Antrix Corporation (India)

Clickbaits, in social media, are exaggerated headlines whose main motive is to mislead the reader to “click” on them. They create a nuisance in the online experience by creating a lure towards poor content. Online content creators are utilizing more of them to get increased page views and thereby more ad revenue without providing the backing content. This paper proposes a model for detection of clickbait by utilizing convolutional neural networks and presents a compiled clickbait corpus. We create a corpus using multiple social media platforms and utilize deep learning for learning features rather than undergoing the long and complex process of feature engineering. Our model achieves high performance in identification of clickbaits.

Ostinato is a network traffic generator tool. This paper addresses the features of Ostinato, its architecture and a basic capture and replay analysis, followed by scope of future integration with newer protocols. A packet trace of Address Resolution Protocol(ARP) is used to show how Ostinato can be used to edit and replay packets. Various snapshots of Ostinato's Graphical User Interface(GUI) are used to explain the process.

Large scale software systems keep on generating logs for the events carried out in the past. The information recorded in these log files is very useful in debugging operation as well as for regression testing. Now days, companies are required to review their log records on regular intervals to detect and analyze the anomalies, faults or any unwanted activity that is not normal. However, when the system is complex, these log files become huge and are almost impossible to read. Often, entries are irrelevant, so combining and correlating events in huge logs is difficult, time consuming process and requires enormous computational resources. Thus this paper aims at development of generic web based framework to analyze the log files provided by the user. The built tool will parse the log files based on user selected text phrases. The developed prototype based on the assumption that a log file generally records different events based on timestamps. And each event will have its corresponding entity and pattern pairs. An entity is the attribute name given to particular entity present in similar events. A pattern is basically a value for the attribute corresponding to each entity and it is the actual point of interest. In the proposed framework timestamp is considered as the metadata for the log file and the user is required to highlight the entity and any pattern corresponding to that entity. The entity and its corresponding value are searched in the entire log file by generating regular expression dynamically. Finally, the proposed log analysis tool in this paper visualizes the highlighted entity against time using Google charts. The proposed web enabled tool is light-weight framework supporting data streaming capabilities. It is different from the existing log analysis tools in three ways. Firstly, it supports the feature of highlighting the entity-pattern pair and provides the visualizations in terms of graphs, listings, etc for the highlighted entity-pattern pair. Secondly, the tool supports generation of Regular Expressions dynamically for the highlighted entity-pattern pair. Lastly, to print and save the visualization reports as JPeg images for latter reference.

The paper introduces a new approach to network routing using the adaptive learning techniques of Ant Colony Optimization (ACO) framework. The proposed algorithm is based on the two ACO algorithms AntNet and Omicron Ant Colony Optimization (OA). In principle, the algorithm uses the mobile agents (ants) to collect information about the network. The ants exchange this collected data using stigmergic communication. In an attempt to decrease the packet delay and improve throughput new methods and data structures have been introduced. The algorithm adopts OA's approach to initialize and update the pheromone values. In addition, it introduces solution tables to hold a set of good solutions at any given time. Further, to select the next-hops the algorithm includes methods, namely, deterministic dual step method and roulette-wheel selection. The algorithm is simulated on the NSFNET topology using ns-2. On comparing the delay and throughput values with standard AntNet algorithm, a considerable improvement is observed, thereby denoting an enhanced efficiency of routing.

Present day software testing demands effective ways to find software vulnerabilities through testing. This is especially true in case of network security that employ digital certificates for authentication. Digital certificates are the de-facto standard for verification of users and an integral part of public key infrastructure used to secure channels of communication within networks. An effective approach to testing digital certificates is to implement protocol based fuzzing. Fuzzing in general terms is the process of inserting high volume of invalid or random inputs into a program with the aim of obtaining unexpected results, thus identifying errors and potential vulnerabilities. This paper aims to introduce a protocol aware, user friendly graphical user interface (GUI) based digital certificate fuzzing tool. The tool aims to provide an effective means of black box testing through the use of mutation based fuzzing and OpenSSL to create digital certificates with user provided test-case specific fields. The fuzzed certificates are used as inputs in order to expose defects in digital certificate validation systems.

Tropical cyclone is one of the most violent natural disasters causing massive devastation. Accurate forecasting of cyclones with high lead times is an important problem. We propose a framework to predict tropical cyclogenesis (i.e. cyclone formation). This framework executes along with a parallel weather simulation model (WRF) and analyzes the simulation output as soon as they are generated. Our framework has two major components – a trigger function and a deep predictive model. The trigger function acts as a basic filter to identify cyclones from non-cyclones. The proposed deep learning model is based on convolutional neural networks (CNNs). The best track data from Indian Meteorological Department (IMD) is used as a reference for labeling data points into disturbances and tropical cyclones. The framework achieves a probability of detection (POD) value of approximately 95% with a false alarm ratio (FAR) of 21.69% overall. The predictions made by the framework have a lead time of up to 150 hours from the time that a disturbance transforms into a tropical cyclone.

