Professionalising Election Campaigns:The Emergence of Political Consulting

The 2014 and 2019 general elections in India were referred to as “WhatsApp elections,” which had IT cells, bots, and political consultants strategically using data mining tools to build resonant narratives to tell voters what they wanted to hear. By the 2014 national election, the industry was reported to be worth $40–$47 million. Between 2014 and 2018, industry specialists approximated that the number of firms in this market had at least doubled. These unprecedented tools of technological campaigning come with new forms of identifying, targeting, and defining issues of political importance. This article suggests that such developments are turning electoral politics into a thriving business being data-driven, technologically oriented, and having far-reaching implications for democratic processes

miRPlant : an integrated tool for identification of plant miRNA from RNA sequencing data

Background Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep’s probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction. Result We have developed a user-friendly plant miRNA prediction tool called miRPlant. We show using 16 plant miRNA datasets from four different plant species that miRPlant has at least a 10% improvement in accuracy compared to miRDeep-P, which is the most popular plant miRNA prediction tool. Furthermore, miRPlant uses a Graphical User Interface for data input and output, and identified miRNA are shown with all RNAseq reads in a hairpin diagram. Conclusions We have developed miRPlant which extends miRDeep* to various plant species by adopting suitable strategies to identify hairpin excision regions and hairpin structure filtering for plants. miRPlant does not require any third party tools such as mapping or RNA secondary structure prediction tools. miRPlant is also the first plant miRNA prediction tool that dynamically plots miRNA hairpin structure with small reads for identified novel miRNAs. This feature will enable biologists to visualize novel pre-miRNA structure and the location of small RNA reads relative to the hairpin. Moreover, miRPlant can be easily used by biologists with limited bioinformatics skills

Network anomaly detection by using a time-decay closed frequent pattern

© 2019 by the authors. Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods

Object Based Image Ranking Using Neural Networks

In this paper an object-based image ranking is performed using both supervised and unsupervised neural networks. The features are extracted based on the moment invariants, the run length, and a composite method. This paper also introduces a likeness parameter, namely a similarity measure using the weights of the neural networks. The experimental results show that the performance of image retrieval depends on the method of feature extraction, types of learning, the values of the parameters of the neural networks, and the databases including query set. The best performance is achieved using supervised neural networks for internal query set

Caregiver perceptions and experiences of paediatric emergency department attendance during the COVID-19 pandemic: a mixed-methods study

BACKGROUND: During the early stages of the COVID-19 pandemic, concerns were raised about reduced attendance at hospitals, particularly in paediatric emergency departments, which could result in preventable poorer outcomes and late presentations among children requiring emergency care. We aimed to investigate the impact of COVID-19 on health-seeking behaviour and decision-making processes of caregivers presenting to paediatric emergency services at a National Health Service (NHS) Trust in London. MATERIALS AND METHODS: We conducted a mixed-methods study (survey and semi-structured interviews) across two hospital sites between November-December 2020. Data from each study were collected concurrently followed by data comparison. RESULTS: Overall, 100 caregivers participated in our study; 80 completed the survey only, two completed the interview only and 18 completed both. Our quantitative study found that almost two-thirds (63%, n = 62) of caregivers attended the department within two days of their child becoming ill. Our qualitative study identified three major themes which were underpinned by concepts of trust, safety and uncertainty and were assessed in relation to the temporal nature of the pandemic and the caregivers' journey to care. We found most caregivers balanced their concerns of COVID-19 and a perceived "overwhelmed" NHS by speaking to trusted sources, predominantly general practitioners (GPs). CONCLUSION: Caregivers have adapted their health-seeking behaviour throughout the pandemic as new information and guidance have been released. We identified several factors affecting decisions to attend; some existed before the pandemic (e.g., concerns for child's health) whilst others were due to the pandemic (e.g., perceived risks of transmission when accessing healthcare services). We recommend trusted medical professionals, particularly GPs, continue to provide reassurance to caregivers to seek emergency paediatric care when required. Communicating the hospital safety procedures and the importance of early intervention to caregivers could additionally provide reassurance to those concerned about the risks of accessing the hospital environment

Automatic target recognition based on cross-plot

Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository.Kelvin Kian Loong Wong and Derek Abbot

Connectivity-based shape descriptors

In this paper, we propose a method for indexing and retrieval of images based on shapes of objects. The concept of connectivity is introduced. 3D models are used to represent 2D images. 2D images are decomposed a priori using connectivity which is followed by 3D model construction. 3D model descriptors are obtained for 3D models and used to represent the underlying 2D shapes. We have used spherical harmonics descriptors as the 3D model descriptors. Difference between two images is computed as the Euclidean distance between their descriptors. Experiments are performed to test the effectiveness of spherical harmonics for retrieval of 2D images. The proposed method is compared with methods based on principal components analysis (PCA) and generic Fourier descriptors (GFD). It is found that the proposed method is effective. Item S8 within the MPEG-7 still images content set is used for performing experiments