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he Glagolitic script is the oldest Slavic script and one of the two Slavic scripts (the other is Cyrillic). It was actively used in Croatia until the 19th century. Today, the Glagolitic script is a symbol of Croatian national identity. It has significant cultural, artistic, and esthetic value. The goals of the Old Church Slavonic Institute are researching, discovering, recognizing, systematically listing, and editing Glagolitic manuscripts, inscriptions, and printed books. One of the Institute’s main tasks is to create a digital version of the Dictionary of the Croatian Redaction of Church Slavonic. This is a long-term project, and the dictionary has so far only been published in printed form (A to I). In addition to the creation of the digital dictionary, additional web content is being developed (e.g. games), which will provide an additional source and tool for dictionary compilers and users. One of these additional contents is the reverse dictionary, which consists of the headwords written in Glagolitic from the Dictionary. A reverse dictionary is a dictionary in which the words are sorted alphabetically by their final rather than initial letters, i.e. the words are sorted based on their endings. This allows users to look up words by focusing on their final segments. Reverse dictionaries are useful for scientific research, especially for the study of word-formation (e.g. the study of masculine-feminine pairs), but they can also be used for other purposes, such as finding rhymes and compiling educational games. A reverse dictionary for Croatian Church Slavonic is thus a useful tool for further research. The problem is not only that the word search is not done in a classic matter (where the string is searched from the beginning of the word) but also that the Glagolitic script has some specific letters which have to be mapped to certain characters in the Latin script and that the reverse dictionary has headwords in two scripts (Latin and Glagolitic). Currently, no available software solution can accommodate for these problems, so the reverse dictionary is being developed from scratch using a custom code. This paper presents the challenges and solutions in the development of the Croatian Church Slavonic Reverse Dictionary.

In view of the limitation of the forward dictionaries to attend to the needs specific to the language producers, an alternate resource in the form of ‘Reverse Dictionary’ needs to be built. Reverse Dictionary aims to lexicalize the concept in the user’s mind by taking as input a natural language description of the concept and returning word/s that are in semantic correspondence to the description. A critical survey of the existing Reverse Dictionary works is presented in this paper. It is concluded that this problem has been addressed through five categories of approaches, namely, Information Retrieval-based approach, Graph-based approach, Mental dictionary-based approach, Vector Space Model-based approach, and Neural Language Model-based approach. We identify and highlight that the works reported so far do not account for human perceptions in the user input. However, as a NL is a system of perceptions and a Reverse Dictionary deals with natural language input, dealing with perception based information in the user input is important to capture his/her intent. To address the identified research gap, we have considered the concept of Precisiated Natural Language (PNL) based on Zadeh’s paradigm of Computational Theory of Perceptions. We have proposed to incorporate it into the traditional Information Retrieval (IR) architecture in building a Reverse Dictionary. To gain insights for the same, we have reported an experimental analysis of IR system based Wordster Reverse Dictionary.

A Reverse Dictionary maps a natural language description to corresponding semantically appropriate words. It is of assistance, particularly to the language producers, in finding the correct word for a concept in mind while writing/speaking. As the COVID-19 pandemic intensely impacted almost all the functionalities across the globe, texts on this subject appear in a significant amount in various forms, including news updates, awareness and safety articles, notices and circulars, research articles, social media posts, etc. A Reverse Dictionary on this subject is a requisite in view of the following reasons, hence addressed. Firstly, the varied text forms involve a diverse range of language producers ranging from professional doctors to the general mass. Secondly, the COVID-19 pandemic’s glossary is more specific than the general English language, hence unfamiliar to the language producers. We have carried out an implementation based on the Wordster Reverse Dictionary architecture, owing to its outperformance of the commercial Onelook Reverse Dictionary benchmark. We report an accuracy of 0.49 based on top-3 system responses. To address the limitations of the current implementation, we bring into consideration Zadeh’s paradigm of the Computational Theory of Perceptions. Notably, the compilation of the COVID-19 glossary as a part of this study is another contribution in view that it is of assistance to the concerned readers.

