Towards a Global, Multilingual Framenet
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Malware Classification with BERT
San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 5-25-2021 Malware Classification with BERT Joel Lawrence Alvares Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Artificial Intelligence and Robotics Commons, and the Information Security Commons Malware Classification with Word Embeddings Generated by BERT and Word2Vec Malware Classification with BERT Presented to Department of Computer Science San José State University In Partial Fulfillment of the Requirements for the Degree By Joel Alvares May 2021 Malware Classification with Word Embeddings Generated by BERT and Word2Vec The Designated Project Committee Approves the Project Titled Malware Classification with BERT by Joel Lawrence Alvares APPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE San Jose State University May 2021 Prof. Fabio Di Troia Department of Computer Science Prof. William Andreopoulos Department of Computer Science Prof. Katerina Potika Department of Computer Science 1 Malware Classification with Word Embeddings Generated by BERT and Word2Vec ABSTRACT Malware Classification is used to distinguish unique types of malware from each other. This project aims to carry out malware classification using word embeddings which are used in Natural Language Processing (NLP) to identify and evaluate the relationship between words of a sentence. Word embeddings generated by BERT and Word2Vec for malware samples to carry out multi-class classification. BERT is a transformer based pre- trained natural language processing (NLP) model which can be used for a wide range of tasks such as question answering, paraphrase generation and next sentence prediction. However, the attention mechanism of a pre-trained BERT model can also be used in malware classification by capturing information about relation between each opcode and every other opcode belonging to a malware family. -
Construction of English-Bodo Parallel Text
International Journal on Natural Language Computing (IJNLC) Vol.7, No.5, October 2018 CONSTRUCTION OF ENGLISH -BODO PARALLEL TEXT CORPUS FOR STATISTICAL MACHINE TRANSLATION Saiful Islam 1, Abhijit Paul 2, Bipul Syam Purkayastha 3 and Ismail Hussain 4 1,2,3 Department of Computer Science, Assam University, Silchar, India 4Department of Bodo, Gauhati University, Guwahati, India ABSTRACT Corpus is a large collection of homogeneous and authentic written texts (or speech) of a particular natural language which exists in machine readable form. The scope of the corpus is endless in Computational Linguistics and Natural Language Processing (NLP). Parallel corpus is a very useful resource for most of the applications of NLP, especially for Statistical Machine Translation (SMT). The SMT is the most popular approach of Machine Translation (MT) nowadays and it can produce high quality translation result based on huge amount of aligned parallel text corpora in both the source and target languages. Although Bodo is a recognized natural language of India and co-official languages of Assam, still the machine readable information of Bodo language is very low. Therefore, to expand the computerized information of the language, English to Bodo SMT system has been developed. But this paper mainly focuses on building English-Bodo parallel text corpora to implement the English to Bodo SMT system using Phrase-Based SMT approach. We have designed an E-BPTC (English-Bodo Parallel Text Corpus) creator tool and have been constructed General and Newspaper domains English-Bodo parallel text corpora. Finally, the quality of the constructed parallel text corpora has been tested using two evaluation techniques in the SMT system. -
Bros:Apre-Trained Language Model for Understanding Textsin Document
