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Data mining is a way to extract knowledge out of generally large data sets; in other words, it is an approach to discover hidden relationships among data by using artificial intelligence methods. This has made it an important field in research. Law is one of the most important fields for applying data mining given the plethora of data from law cases stenographer data to lawsuit data. Text summarization in NLP (Natural Language Processing) is the process of summarizing the information on large texts for quicker consumption it is an extremely useful technique in NLP. Identifying law case characteristics is the first step for developing further analysis. An approach based on data mining techniques is discussed in this paper to extract important entities from law cases which are written in plain text. The process will involve different Artificial intelligence techniques including clustering or other unsupervised or supervised learning techniques.

Current systems for predicting prosodic prominence and boundaries in texts focus on syntax/semantic-based automatic decoding of sentences that need to be annotated syntactically (Atterer & Klein 2002; Windmann et al. 2011). However, to date, there is no phonetically validated replicable system for manually coding prosodic boundaries and syllable prominence in longer sentences or texts. Based on work in the fields of metrical phonology (Liberman & Prince 1977), phrase formation (Hayes 1989) and existing pause coding systems (Gee and Grosjean 1983), we developed a manual for coding prosodic boundaries (with 6 degrees of juncture) and syllable prominence (8 degrees). Three independent annotators applied the coding system to the beginning pages of four German novels and to four short stories (20 058 syllables, Fleiss kappa .82). For the phonetic validation, eight professional speakers read the excerpts of the novels aloud. We annotated the speech signal automatically with MAUS (Schiel 1999). Using PRAAT (Boersma & Weenink 2019), we extracted pitch, duration, and intensity for each syllable, as well as several phonetic parameters for pauses, and compared all measures obtained to the theoretically predicted levels of syllable prominence and prosodic boundary strength. The validation with the speech signal shows that our annotation system reliably predicts syllable prominence and prosodic boundaries. Since our annotation works with plain text, there are many potential applications of the coding system, covering research on prose rhythm, synthetic speech and (psycho)linguistic research on prosody.

With the rise in use of internet in various fields like education, military, government sector, banking, the security and privacy of the info has been the foremost concern. As in today's era, most of the handling of data and transactions are done online. When the data is transferred from the one end of sender to the other end of receiver online, it's eavesdropped by an intruder and thus could be a threat to the secrecy or confidentiality of the info. The hottest technique that protects the confidentiality of the data is cryptography which converts the plain text into scrambled form which is unreadable. Then the receiver applies a reverse mechanism to decrypt the unreadable data to readable form. This mechanism is known as encryption-decryption process or cryptography. Cryptography can be both symmetric and asymmetric. Here the authors discuss symmetric and asymmetric algorithms.

In this paper, we present a novel multi-threaded parallel permutation and channel-combined diffusion for image encryption which is independent of plain text. In our proposed method, the coupled map lattice is used to generate the key sequences for multi-thread permutation and diffusion. Then intra- and inter-thread permutations are achieved using multi-threading in combination with the tent mapping. For the subsequent diffusion, this paper introduces a method based on channel-combined diffusing which simultaneously diffuses three channels. Experimental results indicate a high encryption performance with the capability of effectively resisting the known plain text and differential attacks. Our proposed method also has a lower computational complexity which enables its applicability in practical scenarios.

Excited-state quantum chemical calculations typically report excitation energies and oscillator strengths, ƒ, for each electronic transition. On the other hand, UV-visible spectrophotometric experiments report energy-dependent molar extinction/attenuation coefficients, ε(v), that determine the absorption band line shapes. ε(v) and ƒ are related, but this relation is complicated by various broadening and solvation effects. We fit and integrated experimental UV-visible spectra to obtain ƒexp values for absorption bands and estimated the uncertainty in the fitting. We derived 164 ƒexp values from 100 organic molecules ranging in size from 6-34 atoms. The corresponding computed oscillator strengths (ƒcomp) were obtained with time-dependent density functional theory and a polarizable continuum solvent model. By expressing experimental and computed absorption strengths using a common quantity, we directly compared ƒcomp and ƒexp. While ƒcomp and ƒexp are well correlated (linear regression R2=0. 921), ƒcomp in most cases significantly overestimates ƒexp (regression slope=1.34). The agreement between absolute ƒcomp and ƒexp values was substantially improved by accounting for a solvent refractive index factor, as suggested in some derivations in the literature. The 100 digitized UV-visible spectra are included as plain text files in the supporting information to aid in benchmarking computational or machine-learning approaches that aim to simulate realistic UV-visible absorption spectra.