En los últimos años se ha debatido si el desarrollo de la conciencia metalingüística permite dominar mejor la lengua materna. En este trabajo se expone un modelo didáctico para la reflexión metalingüística del proceso derivativo en español que, tras su experimentación en un aula con alumnos de bachillerato, prueba su validez para el enriquecimiento léxico. El modelo se fundamenta en las teorías de procesamiento y organización morfológica del léxico mental de Bybee, el conexionismo y la lingüística cognitiva. Consiste en presentar palabras conectadas, bien por formar parte de la misma familia léxica o bien por compartir afijos, para que los mismos estudiantes, a partir de bases de datos on line (un lematizador y un diccionario inverso), amplíen los esquemas de conexión y relacionen las palabras dadas con otras. La experimentación del modelo se ha llevado al aula con las familias léxicas de bueno y genio y con formas derivadas con -al y ex-. La práctica demuestra su eficacia y rentabilidad.

Danowitz reviews Music Abbreviations: A Reverse Dictionary by Donald L. Hixon.

Amid the rapid advancement of neural machine translation, the challenge of data sparsity has been a major obstacle. To address this issue, this study proposes a general data augmentation technique for various scenarios. It examines the predicament of parallel corpora diversity and high quality in both rich- and low-resource settings, and integrates the low-frequency word substitution method and reverse translation approach for complementary benefits. Additionally, this method improves the pseudo-parallel corpus generated by the reverse translation method by substituting low-frequency words and includes a grammar error correction module to reduce grammatical errors in low-resource scenarios. The experimental data are partitioned into rich- and low-resource scenarios at a 10:1 ratio. It verifies the necessity of grammatical error correction for pseudo-corpus in low-resource scenarios. Models and methods are chosen from the backbone network and related literature for comparative experiments. The experimental findings demonstrate that the data augmentation approach proposed in this study is suitable for both rich- and low-resource scenarios and is effective in enhancing the training corpus to improve the performance of translation tasks.

Background Streptomyces roseochromogenes NRRL 3504 produces clorobiocin, an aminocoumarin antibiotic that inhibits DNA replication. No other natural products have been isolated from this bacterium so far, despite the presence of a rich repertoire of specialized metabolite biosynthesis gene clusters (smBGCs) within its genome. Heterologous expression of smBGCs in suitable chassis speeds up the discovery of the natural products hidden behind these sets of genes. Results In this work we focus on one intriguing smBGC of NRRL 3504 bearing some similarity to gene clusters involved in production of manumycin family polyketides. Through heterologous expression in Streptomyces chassis strains S. albus Del14 and S. lividans ΔYA9, this smBGC (hereafter referred to as lim BGC) was shown to direct the production of unusual polyketide limocrocin (LIM) known for its ability to interfere with viral reverse transcriptases. The organization of lim BGC, data on the structures of revealed metabolites as well as manipulations of lim genes allowed us to put forward an initial hypothesis about a biosynthetic pathway leading to LIM. We provide initial data on two LIM derivatives as well as updated NMR spectra for the main product. Conclusion This study reveals the genetic control of biosynthesis of LIM that remained hidden for the last 70 years. This, in turn, opens the door to biological routes towards overproduction of LIM as well as generation of its derivatives.

Botnets and malware continue to avoid detection by static rule engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the “bagging” model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, F 1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large enterprise. In 4 h of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.

In engineering practice, ground penetrating radar (GPR) records are often hindered by clutter resulting from uneven underground media distribution, affecting target signal characteristics and precise positioning. To address this issue, we propose a method combining deep learning preprocessing and reverse time migration (RTM) imaging. Our preprocessing approach introduces a novel deep learning framework for GPR clutter, enhancing the network’s feature-capture capability for target signals through the integration of a contextual feature fusion module (CFFM) and an enhanced spatial attention module (ESAM). The superiority and effectiveness of our algorithm are demonstrated by RTM imaging comparisons using synthetic and laboratory data. The processing of actual road data further confirms the algorithm’s significant potential for practical engineering applications.

Background Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner. Results This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations. Conclusions Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.