Under review as a conference paper at ICLR 2021 BROS: A PRE-TRAINED LANGUAGE MODEL FOR UNDERSTANDING TEXTS IN DOCUMENT Anonymous authors Paper under double-blind review ABSTRACT Understanding document from their visual snapshots is an emerging and chal- lenging problem that requires both advanced computer vision and NLP methods. Although the recent advance in OCR enables the accurate extraction of text seg- ments, it is still challenging to extract key information from documents due to the diversity of layouts. To compensate for the difficulties, this paper introduces a pre-trained language model, BERT Relying On Spatiality (BROS), that represents and understands the semantics of spatially distributed texts. Different from pre- vious pre-training methods on 1D text, BROS is pre-trained on large-scale semi- structured documents with a novel area-masking strategy while efficiently includ- ing the spatial layout information of input documents. Also, to generate structured outputs in various document understanding tasks, BROS utilizes a powerful graph- based decoder that can capture the relation between text segments. BROS achieves state-of-the-art results on four benchmark tasks: FUNSD, SROIE*, CORD, and SciTSR. Our experimental settings and implementation codes will be publicly available. 1 INTRODUCTION Document intelligence (DI)1, which understands industrial documents from their visual appearance, is a critical application of AI in business. One of the important challenges of DI is a key information extraction task (KIE) (Huang et al., 2019; Jaume et al., 2019; Park et al., 2019) that extracts struc- tured information from documents such as financial reports, invoices, business emails, insurance quotes, and many others. -
Usage of IT Terminology in Corpus Linguistics Mavlonova Mavluda Davurovna
International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8, Issue-7S, May 2019 Usage of IT Terminology in Corpus Linguistics Mavlonova Mavluda Davurovna At this period corpora were used in semantics and related Abstract: Corpus linguistic will be the one of latest and fast field. At this period corpus linguistics was not commonly method for teaching and learning of different languages. used, no computers were used. Corpus linguistic history Proposed article can provide the corpus linguistic introduction were opposed by Noam Chomsky. Now a day’s modern and give the general overview about the linguistic team of corpus. corpus linguistic was formed from English work context and Article described about the corpus, novelties and corpus are associated in the following field. Proposed paper can focus on the now it is used in many other languages. Alongside was using the corpus linguistic in IT field. As from research it is corpus linguistic history and this is considered as obvious that corpora will be used as internet and primary data in methodology [Abdumanapovna, 2018]. A landmark is the field of IT. The method of text corpus is the digestive modern corpus linguistic which was publish by Henry approach which can drive the different set of rules to govern the Kucera. natural language from the text in the particular language, it can Corpus linguistic introduce new methods and techniques explore about languages that how it can inter-related with other. for more complete description of modern languages and Hence the corpus linguistic will be automatically drive from the these also provide opportunities to obtain new result with text sources. -
Information Extraction Based on Named Entity for Tourism Corpus
Information Extraction based on Named Entity for Tourism Corpus Chantana Chantrapornchai Aphisit Tunsakul Dept. of Computer Engineering Dept. of Computer Engineering Faculty of Engineering Faculty of Engineering Kasetsart University Kasetsart University Bangkok, Thailand Bangkok, Thailand [email protected] [email protected] Abstract— Tourism information is scattered around nowa- The ontology is extracted based on HTML web structure, days. To search for the information, it is usually time consuming and the corpus is based on WordNet. For these approaches, to browse through the results from search engine, select and the time consuming process is the annotation which is to view the details of each accommodation. In this paper, we present a methodology to extract particular information from annotate the type of name entity. In this paper, we target at full text returned from the search engine to facilitate the users. the tourism domain, and aim to extract particular information Then, the users can specifically look to the desired relevant helping for ontology data acquisition. information. The approach can be used for the same task in We present the framework for the given named entity ex- other domains. The main steps are 1) building training data traction. Starting from the web information scraping process, and 2) building recognition model. First, the tourism data is gathered and the vocabularies are built. The raw corpus is used the data are selected based on the HTML tag for corpus to train for creating vocabulary embedding. Also, it is used building. The data is used for model creation for automatic for creating annotated data. -
