12,179 research outputs found
Optimized Ensemble Approach for Multi-model Event Detection in Big data
Event detection acts an important role among modern society and it is a popular computer process that permits to detect the events automatically. Big data is more useful for the event detection due to large size of data. Multimodal event detection is utilized for the detection of events using heterogeneous types of data. This work aims to perform for classification of diverse events using Optimized Ensemble learning approach. The Multi-modal event data including text, image and audio are sent to the user devices from cloud or server where three models are generated for processing audio, text and image. At first, the text, image and audio data is processed separately. The process of creating a text model includes pre-processing using Imputation of missing values and data normalization. Then the textual feature extraction using integrated N-gram approach. The Generation of text model using Convolutional two directional LSTM (2DCon_LSTM). The steps involved in image model generation are pre-processing using Min-Max Gaussian filtering (MMGF). Image feature extraction using VGG-16 network model and generation of image model using Tweaked auto encoder (TAE) model. The steps involved in audio model generation are pre-processing using Discrete wavelet transform (DWT). Then the audio feature extraction using Hilbert Huang transform (HHT) and Generation of audio model using Attention based convolutional capsule network (Attn_CCNet). The features obtained by the generated models of text, image and audio are fused together by feature ensemble approach. From the fused feature vector, the optimal features are trained through improved battle royal optimization (IBRO) algorithm. A deep learning model called Convolutional duo Gated recurrent unit with auto encoder (C-Duo GRU_AE) is used as a classifier. Finally, different types of events are classified where the global model are then sent to the user devices with high security and offers better decision making process. The proposed methodology achieves better performances are Accuracy (99.93%), F1-score (99.91%), precision (99.93%), Recall (99.93%), processing time (17seconds) and training time (0.05seconds). Performance analysis exceeds several comparable methodologies in precision, recall, accuracy, F1 score, training time, and processing time. This designates that the proposed methodology achieves improved performance than the compared schemes. In addition, the proposed scheme detects the multi-modal events accurately
Research and Development Workstation Environment: the new class of Current Research Information Systems
Against the backdrop of the development of modern technologies in the field
of scientific research the new class of Current Research Information Systems
(CRIS) and related intelligent information technologies has arisen. It was
called - Research and Development Workstation Environment (RDWE) - the
comprehensive problem-oriented information systems for scientific research and
development lifecycle support. The given paper describes design and development
fundamentals of the RDWE class systems. The RDWE class system's generalized
information model is represented in the article as a three-tuple composite web
service that include: a set of atomic web services, each of them can be
designed and developed as a microservice or a desktop application, that allows
them to be used as an independent software separately; a set of functions, the
functional filling-up of the Research and Development Workstation Environment;
a subset of atomic web services that are required to implement function of
composite web service. In accordance with the fundamental information model of
the RDWE class the system for supporting research in the field of ontology
engineering - the automated building of applied ontology in an arbitrary domain
area, scientific and technical creativity - the automated preparation of
application documents for patenting inventions in Ukraine was developed. It was
called - Personal Research Information System. A distinctive feature of such
systems is the possibility of their problematic orientation to various types of
scientific activities by combining on a variety of functional services and
adding new ones within the cloud integrated environment. The main results of
our work are focused on enhancing the effectiveness of the scientist's research
and development lifecycle in the arbitrary domain area.Comment: In English, 13 pages, 1 figure, 1 table, added references in Russian.
Published. Prepared for special issue (UkrPROG 2018 conference) of the
scientific journal "Problems of programming" (Founder: National Academy of
Sciences of Ukraine, Institute of Software Systems of NAS Ukraine
Optimisation Method for Training Deep Neural Networks in Classification of Non- functional Requirements
Non-functional requirements (NFRs) are regarded critical to a software system's success. The majority of NFR detection and classification solutions have relied on supervised machine
learning models. It is hindered by the lack of labelled data for training and necessitate a significant amount of time spent on feature engineering.
In this work we explore emerging deep learning techniques to reduce the burden of feature engineering. The goal of this study is to develop an autonomous system that can classify NFRs into multiple classes based on a labelled corpus. In the first section of the thesis, we standardise the NFRs ontology and annotations to produce a corpus based on five attributes: usability, reliability, efficiency, maintainability, and portability. In the second section, the design and implementation of four neural networks, including the artificial neural network, convolutional neural network, long short-term memory, and gated recurrent unit are examined to classify NFRs.
These models, necessitate a large corpus. To overcome this limitation, we proposed a new paradigm for data augmentation. This method uses a sort and concatenates strategy to combine two phrases from the same class, resulting in a two-fold increase in data size while keeping the domain vocabulary intact. We compared our method to a baseline (no augmentation) and an existing approach Easy data augmentation (EDA) with pre-trained word embeddings. All training has been performed under two modifications to the data; augmentation on the entire data before train/validation split vs augmentation on train set only. Our findings show that as compared to EDA and baseline, NFRs classification model improved greatly, and CNN outperformed when trained using our suggested technique in the first setting. However, we saw a slight boost in the second experimental setup with just train set augmentation. As a result, we can determine that augmentation of the validation is required in order to achieve acceptable results with our proposed approach. We hope that our ideas will inspire new data augmentation techniques, whether they are generic or task specific. Furthermore, it would also be useful to implement this strategy in other languages
NLP-Based Techniques for Cyber Threat Intelligence
In the digital era, threat actors employ sophisticated techniques for which,
often, digital traces in the form of textual data are available. Cyber Threat
Intelligence~(CTI) is related to all the solutions inherent to data collection,
processing, and analysis useful to understand a threat actor's targets and
attack behavior. Currently, CTI is assuming an always more crucial role in
identifying and mitigating threats and enabling proactive defense strategies.
