g., monitoring heart rate, exercising) performed on wearable devices ranging from fitness trackers to smartwatches, (c) the mere opening of specific applications on a Wear OS smartwatch (e.g., the opening of a medical app, which can immediately reveal a condition of the wearer), (d) fine-grained actions (e.g., recording an insulin injection) within a specific application that helps diabetic users to monitor their condition, and (e) the profile and habits of the wearer by continuously monitoring her traffic over an extended period. We run traffic-analysis attacks by collecting a dataset of Bluetooth communications concerning a diverse set of wearable devices, by designing features based on packet sizes and timings, and by using machine learning to classify the encrypted traffic to actions performed by the wearer. Then, we explore standard defense strategies against traffic-analysis attacks such as padding, delaying packets, or injecting dummy traffic. We show that these defenses do not provide sufficient protection against our attacks and introduce significant costs. Overall, our research highlights the need to rethink how applications exchange sensitive information over Bluetooth, to minimize unnecessary data exchanges, and to research and design new defenses against traffic-analysis tailored to the wearable setting.

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Tomei, Matthew, Shomit Das, Mohammad Seyedzadeh, Philip Bedoukian, Bradford Beckmann, Rakesh Kumar, and David Wood. "Byte-Select Compression." ACM Transactions on Architecture and Code Optimization 18, no. 4 (December 31, 2021): 1–27. http://dx.doi.org/10.1145/3462209.

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Cache-block compression is a highly effective technique for both reducing accesses to lower levels in the memory hierarchy (cache compression) and minimizing data transfers (link compression). While many effective cache-block compression algorithms have been proposed, the design of these algorithms is largely ad hoc and manual and relies on human recognition of patterns. In this article, we take an entirely different approach. We introduce a class of “byte-select” compression algorithms, as well as an automated methodology for generating compression algorithms in this class. We argue that, based on upper bounds within the class, the study of this class of byte-select algorithms has potential to yield algorithms with better performance than existing cache-block compression algorithms. The upper bound we establish on the compression ratio is 2X that of any existing algorithm. We then offer a generalized representation of a subset of byte-select compression algorithms and search through the resulting space guided by a set of training data traces. Using this automated process, we find efficient and effective algorithms for various hardware applications. We find that the resulting algorithms exploit novel patterns that can inform future algorithm designs. The generated byte-select algorithms are evaluated against a separate set of traces and evaluations show that Byte-Select has a 23% higher compression ratio on average. While no previous algorithm performs best for all our data sets which include CPU and GPU applications, our generated algorithms do. Using an automated hardware generator for these algorithms, we show that their decompression and compression latency is one and two cycles respectively, much lower than any existing algorithm with a competitive compression ratio.

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30

Hinchcliffe, J. "Bite to byte." British Dental Journal 200, no. 10 (May 2006): 595. http://dx.doi.org/10.1038/sj.bdj.4813672.

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31

BENNER, JANE. "Take a byte." Nursing 35, no. 6 (June 2005): 73. http://dx.doi.org/10.1097/00152193-200506000-00050.

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32

Maynard Smith, John. "Byte-sized evolution." Nature 355, no. 6363 (February 1992): 772–73. http://dx.doi.org/10.1038/355772a0.

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33

Baumann, Paul, and Silvia Santini. "Every Byte Counts." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, no. 2 (June 30, 2017): 1–29. http://dx.doi.org/10.1145/3090052.

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34

Fraser, Peter J. "CABIOS FIRST BYTE." Bioinformatics 1, no. 4 (1985): 219–24. http://dx.doi.org/10.1093/bioinformatics/1.4.219.

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Henderson, Roger. "Henderson byte: Multiferroics." Preview 2020, no. 205 (March 3, 2020): 30. http://dx.doi.org/10.1080/14432471.2020.1720675.

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Domanski, Grzegorz, Bogumil Konarzewski, Robert Kurjata, Janusz Marzec, Krzysztof Zaremba, Michal Dziewiecki, Marcin Ziembicki, and Andrzej Rychter. "The study of radiation damage of EPROM 2764 memory." Nuclear Technology and Radiation Protection 31, no. 3 (2016): 233–39. http://dx.doi.org/10.2298/ntrp1603233d.

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A simple statistical theory of radiation damage of semiconductor memory has been constructed. The radiation damage of EPROM memory has been investigated. The measured number of damaged bytes is significantly lower than the expected number resulting from the purely random distribution of the damaged bits. In this way it has been proven that there is a correlation between the failures of individual memory bits which are located in the same byte.

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Hu, Xinyi, and Yaqun Zhao. "Research on Plaintext Restoration of AES Based on Neural Network." Security and Communication Networks 2018 (November 18, 2018): 1–9. http://dx.doi.org/10.1155/2018/6868506.

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Known plaintext attack is a common attack method in cryptographic attack. For ciphertext, only known part of the plaintext but unknown key, how to restore the rest of the plaintext is an important part of the known plaintext attack. This paper uses backpropagation neural networks to perform cryptanalysis on AES in an attempt to restore plaintext. The results show that the neural network can restore the entire byte with a probability of more than 40%, restoring more than half of the plaintext bytes with a probability of more than 63% and restoring more than half of the bytes above 89%.

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H, Mahmudul, Sirajum M, Syed N.S, A. K.M. F.H, Shahina H, and Tasnim I.R. "Design and Development of an LNB based DVB-S receiver." International Journal of Engineering & Technology 7, no. 3.19 (September 7, 2018): 66. http://dx.doi.org/10.14419/ijet.v7i3.19.16989.