Liste Des Jeux Nintendo NES Chase Bubble Bobble Part 2
Liste des jeux Nintendo NES Chase Bubble Bobble Part 2 Cabal International Cricket Color a Dinosaur Wayne's World Bandai Golf : Challenge Pebble Beach Nintendo World Championships 1990 Lode Runner Tecmo Cup : Football Game Teenage Mutant Ninja Turtles : Tournament Fighters Tecmo Bowl The Adventures of Rocky and Bullwinkle and Friends Metal Storm Cowboy Kid Archon - The Light And The Dark The Legend of Kage Championship Pool Remote Control Freedom Force Predator Town & Country Surf Designs : Thrilla's Surfari Kings of the Beach : Professional Beach Volleyball Ghoul School KickMaster Bad Dudes Dragon Ball : Le Secret du Dragon Cyber Stadium Series : Base Wars Urban Champion Dragon Warrior IV Bomberman King's Quest V The Three Stooges Bases Loaded 2: Second Season Overlord Rad Racer II The Bugs Bunny Birthday Blowout Joe & Mac Pro Sport Hockey Kid Niki : Radical Ninja Adventure Island II Soccer NFL Track & Field Star Voyager Teenage Mutant Ninja Turtles II : The Arcade Game Stack-Up Mappy-Land Gauntlet Silver Surfer Cybernoid - The Fighting Machine Wacky Races Circus Caper Code Name : Viper F-117A : Stealth Fighter Flintstones - The Surprise At Dinosaur Peak, The Back To The Future Dick Tracy Magic Johnson's Fast Break Tombs & Treasure Dynablaster Ultima : Quest of the Avatar Renegade Super Cars Videomation Super Spike V'Ball + Nintendo World Cup Dungeon Magic : Sword of the Elements Ultima : Exodus Baseball Stars II The Great Waldo Search Rollerball Dash Galaxy In The Alien Asylum Power Punch II Family Feud Magician Destination Earthstar Captain America and the Avengers Cyberball Karnov Amagon Widget Shooting Range Roger Clemens' MVP Baseball Bill Elliott's NASCAR Challenge Garry Kitchen's BattleTank Al Unser Jr. -
NLP with BERT: Sentiment Analysis Using SAS® Deep Learning and Dlpy Doug Cairns and Xiangxiang Meng, SAS Institute Inc
Paper SAS4429-2020 NLP with BERT: Sentiment Analysis Using SAS® Deep Learning and DLPy Doug Cairns and Xiangxiang Meng, SAS Institute Inc. ABSTRACT A revolution is taking place in natural language processing (NLP) as a result of two ideas. The first idea is that pretraining a deep neural network as a language model is a good starting point for a range of NLP tasks. These networks can be augmented (layers can be added or dropped) and then fine-tuned with transfer learning for specific NLP tasks. The second idea involves a paradigm shift away from traditional recurrent neural networks (RNNs) and toward deep neural networks based on Transformer building blocks. One architecture that embodies these ideas is Bidirectional Encoder Representations from Transformers (BERT). BERT and its variants have been at or near the top of the leaderboard for many traditional NLP tasks, such as the general language understanding evaluation (GLUE) benchmarks. This paper provides an overview of BERT and shows how you can create your own BERT model by using SAS® Deep Learning and the SAS DLPy Python package. It illustrates the effectiveness of BERT by performing sentiment analysis on unstructured product reviews submitted to Amazon. INTRODUCTION Providing a computer-based analog for the conceptual and syntactic processing that occurs in the human brain for spoken or written communication has proven extremely challenging. As a simple example, consider the abstract for this (or any) technical paper. If well written, it should be a concise summary of what you will learn from reading the paper. As a reader, you expect to see some or all of the following: • Technical context and/or problem • Key contribution(s) • Salient result(s) If you were tasked to create a computer-based tool for summarizing papers, how would you translate your expectations as a reader into an implementable algorithm? This is the type of problem that the field of natural language processing (NLP) addresses. -
Enriching an Explanatory Dictionary with Framenet and Propbank Corpus Examples
