Abstract

The management and regulation of the waste generated from coal-based power plants have been a key issue throughout the world. There is a need for the voluminous utilization of coal ash in India. The bulk utilization of the industrial waste materials like coal ash can control the exploitation of the natural resources like soil in diferent aspects. Here, it has been attempted to evaluate the potential use of coal ash as a foundation/fll material. In addition, physical, geotechnical, morphological, and chemical characterization of the pond ash has been done. Then, fnite element modeling of the vibratory roller compaction was simulated using PLAXIS3D software for the recommendation of the pond ash as a construction material in place of soil. Also, the reduction in CO2 emission with pond ash was compared with that of the soil. The reduction in CO2 emission was found to be about 15% considering several factors like distance from the source station to the test site, types of coal ash used, types of construction equipment employed, etc. The utilization of pond ash as fll material was found to be economical, and its utilization saves around INR 32 to INR 399 per cubic meter. Keywords Coal ash · Foundation/fll material · Finite element modeling · Vibratory roller compaction · CO2 emission

Abstract

The authenticated key agreement is one of the major security services that can be used to secure an Internet of Things (IoT) environment, where the devices collect the data and the data is then aggregated at the cloud server, and then a user needs to access the data stored at the server(s) securely. For this purpose, after a mutual authentication performed between a user and the accessed server, a session key needs to be established among them for secure communication. In this article, we design an efficient lattice-based authenticated key exchange protocol using ring-based version of learning with errors assumption for the IoT-enabled smart devices. The proposed protocol is basically a key exchange that uses the reconciliation mechanism. The detailed security analysis under the standard model has been performed along with the informal security analysis to show that the proposed protocol is robust against different attacks. We then simulate the proposed protocol under the NS-3 simulator to measure the network performance parameters like network throughput and latency. A comparative analysis shows that the proposed protocol has superior security, less computational cost, and comparable communication cost when compered these parameters with the other competing schemes.

Abstract

Cognitive Internet of Things (CIoT) supports the organizations to learn from the information (data) arriving from various connected devices, sensors, machines and other sources, and at the same time it inspires intelligence into different business operations, products, customer experiences, and people. Data poising attacks are very serious concerns because they may play a significant factor for businesses and organizations for both financial terms and damaging their reputations, when the Big data analytics on the analyzed data is itself corrupted. To mitigate this issue, in this paper, we suggest a blockchain-based Artificial Intelligence(AI)/Machine Learning(ML)-enabled Big data analytics mechanism for CIoT environment. The comprehensive experimental results have been provided under two circumstances: (1) performance of the ML model under data poisoning attacks and (2) performance of the ML model without data poisoning attacks. In the first case, we show how the data poison attacks can effect the ML model when the data is on some cloud storage (i.e. not in the blockchains), whereas in the second case we show the effect when the data is in the blockchains (i.e., without data poisoning attacks). The experimental results demonstrate that we have significant gains in performance in terms of accuracy, recall, precision and F1 score when there are no data poisoning attacks on the data. Moreover, a detailed blockchain simulation has carried out to demonstrate the practical aspects of the proposed security framework.

Abstract

—With Cardiovascular Diseases on the rise around the world, Electrocardiograms (ECGs) play a crucial role in their diagnosis owing to their non-invasive nature and simplicity. Medical professionals typically use 12-lead ECGs for medical analysis but gathering 12-lead ECG data is an arduous task outside clinical setting. Modern wearables can collect an ECG with fewer leads than the standard 12 leads. However, medical professionals and conventional ECG analysis software find this reduced lead set data challenging to interpret. By using the reduced lead set data to create standard 12-lead ECG data, ECG reconstruction can solve this issue. This paper proposes a novel single-lead to multi-lead ECG reconstruction solution using a modified Attention U-net framework. Using only the lead II of ECG, our model is able to reproduce the other 11 leads of conventional 12-lead ECG with a Pearson correlation, Mean square error and R-squared error of 0.805, 0.0122 and 0.639, respectively. Further, a single combined model is used to reconstruct all 11 leads simultaneously, improving performance and simultaneously reducing the computational resources needed for training compared to current literature in the field. In comparison to previous works, which only reconstruct small ECG segments, our model is trained to reconstruct longer 10-second ECG signals. We demonstrate our model’s ability for real-life utilisation using a cardiovascular disease classification task. A deep learning model was trained for multi-disease classification on actual 12-lead ECG data and was tested on both original and reconstructed 12-lead ECG signals. The classification accuracies for the original and reconstructed signals were comparable, portraying that our reconstruction model can preserve diagnostically relevant artefacts in its reconstructed signals. This work provides a new promising solution in the field of single-lead ECG reconstruction, taking us a step closer to bridging the divide between reduced lead set data and existing 12-lead ECG end users like clinicians and automatic ECG classifiers. Index Terms—Healthcare, Cardiology, Electrocardiograms, Machine Learning, Neural Networks

Abstract

The particle flow Gaussian particle filter (PFGPF) uses an invertible particle flow to generate a proposal density. It approximates the predictive and posterior distributions as Gaussian densities. In this paper, we use a bank of PFGPF filters to construct a Particle flow Gaussian sum particle filter (PFGSPF), which approximates the prediction and posterior as Gaussian mixture model. This approximation is useful in complex estimation problems where a single Gaussian approximation is inadequate. We compare the performance of this proposed filter with the PFGPF and others in challenging numerical simulations. Index Terms— Gaussian mixture, invertible particle flow, Gaussian sum particle filer, Gaussian particle filter, PFGPF.

