Annotation of multimedia learning materials for semantic search

Multimedia is the main source for online learning materials, such as videos, slides and textbooks, and its size is growing with the popularity of online programs offered by Universities and Massive Open Online Courses (MOOCs). The increasing amount of multimedia learning resources available online makes it very challenging to browse through the materials or find where a specific concept of interest is covered. To enable semantic search on the lecture materials, their content must be annotated and indexed. Manual annotation of learning materials such as videos is tedious and cannot be envisioned for the growing quantity of online materials. One of the most commonly used methods for learning video annotation is to index the video, based on the transcript obtained from translating the audio track of the video into text. Existing speech to text translators require extensive training especially for non-native English speakers and are known to have low accuracy. This dissertation proposes to index the slides, based on the keywords. The keywords extracted from the textbook index and the presentation slides are the basis of the indexing scheme. Two types of lecture videos are generally used (i.e., classroom recording using a regular camera or slide presentation screen captures using specific software) and their quality varies widely. The screen capture videos, have generally a good quality and sometimes come with metadata. But often, metadata is not reliable and hence image processing techniques are used to segment the videos. Since the learning videos have a static background of slide, it is challenging to detect the shot boundaries. Comparative analysis of the state of the art techniques to determine best feature descriptors suitable for detecting transitions in a learning video is presented in this dissertation. The videos are indexed with keywords obtained from slides and a correspondence is established by segmenting the video temporally using feature descriptors to match and align the video segments with the presentation slides converted into images. The classroom recordings using regular video cameras often have poor illumination with objects partially or totally occluded. For such videos, slide localization techniques based on segmentation and heuristics is presented to improve the accuracy of the transition detection. A region prioritized ranking mechanism is proposed that integrates the keyword location in the presentation into the ranking of the slides when searching for a slide that covers a given keyword. This helps in getting the most relevant results first. With the increasing size of course materials gathered online, a user looking to understand a given concept can get overwhelmed. The standard way of learning and the concept of “one size fits all” is no longer the best way to learn for millennials. Personalized concept recommendation is presented according to the user’s background knowledge. Finally, the contributions of this dissertation have been integrated into the Ultimate Course Search (UCS), a tool for an effective search of course materials. UCS integrates presentation, lecture videos and textbook content into a single platform with topic based search capabilities and easy navigation of lecture materials

Steganography and Steganalysis : Different Approaches

Steganography is the technique of hiding confidential information within any media. Steganography is often confused with cryptography because the two are similar in the way that they both are used to protect confidential information. The difference between the two is in the appearance in the processed output; the output of steganography operation is not apparently visible but in cryptography the output is scrambled so that it can draw attention. Steganlysis is process to detect of presence of steganography. In this article we have tried to elucidate the different approaches towards implementation of steganography using ‘multimedia’ file (text, static image, audio and video) and Network IP datagram as cover. Also some methods of steganalysis will be discussed

Enhanced Study of Deep Learning Algorithms for Web Vulnerability Scanner

The detection of online vulnerabilities is the most important task for network security. In this paper, deep learning methodologies for dealing with tough or complicated challenges are investigated using convolutional neural networks, long-short-term memory, and generative adversarial networks.Experimental results demonstrate that deep learning approaches can significantly outperform standard methods when compared to them. In addition, we examine the various aspects that affect performance. This work can provide researchers with useful direction when designing network architecture and parameters for identifying web attacks

Road-based proactive routing protocols for vehicular networks

Vehicular networks are formed by vehicles communicating in an ad hoc manner or through cellular and IEEE 802.11 base stations. Routing in vehicular networks is challenging due to highly dynamic network topologies. Previous research done at the UbiNetS lab at NJIT has proposed the idea of Road-Based using Vehicular Traffic information (RBVT) routing. In RBVT, routes are sequences of road intersections, and geographical forwarding is used for sending packets between the intersections. This thesis proposes two proactive RBVT protocols (RBVT-P and RBVT-PS) for city-based Vehicular Ad hoc NETworks (VANETs). RBVT-P is a completely distributed protocol that unicasts connectivity packets in the network to find the road- based topology graph. This graph represents a real-time view of the roads with enough vehicular traffic to ensure connectivity between intersections and is disseminated to all nodes. RBVT-PS uses a similar concept, but the road connectivity information is gathered and sent separately to a server via cellular links The server centrally computes the connectivity graph and disseminates it to the nodes in the network. The proposed protocols are compared against existing routing protocols. Simulation results for city scenarios show that both RBVT-P and RBVT-PS perform better than the existing protocols, with reduction in end-to-end delay as much as 85% and increase in delivery ratio as much three times. RBVT-PS reduces the delay three times and increases the delivery ratio by 10% when compared with RBVT-P

Vanet routing on city roads using real-time vehicular traffic information

This article presents a class of routing protocols called RBVT, Road-Based using Vehicular Traffic informatio

Use of large pieces of printed circuit boards for bioleaching to avoid ‘precipitate contamination problem’ and to simplify overall metal recovery

Very recently bioleaching has been used for removing metals from electronic waste. Most of the research has been targeted to using pulverized PCBs for bioleaching where precipitate formed during bioleaching contaminates the pulverized PCB sample and making the overall metal recovery process more complicated. In addition to that, such mixing of pulverized sample with precipitate also creates problems for the final separation of non metallic fraction of PCB sample. In the present investigation we attempted the use of large pieces of printed circuit boards instead of pulverized sample for removal of metals. Use of large pieces of PCBs for bioleaching was restricted due to the chemical coating present on PCBs, the problem has been solved by chemical treatment of PCBs prior to bioleaching. In short, • Large pieces of PCB can be used for bioleaching instead of pulverized PCB sample. • Metallic portion on PCBs can be made accessible to bacteria with prior chemical treatment of PCBs. • Complete metal removal obtained on PCB pieces of size 4 cm × 2.5 cm with the exception of solder traces. The final metal free PCBs (non metallic) can be easily recycled and in this way the overall recycling process (metallic and non metallic part) of PCBs becomes simple

UCS: Ultimate Course Search

In this system prototype demonstration we present, Ultimate Course Search (UCS), a learning tool developed to provide students ways to efficiently search electronic educational materials. UCS integrates slides, lecture videos and textbooks into a single platform. The keywords extracted from the textbooks and the slides are the basis of the indexing scheme. For the videos, UCS relies on slide transitions and metadata to establish the correspondence between slides and video segments. The video segmentation is based on the slides being presented using the meta-data provided by the video recording software and image processing techniques