Proceedings of eLex 2019 Enriching an Explanatory Dictionary with FrameNet and PropBank Corpus Examples Pēteris Paikens 1, Normunds Grūzītis 2, Laura Rituma 2, Gunta Nešpore 2, Viktors Lipskis 2, Lauma Pretkalniņa2, Andrejs Spektors 2 1 Latvian Information Agency LETA, Marijas street 2, Riga, Latvia 2 Institute of Mathematics and Computer Science, University of Latvia, Raina blvd. 29, Riga, Latvia E-mail: [email protected], [email protected] Abstract This paper describes ongoing work to extend an online dictionary of Latvian – Tezaurs.lv – with representative semantically annotated corpus examples according to the FrameNet and PropBank methodologies and word sense inventories. Tezaurs.lv is one of the largest open lexical resources for Latvian, combining information from more than 300 legacy dictionaries and other sources. The corpus examples are extracted from Latvian FrameNet and PropBank corpora, which are manually annotated parallel subsets of a balanced text corpus of contemporary Latvian. The proposed approach augments traditional lexicographic information with modern cross-lingually interpretable information and enables analysis of word senses from the perspective of frame semantics, which is substantially different from (complementary to) the traditional approach applied in Latvian lexicography. In cases where FrameNet and PropBank corpus evidence aligns well with the word sense split in legacy dictionaries, the frame-semantically annotated corpus examples supplement the word sense information with clarifying usage examples and commonly used semantic valence patterns. However, the annotated corpus examples often provide evidence of a different sense split, which is often more coarse-grained and, thus, may help dictionary users to cluster and comprehend a fine-grained sense split suggested by the legacy sources. -
Measuring Text Simplification with the Crowd
Measuring Text Simplification with the Crowd Walter S. Lasecki Luz Rello Jeffrey P. Bigham ROC HCI HCI Institute HCI and LT Institutes Dept. of Computer Science School of Computer Science School of Computer Science University of Rochester Carnegie Mellon University Carnegie Mellon University [email protected] [email protected] [email protected] ABSTRACT the U.S. [41]1 and the European Guidelines for the Production of Text can often be complex and difficult to read, especially for peo Easy-to-Read Information in Europe [23]. ple with cognitive impairments or low literacy skills. Text simplifi Text simplification is an area of Natural Language Processing cation is a process that reduces the complexity of both wording and (NLP) that attempts to solve this problem automatically by reduc structure in a sentence, while retaining its meaning. However, this ing the complexity of both wording (lexicon) and structure (syn is currently a challenging task for machines, and thus, providing tax) in a sentence [46]. Previous efforts on automatic text simpli effective on-demand text simplification to those who need it re fication have focused on people with autism [20, 39], people with mains an unsolved problem. Even evaluating the simplicity of text Down syndrome [48], people with dyslexia [42, 43], and people remains a challenging problem for both computers, which cannot with aphasia [11, 18]. understand the meaning of text, and humans, who often struggle to However, automatic text simplification is in an early stage and agree on what constitutes a good simplification. still is not useful for the target users [20, 43, 48]. -
[Japan] SALA GIOCHI ARCADE 1000 Miglia
SCHEDA NEW PLATINUM PI4 EDITION La seguente lista elenca la maggior parte dei titoli emulati dalla scheda NEW PLATINUM Pi4 (20.000). - I giochi per computer (Amiga, Commodore, Pc, etc) richiedono una tastiera per computer e talvolta un mouse USB da collegare alla console (in quanto tali sistemi funzionavano con mouse e tastiera). - I giochi che richiedono spinner (es. Arkanoid), volanti (giochi di corse), pistole (es. Duck Hunt) potrebbero non essere controllabili con joystick, ma richiedono periferiche ad hoc, al momento non configurabili. - I giochi che richiedono controller analogici (Playstation, Nintendo 64, etc etc) potrebbero non essere controllabili con plance a levetta singola, ma richiedono, appunto, un joypad con analogici (venduto separatamente). - Questo elenco è relativo alla scheda NEW PLATINUM EDITION basata su Raspberry Pi4. - Gli emulatori