A gender gap exists in undergraduate studies of different careers related to technology.  Previous research investigated differences among gender in Science, Technology, Engineering, and Mathematics (STEM) careers and other investigated what influences females to choose a career in computer science.  Therefore, an exploratory study was conducted to examine high school student’s perceptions about a technology career in Puerto Rico.  The participants on this study were students in different private and public high schools in Puerto Rico, specifically sophomores, juniors and seniors’ females.  A sample (n) of 26 female students answered a questionnaire after attending an introductory programming workshop.  All of the participants considered the programming workshop as a good experience and they would be interested in attending, and also recommend other girls to attend, future programming events.  Results suggest that the highest influence for them to pursue undergraduate studies on a technology program comes from female teachers, mother, and male teachers.

Human action recognition has been a talked topic since machine vision was coined. With the advent of neural networks and deep learning methods, various architectures were suggested to address the problems within a context. Convolutional neural network has been the primary go-to architecture for image segmentation, flow estimation and action recognition in recent days. As the problem itself is an extended version of various sub-problems, such as frame segmentation, spatial and temporal feature extraction, motion modeling and action classification as a whole, some methods reviewed in this paper addressed sub-problems and some tried to address a single architecture to the action recognition problem. While being a success, convolution neural networks have drawbacks in its pooling methods. CapsNet, on the other hand, uses squashing function to determine the activation. Also it addresses spatiotemporal information with the normalized vector maps while CNN-based methods extracts feature map for spatial and temporal information and later augment them in a fusion layer for combining two separate feature maps. Critical review of papers provided in this work can contribute significantly in addressing human action recognition problem as a whole.

In Social Graph, a set of entities or nodes or vertices interact with each other in a complicated manner that can form multiple types of relationships that depend on time and types of complications. Such graphs include multiple subsystems and layers of connectivity. So it is important to take such multi-layer features into account to make easier of understanding of such complex systems. In this paper, the author focuses on a Social Graph to represent in a multi-layer graph based on its characteristics lies in each node or vertex or entity. For this, the author proposes a general model related to Social Graph. For this model, the author proposes an algorithm, SoGraM for representation of Social Graph with multi-layer features using Graph Mining Techniques. Further, the author tries to prove the proposed algorithm with three examples of Social Graph namely Author Graph, Email Graph, and Telephone Graph.

The problem of resolving references to earlier or later items in the discourse is commonly called as anaphora resolution or pronoun resolution. These items are usually noun phrases representing objects in the real world called referents but can also be verb phrases, whole sentences or paragraphs. Nowadays, anaphora resolution is addressed in numerous NLP (Natural Language Processing) applications. Proper treatment of anaphoric relations improves the performance of applications. Machine translation, information extraction, text summarization, or dialogue systems are some of the common applications of NLP. In early days, the machine translation systems processed on the basis of a sentence-by-sentence level. It did not consider the ties between sentences and resulted in an incoherent text as output. When the researcher forgets to handle the anaphora issue, it results in the striking problem of incorrect facts. It is very much needed to concentrate on the usage of pronoun, as it should match with their antecedents both in number and gender. Assigning inappropriate morphological features to the anaphor often may also lead to an undesirable change in the meaning of the sentence.

With the expansion of worldwide security concerns and a consistently expanding requirement for successful checking of open places, i.e. air terminals, railroad stations, shopping centres, crowded sports fields, army bases or smart healthcare facilities such as daily activity monitoring and fall detection in old people’s homes is increasing very rapidly. The visual occlusions and ambiguities in crowded scenes, usage of suitable method and in addition the perplexing practices and scene semantics make the investigation a challenging task. This research demonstrates comprehensive and critical analysis of crowd scene involves in object detection, tracking, feature extraction and learning from visual surveillance which helps to recognize behavioural pattern. This research refers scene understanding as scene layout, i.e. finding streets, structures, side-walks, vehicles turning, person on foot intersection and scene status such as crowd congestion, split, merge etc. The  significance of the proposed comprehensive review to create crowd administration procedures and help the development of the group or people, to maintain a strategic distance from the group calamities and guarantee general society security. Based on the observation of previous research in three aspects, i.e. review based on methods, frameworks and critical existing results analysis, this research propose a framework for anomaly detection in crowded scene using LSTM (long Short-Term Method).  Proposed comprehensive review is expected to contribute significantly for the investigation of behavior pattern analysis in computer vision research domains.