Stage of networking is quintessential task in which security comes into play. Securing these networks which contains confidential digital data that needs to secured will be the agenda of cryptography. Many cryptographic algorithms increment their strengths over parameters like key size, increasing the rounds of iteration and finally using confusion box as S-box as it has best robustness. So, this paper mainly focusses over securing digital data with the help of S-box function over Data Encryption Standard (DES) algorithm. For this, a plain text and key will be given to this DES as it extracts 8x8(64) bit characters from the key and converting them into its corresponding ASCII value and are concatenating to form an 8 value by taking mod16. These will give to 8 S-box in order to generate its corresponding output to make even more secure and also shows dynamic DES gives much result than other crypto methods. The evaluation of this integrated s-box and DES shows much fruitful results over factors like non-linearity, Avalanche criterion, Balance, Robustness to linear cryptanalysis, Robustness to differential cryptanalysis.

Data confidentiality and resource's limitation issues are challenges for the Internet of Things. To implement good security on IoT systems, cryptography can do it, but it needs an effective encryption algorithm that does not require a lot of resources. The purpose of this study is to secure an IoT system by implementing an algorithm that is successful in maintaining the confidentiality of data transmitted. This research uses an experimental approach, by creating an IoT system for agriculture and adding an encryption algorithm. The IoT system uses NodeMCU as a microcontroller. NodeMCU is a microcontroller with small resources so it needs an efficient algorithm to be implemented in it. One algorithm that has good performance in a desktop computing environment is the Advance Encryption Standard (AES) algorithm. The algorithm is tested in an IoT computing environment with a data exchange architecture using an REST (Representational State Transfer) web service, resulting in an IoT system for agriculture with cryptographic implementations in it. In the tests carried out, the encryption process of 128 and 256 bits of plain text took 266.31 and 274.31 microseconds, while the memory used was 16% and 17% of the total memory, respectively. This shows the encryption time is fast, and the memory usage is relatively small.

Kmer-based methods are widely used in bioinformatics, which raises the question of what is the smallest practically usable representation (i.e. plain text) of a set of kmers. We propose a polynomial algorithm computing a minimum such representation (which was previously posed as a potentially NP-hard open problem), as well as an efficient near-minimum greedy heuristic. When compressing genomes of large model organisms, read sets thereof or bacterial pangenomes, with only a minor runtime increase, we decrease the size of the representation by up to 60% over unitigs and 27% over previous work. Additionally, the number of strings is decreased by up to 97% over unitigs and 91% over previous work. Finally we show that a small representation has advantages in downstream applications, as it speeds up queries on the popular kmer indexing tool Bifrost by 1.66x over unitigs and 1.29x over previous work.

Penggunaan Quick Response (QR) Code untuk berbagi ataupun menyimpan data semakin marak digunakan. Kode QR dapat dengan cepat memberikan informasi baik kontak, plain text, URL ataupun data lainnya hanya dengan memindai QR menggunakan kamera smartphone. Meningkatnya penggunaan kode QR harus diiringi juga dengan tingkat keamanan dalam proteksi data. Jurnal ini membahas beberapa metode yang dapat digunakan untuk meningkatkan keamanan data pada kode QR. Dalam ilmu komputer dan matematika dikenal beberapa istilah terkait proteksi pesan/data yaitu Kriptografi dan Steganografi. Kriptografi akan mengubah pesan menjadi sebuah ciphertext menggunakan algoritma enkripsi dan secret key. Sedangkan pada Steganografi, pesan akan disembunyikan pada sebuah objek. Dengan kedua proses tersebut baik kriptografi dan steganografi ataupun kombinasi dari keduanya maka pesan/data yang disematkan pada kode QR tidak akan mudah untuk diterjemahkan. Metode yang akan dibahas yaitu enkripsi Algoritma Speck dan Advance Encryption Standard (AES) dengan kombinasi Steganografi. Kedua metode tersebut berhasil mengubah informasi rahasia menjadi data yang tidak mudah dikenali. Informasi rahasia yang mengalami proses steganografi menjadi sulit dideteksi dan memberikan pesan yang salah kepada peretas.

The article focuses on transformations which occur in Russian and Armenian translations of G. Orwell’s allegorical novella Animal Farm with special reference to pragma-stylistic analysis of both the source and the target texts. The aim of the analysis is to reveal the so-called Orwellian identity in style and to determine the means of its manifestation in both translations. Viewed from the perspective of pragmatic analysis of the original and the target texts, the Russian translation can be characterized by unnecessary additions to the plain text and tends to be pompous in some cases while the Armenian translation is closer to the style of the source text and tends to remain faithful to its intent. At large, both the Armenian and Russian versions of the fable have retained the chief pragmatic orientation and have almost the same impact on the target reader as the original one: persuasive message, rhetorical narration, manipulative nature.