In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insight into ongoing research. It explores recent research trends and techniques for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions

A huge amount of data is generated every second on social media. Event and topic detection must address both scalability and accuracy challenges when using enormous and noisy data collections from social media. Documents describing the same event and story have a similar set of collocated keywords that can be used to identify the event time and its description. In this work, we propose a novel graph-based approach, called the Enhanced Heartbeat Graph (EHG), which does not only detect events at an early stage but also suppresses event-related topics in the upcoming text stream in order to highlight other micro details. We have compared the proposed approach with ten state-of-the-art approaches for event detection. Experiment results on real-world data (i.e., Football Association Challenge Cup Final, Super Tuesday, and the US Election 2012) show considerable improvement in most cases, while computational complexity remains very attractive.

This study observes the phenomenon of organizational performance with the lens of dynamic capabilities. This study investigates the impact of dynamic capabilities on organizational performance, taking organizational competencies as moderating variable, focusing on a paper industry at Lahore, Pakistan. The measurement of dynamic capabilities is based on the multi-dimensional construct underlying the four main factors which include i.e. Sensing, Learning, strong coordination, and competitive response to the rivals. This will lead to explore relationship of dynamic capabilities with organizational performance. Empirical research posits that dynamic capabilities have a direct impact on the organizational performance of the firm. It also proves that organizational competencies have positive moderating role in relationship of organizational performance and dynamic capabilities. This suggests that the direct relationship between dynamic capabilities and performance is insignificant.

Tweets about everyday events are published on Twitter. Detecting such events is a challenging task due to the diverse and noisy contents of Twitter. In this paper, we propose a novel approach named Weighted Dynamic Heartbeat Graph (WDHG) to detect events from the Twitter stream. Once an event is detected in a Twitter stream, WDHG suppresses it in later stages, in order to detect new emerging events. This unique characteristic makes the proposed approach sensitive to capture emerging events efficiently. Experiments are performed on three real-life benchmark datasets: FA Cup Final 2012, Super Tuesday 2012, and the US Elections 2012. Results show considerable improvement over existing event detection methods in most cases.

Abstract. Huge mounds of data are generated every second on the Internet. People around the globe publish and share information related to real-life events they experience every day. This provides a valuable opportunity to analyze the content of this information to detect real-life happenings, however, it is quite challenging task. Most of the existing methods focus on bursty features to highlight the significance of data entities, but ignore the fact that burstiness often dominates the other minor details which, sometimes, can be very important. Based on this fact, in this work, we propose a novel graph-based approach named the Dynamic Heartbeat Graph (DHG) that not only detects the events at an early stage, but also suppresses them in the upcoming adjacent data stream in order to highlight new emerging events. This characteristic makes the proposed method interesting and efficient in finding emerging events and related topics. The experiment results on real-life datasets (i.e. FA Cup Final and Super Tuesday 2012) show a considerable improvement in most cases, while time complexity remains very attractive.

Massive advances in internet infrastructure are impacting e-healthcare services compared to conventional means. Therefore, extra care and protection is needed for extremely confidential patient medical records. With this intention, we have proposed an enhanced image steganography method, to improve imperceptibility and data hiding capacity of stego images. The proposed Image Region Decomposition (IRD) method, embeds more secret information with better imperceptibility, in patient's medical images. The algorithm decomposes the grayscale magnetic resonance imaging (MRI) images into three unique regions: low-intensity, medium-intensity, and high-intensity. Each region is made up of k number of pixels, and in each pixel we operate the block of n least significant bits (LSBs), where 1 ≤ n ≤ 3. Four classes of MRI images of different dimensions are used for embedding. Data with different volumes are used to test the images for imperceptibility and verified with quality factors. The proposed IRD algorithm is tested for performance, on the set of brain MRI images using peak signal-to-noise ratio (PSNR), mean square error (MSE) and structural similarity (SSIM) index. The results elucidated that the MRI stego image is imperceptible, like the original cover image by adjusting 2 nd and 1 st LSBs in the low-intensity region. Our proposed steganography technique provides a better average PSNR (49.27), than other similar methods. The empirical results show that the proposed IRD algorithm, significantly improves the imperceptibility and data embedding capacity, compared to the existing state-of-the-art methods.

The nonlinear transformation concedes as S-box which is responsible for the certainty of contemporary block ciphers. Many kinds of S-boxes are planned by various authors in the literature. Construction of S-box with a powerful cryptographic analysis is the vital step in scheming block cipher. Through this paper, we give more powerful and worthy S-boxes and compare their characteristics with some previous S-boxes employed in cryptography. The algorithm program planned in this paper applies the action of projective general linear group P G L 2 , G F 2 8 on Galois field G F 2 8 . The proposed S-boxes are constructed by using Mobius transformation and elements of Galois field. By using this approach, we will encrypt an image which is the preeminent application of S-boxes. These S-boxes offer a strong algebraic quality and powerful confusion capability. We have tested the strength of the proposed S-boxes by using different tests, BIC, SAC, DP, LP, and nonlinearity. Furthermore, we have applied these S-boxes in image encryption scheme. To check the strength of image encryption scheme, we have calculated contrast, entropy, correlation, energy, and homogeneity. The results assured that the proposed scheme is better. The advantage of this scheme is that we can secure our confidential image data during transmission.

Social media has become a useful source for detecting real-life events. This paper presents an event detection application EveSense. It detects real-life events and related trending topics from the Twitter stream and allows users to find interesting events that have recently occurred. It uses a novel Dynamic Heartbeat Graph (DHG) approach, which efficiently extracts distinguishing features and performs better than the existing event detection methods. We tested and evaluated the application on three case studies, including a sports event (FA cup Final) and two political events (Super Tuesday and US Election).