Teaching and exam proctoring represent key pillars of the education system. Human proctoring, which involves visually monitoring examinees throughout exams, is an important part of assessing the academic process. The capacity to proctor examinations is a critical component of educational scalability. However, such approaches are time-consuming and expensive. In this paper, we present a new framework for the learning and classification of cheating video sequences. This kind of study aids in the early detection of students’ cheating. Furthermore, we introduce a new dataset, “actions of student cheating in paper-based exams”. The dataset consists of suspicious actions in an exam environment. Five classes of cheating were performed by eight different actors. Each pair of subjects conducted five distinct cheating activities. To evaluate the performance of the proposed framework, we conducted experiments on action recognition tasks at the frame level using five types of well-known features. The findings from the experiments on the framework were impressive and substantial.

Bird’s-eye surround view is a new type of Advanced Driv er Assistance System (ADASs) that provides drivers with a real-time ${360}^{\circ }$ top-down view of their vehicle. This paper presents an architecture for a surround view system based on a Tensilica Vision P5 embedded DSP. Fish-eye cameras mounted on each side of a vehicle are employed in the proposed model. The cameras are able to cover the entire view around the vehicle with overlapping areas between neighboring cameras. We use a morphing approach for joint lens and perspective correction of each view. For stitching frames from different cameras, we assume a fixed geometry which permits off-line calculation of frame transformations, thereby reducing the real-time processing load. To further reduce computational complexity, we employ lookup-tables in the view morphing and blending phases. The Vision P5 core combines the four frames with XGA resolution into a single XGA surround view at 30FPS.

The work presented in this paper is part of a project to enable humanoid robots to build a semantic understanding of their environment adopting unsupervised self-learning techniques. Here, we propose an approach to learn 3-dimensional human-pose conformations, i.e. structural arrangements of a (simplified) human skeleton model, given only a minimal verbal description of a human posture (e.g. "sitting", "standing", "tree pose"). The only tools given to the robot are knowledge about the skeleton model, as well as a connection to the labeled images database "google images". Hence the main contribution of this work is to filter relevant results from an images database, given a human-pose specific query words, and to transform the information in these (2D) images into a 3D pose that is the most likely to fit the human understanding of the keywords. Steps to achieve this goal integrate available 2D human-pose estimators using still images, clustering techniques to extract representative 2D

Distributed clustering algorithms have proven to be effective in dramatically reducing execution time. However, distributed environments are characterized by a high rate of failure. Nodes can easily become unreachable. Furthermore, it is not guaranteed that messages are delivered to their destination. As a result, fault tolerance mechanisms are of paramount importance to achieve resiliency and guarantee continuous progress. In this paper, a fault-tolerant distributed k-means algorithm is proposed on a grid of commodity machines. Machines in such an environment are connected in a peer-to-peer fashion and managed by a gossip protocol with the actor model used as the concurrency model. The fact that no synchronization is needed makes it a good fit for parallel processing. Using the passive replication technique for the leader node and the active replication technique for the workers, the system exhibited robustness against failures. The results showed that the distributed k-means algorithm with no fault-tolerant mechanisms achieved up to a 34% improvement over the Hadoop-based k-means algorithm, while the robust one achieved up to a 12% improvement. The experiments also showed that the overhead, using such techniques, was negligible. Moreover, the results indicated that losing up to 10% of the messages had no real impact on the overall performance.