LOCAL TRANSLATION SERVICES FOR NEGLECTED LANGUAGES

Author :  David Noever

Affiliation :  Auburn University

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 01, January, 2021

Abstract :

Taking advantage of computationally lightweight, but high-quality translators prompt consideration of new applications that address neglected languages. For projects with protected or personal data, translators for less popular or low-resource languages require specific compliance checks before posting to a public translation API. In these cases, locally run translators can render reasonable, cost-effective solutions if done with an army of offline, smallscale pair translators. Like handling a specialist’s dialect, this research illustrates translating two historically interesting, but obfuscated languages: 1) hacker-speak (“l33t”) and 2) reverse (or “mirror”) writing as practiced by Leonardo da Vinci. The work generalizes a deep learning architecture to translatable variants of hacker-speak with lite, medium, and hard vocabularies. The original contribution highlights a fluent translator of hacker-speak in under 50 megabytes and demonstrates a companion text generator for augmenting future datasets with greater than a million bilingual sentence pairs. A primary motivation stems from the need to understand and archive the evolution of the international computer community, one that continuously enhances their talent for speaking openly but in hidden contexts. This training of bilingual sentences supports deep learning models using a long short-term memory, recurrent neural network (LSTM-RNN). It extends previous work demonstrating an English-to-foreign translation service built from as little as 10,000 bilingual sentence pairs. This work further solves the equivalent translation problem in twenty-six additional (non-obfuscated) languages and rank orders those models and their proficiency quantitatively with Italian as the most successful and Mandarin Chinese as the most challenging.

Keyword :  Recurrent Neural Network, Long Short-Term Memory (LSTM) Network, Machine Translation, Encoder-Decoder Architecture, Obfuscation.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110110.pdf

MEGALITE: A NEW SPANISH LITERATURE CORPUS FOR NLP TASKS

Author :  Luis-Gil Moreno-Jiménez

Affiliation :  Université d’Avignon

Country :  France

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 01, January, 2021

Abstract :

In this work we introduce the Spanish Literary corpus MegaLite, a new corpus well adapted to Natural Language Processing (NLP), Computational Creativity (CC), Text generation and others studies. We address the creation of this corpus of literary documents to evaluate or design algorithms in automatic text generation, classification, stylometry and rhetorical analysis, sentiment detection, among other tasks. We have constituted this corpus manually in order to avoir genre classification errors. Near of 5 200 works on the genres narrative, poetry and plays constitute this corpus. Some statistics and applications of MegaLite corpus are presented and discussed. The MegaLite corpus will be available to the community as a free resource, under several adequate formats.

Keyword :  Emotion Corpus, Spanish Literary Corpus, Learning algorithms, Linguistic resources.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110109.pdf

MAKING CROSS-DOMAIN RECOMMENDATIONS BY ASSOCIATING DISJOINT USERS AND ITEMS THROUGH THE AFFECTIVE AWARE PSEUDO ASSOCIATION METHOD

Author :  John Kalung Leung

Affiliation :  George Mason University

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 01, January, 2021

Abstract :

This paper utilizes an ingenious text-based affective aware pseudo association method (AAPAM) to link disjoint pseudo users and items across different information domains and leverage them to make cross-domain content-based and collaborative filtering recommendations. This paper demonstrates that the AAPAM method could seamlessly join different information domain datasets to act as one without any additional cross-domain information retrieval protocols. Besides making cross-domain recommendations, the benefit of joining datasets from different information domains through AAPAM is that it eradicates cold start issues while making serendipitous recommendations.

Keyword :  Behavioral Analysis, Emotion-aware Recommender System, Emotion prediction, Personality, Pseudo Users Association.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110108.pdf

A DATA-DRIVEN STRATEGY TO COMBINE WORD EMBEDDINGS IN INFORMATION RETRIEVAL

Author :  Alfredo Silva

Affiliation :  Universidad Técnica Federico Santa María

Country :  Chile

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 01, January, 2021

Abstract :

Word embeddings are vital descriptors of words in unigram representations of documents for many tasks in natural language processing and information retrieval. The representation of queries has been one of the most critical challenges in this area because it consists of a few terms and has little descriptive capacity. Strategies such as average word embeddings can enrich the queries' descriptive capacity since they favor the identification of related terms from the continuous vector representations that characterize these approaches. We propose a datadriven strategy to combine word embeddings. We use Idf combinations of embeddings to represent queries, showing that these representations outperform the average word embeddings recently proposed in the literature. Experimental results on benchmark data show that our proposal performs well, suggesting that data-driven combinations of word embeddings are a promising line of research in ad-hoc information retrieval.

