Information visualization is a prominent technique to visually explore and analyze large volumes of data effectively. Visualization must be aesthetically appealing and perceptually pleasing to the human cognition. This needs necessitates a framework to predict visualization technique based on two aspects: the underlying dataset and the task to be performed on it. Additionally, the resultant visualization must be optimal in the context of aesthetics and human perception. This dissertation contributes in three perspectives that subsume information visualization aspects: automatic technique selection of a visualization, quantifying and optimizing visualization layout, and visualizing software trace. The study provides computational intelligence (CI) model to predict a visualization technique based on the metadata of original dataset and relevant tasks. Similarly, visualization metrics are formulated to objectively measure the visualization quality. Based on these metrics, an evolutionary algorithm optimizes the visualization layout. Finally, the hierarchical visualization technique is used to study the usage of application programming interface (API) objects in the program trace. The trace is collected using the bytecode instrumentation. This dissertation has three parts. First part aims to predict an appropriate visualization technique for a specific dataset. A customize dataset is built using the knowledge that exists in the contemporary literature on various visualization techniques. The dataset comprise of four metadata attributes, relevant task, and the visualization techniques. The study develops an artificial neural network (ANN) to predict a visualization technique using five input and eight output neurons. Optimal neural network architecture is obtained by evaluating various structures with different network configuration. Several well-known performance metrics, i.e., confusion matrix, accuracy, precision, and sensitivity of the classification are used to compare various neural network architectures. Additionally, the best ANN Abstract model is compared with five other well-known classifiers: k-nearest neighbor (k-NN), naïve Bayes (NB), decision tree (DT), random forest (RF), and support vector machine (SVM). Second part provides design of an optimal visualization using visualization quality metrics. Initially, the study focuses on the design parameters which contribute towards the quality of a visualization technique. Visualization metrics are proposed to measure the aesthetic and perceptual characteristics of visualization. They include: effectiveness, expressiveness, readability, and interactivity. An evolutionary algorithm (EA)-based framework to optimize the layout of a visualization technique is also proposed. Treemap visualization technique is used for layout optimization using the EA. These results are evaluated using control experiments and benchmark tasks. The last part uses treemap-based visualization to analyze API objects used in the software, particularly to understand API’s objects during runtime of Java programs. The work consists of two aspects: the extraction of APIs information using bytecode instrumentation, and development of a visualization tool to analyse the traces using treemaps. Initially, a bytecode instrumentation tool is developed to probe and collect runtime information. The extracted information is logged into an extensible markup language (XML) file. The log file is synthesized using treemap. The instrumentation part is evaluated using twenty benchmark and ten real world applications. The results show that the instrumentation tool causes minimal runtime overheads.
پی پی پی چیئر مین بلاول بھٹو زرداری کا خیر پور ناتھن شا ہ سے تعلق رکھنے والے ایم آ ر ڈی کے شہدا ء کی برسی پر پیغام
خیر پور ناتھن شاہ کے بارہ شہید کارکنان کو خراج عقیدت پیش کر تا ہوں ۔تاریخ ان شہیدوں کی قربانی کا ذکر سنہری حروف میں لکھے گی :بلاول بھٹو زرداری
پاکستان میں جمہوریت مفت میں نہیں ملی ،پیپلز پارٹی نے اس کی خاطر جانوں کی قربانیاں دیں :بلاول بھٹو زرداری
پی پی پی قیادت و کارکنان آج بھی جمہوریت کی حفاظت کے لیے سیاسی انتقام کا مقابلہ کر رہے ہیں ۔بلاول بھٹو زرداری
پارٹی کے شہیدوں کی قربانیاں رائیگاں نہیں جائیں گی ۔بلاول بھٹو زرداری
شہدا ء کوخراج عقید ت پیش کر نے کا درست طریقہ ان کے مشن کو آگے بڑھا نا ہے ۔بلاول بھٹو زرداری
پیپلز پارٹی اپنے نظریے اور جدو جہد سے ایک سیکنڈ کے لیے بھی غافل نہیں ہو ئی ۔بلاول بھٹو زرداری
پاکستانی عوام اب ایک نینو سیکنڈ کے لیے بھی جمہوری نظام کی معطلی برداشت کر نے کے لیے تیار نہیں ۔بلاول بھٹو زرداری