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This paper introduces an LNB-based DVB-S receiver with respect to sync byte detection. The basic configuration of DVB-S for decoding is the major focus of this work, where the main task is to decode the received signal from the satellite with a good SNR. Sync-byte detection is one of the challenges of receiver design. An algorithm is developed to overcome the problems associated with Sync-byte detection which can efficiently perform Sync-byte detection. After decoding the signal, the SNR is found to be 9.62 dB, which represents quiet a good signal. A channel list is identified in the final result.

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Radha, K., B. Thirumala Rao, Shaik Masthan Babu, K. Thirupathi Rao, V. Krishna Reddy, and P. Saikiran. "Service Level Agreements in Cloud Computing and Big Data." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 1 (February 1, 2015): 158. http://dx.doi.org/10.11591/ijece.v5i1.pp158-165.

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Now-a-days Most of the industries are having large volumes of data. Data has range of Tera bytes to Peta byte. Organizations are looking to handle the growth of data. Enterprises are using cloud deployments to address the big data and analytics with respect to the interaction between cloud and big data. This paper presents big data issues and research directions towards the ongoing work of processing of big data in the distributed environments.

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Sun, Shaofei, Hongxin Zhang, Xiaotong Cui, Liang Dong, Muhammad Saad Khan, and Xing Fang. "Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm." Applied Sciences 11, no. 2 (January 18, 2021): 839. http://dx.doi.org/10.3390/app11020839.

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This paper focuses on electromagnetic information security in communication systems. Classical correlation electromagnetic analysis (CEMA) is known as a powerful way to recover the cryptographic algorithm’s key. In the classical method, only one byte of the key is used while the other bytes are considered as noise, which not only reduces the efficiency but also is a waste of information. In order to take full advantage of useful information, multiple bytes of the key are used. We transform the key into a multidimensional form, and each byte of the key is considered as a dimension. The problem of the right key searching is transformed into the problem of optimizing correlation coefficients of key candidates. The particle swarm optimization (PSO) algorithm is particularly more suited to solve the optimization problems with high dimension and complex structure. In this paper, we applied the PSO algorithm into CEMA to solve multidimensional problems, and we also add a mutation operator to the optimization algorithm to improve the result. Here, we have proposed a multibyte correlation electromagnetic analysis based on particle swarm optimization. We verified our method on a universal test board that is designed for research and development on hardware security. We implemented the Advanced Encryption Standard (AES) cryptographic algorithm on the test board. Experimental results have shown that our method outperforms the classical method; it achieves approximately 13.72% improvement for the corresponding case.

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WANG, XING-YUAN, FENG CHEN, TIAN WANG, DAHAI XU, and YUTIAN MA. "ATTACK TO AN IMAGE ENCRYPTION BASED ON CHAOTIC LOGISTIC MAP." International Journal of Modern Physics B 27, no. 31 (November 28, 2013): 1350196. http://dx.doi.org/10.1142/s0217979213501968.

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This paper offers two different attacks on a freshly proposed image encryption based on chaotic logistic map. The cryptosystem under study first uses a secret key of 80-bit and employed two chaotic logistic maps. We derived the initial conditions of the logistic maps from using the secret key by providing different weights to all its bits. Additionally, in this paper eight different types of procedures are used to encrypt the pixels of an image in the proposed encryption process of which one of them will be used for a certain pixel which is determined by the product of the logistic map. The secret key is revised after encrypting each block which consisted of 16 pixels of the image. The encrypting process have weakness, worst of which is that every byte of plaintext is independent when substituted, so the cipher text of the byte will not change even the other bytes have changed. As a result of weakness, a chosen plaintext attack and a chosen cipher text attack can be completed without any knowledge of the key value to recuperate the ciphered image.

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Johnson, Barbara A., R. Neff, and D. Rehm. "First Byte in Spanish." Modern Language Journal 69, no. 1 (1985): 107. http://dx.doi.org/10.2307/327918.

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Henderson, Roger. "Henderson byte: Anders Celsius." Preview 2021, no. 213 (July 4, 2021): 21. http://dx.doi.org/10.1080/14432471.2021.1963595.

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Etzion, T. "Perfect byte-correcting codes." IEEE Transactions on Information Theory 44, no. 7 (1998): 3140–46. http://dx.doi.org/10.1109/18.737544.

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Cheshire, Stuart, and Mary Baker. "Consistent overhead byte stuffing." ACM SIGCOMM Computer Communication Review 27, no. 4 (October 1997): 209–20. http://dx.doi.org/10.1145/263109.263168.

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Allman, Mark. "TCP byte counting refinements." ACM SIGCOMM Computer Communication Review 29, no. 3 (July 1999): 14–22. http://dx.doi.org/10.1145/505724.505727.

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Cheshire, S., and M. Baker. "Consistent overhead byte stuffing." IEEE/ACM Transactions on Networking 7, no. 2 (April 1999): 159–72. http://dx.doi.org/10.1109/90.769765.

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Henderson, Roger. "Henderson byte: Georg Ohm." Preview 2019, no. 200 (May 4, 2019): 16. http://dx.doi.org/10.1080/14432471.2019.1623158.

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Henderson, Roger. "Henderson byte: Michael Faraday." Preview 2019, no. 202 (September 3, 2019): 20. http://dx.doi.org/10.1080/14432471.2019.1673281.

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SNOB, THEO D. "Bark Worse Than Byte." New Library World 87, no. 2 (February 1986): 24. http://dx.doi.org/10.1108/eb038667.

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