A Smart Home essentially is a communication network that connects smart devices, sensors and actuators enabling the owner to locally and remotely access, monitor and control them. However, SHs are currently facing increasing challenges due to the underlying home automation systems which are affected by network security issues. This paper presents a SH testbed comprising SH devices that employ the IEEE 802.11 standard protocol for communication. Comprehensive tests were conducted with an aim to observe and understand vulnerabilities that exist in smart device networks when they are attacked using different types of attacks such as Eavesdropping, Denial of Service (DoS), and Man-In-TheMiddle (MITM). This paper reports and discusses the findings obtained from these experiments.
This paper aims to provide a clearer view of container technology, such as its advantages and disadvantages, how it can cooperate with Openstack, and document the improvements made by this cooperation. In terms of containerization technology, this paper will illustrate the components of Docker and the impact it has compared to the already classic technology of virtual machines. Another element to be addressed in this paper is the importance of moving some of the computing power to the periphery of networks. This can be done using existing peripheral devices to the extent that the computing resources on this equipment allow. This move is necessary to provide an infrastructure capable of supporting services with low latency needs, minimal delay (traffic sensor, vehicle sensors) and at the same time an infrastructure that will free the core of networks from a large volume of data. This paper will follow the comparison of household equipment that can play an active role in computing at the periphery of networks to highlight what types of applications or calculations can be performed on them. We will follow in this paper the comparison of household equipment that can play an active role in computing at the periphery of networks to highlight what types of applications or calculations can be performed on them. Keywords-container; Docker; Network Function Virtualization (NFV).
This paper addresses an effective scheme for sensing data collection and management in future smart city applications for rapid urbanization. The main contribution of this paper provides an application of Internet of things as a new Internet technology as case study. In particular, we focus on two key technologies, an information-centric network and unmanned aerial vehicles. We propose a novel joint sensing, forwarding, and storing scheme, for which we introduce an erase code technique and cross-layer optimization. We provide the overall blueprint of our study, and we present a preliminary evaluation. The numerical results illustrate that the scheme can improve data caching capability by 29.3% in the deployment of future wireless sensor networks. KeywordsInformation-centric network (ICN); Wireless sensor network (WSN); Unmanned aerial vehicle (UAV); Cross-
We propose a novel, attention-based self-supervised approach to identify claim-worthy sentences in a fake news article, an important first step in automated fact-checking. We leverage aboutness of headline and content using attention mechanism for this task. The identified claims can be used for downstream task of claim verification for which we are releasing a benchmark dataset of manually selected compelling articles with veracity labels and associated evidence. This work goes beyond stylistic analysis to identifying content that influences reader belief. Experiments with three datasets show the strength of our model. Data and code available at https://github.com/architapathak/Self-Supervised-ClaimIdentification
The common wisdom about Sanskrit is that it is free word order language. This word order poses challenges such as handling non-projectivity in parsing. The earlier works on the word order of Sanskrit have shown that there are syntactic structures in Sanskrit which cannot be covered under even the non-planarity. In this paper, we study these structures further to investigate if they can fall under well-nestedness or not. A small manually tagged corpus of the verses of ´Sr ¯ ımad-Bhagavad-G ¯ ıt ¯ a (BhG) was considered for this study. It was noticed that there are as many well-nested trees as there are ill-nested ones. From the linguistic point of view, we could get a list of relations that are involved in the planarity violations. All these relations had one thing in common—that they have unilateral expectancy. It was this loose binding, as against the mutual expectancy with certain other relations, that allowed them to cross the phrasal boundaries.
Question Answer pair generation is a task that has been worked upon by multiple researchers in many languages. It has been a topic of interest due to its extensive uses in different fields like self assessment, academics, busi ness website FAQs etc. Many experiments were conducted on Question Answering pair generation in English, concentrating on basic Whquestions with a rulebased approach. We have built the first hybrid machine learning and rulebased solution in Telugu which is ef ficient for short stories or short passages in children’s books. Our work covers the funda mental question forms with the question types: adjective, yes/no, adverb, verb, when, where, whose, quotative, and quantitative(how many/ how much). We constructed rules for question generation using POS tags and UD tags along with linguistic information of the surrounding context of the word.