In this context, NLP, an artificial intelligence branch, has emerged as a
powerful tool for enhancing threat intelligence capabilities. This survey paper
provides a comprehensive overview of NLP-based techniques applied in the
context of threat intelligence. It begins by describing the foundational
definitions and principles of CTI as a major tool for safeguarding digital
assets. It then undertakes a thorough examination of NLP-based techniques for
CTI data crawling from Web sources, CTI data analysis, Relation Extraction from
cybersecurity data, CTI sharing and collaboration, and security threats of CTI.
Finally, the challenges and limitations of NLP in threat intelligence are
exhaustively examined, including data quality issues and ethical
considerations. This survey draws a complete framework and serves as a valuable
resource for security professionals and researchers seeking to understand the
state-of-the-art NLP-based threat intelligence techniques and their potential
impact on cybersecurity
Detect Depression from Social Networks with Sentiment Knowledge Sharing
Social network plays an important role in propagating people's viewpoints,
emotions, thoughts, and fears. Notably, following lockdown periods during the
COVID-19 pandemic, the issue of depression has garnered increasing attention,
with a significant portion of individuals resorting to social networks as an
outlet for expressing emotions. Using deep learning techniques to discern
potential signs of depression from social network messages facilitates the
early identification of mental health conditions. Current efforts in detecting
depression through social networks typically rely solely on analyzing the
textual content, overlooking other potential information. In this work, we
conduct a thorough investigation that unveils a strong correlation between
depression and negative emotional states. The integration of such associations
as external knowledge can provide valuable insights for detecting depression.
Accordingly, we propose a multi-task training framework, DeSK, which utilizes
shared sentiment knowledge to enhance the efficacy of depression detection.
Experiments conducted on both Chinese and English datasets demonstrate the
cross-lingual effectiveness of DeSK
xFraud: Explainable Fraud Transaction Detection
At online retail platforms, it is crucial to actively detect the risks of
transactions to improve customer experience and minimize financial loss. In
this work, we propose xFraud, an explainable fraud transaction prediction
framework which is mainly composed of a detector and an explainer. The xFraud
detector can effectively and efficiently predict the legitimacy of incoming
transactions. Specifically, it utilizes a heterogeneous graph neural network to
learn expressive representations from the informative heterogeneously typed
entities in the transaction logs. The explainer in xFraud can generate
meaningful and human-understandable explanations from graphs to facilitate
further processes in the business unit. In our experiments with xFraud on real
transaction networks with up to 1.1 billion nodes and 3.7 billion edges, xFraud
is able to outperform various baseline models in many evaluation metrics while
remaining scalable in distributed settings. In addition, we show that xFraud
explainer can generate reasonable explanations to significantly assist the
business analysis via both quantitative and qualitative evaluations.Comment: This is the extended version of a full paper to appear in PVLDB 15
(3) (VLDB 2022
Sarcasm Detection on Text for Political Domain— An Explainable Approach
In the era of social media, a large volume of data is generated by applications such as the industrial internet of things, IoT, Facebook, Twitter, and individual usage. Artificial intelligence and big data tools plays an important role in devising mechanisms for handling this vast volume of data as per the required usage of data to form important information from this unstructured data. When the data is publicly available on the internet and social media, it is imperative to treat the data carefully to respect the sentiments of the individuals. In this paper, the authors have attempted to solve three problems for treating the data using AI and data science tools, weighted statistical methods, and explainability of sarcastic comments. The first objective of this research study is sarcasm detection, and the next objective is to apply it to a domain-specific political Reddit dataset. Moreover, the last is to predict sarcastic words using counterfactual explainability. The textare extracted from the self-annotated Reddit corpus dataset containing 533 million comments written in English language, where 1.3 million comments are sarcastic. The sarcasm detection based model uses a weighted average approach and deep learning models to extract information and provide the required output in terms of content classification. Identifying sarcasm from a sentence is very challenging when the sentence has content that flips the polarity of positive sentiment into negative sentiment. This cumbersome task can be achieved with artificial intelligenceand machine learningalgorithms that train the machine and assist in classifying the required content from the sentences to keep the social media posts acceptable to society. There should be a mechanism to determine the extent to which the model's prediction could be relied upon. Therefore, the explination of the prediction is essential. We studied the methods and developed a model for detecting sarcasm and explaining the prediction. Therefore, the sarcasm detection model with explainability assists in identifying the sarcasmfrom the reddit post and its sentiment score to classify given textcorrectly. The F1-score of 75.75% for sarcasm and 80% for the explainability model proves the robustness of the proposed model
Exploiting Emotions via Composite Pretrained Embedding and Ensemble Language Model
Decisions in the modern era are based on more than just the available data; they also incorporate feedback from online sources. Processing reviews known as Sentiment analysis (SA) or Emotion analysis. Understanding the user's perspective and routines is crucial now-a-days for multiple reasons. It is used by both businesses and governments to make strategic decisions. Various architectural and vector embedding strategies have been developed for SA processing. Accurate representation of text is crucial for automatic SA. Due to the large number of languages spoken and written, polysemy and syntactic or semantic issues were common. To get around these problems, we developed effective composite embedding (ECE), a method that combines the advantages of vector embedding techniques that are either context-independent (like glove & fasttext) or context-aware (like XLNet) to effectively represent the features needed for processing. To improve the performace towards emotion or sentiment we proposed stacked ensemble model of deep lanugae models.ECE with Ensembled model is evaluated on balanced dataset to prove that it is a reliable embedding technique and a generalised model for SA.In order to evaluate ECE, cutting-edge ML and Deep net language models are deployed and comapared. The model is evaluated using benchmark datset such as MR, Kindle along with realtime tweet dataset of user complaints . LIME is used to verify the model's predictions and to provide statistical results for sentence.The model with ECE embedding provides state-of-art results with real time dataset as well
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