Keyword :  Word embeddings, information retrieval, query representation

For More Details :  https://aircconline.com/csit/papers/vol11/csit110107.pdf

CONVOLUTIONAL NEURAL NETWORK FOR MALWARE CLASSIFICATION BASED ON API CALL SEQUENCE

Author :  Matthew Schofield

Affiliation :  Rowan University

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 01, January, 2021

Abstract :

Malicious software is constantly being developed and improved, so detection and classification of malicious applications is an ever-evolving problem. Since traditional malware detection techniques fail to detect new or unknown malware, machine learning algorithms have been used to overcome this disadvantage. We present a Convolutional Neural Network (CNN) for malware type classification based on the Windows system API (Application Program Interface) calls. This research uses a database of 5385 instances of API call streams labeled with eight types of malware of the source malicious application. We use a 1-Dimensional CNN by mapping API call streams as categorical and term frequency-inverse document frequency (TF-IDF) vectors respectively. We achieved accuracy scores of 98.17% using TF-IDF vector and 95.40% via categorical vector. The proposed 1-D CNN outperformed other traditional classification techniques with overall accuracy score of 91.0%.

Keyword :  Convolutional Neural Network, Malware Classification, Windows API Calls, Term Frequency Inverse Document Frequency Vectors

For More Details :  https://aircconline.com/csit/papers/vol11/csit110106.pdf

MITIGATING PHISHING ATTACK IN ORGANISATIONS: A LITERATURE REVIEW

Author :  Wosah Peace Nmachi

Affiliation :  School of Computing & Engineering University of Gloucestershire

Country :  United Kingdom

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 01, January, 2021

Abstract :

Email is a channel of communication which is increasingly used by individuals and organisations for exchange of information. It is considered to be a confidential medium of communication but this is no longer the case as attackers send malicious emails to users to deceive them into disclosing their private personal information such as username, password, and bank card details, etc. In search of a solution to combat phishing cybercrime attacks, different approaches have been developed. However, the traditional exiting solutions have been limited in assisting email users to identify phishing emails from legitimate ones. This paper reveals the different email and website phishing solutions in phishing attack detection. It first provides a literature analysis of different existing phishing mitigation approaches. It then provides a discussion on the limitations of the techniques, before concluding with an exploration into how phishing detection can be improved.

Keyword :  Cyber-security, Phishing Email Attack, Deep Learning, Stylometric Analysis

For More Details :  https://aircconline.com/csit/papers/vol11/csit110105.pdf

SOCIAL ENGINEERING INFOSEC POLICIES (SE-IPS)

Author :  Dalal Alharthi

Affiliation :  University of California Irvine

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 01, January, 2021

Abstract :

The sudden increase in employees working primarily or even exclusively at home has generated unique societal and economic circumstances which makes the protection of information assets a major problem for organizations. The application of security policies is essential for mitigating the risk of social engineering attacks. However, incorporating and enforcing successful security policies in an organization is not a straightforward task. To that end, this paper develops a model of Social Engineering InfoSec Policies (SE-IPs) and investigates the incorporation of those SE-IPs in organizations. This paper proposes a customizable model of SE-IPs that can be adopted by a wide variety of organizations. The authors designed and distributed a survey to measure the incorporation level of formal SE-IPs in organizations. After collecting and analyzing the data which included over fifteen hundred responses, the authors found that on average, organizations incorporated just over fifty percent of the identified formal Social Engineering InfoSec Policies.

Keyword :  Cybersecurity, InfoSec, Security Policies, Social Engineering.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110104.pdf