Abstract We assessed the mechanisms by which non-encapsulated heme, released in the plasma of mice post exposure to chlorine (Cl 2 ) gas, resulted in the initiation and propagation of acute lung injury. We exposed adult C57BL/6 male and female to Cl 2 (500 ppm for 30 min) in environmental chambers and returned them to room air and injected them intramuscularly with a single dose of human hemopexin (hHPX; 5 µg/ g BW), the most efficient scavenger of heme, 30-60 min post exposure. Concentrations of hHPX in plasma of air and Cl 2 exposed mice were 9081±900 vs. 1879± 293 at 6 h and 2966±463 vs. 1555±250 at 50 h post injection (ng/ml; X±1 SEM=3; p<0.01). Cl 2 exposed mice developed progressive acute lung injury post exposure characterized by increased concentrations of plasma heme, marked inflammatory response, respiratory acidosis and increased concentrations of plasma proteins in the alveolar space. Injection of hHPX decreased the onset of acute lung injury at 24 h post exposure; mean survival, for the saline and hHPX groups were 40 vs. 80% (P<0.001) at 15 d post exposure. Non-supervised global proteomics analysis of mouse lungs at 24 h post exposure, revealed the upregulation of 92 and downregulation of 145 lung proteins. Injection of hHPX at one h post exposure moderated the Cl 2 induced changes in eighty-three of these 237 lung proteins. System biology analysis of the global proteomics data showed that hHPX reversed changes in mitochondrial dysfunction and elF2 and integrin signaling. Western blot analysis of lung tissue showed significant increase of phosphorylated elF2 at 24 h post exposure in vehicle treated mice but normal levels in those injected with hHPX. Similarly, RT-PCR analysis of lung tissue showed that hHPX reversed the onset of mtDNA lesions. A form of recombinant human hemopexin generated in tobacco plants was equally effective in reversing acute lung and mtDNA injury. The results of this study offer new insights as to the mechanisms by which exposure to Cl 2 results in acute lung injury and to the therapeutic effects of hemopexin.

As a means of transportation of small loads, wheels were attached to carts and chariots. Around the same time constituting to transportation history, people developed simple logs and controllable riverboats to direct the vehicle. From here people went on to tame animals like horses, bullock carts .In 18th and 19th century there were significant development in transportation with invention of trains, cars, aeroplane and satellites .The vehicles were driven with man as a mode of driver. This led to easy and faster mode of movement of goods and people. The transportation industry has gone through several research, studies and refinements to reach where it is now. Thus signification transformation is seen in recent years. Thus the industry has reached an unprecedented level where vehicles don’t even require human intervention to zoom around the road. The technological advancements have laid a hand in its remarkable journey of innovation and evolution. Today in this digital 21st Century there is invention of automation in field of transportation with driver less. AI technology brings manifold benefits to the transportation sector. It increases efficiency and reduces emissions while enhancing safety by reducing human error, that’s leading cause of accidents. Personalized user experience, are becoming the norm due to AI. In the future ,AI-based solutions will add more value to cars, resulting in further advancement in the development of autonomous driving ,maximizing production capacity, increase in production and gathering data for improved road safety and passenger experience .We are not at the age where AI in transportation helps achieve major breakthrough ,but also catching the eyes of transportation bosses worldwide. The field of autonomous vehicles is undergoing a significant transition, largely due to the swift progress made in Artificial Intelligence .These advances go beyond self-driving cars to include drones, truck ,taxies and other type of transportation. The incorporation of artificial intelligence into driverless cars is paradigm change that might completely alter how we view and engage the transportation.

A study conducted by Bryce Space and Technology on the quantification and cause-mapping of launch delays of small satellites revealed that all 1,078 small satellites launched commercially in the past five years had suffered launch delays. The study attributed the primary reasons for this delay to schedule slippages of launches and the primary satellite. Such delays are a huge financial drain for the small satellite manufacturers. As a critical step toward addressing such problems by streamlining the supply chain and imparting the elements of interoperability and cross-compatibility, there are renewed calls for extending the scope of standardization of satellites—sizes, interface, and other aspects—from the currently existing CubeSat standards (CDS) to cover larger small satellites up to 500 kg. There are several initiatives such as Launch U by Aerospace Corporation, for example, aiming at addressing this area. In this context, the article tries to assess standardization through several strategic frameworks such as the Transaction asset model, Kraljic matrix, and Project planning model to identify the positive effects of standardization and also the negatives to impress on the need to define a proper extent until which standardization can be allowed to happen.

Come hear about the creation of Nickelodeon's hit new animated action/adventure series Max & the Midknights! The show's Supervising Producer and CG Supervisor, along with production partner Xentrix's Head of Pipeline and Creative Director, will share a behind-the-scenes look at the challenges they faced creating this ambitious cinematic CG show with its hand-made and stop-motion look and feel.