In the field of Natural Language Processing (NLP) the process of stemming plays a significant role. Stemmer transforms an inflected word to its root form. Stemmer significantly increases the efficiency of Information Retrieval (IR) systems. It is a very basic yet fundamental text pre-processing task widely used in many NLP tasks. Several important works on stemming have been carried out by researchers in English and other major languages. In this paper, we study and review existing works on stemming in Bengali and other Indian languages. Finally we propose a rule based approach that explores Bengali morphology and leverages WordNet to achieve better accuracy. Our algorithm produced stemming accuracy of 98.86% for Nouns and 99.75% for Verbs.
The anthology of spoken languages today is inundated with textual information, necessitating the development of automatic summarization models. In this manuscript, we propose an extractor-paraphraser based abstractive summarization system that exploits semantic overlap as opposed to its predecessors that focus more on syntactic information overlap. Our model outperforms the state-of-the-art baselines in terms of ROUGE, METEOR and word mover similarity (WMS), establishing the superiority of the proposed system via extensive ablation experiments. We have also challenged the summarization capabilities of the state of the art Pointer Generator Network (PGN), and through thorough experimentation, shown that PGN is more of a paraphraser, contrary to the prevailing notion of a summarizer; illustrating it's incapability to accumulate information across multiple sentences.
The recognition task of Multi-Word Named Entities (MNEs) in itself is a challenging task when the language is inflectional and agglutinative. Having break-through NLP researches with deep neural network and language modelling techniques, the applicability of such techniques/algorithms for Indian language like Manipuri remains unanswered. In this paper an attempt to recognize Manipuri MNE is performed using a Long Short Term Memory (LSTM) recurrent neural network model in conjunction with Part Of Speech (POS) embeddings. To further improve the classification accuracy, word cluster information using K-means clustering approach is added as a feature embedding. The cluster information is generated using a Skip-gram based words vector that contains the semantic and syntactic information of each word. The model so proposed does not use extensive language morphological features to elevate its accuracy. Finally the model’s performance is compared with the other machine learning based Manipuri MNE models.
Language detection is considered a difficult task especially for similar languages, varieties, and dialects. With the growing number of on-line content in different languages, the need for reliable and robust language detection tools also increased. In this work, we use supervised autoencoders with a bayesian optimizer for language detection and highlight its effi-ciency in detecting similar languages with dialect variance in comparison to other state-of-the-art techniques. We evaluated our approach on multiple datasets (Ling10, Discriminating between Similar Language (DSL), and Indo-Aryan Language Identification (ILI)). Obtained results demonstrate that SAE is highly effective in detecting languages, up to a 100% accuracy in the Ling10. Similarly, we obtain a competitive performance in identifying similar languages, and dialects, 92%, and 85% for DSL and ILI datasets respectively.
The behavior, mental-health, emotion, life choices, social nature, and thought patterns of an individual are revealed by personality. Cyber forensics, personalized services, recommender systems are some of the examples of automatic personality prediction. A deep learning based personality prediction system has been developed in this work. Facial and ambient features are extracted from the visual modality using Multi-task Cascaded Convolutional Networks (MTCNN) and ResNet, respectively; the audio features are extracted using the VGGish Convolutional Neural Networks (VGGish CNN), and the text features are extracted using n-gram Convolutional Neural Networks (CNN). The extracted features are then passed to a fully connected layer followed by sigmoid for the final output prediction. Finally, the text, visual and audio modalities are combined in different ways: (i) concatenation of features in multi-modal setting, and (ii) application of different attention mechanisms for fusing features. The dataset released in Chalearn-17 is used for evaluating the performance of the system. From the obtained results, it can be concluded that, the concatenation of features extracted from different modalities attains comparable results with the averaging method (late fusion). It is also shown that a hand full of images are enough for attaining comparable performance.
