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The Suraj Punj Joural for Multidisciplinary Research, e-ISSN : 2394-2886 is a monthly publication, with peer arbitration in Engineering, Science and Management. SPJMR is an open-access, Multidisciplinary journal, driven by the belief that all types of knowledge must be globally available. Its target audience are original researchers who dialogue with the interdisciplinary theme in science, technology and management. The journal publishes unpublished research, of recognized theoretical rigor, intellectual and scientific relevance and that involves multidisciplinary discussions that revolve around four main thematic axes: Science, Engineering and Management. Suraj Punj Joural for Multidisciplinary Research is an open access journal, which means that all articles are available on the internet to all users immediately upon publication. Non-commercial use and distribution in any medium is permitted, provided the author and the journal are properly credited. Benefits of open access for authors include: free access for all users worldwide, authors retain copyright to their work, increased visibility and readership, rapid publication, no spatial constraints. Special issues dedicated to international conferences in the topics of the journal are brought out, as well. All submitted manuscripts are initially evaluated by the Editor and, if are found suitable, are sent for further consideration, to peer reviewers for an independent and anonymous expert review process. High Visibility: ...

Download a PDF of the paper titled A Semi-supervised Object Detection Algorithm for Underwater Imagery, by Suraj Bijjahalli and 2 other authors Abstract: Detection of artificial objects from underwater imagery gathered by Autonomous Underwater Vehicles (AUVs) is a key requirement for many subsea applications. Real-world AUV image datasets tend to be very large and unlabelled. Furthermore, such datasets are typically imbalanced, containing few instances of objects of interest, particularly when searching for unusual objects in a scene. It is therefore, difficult to fit models capable of reliably detecting these objects. Given these factors, we propose to treat artificial objects as anomalies and detect them through a semi-supervised framework based on Variational Autoencoders (VAEs). We develop a method which clusters image data in a learned low-dimensional latent space and extracts images that are likely to contain anomalous features. We also devise an anomaly score based on extracting poorly reconstructed regions of an image. We demonstrate that by applying both methods on large image datasets, human operators can be shown candidate anomalous samples with a low false positive rate to identify objects of interest. We apply our approach to real seafloor imagery gathered by an AUV and evaluate its sensitivity to the dimensionality of the latent representation used by the VAE. We evaluate the precision-recall tradeoff and demonstrate that by choosing an appropriate latent dim...

सुराज्य डिजिटल हा ब्रँड जगाच्या काना-कोपऱ्यातील सर्वंकष माहितीचा अचूक खजिना आपल्यापर्यंत पोहोंचविण्याचे काम करतोय. टाईम मीडिया हाऊसचा हा ब्रँड अल्पावधीतच मराठी वाचकांचा अत्यंत विश्वासार्ह ब्रँड बनला आहे.जगभरातील घटनांचा वेगवान आढावा घेत त्यावर भाष्य करण्याबरोबरच वाचकांच्या आवडीचे विषय, करमणूक यासारखे दृक्श्राव्य व्यासपीठ उपलब्ध करून देण्यासाठी सुराज्य डिजिटल कटीबद्ध आहे. अधिक माहितीसाठी संपर्क करा [email protected] [email protected]

Download a PDF of the paper titled Full-Gradient Representation for Neural Network Visualization, by Suraj Srinivas and 1 other authors Abstract: We introduce a new tool for interpreting neural net responses, namely full-gradients, which decomposes the neural net response into input sensitivity and per-neuron sensitivity components. This is the first proposed representation which satisfies two key properties: completeness and weak dependence, which provably cannot be satisfied by any saliency map-based interpretability method. For convolutional nets, we also propose an approximate saliency map representation, called FullGrad, obtained by aggregating the full-gradient components. We experimentally evaluate the usefulness of FullGrad in explaining model behaviour with two quantitative tests: pixel perturbation and remove-and-retrain. Our experiments reveal that our method explains model behaviour correctly, and more comprehensively than other methods in the literature. Visual inspection also reveals that our saliency maps are sharper and more tightly confined to object regions than other methods.

Download a PDF of the paper titled Full-Gradient Representation for Neural Network Visualization, by Suraj Srinivas and 1 other authors Abstract: We introduce a new tool for interpreting neural net responses, namely full-gradients, which decomposes the neural net response into input sensitivity and per-neuron sensitivity components. This is the first proposed representation which satisfies two key properties: completeness and weak dependence, which provably cannot be satisfied by any saliency map-based interpretability method. For convolutional nets, we also propose an approximate saliency map representation, called FullGrad, obtained by aggregating the full-gradient components. We experimentally evaluate the usefulness of FullGrad in explaining model behaviour with two quantitative tests: pixel perturbation and remove-and-retrain. Our experiments reveal that our method explains model behaviour correctly, and more comprehensively than other methods in the literature. Visual inspection also reveals that our saliency maps are sharper and more tightly confined to object regions than other methods.

