Abstract

A secure authentication protocol plays a crucial role in securing communications over wireless and mobile networks. Due to resource-limitations and the nature of the wireless channel, the global mobile networks are highly susceptible to various attacks. Recently, an efficient authentication system for global roaming has been proposed in the literature. In this article, we first show that the analyzed authentication system is vulnerable man-in-the-middle attack, replay attack and Denial-of-Service (DoS) attack, and it does not ensure untraceability and local password-verification process to identify wrong passwords. To fix these security flaws, we propose a more efficient and robust authentication system for roaming in mobility networks. We use the formal verification tools like ProVerif, Automated Validation of Internet Security Protocols and Applications (AVISPA) and Burrows-Abadi-Needham (BAN) logic to check the regularity of the authentication protocol. Moreover, we prove the secrecy of a session key through the formal security using the random oracle model, known as Real-Or-Random (ROR) model. Finally, a detailed performance evaluation proves that the security protocol not only provides a security strength, but also preserves the low computational overhead. Thus, the proposed authentication protocol is secure and computationally efficient as compared to other relevant schemes.

Abstract

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on device-to-device (D2D) ecosystems (eg, crowdsensing). Sophisticated applications, like cryptocurrencies, need distributed ledgers to function. Distributed ledgers, such as blockchains and directed acyclic graphs (DAGs), employ consensus protocols to add data in the form of blocks. However, such protocols are designed for resourceful devices that are interconnected via the Internet. Moreover, existing distributed ledgers are not deployable to D2D ecosystems since their storage needs are continuously increasing. In this work, we introduce and analyse Mneme, a DAG-based distributed ledger that can be maintained solely by mobile devices. Mneme utilizes two novel consensus protocols: Proof-of-Context (PoC) and Proof-of-Equivalence (PoE). PoC employs users' context to add data on Mneme. PoE is executed periodically to summarize data and produce equivalent blocks that require less storage. We analyze Mneme's security and justify the ability of PoC and PoE to guarantee the characteristics of distributed ledgers: persistence and liveness. Furthermore, we analyze potential attacks from malicious users and prove that the probability of a successful attack is inversely proportional to the square of the number of mobile users who maintain Mneme.

Abstract

This paper presents a methodology submitted to the FinSBD-2 shared task to extract well formed sentences, lists and items from noisy unstructured financial PDF documents in English language. The proposed architecture for document structure identification, is a combination of deep learning and heuristic based approaches. We use two unidirectional Long Short-Term Memory (LSTM) encoders to get the sentence split tokens from the set of all the possible split points. Further, the outputs are passed on to an attention based LSTM network to select only the well formed sentences from all the possible sentences. These outputs are merged to ultimately produce all possible well formed sentences. Apart from the sentences, lists and items are identified using a combination of heuristics which identify patterns in the data. The final F1 score, 0.217 on this task, is obtained by comparing the start and end indices of sentences, lists and items. We have presented another parameter, which is used to evaluate the class coverage by checking the overlap between the predicted and ground truth sentences and obtained an average 40% class coverage score. This metric is more useful for industry researchers who require coverage of the content rather than character level precision. The proposed approach will empower both academia and industry researchers in their effort to handle noisy documents for various NLP tasks by providing a simple, fast and robust approach to identify structure in their documents.

Abstract

In one aspect, a computerized method for automatically generating digital video content from scripts includes a media engine. The media engine receives a script and a plurality of flags; sending the script to a natural language processing (NLP) engine. The NLP engine parses the script. The script is broken into a set of keywords and phrases in a JSON format. The NLP engine, based on the keywords and phrases and the plurality of flags, obtains a relevant background scene for the video and a relevant set of assets for the video, and a set of associated attributes of each of the set of assets. In an asset is a character or object for the video. The NLP engine provides the parsed script in the JSON format and the relevant background scene of the video and the relevant set of assets of the video, and the set of associated attributes of each of the set of assets to a layout engine. The layout engine, based on the parsed script …