In this paper we explore the use of Prediction by partial matching (PPM) compression based to segment Hadith into its two main components (Isnad and Matan). The experiments utilized the PPMD variant of the PPM, showing that PPMD is effective in Hadith segmentation. It was also tested on Hadith corpora of different structures. In the first experiment we used the nonauthentic Hadith (NAH) corpus for training models and testing, and in the second experiment we used the NAH corpus for training models and the Leeds University and King Saud University (LK) Hadith corpus for testing PPMD segmenter. PPMD of order 7 achieved an accuracy of 92.76% and 90.10% in the first and second experiments, respectively.
© 2020 The Authors. Published by ACL. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://aclanthology.org/2020.icon-main.12
The paper explores how an attention-based approach can increase performance on the task of native-language identification (NLI), i.e., to identify an author’s first language given information expressed in a second language. Previously, Support Vector Machines have consistently outperformed deep learning-based meth-ods on the TOEFL11 data set, the de facto standard for evaluating NLI systems. The attention-based system BERT (Bidirectional Encoder Representations from Trans-formers) was first tested in isolation on the TOEFL11 data set, then used in a meta-classifier stack in combination with traditional techniques to produce an accuracy of 0.853. However, more labelled NLI data is now available, so BERT was also trained on the much larger Reddit-L2 data set, containing 50 times as many examples as previously used for English NLI, giving an accuracy of 0.902 on the Reddit-L2 in-domain test scenario, improving the state-of-the-art by 21.2 percentage points.
Smart devices are often deployed in some edge-devices, which require quality solutions in limited amount of memory usage. In most of the user-interaction based smart devices, coreference resolution is often required. Keep-ing this in view, we have developed a fast and lightweight coreference resolution model which meets the minimum memory requirement and converges faster. In order to generate the embeddings for solving the task of coreference resolution, DistilBERT, a light weight BERT module is utilized. DistilBERT con-sumes less memory (only 60% of memory in comparison to BERT-based heavy model) and it is suitable for deployment in edge devices. DistilBERT embedding helps in 60% faster convergence with an accuracy compromise of 2.59%, and 6.49% with respect to its base model and current state-of-the-art, respec-tively.
This paper is an attempt to study polarisation on social media data. We focus on two hugely controversial and talked about events in the Indian diaspora, namely 1) the Sabarimala Temple (located in Kerala, India) incident which became a nationwide controversy when two women under the age of 50 secretly entered the temple breaking a long standing temple rule that disallowed women of menstruating age (10-50) to enter the temple and 2) the Indian government’s move to demonetise all existing 500 and 1000 denomination banknotes, comprising of 86% of the currency in circu-lation, in November 2016. We gather tweets around these two events in various time periods, preprocess and annotate them with their sentiment polarity and emotional category, and analyse trends to help us understand changing polarity over time around controversial events. The tweets collected are in English, Hindi and code-mixed Hindi-English. Apart from the analysis on the annotated data, we also present the twitter data comprising a total of around 1.5 million tweets.
Acoustically, English lexical stress is multidimensional and involving manipulation of duration, intensity, fundamental frequency (F0) and vowel quality. The current study investigates the acquisition of English lexical stress by L1 Bengali speakers at the phonological level in terms of the properties of acoustic cues. For this purpose, this study compares 20 L1 Bengali speakers' use of acoustic correlates for the production of English lexical stress in context sentence and neutral frame sentence. The result of this study showed that L1 Bengali speakers were not able to achieve neutral frame sentence like control over duration, intensity, F0 and to a limited extent vowel quality in context sentence. As a result, unlike neutral frame sentence, L1 Bengali speakers were not sensitive to English lexical stress contrast in context sentence. This analysis reveals that, the difference between the neutral frame and context sentences in terms of L1 Bengali speakers’ realization of phonology of English lexical stress contrast was probably due to the influence of Bengali phonology of lexical stress placement (restricted to the initial syllable of a word) on L1 Bengali speakers’ English speech.