ضلع دادو کے قصبے خیر پور ناتھن شاہ میں آمرانہ حکومت نے پیپلز پارٹی کے 12کارکنوں کو شہید کیا تھا ۔12ستمبر 1983ء کو عبدالغنی ابڑو ،عبدالعزیز لاکھیر ،نظام الدین نائچ ،عبدالنبی کھوسو اور اﷲ واریو لانگاہ کو شہید کیا گیا تھا ۔
شاہنواز کھو سو ،حبیب اﷲ لغاری ،دیدار علی کھوکھر ،ضمیر حسین جاگیرانی ،اعجاز حسین کھو نھارو اور منظور احمد چانڈیو بھی شہدا میں شامل ہیں ۔
This paper is formulated under the conceptual framework of Gayatri spivak’s theory of Can Subaltern Speak? And explores the potential permeability of visual resources as a form of discourse through which subalterns found opportunity to counter media hegemony and make their voices heard. In order to analyze the selected graphic novel Munnu: A Boy From Kashmir (2015) the present qualitative study applies Grunter Kress and Theo Van Leeuwen’s model of visual design (2010) that connects the representational meaning to the interactive one. The analyst reflected upon how the interplay of visuals images and words together displayed the theme of Kashmir’s subjugation as well as how this genre has proved supportive to author to counter the said hegemony. Hence, the study found consent, political domination and media control as the broad elements that can be seen in the novel and also the study concludes that counter hegemony is possible through such literary genres, as the novel’s narrator has communicated those aspects of hegemonic situation in Kashmir to a large audience through literary discourse of graphic novel genre that are chiefly absent from mainstream media’s treatments of the Kashmir’s conflict. ______
This dissertation addresses the problem of building collaboration in a team of autonomous agents and presents imitation learning as an effective mechanism to build this collaboration. Imitation learning involves learning from an expert by observing her demonstrating a task and then mimicking her. This mechanism requires less time and technical expertise on behalf of domain experts/ knowledge engineers and makes it convenient for them to transfer knowledge to a software agent. The research extends the idea of a demonstration to multi-human demonstrations and presents a framework of Team Learning from Demonstration (TLfD) that allows a group of human experts to train a team of agents via demonstrations. A major challenge faced by the research is to cope with the overhead of demonstrations and inconsistencies in human demonstrations. To reduce the demonstration overhead, the dissertation emphasizes on a modular approach and enables the framework to train a team of a large number of agents via smaller numbers of demonstrators. The framework learns the collaborative strategy in the form of weighted naïve Bayes model where the parameters of the model are learned from the demonstration data and its weights are optimized using Artificial Immune Systems. The framework is thoroughly evaluated in the domain of RoboCup Soccer Simulation 3D which is a promising platform for a multi-agent domain and addresses many complex real-world problems. A series of experiments were conducted using RoboCup Soccer in which the agents were trained to perform different types of tasks through TLfD framework. The experiments were started with training a single agent how to score a goal in an empty soccer field. The later experiments increased the complexity of the task and the number of agents involved. The final experiment eventually trained a full-fledged team of nine soccer players and enabled them to play soccer against other competition quality teams. A number of test matches were played against different opponent teams, and the results of the matches were evaluated on the basis of different performance and behavioral metrics. The performance metrics described how well the imitating team played in the field whereas the behavioral metrics assessed how closely they had imitated the human demonstrations. Our soccer simulation 3D team KarachiKoalas served as a benchmark to evaluate the quality of the imitating team, and the dissertation closely compared the two teams and found that the team that was trained via imitation gave comparable performance to KarachiKoalas. The results showed the effectiveness of TLfD framework and supported the idea of using imitation to build collaboration among multiple agents. However, the framework, in its current form, does not support strategy building in an incremental manner in which a naïve strategy is learned via imitation and is refined in stages. The ability to build strategies incrementally can be a crucial requirement in complex systems. In future, the framework can be extended to incorporate the ability to refine an already learned strategy via human expert’s feedback.