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1. An Analysis of Deep Contextual Word Embeddings and Neural Architectures for Toponym Mention Detection in Scientific Publications

   https://doi.org/10.18653/v1/W19-2607  

   Magnusson, Matthew; Dietz, Laura (May 2019, Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications)

   Toponym detection in scientific papers is an open task and a key first step in place entity enrichment of documents. We examine three common neural architectures in NLP: 1) convolutional neural network, 2) multi-layer perceptron (both applied in a sliding window context) and 3) bidirectional LSTM and apply contextual and non-contextual word embedding layers to these models. We find that deep contextual word embeddings improve the performance of the bi-LSTM with CRF neural architecture achieving the best performance when multiple layers of deep contextual embeddings are concatenated. Our best performing model achieves an average F1 of 0.910 when evaluated on overlap macro exceeding previous state-of-the-art models in the toponym detection task. 

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2. Attention Based Transformer for Student Answers Assessment

   Ait Khayi, N. & (January 2020, Proceedings of the Thirty-Third International FLAIRS Conference (FLAIRS-32))

   Assessing the correctness of student answers in a dialog-based intelligent tutoring system (ITS) is a well-defined Natural Language Processing (NLP) task that has attracted the attention of many researchers in the field. Inspired by Vaswani’s transformer, we propose in this paper an attention-based transformer neural network with a multi-head attention mechanism for the task of student answer assessment. Results show the competitiveness of our proposed model. A highest accuracy of 71.5% was achieved when using ELMo embeddings, 10 heads of attention, and 2 layers. This is very competitive and rivals the highest accuracy achieved by a previously proposed BI-GRU-Capsnet deep network (72.5%) on the same dataset. The main advantages of using transformers over BI-GRU-Capsnet is reducing the training time and giving more space for parallelization. 

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3. The Performance of Sequential Deep Learning Models in Detecting Phishing Websites Using Contextual Features of URLs

   Gopali, S; Namin, A; Abri, F; Jones, K (May 2024, ACM SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing)

   Cyber attacks continue to pose significant threats to individuals and organizations, stealing sensitive data such as personally identifiable information, financial information, and login credentials. Hence, detecting malicious websites before they cause any harm is critical to preventing fraud and monetary loss. To address the increasing number of phishing attacks, protective mechanisms must be highly responsive, adaptive, and scalable. Fortunately, advances in the field of machine learning, coupled with access to vast amounts of data, have led to the adoption of various deep learning models for timely detection of these cyber crimes. This study focuses on the detection of phishing websites using deep learning models such as Multi-Head Attention, Temporal Convolutional Network (TCN), BI-LSTM, and LSTM where URLs of the phishing websites are treated as a sequence. The results demonstrate that Multi-Head Attention and BI-LSTM model outperform some other deep learning-based algorithms such as TCN and LSTM in producing better precision, recall, and F1-scores. 

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4. Detection of dementia on voice recordings using deep learning: a Framingham Heart Study

   https://doi.org/10.1186/s13195-021-00888-3  

   Xue, Chonghua; Karjadi, Cody; Paschalidis, Ioannis Ch.; Au, Rhoda; Kolachalama, Vijaya B. (December 2021, Alzheimer's Research & Therapy)

   Abstract Background Identification of reliable, affordable, and easy-to-use strategies for detection of dementia is sorely needed. Digital technologies, such as individual voice recordings, offer an attractive modality to assess cognition but methods that could automatically analyze such data are not readily available. Methods and findings We used 1264 voice recordings of neuropsychological examinations administered to participants from the Framingham Heart Study (FHS), a community-based longitudinal observational study. The recordings were 73 min in duration, on average, and contained at least two speakers (participant and examiner). Of the total voice recordings, 483 were of participants with normal cognition (NC), 451 recordings were of participants with mild cognitive impairment (MCI), and 330 were of participants with dementia (DE). We developed two deep learning models (a two-level long short-term memory (LSTM) network and a convolutional neural network (CNN)), which used the audio recordings to classify if the recording included a participant with only NC or only DE and to differentiate between recordings corresponding to those that had DE from those who did not have DE (i.e., NDE (NC+MCI)). Based on 5-fold cross-validation, the LSTM model achieved a mean (±std) area under the receiver operating characteristic curve (AUC) of 0.740 ± 0.017, mean balanced accuracy of 0.647 ± 0.027, and mean weighted F1 score of 0.596 ± 0.047 in classifying cases with DE from those with NC. The CNN model achieved a mean AUC of 0.805 ± 0.027, mean balanced accuracy of 0.743 ± 0.015, and mean weighted F1 score of 0.742 ± 0.033 in classifying cases with DE from those with NC. For the task related to the classification of participants with DE from NDE, the LSTM model achieved a mean AUC of 0.734 ± 0.014, mean balanced accuracy of 0.675 ± 0.013, and mean weighted F1 score of 0.671 ± 0.015. The CNN model achieved a mean AUC of 0.746 ± 0.021, mean balanced accuracy of 0.652 ± 0.020, and mean weighted F1 score of 0.635 ± 0.031 in classifying cases with DE from those who were NDE. Conclusion This proof-of-concept study demonstrates that automated deep learning-driven processing of audio recordings of neuropsychological testing performed on individuals recruited within a community cohort setting can facilitate dementia screening. 

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5. Deep Learning-Based Time-Series Analysis for Detecting Anomalies in Internet of Things

   https://doi.org/10.3390/electronics11193205  

   Gopali, Saroj; Siami Namin, Akbar (October 2022, Electronics)

   Anomaly detection in time-series data is an integral part in the context of the Internet of Things (IoT). In particular, with the advent of sophisticated deep and machine learning-based techniques, this line of research has attracted many researchers to develop more accurate anomaly detection algorithms. The problem itself has been a long-lasting challenging problem in security and especially in malware detection and data tampering. The advancement of the IoT paradigm as well as the increasing number of cyber attacks on the networks of the Internet of Things worldwide raises the concern of whether flexible and simple yet accurate anomaly detection techniques exist. In this paper, we investigate the performance of deep learning-based models including recurrent neural network-based Bidirectional LSTM (BI-LSTM), Long Short-Term Memory (LSTM), CNN-based Temporal Convolutional (TCN), and CuDNN-LSTM, which is a fast LSTM implementation supported by CuDNN. In particular, we assess the performance of these models with respect to accuracy and the training time needed to build such models. According to our experiment, using different timestamps (i.e., 15, 20, and 30 min), we observe that in terms of performance, the CuDNN-LSTM model outperforms other models, whereas in terms of training time, the TCN-based model is trained faster. We report the results of experiments in comparing these four models with various look-back values. 

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