di sistemi 3D (Playstation, Nintendo64, Dreamcast) e PC (Amiga, Commodore) sono presenti SOLO nella NEW PLATINUM Pi4 e non sulle versioni Pi3 Plus e Gold. - Gli emulatori Atomiswave, Sega Naomi (Virtua Tennis, Virtua Striker, etc.) sono presenti SOLO nelle schede Pi4. - La versione PLUS Pi3B+ emula solo 550 titoli ARCADE, generati casualmente al momento dell'acquisto e non modificabile. Ultimo aggiornamento 2 Settembre 2020 NOME GIOCO EMULATORE 005 SALA GIOCHI ARCADE 1 On 1 Government [Japan] SALA GIOCHI ARCADE 1000 Miglia: Great 1000 Miles Rally SALA GIOCHI ARCADE 10-Yard Fight SALA GIOCHI ARCADE 18 Holes Pro Golf SALA GIOCHI ARCADE 1941: Counter Attack SALA GIOCHI ARCADE 1942 SALA GIOCHI ARCADE 1943 Kai: Midway Kaisen SALA GIOCHI ARCADE 1943: The Battle of Midway [Europe] SALA GIOCHI ARCADE 1944 : The Loop Master [USA] SALA GIOCHI ARCADE 1945k III SALA GIOCHI ARCADE 19XX : The War Against Destiny [USA] SALA GIOCHI ARCADE 2 On 2 Open Ice Challenge SALA GIOCHI ARCADE 4-D Warriors SALA GIOCHI ARCADE 64th. -
MASC: the Manually Annotated Sub-Corpus of American English
MASC: The Manually Annotated Sub-Corpus of American English Nancy Ide*, Collin Baker**, Christiane Fellbaum†, Charles Fillmore**, Rebecca Passonneau†† *Vassar College Poughkeepsie, New York USA **International Computer Science Institute Berkeley, California USA †Princeton University Princeton, New Jersey USA ††Columbia University New York, New York USA E-mail: [email protected], [email protected], [email protected], [email protected], [email protected] Abstract To answer the critical need for sharable, reusable annotated resources with rich linguistic annotations, we are developing a Manually Annotated Sub-Corpus (MASC) including texts from diverse genres and manual annotations or manually-validated annotations for multiple levels, including WordNet senses and FrameNet frames and frame elements, both of which have become significant resources in the international computational linguistics community. To derive maximal benefit from the semantic information provided by these resources, the MASC will also include manually-validated shallow parses and named entities, which will enable linking WordNet senses and FrameNet frames within the same sentences into more complex semantic structures and, because named entities will often be the role fillers of FrameNet frames, enrich the semantic and pragmatic information derivable from the sub-corpus. All MASC annotations will be published with detailed inter-annotator agreement measures. The MASC and its annotations will be freely downloadable from the ANC website, thus providing -
Special Article 2
Special Article 2 By KIM Yungduk Korea’s Pop Culture Policy & icy, the South Korean government has announced additional opening Japanese Animation policy initiatives four times since the first opening action in 1998. Anime films for theater showings were allowed in June 2000 as The inflow and acceptance of Japanese the third step. The scope of anime films allowed was limited to culture in South Korea were under institu- those that won international movie awards, but anime has become tional control for a long time. The South totally available in any form since Jan. 1, 2006. Korean government kept a policy of prohibit- The South Korean government’s gradual opening was meant to ing the inflow of Japanese culture for over allow sufficient time for a complete lifting of the ban and political Author Kim Yungduk 60 years. The ban originated from “an judgment to mitigate any negative influence stemming from anti- unhappy history” between South Korea and Japan in the early part of Japan sentiment and forestall any adverse consequence to domestic the 20th century. Directly after liberation from Japanese colonial rule cultural industries. in 1945, Lee Seung Man, the first president of South Korea, prohibit- ed the inflow and acceptance of Japanese culture. At that time, the Acceptance of Japanese Animation: Track Record prohibition policy was based on “dominative anti-Japan sentiment” among most Korean people, but it did not have any distinct legal Broadcasting ground. According to 2007 import and export statistics on broadcast pro- The ban on Japanese culture remained effective even after South grams reported by the Korea Communications Commission (KCC), Korea and Japan normalized diplomatic relations in 1965.