Abstract

We propose thetextbf {AViNet} architecture for audiovisual saliency prediction. AViNet is a fully convolutional encoder-decoder architecture. The encoder combines visual features learned for action recognition, with audio embeddings learned via an aural network designed to classify objects and scenes. The decoder infers a saliency map via trilinear interpolation and 3D convolutions, combining hierarchical features. The overall architecture is conceptually simple, causal, and runs in real-time (60 fps). AViNet outperforms the state-of-the-art on ten (seven audiovisual and three visual-only) datasets while surpassing human performance on the CC, SIM, and AUC metrics for the AVE dataset. Visual features maximally account for saliency on existing datasets with audio-only contributing to minor gains, except in specific contexts like social events. Our work, therefore, motivates the need to curate saliency datasets reflective of real-life, where both the visual and aural modalities complimentarily drive saliency. Our code and pre-trained models are available at this https URL

Abstract

Semantics play an important role when it comes to automated systems using text or language and it is different for different domains. In this paper, we tackle the FinSim 2020 shared task at IJCAIPRICAI 2020. The task deals with designing a semantic model which can automatically classify short phrases/terms from financial domain into the most relevant hypernym (or top-level) concept in an external ontology. We perform several experiments using different kinds of word and phrase level embeddings to solve the problem in an unsupervised manner. We also explore the use of a supplementary financial domain data; either to learn better concept representation or generate more training samples. We discuss both the positive and negative results that we observed while applying these approaches.

Abstract

n this paper, we study current and upcoming frontiers across the landscape of skeleton-based human action recognition. To begin with, we benchmark state-of-the-art models on the NTU-120 dataset and provide multi-layered assessment of the results. To examine skeleton action recognition'in the wild', we introduce Skeletics-152, a curated and 3-D pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale action dataset. The results from benchmarking the top performers of NTU-120 on Skeletics-152 reveal the challenges and domain gap induced by actions' in the wild'. We extend our study to include out-of-context actions by introducing Skeleton-Mimetics, a dataset derived from the recently introduced Mimetics dataset. Finally, as a new frontier for action recognition, we introduce Metaphorics, a dataset with caption-style annotated YouTube videos of the popular social game Dumb Charades and interpretative dance performances. Overall, our work characterizes the strengths and limitations of existing approaches and datasets. It also provides an assessment of top-performing approaches across a spectrum of activity settings and via the introduced datasets, proposes new frontiers for human action recognition.

Abstract

The problem of bound entanglement detection is a challenging aspect of quantum information theory for higher dimensional systems. Here, we propose an indecomposable positive map for two-qutrit systems, which is shown to generate a class of positive partial transposed (PPT) states. A corresponding witness operator is constructed and shown to be weakly optimal and locally implementable. Further, we perform a structural physical approximation of the indecomposable map to make it a completely positive one, and find a new PPT entangled state which is not detectable by certain other well-known entanglement detection criteria.

Abstract

India is a country with thousands of languages and dialects spoken across a billion-strong population. For multi-lingual content creation and accessibility, text-to-speech systems will play a crucial role. However, the current neural TTS systems are data-hungry and need about 20 hours of clean single-speaker speech data for each language and speaker. This is not scalable for the large number of Indian languages and dialects. In this work, we demonstrate three simple, yet effective pre-training strategies that allow us to train neural TTS systems with just about one-tenth of the data needs while also achieving better accuracy and naturalness. We show that such pre-trained neural TTS systems can be quickly adapted to different speakers across languages and genders with less than 2 hours of data, thus significantly reducing the effort for future expansions to the thousands of rare Indian languages. We specifically …

Abstract

In this work, we re-think the task of speech enhancement in unconstrained real-world environments. Current state-of-the-art methods use only the audio stream and are limited in their performance in a wide range of real-world noises. Recent works using lip movements as additional cues improve the quality of generated speech over" audio-only" methods. But, these methods cannot be used for several applications where the visual stream is unreliable or completely absent. We propose a new paradigm for speech enhancement by exploiting recent breakthroughs in speech-driven lip synthesis. Using one such model as a teacher network, we train a robust student network to produce accurate lip movements that mask away the noise, thus acting as a" visual noise filter". The intelligibility of the speech enhanced by our pseudo-lip approach is comparable (< 3% difference) to the case of using real lips. This implies that we can exploit the advantages of using lip movements even in the absence of a real video stream. We rigorously evaluate our model using quantitative metrics as well as human evaluations. Additional ablation studies and a demo video on our website containing qualitative comparisons and results clearly illustrate the effectiveness of our approach.