Abstract

The tourism industry is important for the benefits it brings and due to its role as a commercial activity that creates demand and growth for many more industries. Yet there is not much work on data science problems in tourism. Unfortunately, there is not even a standard benchmark for evaluation of tourismspecific data science tasks and models. In this paper, we propose a benchmark, TOURISMNLG, of five natural language generation (NLG) tasks for the tourism domain and release corresponding datasets with standard train, validation and test splits1 . Further, previously proposed data science solutions for tourism problems do not leverage the recent benefits of transfer learning. Hence, we also contribute the first rigorously pretrained mT5 and mBART model checkpoints for the tourism domain. The models have been pretrained on four tourism-specific datasets covering different aspects of tourism. Using these models, we present initial baseline results on the benchmark tasks. We hope that the dataset will promote active research for natural language generation for travel and tourism. Keywords: NLG for Tourism · Long QA · Blog-Title Generation · Forum-Title Generation · Paragraph Generation · Short QA

Abstract

We say that two given polynomials f, g ∈ R[x_1, ..., x_n], over a ring R, are equivalent under shifts if there exists a vector (a_1, ..., a_n) ∈ Rⁿ such that f(x_1+a_1, ..., x_n+a_n) = g(x_1, ..., x_n). This is a special variant of the polynomial projection problem in Algebraic Complexity Theory. Grigoriev and Karpinski (FOCS 1990), Lakshman and Saunders (SIAM J. Computing, 1995), and Grigoriev and Lakshman (ISSAC 1995) studied the problem of testing polynomial equivalence of a given polynomial to any t-sparse polynomial, over the rational numbers, and gave exponential time algorithms. In this paper, we provide hardness results for this problem. Formally, for a ring R, let SparseShift_R be the following decision problem - Given a polynomial P(X), is there a vector 𝐚 such that P(X+𝐚) contains fewer monomials than P(X). We show that SparseShift_R is at least as hard as checking if a given system of polynomial equations over R[x_1,..., x_n] has a solution (Hilbert’s Nullstellensatz). As a consequence of this reduction, we get the following results. 1) SparseShift_ℤ is undecidable. 2) For any ring R (which is not a field) such that HN_R is NP_R-complete over the Blum-Shub-Smale model of computation, SparseShift_ is also NP_R-complete. In particular, SparseShift_ℤ is also NP_ℤ-complete. We also study the gap version of the SparseShift_R and show the following. 1) For every function β:ℕ → ℝ_+ such that β ∈ o(1), N^β-gap-SparseShift_ℤ is also undecidable (where N is the input length). 2) For R = 𝔽_p, ℚ, ℝ or ℤ_q and for every β > 1 the β-gap-SparseShift_R problem is NP-hard. Furthermore, there exists a constant α > 1 such that for every d = O(1) in the sparse representation model, and for every d ≤ n^O(1) in the arithmetic circuit model, the α^d-gap-SparseShift_R problem is NP-hard when given polynomials of degree at most d, in O(nd) many variables, as input.

Abstract

Writing a good job description is an important step in the online recruitment process to hire the best candidates. Most recruiters forget to include some relevant skills in the job description. These missing skills affect the performance of recruitment tasks such as job suggestions, job search, candidate recommendations, etc. Existing approaches are limited to contextual modelling, do not exploit inter-relational structures like job-job and job-skill relationships, and are not scalable. In this paper, we exploit these structural relationships using a graph-based approach. We propose a novel skill prediction framework called JobXMLC, which uses graph neural networks with skill attention to predict missing skills using job descriptions. JobXMLC enables joint learning over a job-skill graph consisting of 22.8K entities (jobs and skills) and 650K relationships. We experiment with real-world recruitment datasets to evaluate our proposed approach. We train JobXMLC on 20, 298 jobs and 2, 548 skills within 30 minutes on a single GPU machine. JobXMLC outperforms the state-of-the-art approaches by 6% on precision and 3% on recall. JobXMLC is 18X faster for training tasks and up to 634X faster in skill prediction on benchmark datasets enabling JobXMLC to scale up on larger datasets. We have made our code and dataset public at https://precog.iiit.ac.in/resources.html.

Abstract

Employment scams, such as scapegoat positions, clickbait and non-existing jobs, etc., are among the top five scams registered over online platforms.1 Generally, scam complaints contain heterogeneous information (money, location, employment type, organization, email, and phone number), which can provide critical insights for appropriate interventions to avoid scams. Despite substantial efforts to analyze employment scams, integrating relevant scam-related information in structured form remains unexplored. In this work, we extract this information and construct a large-scale Employment Scam Knowledge Graph consisting of 0.1M entities and 0.2M relationships. Our findings include discovering different modes of employment scams, entities, and relationships among entities to alert job seekers. We plan to extend this work by utilizing a knowledge graph to identify and avoid potential scams in the future

Abstract

Suspect Identification is one of the most pivotal aspects of a forensic and criminal investigation. A significant amount of time and skill is devoted to creating sketches for it and requires a fair amount of recollections from the witness to provide a useful sketch. We devise a method that aims to automate the process of suspect identification and model this problem by iteratively retrieving images from feedback provided by the user. Compared to standard image retrieval tasks, interactive facial image retrieval is specifically more challenging due to the high subjectivity involved in describing a person’s facial attributes and appropriately evolving with the preferences put forward by the user. Our method uses a relatively simpler form of supervision by utilizing the user’s feedback to label images as either similar or dissimilar to their mental image of the suspect based on which we propose a loss function using the contrastive learning paradigm that is optimized in an online fashion. We validate the efficacy of our proposed approach using a carefully designed testbed to simulate user feedback and a large-scale user study. We empirically show that our method iteratively improves personalization, leading to faster convergence and enhanced recommendation relevance, thereby, improving user satisfaction. Our proposed framework is being designed for real-time use in the metropolitan crime investigation department, and thus is also equipped with a user-friendly web interface with a real-time experience for suspect retrieval.