सुराज्य डिजिटल हा ब्रँड जगाच्या काना-कोपऱ्यातील सर्वंकष माहितीचा अचूक खजिना आपल्यापर्यंत पोहोंचविण्याचे काम करतोय. टाईम मीडिया हाऊसचा हा ब्रँड अल्पावधीतच मराठी वाचकांचा अत्यंत विश्वासार्ह ब्रँड बनला आहे.जगभरातील घटनांचा वेगवान आढावा घेत त्यावर भाष्य करण्याबरोबरच वाचकांच्या आवडीचे विषय, करमणूक यासारखे दृक्श्राव्य व्यासपीठ उपलब्ध करून देण्यासाठी सुराज्य डिजिटल कटीबद्ध आहे. अधिक माहितीसाठी संपर्क करा [email protected] [email protected]

Welcome To Suraj.com Business Centre Create your brand here… Our product range covers all Digital Printing formats from minimal to large size printing, serving professionals from Graphic Designers to Architects/Engineers. With ourCutting Edge Technology from Black & White to Multicolour printing and wide range of Quality Papers to Art Effect Materials. Suraj.com Business Centre is accomplished at customizing solutions by developing a deep understanding of long-term needs of clients and allocating permanent resources to service their unique requirements. Therefore, we cater to clients’ unique requirements and work accordingly to produce the desired outcome. Besides, we use the latest technology to keep pace with the rising market trends. Becoming the Goa’s Most Reliable Printer We're full service which means we've got you covered on design and content right through to digital. You'll form a lasting relationship with us, collaboration is central to everything we do. Suraj.com Key Points: • • Quality matters here. • • Printing made easier. • • Fast and quality service. • • Not just an ordinary work. • • For all printing solutions. • • Listening to your printing needs. • • Taking your needs seriously. • • Cheap prices, not quality. • • The write kind of solutions. • • Your priority is ours. Mr. Prakash. M. Gawas, the owner of Suraj.com Business Centre is a simple and hard working man who you will always find at the main counter of the store ready to help you all with your prin...

The Suraj Punj Joural for Multidisciplinary Research, e-ISSN : 2394-2886 is a monthly publication, with peer arbitration in Engineering, Science and Management. SPJMR is an open-access, Multidisciplinary journal, driven by the belief that all types of knowledge must be globally available. Its target audience are original researchers who dialogue with the interdisciplinary theme in science, technology and management. The journal publishes unpublished research, of recognized theoretical rigor, intellectual and scientific relevance and that involves multidisciplinary discussions that revolve around four main thematic axes: Science, Engineering and Management. Suraj Punj Joural for Multidisciplinary Research is an open access journal, which means that all articles are available on the internet to all users immediately upon publication. Non-commercial use and distribution in any medium is permitted, provided the author and the journal are properly credited. Benefits of open access for authors include: free access for all users worldwide, authors retain copyright to their work, increased visibility and readership, rapid publication, no spatial constraints. Special issues dedicated to international conferences in the topics of the journal are brought out, as well. All submitted manuscripts are initially evaluated by the Editor and, if are found suitable, are sent for further consideration, to peer reviewers for an independent and anonymous expert review process. High Visibility: ...

Download a PDF of the paper titled A Semi-supervised Object Detection Algorithm for Underwater Imagery, by Suraj Bijjahalli and 2 other authors Abstract: Detection of artificial objects from underwater imagery gathered by Autonomous Underwater Vehicles (AUVs) is a key requirement for many subsea applications. Real-world AUV image datasets tend to be very large and unlabelled. Furthermore, such datasets are typically imbalanced, containing few instances of objects of interest, particularly when searching for unusual objects in a scene. It is therefore, difficult to fit models capable of reliably detecting these objects. Given these factors, we propose to treat artificial objects as anomalies and detect them through a semi-supervised framework based on Variational Autoencoders (VAEs). We develop a method which clusters image data in a learned low-dimensional latent space and extracts images that are likely to contain anomalous features. We also devise an anomaly score based on extracting poorly reconstructed regions of an image. We demonstrate that by applying both methods on large image datasets, human operators can be shown candidate anomalous samples with a low false positive rate to identify objects of interest. We apply our approach to real seafloor imagery gathered by an AUV and evaluate its sensitivity to the dimensionality of the latent representation used by the VAE. We evaluate the precision-recall tradeoff and demonstrate that by choosing an appropriate latent dim...