Plagiarism As an Act of Academic Crime: A Critical Overview in an Islamic & Modern Perspective

Plagiarism is a cheating in form of stealing the ideas or language of others, which is morally and ethically crime and it's also an offense. In the field of education plagiarism is an academic fraud, therefore it is considered a theft. In material world the things which belong to us are considered our property; same is the case in academic world ideas, concepts words and work which belong to us are known as "intellectual property". Thus, we cannot use or cite others’ academic work without permission. Islam is the only religion that advocates for knowledge seeking and sharing with others. So sharing useful knowledge is Sadqa-e-Jāriyia. But this does not mean to attain knowledge and overlook the real source of knowledge. In this connection individual has to give credit whenever one uses the work of others anywhere. Hence, if we are using the work of others and do not cite the source and acknowledge the real author, we indulge ourselves in "Plagiarism" or in academic fraud. So in this scenario, academic world copyright or authorship is important. In recent terminology these are known as "intellectual property rights". In present era deceiving and harming of others has become common. In academia, "intellectual frauds" are more common nowadays. The objective of this article is to highlight the Islamic perspective in the avoidances of plagiarism. In this regard, Qur᾽ānic verses and Ahādīth have been used for exploring how Islam has indicated the avoidances of plagiarism. The main theme of this paper is to highlight and explore the Islamic vision regarding the academic theft and current preventive practices in Pakistan. It is suggested & concluded that in Islam plagiarism is prohibited. It also indicates the terms & conditions for the use of academic work of other scholars. There is dire need that the Muslim experts should also revive new Islamic copyright rules and regulation for honest academic writing.

A Multi-Feature Hybrid Object Tracking Algorithm

The availability of low-cost video cameras and digital media storage has invited huge investments in developing state-of-the-art algorithms that automatically evaluate and understand video datasets. One such class of algorithm is object tracking which analyzes the data and automatically determines the location of the object in a video sequence. As these algorithms are a prelude to a higher level decision making algorithms, therefore estimation of the trajectory of the object must be accurate and robust under many challenging constraints. A very popular class of object tracking algorithm is the hybrid object tracking category based on integrating Meanshift (MS) and Particle Filter (PF) (MSPF). The purpose of this integration was to overcome the limitation of the PF methods that required a large number of samples/particles PF method to approximate the object state. Consequently, this integration uses the MS optimization procedure to move fewer particles, in the direction of gradient ascent, which represents the dynamics of the target more accurately. The existing methods employ a pre-determined combination of features, inherently assuming that the background would not change. However in uncontrolled environment, it is difficult to specify the background of the object in advance as it moves around the field of view of the camera and thereof this assumption may not often hold. Moreover, hybrid tracking systems based on the MSPF methodology are very compute intensive and it is desirable to reduce this complexity. In the first part of this research, the dissertation aims to investigate an adaptive multi-feature framework that is implemented on top of the MSPF methodology that tracks the object in the local perspective. Essentially that takes care of the dynamic and changing characteristic of the background, which is one of the most important challenges in the object tracking domain. In this research work, an Adaptive Multi-Feature framework is proposed and implemented on top of the MSPF methodology (AMF-MSPF). An adaptive ranking module is proposed that is triggered after a certain criteria is violated, in which case a new set of features are selected for tracking the object. The top ranked features are selected to represent the object, which gives the tracker the ability to adapt to locate the object with an upgraded set of feature. Consequently, this improved local discrimination of the target from its immediate neighboring pixels. In most applications a small portion of computational resources are dedicated to trackers and rest is reserved for higher level decision making tasks, which mandate trackers to be efficient and less complex. Thereby, the second part of the dissertation looks into the complexity of the MSPF methodology. As the MSPF methodology is already a computationally intensive processing task, implementing a feature ranking module on top of it might complicate matters. The feature ranking module also requires a significant portion of the power, thereby a novel MS technique is proposed to free some resources for the ranking module. This novelty comes from an observation that only a fraction of random samples were required by the MS optimization to approximate the similarity metric without inducing significant error. This computational reduction would be advantageous given the complex integration of the MS and PF, because the MS procedure is directly proportional to the number of particles that would take many MS iterations to converge. The proposed novelty in the MS method has reduced its complexity that has greatly impacting the overall complexity of the proposed AMF-MSPF. The proposed AMF-MSPF framework is tested on sequences from the CAVIAR datasets such as Browse and Walkbyshop1and an s8 sequence was taken from the PET dataset. These datasets are known for a number of challenging constraints such as abrupt intensity variations, full occlusions, cluttered background etc. Qualitative results have shown robust and accurate tracking under stringent constraints. In the quantitative analysis, a comparison with the existing methods has been carried out. The proposed framework has shown significant improvements in terms of root mean square error (RMSE), false alarm rate (FAR), and F-SCORE. The average RMSE, FAR, and F_SCORE, over all the video sets, of the proposed AMF-MSPF are 8.68, 0.15, and 0.92, which has improved manifold as compared to the chosen reference methods. Experimental results have proved the effectiveness of the proposed framework.