Abstract

Access control is one of the important security services that is essential for an Internet of Things (IoT)-enabled authorized user using his/her smart mobile device to authenticate with the trusted Hospital Authority (HA) in a hospital. After mutual authentication, a secret key is established among the user and HA for secure data transmission. The secure data (transactions) gathered by the HA from the users in the hospital is encrypted using a shared key among various trusted hospital authorities involved in the private blockchain network of hospitals. The HA of each hospital is responsible for constructing the blocks in the blockchain using the encrypted transactions because the data in healthcare application is treated as confidential and private. To deal with this important problem, we design a novel access control scheme using private blockchain technology. The proposed scheme is shown to be secure against various well-known attacks. Moreover, the proposed scheme provides better security and functionality features, and also requires low communication and computational costs as compared to relevant approaches.

Abstract

6G-enabled network in a box (NIB) is a multi- generational, rapidly deployable hardware and software technology for the communication. 6G-enabled NIB provides high level of flexibility which makes it capable to provide connectivity services for different types of applications as it is effective for the communications of after disaster scenario battlefields scenario and industrial scenario. In 6G-enabled NIB deployed industrial applications, various passive and active attacks are possible because the involved entities communicate over insecure channel. In this paper, a new remote user authentication and key management scheme is proposed for securing 6G-enabled NIB deployed for industrial applications, which we call in short as UAKMS-NIB. The security analysis shows the resilience of the UAKMS-NIB against various types of possible attacks. The practical demonstration of UAKMS-NIB is also provided to measure its impact on the network performance parameters.

Abstract

With the emergence of the concept of smart city and the increasing demands for a range of vehicles, Internet of Vehicles (IoV) has achieved a lot of attention by providing multiple benefits, including vehicle emergence, accidents, levels of pollution, and traffic congestion. Moreover, IoV provides various services by combining vehicular ad-hoc networks (VANET) with the Internet of Things (IoT) in smart cities. However, the communication among vehicles is susceptible to various security threats because the sensitive message is transmitted via a insecure channel in the IoV-based smart city environment. Thus, a secure message authentication protocol is indispensable to ensure various services for IoV in a smart city environment. In 2020, a secure message authentication protocol for IoV communication in smart cities has been proposed. However, we discover that the analyzed scheme suffers from various potential attacks such as impersonation, secret key disclosure, and off-line guessing attacks, and also does not ensure authentication. To solve the security threats of the analyzed scheme, we design a secure and efficient message authentication protocol for IoV in a smart city environment, called IoV-SMAP. The proposed IoV-SMAP can resist security drawbacks and provide user anonymity, and mutual authentication. We demonstrate the security of IoV-SMAP by performing informal and formal analyses such as the Real-or-Random (ROR) model, and Automated Validation of Internet Security Protocols and Application (AVISPA) simulations. In addition, we compare the performance of IoV-SMAP with related existing competing authentication schemes. We demonstrate that IoV-SMAP provides better security along with efficiency than related competing schemes and is suitable for the IoV-based smart city environment.

Abstract

The real-world data is often susceptible to label noise, which might constrict the effectiveness of the existing state of the art algorithms for ordinal regression. Existing works on ordinal regression do not take label noise into account. We propose a theoretically grounded approach for class conditional label noise in ordinal regression problems. We present a deep learning implementation of two commonly used loss functions for ordinal regression that is both-1) robust to label noise, and 2) rank consistent for a good ranking rule. We verify these properties of the algorithm empirically and show robustness to label noise on real data and rank consistency. To the best of our knowledge, this is the first approach for robust ordinal regression models.

Abstract

Spoken dialogue systems typically use one or several (top-N) ASR sequence(s) for inferring the semantic meaning and tracking the state of the dialogue. However, ASR graphs, such as confusion networks (confnets), provide a compact representation of a richer hypothesis space than a top-N ASR list. In this paper, we study the benefits of using confusion networks with a neural dialogue state tracker (DST). We encode the 2-dimensional confnet into a 1-dimensional sequence of embed-dings using a confusion network encoder which can be used with any DST system. Our confnet encoder is plugged into the 'Global-locally Self-Attentive Dialogue State Tacker' (GLAD) model for DST and obtains significant improvements in both accuracy and inference time compared to using top-N ASR hypotheses .

