Recently, Twitter has been used as a citizen-engaging platform by Indian Railway Ministry (IRM) for collecting civic issues. However, due to the manual inspection model, a large percentage of complaints are left unaddressed affecting the credibility of the citizen-sourcing mediums. The existing solutions are for English reports and unable to capture the diverse range of code-mixed languages used in the complaints. In this paper, we developed a multilingual cased version of BERT (mBERT) for automated identification of monolingual and multilingual (aka code- mixed) complaints. The proposed mBERT is a transformers- based model pre-trained on multilingual Wikipedia corpus. In addition to the grievances classification, we also employ a BERT multi-label classifier for labelling the tweets with the issues reported in the complaints. The proposed solution approach obtains an accuracy of 82% with the binary classifier and overall accuracy of 95% with the multi-label model. Additionally, a critical analysis is presented on the classified reports and a location-based analysis to visualise the velocity and veracity of complaints across India.

Device-to-Device (D2D) is a novel communication architecture that is expected to significantly improve the performance of next-generation 5G cellular networks by efficient reuse of licensed spectrum. However, excessive and uncontrolled spectrum reuse comes with the cost of co-channel interference, which deteriorates the overall network’s performance. To maximize the gains of D2D deployments, it is crucial to develop interference-aware spectrum reuse techniques. This paper proposes a social-aware clustering technique to maximize spectrum reuse and minimize co-channel interference for parallel D2D transmissions. Furthermore, we propose a Particle Swarm Optimisation based transmit power control scheme to maximize the system throughput and energy efficiency. Our simulation results show a significant improvement in network performance for different activation probabilities.

The fields of business process analysis and process mining (PM) analyze business operations to identify, validate, improve, and automate business processes. Most information about business processes is available as unstructured or semi-structured data in IT systems implementing them. Examples of the information include design documents, XML-format configurations, system logs, and database schema descriptions. The information focuses on the IT algorithms for implementing the processes and does not directly map to the business description of the processes. Advances in Natural Language Processing (NLP) techniques related to semantic tagging, topic modelling, and text classification present opportunities to analyze unstructured data. NLP tasks such as text classification rely on an annotated corpus suitable for the domain. The availability of corpus annotated with implementation-specific tags is a well-known limitation. This paper addresses the challenge of mapping annotations from language and domain-level (generic) descriptions of processes into implementation-specific process data. We present a transfer learning-based approach trained on a corpus of annotated domain-level text and semantic tags. We demonstrate that such a learning technique can effectively annotate implementation-level process information. We further compare the use of state of the art Skip-gram, GloVe, ELMO, and BERT-based learning models in implementing our framework.

In recent years, there has been an explosion in the number of research publications from various conferences and journals. This large volume of scholarly articles is an invaluable source of information and knowledge which need to be effectively summarized to be useful. In contrast to the base summary, an extended summary gives low-level and structured information about the scholarly articles. In this paper, we propose to develop an attention-based BiLSTM-CNN framework for the purpose of generating extended summaries of scientific articles. To validate our approach, we conduct experiments on the dataset published by LongSumm shared task 2020. The performances of all our systems were evaluated in terms of a well-known metric ROUGE. We also benchmark our model against baseline techniques and our results reveal that the proposed attention-based deep neural network outperforms other models with a significant margin.

After providing ubiquitous and high-speed network connectivity to mobile users, cellular operators are exploring unique domains to extend the reach of cellular networks. In this direction, the use of Unmanned Aerial Vehicles (UAVs) has received significant interest from both industry and academia. UAVs equipped with a transceiver module can act as relays and/or base stations to extend coverage and provide line-of-sight connectivity to mobile users, especially during emergencies such as earthquakes and floods. To reap the gains of UAV-based cellular networks, deployment and operational parameters of UAVs such as altitude and transmit power need to be carefully controlled. In this paper, we propose two algorithms for independently adjusting the altitude and transmit power of UAVs to maximize the system throughput. These algorithms are based on Particle Swarm Optimization and are shown to quickly converge to a better solution when compared to the traditional fixed altitude and fixed transmit power approaches.

To handle unprecedented mobile data demands in the next-generation 5G networks, dense deployments of base stations is the most promising solution. However, dense deployments not only leads to co-channel interference but also the underutilization of wireless resources in most scenarios. One way to maximize the gains of such deployments is to allow inter-operator collaborations where multiple operators can share their base stations and licensed spectrum with each other users. In addition to outdoor base stations, ultra-dense deployment of small cells such as femtocells inside homes/offices/public areas can further improve frequency reuse and system throughput. Femtocells are usually owned by end-users who are often reluctant to share them with other users due to trust and performance concerns. Hence, apart from operator collaboration, it is necessary to have collaborations among end-users to share femtocells. In this direction, we propose a unique cellular communication paradigm called Socio-Cellular Network along with an efficient cell selection scheme to facilitate operator and user collaborations to share base stations. Our simulation results show that collaborations among operators and end-users via social networks help to improve the performance of cellular networks in terms of throughput and energy efficiency.

YouTube is one of the most popular and largest video sharing websites (with social networking features) on the Internet. A significant percentage of videos uploaded on YouTube contains objectionable content and violates YouTube community guidelines. YouTube contains several copyright violated videos, commercial spam, hate and extremism promoting videos, vulgar and pornographic material and privacy invading content. This is primarily due to the low publication barrier and anonymity. We present an approach to identify privacy invading harassment and misdemeanor videos by mining the video metadata. We divide the problem into sub-problems: vulgar video detection, abuse and violence in public places and ragging video detection in school and colleges. We conduct a characterization study on a training dataset by downloading several videos using YouTube API and manually annotating the dataset. We define several discriminatory features for recognizing the target class objects. We employ a one class classifier approach to detect the objectionable video and frame the problem as a recognition problem. Our empirical analysis on test dataset reveals that linguistic features (presence of certain terms and people in the title and description of the main and related videos), popularity based, duration and category of videos can be used to predict the video type. We validate our hypothesis by conducting a series of experiments on evaluation dataset acquired from YouTube. Empirical results reveal that accuracy of proposed approach is more than 80% demonstrating the effectiveness of the approach.