Abstract

Dysarthria is a motor speech disorder leading to imprecise articulation of speech. Acoustic analysis capable of detecting and assessing articulation errors is useful in dysarthria diagnosis and therapy. Since speakers with dysarthria experience difficulty in producing rhotics due to complex articulatory gestures of these sounds, the hypothesis of the present study is that duration of the rhotic approximant /ɹ/ distinguishes dysarthric speech of different severity levels. Duration measurements were conducted using the third formant (F3) trajectories estimated from quasi-closed-phase (QCP) spectrograms. Results indicate that the severity level of spastic dysarthria has a significant effect on duration of /ɹ/. In addition, the phonetic context has a significant effect on duration of /ɹ/, the ɪ-r-ɛ context showing the largest difference in /ɹ/ duration between dysarthric speech of the highest severity levels and healthy speech. The results of this preliminary study can be used in the future to develop signal processing and machine learning methods to automatically predict the severity level of spastic dysarthria from speech signals.

Abstract

We consider the task of Medical Concept Normalization (MCN) which aims to map informal medical phrases such as “loosing weight” to formal medical concepts, such as “Weight loss”. Deep learning models have shown high performance across various MCN datasets containing small number of target concepts along with adequate number of training examples per concept. However, scaling these models to millions of medical concepts entails the creation of much larger datasets which is cost and effort intensive. Recent works have shown that training MCN models using automatically labeled examples extracted from medical knowledge bases partially alleviates this problem. We extend this idea by computationally creating a distant dataset from patient discussion forums. We extract informal medical phrases and medical concepts from these forums using a synthetically trained classifier and an off-the-shelf medical entity linker respectively. We use pretrained sentence encoding models to find the k-nearest phrases corresponding to each medical concept. These mappings are used in combination with the examples obtained from medical knowledge bases to train an MCN model. Our approach outperforms the previous state-of-the-art by 15.9% and 17.1% classification accuracy across two datasets while avoiding manual labeling.

Abstract

Automatic text summarization has been widely studied as an important task in natural language processing. Traditionally, various feature engineering and machine learning based systems have been proposed for extractive as well as abstractive text summarization. Recently, deep learning based, specifically Transformer-based systems have been immensely popular. Summarization is a cognitively challenging task–extracting summary worthy sentences is laborious, and expressing semantics in brief when doing abstractive summarization is complicated. In this paper, we specifically look at the problem of summarizing scientific research papers from multiple domains. We differentiate between two types of summaries, namely,(a) LaySumm: A very short summary that captures the essence of the research paper in layman terms restricting overtly specific technical jargon and (b) LongSumm: A much longer detailed summary aimed at providing specific insights into various ideas touched upon in the paper. While leveraging latest Transformer-based models, our systems are simple, intuitive and based on how specific paper sections contribute to human summaries of the two types described above. Evaluations against gold standard summaries using ROUGE (Lin, 2004) metrics prove the effectiveness of our approach. On blind test corpora, our system ranks first and third for the LongSumm and LaySumm tasks respectively.

Abstract

In recent years, the fields of NLP and information retrieval have made tremendous progress thanks to deep learning models like RNNs and LSTMs, and Transformer[35] based models like BERT[9]. But these models are humongous in size. Real world applications however demand small model size, low response times and low computational power wattage. We will discuss six different types of methods (pruning, quantization, knowledge distillation, parameter sharing, matrix decomposition, and other Transformer based methods) for compression of such models to enable their deployment in real industry NLP projects. Given the critical need of building applications with efficient and small models, and the large amount of recently published work in this area, we believe that this tutorial is very timely. We will organize related work done by the 'deep learning for NLP' community in the past few years and present it as a coherent story.

Abstract

Sexism, a pervasive form of oppression, causes profound suffering through various manifestations. Given the rising number of experiences of sexism reported online, categorizing these recollections automatically can aid the fight against sexism, as it can facilitate effective analyses by gender studies researchers and government officials involved in policy making. In this paper, we explore the fine-grained, multi-label classification of accounts (reports) of sexism. To the best of our knowledge, we consider substantially more categories of sexism than any related prior work through our 23-class problem formulation. Moreover, we present the first semi-supervised work for the multi-label classification of accounts describing any type(s) of sexism wherein the approach goes beyond merely fine-tuning pre-trained models using unlabeled data. We devise self-training based techniques tailor-made for the multi-label nature of the problem to utilize unlabeled samples for augmenting the labeled set. We identify high textual diversity with respect to the existing labeled set as a desirable quality for candidate unlabeled instances and develop methods for incorporating it into our approach. We also explore ways of infusing class imbalance alleviation for multi-label classification into our semi-supervised learning, independently and in conjunction with the method involving diversity. Several proposed methods outperform a variety of baselines on a recently released dataset for multi-label sexism categorization across several standard metrics.