Anupam Basu

Bio: Anupam Basu is an academic researcher from University of Burdwan. The author has contributed to research in topics: Bengali & Speech synthesis. The author has an hindex of 24, co-authored 232 publications receiving 2562 citations. Previous affiliations of Anupam Basu include Academy of Scientific and Innovative Research & National Institute of Technology, Durgapur.

Investigation and modeling of the structure of texting language

Abstract: Language usage over computer mediated discourses, such as chats, emails and SMS texts, significantly differs from the standard form of the language and is referred to as texting language (TL). The presence of intentional misspellings significantly decrease the accuracy of existing spell checking techniques for TL words. In this work, we formally investigate the nature and type of compressions used in SMS texts, and develop a Hidden Markov Model based word-model for TL. The model parameters have been estimated through standard machine learning techniques from a word-aligned SMS and standard English parallel corpus. The accuracy of the model in correcting TL words is 57.7%, which is almost a threefold improvement over the performance of Aspell. The use of simple bigram language model results in a 35% reduction of the relative word level error rates.

Foreign Direct Investment in Africa-Some Case Studies

Abstract: This paper reviews the experiences of a few countries in Sub-Saharan Africa that have succeeded in attracting fairly large amounts of foreign investment. The review indicates that sustained efforts to promote political and macroeconomic stability and implement essential structural reforms have been the key elements contributing to the success that certain countries in Africa have achieved in attracting a substantial volume of FDI. Strong leadership, which has helped promote democracy and overcome social and political strife, and a firm commitment to economic reform have been important determinants. The adoption of sound fiscal and monetary policies, supported by an appropriate exchange rate policy, and a proactive approach to removing structural impediments to private sector activity have had a positive bearing on investor sentiment. The analysis underscores the importance of relying on stability and a broad-based reform effort to encourage foreign investment in Africa.

Microfinance in Africa: Experience and Lessons from Selected African Countries

Abstract: Based on the experience of selected countries, this paper offers a critical presentation of the development of the microfinance sector in Africa. The paper supports the view that microfinance institutions, especially those engaged in full financial intermediation, complement effectively the banking sector in extending financial services and successfully draw on the rich experience of community-based development and preexisting informal methods of financial intermediation in Africa. Growing linkages between microfinance institutions and the banking system and the dissemination of good practices by nongovernment organizations contribute to the sound development of the sector, supported by regulation and supervision by local authorities.

Hardware software partitioning using genetic algorithm

Abstract: Hardware software co-design is gaining importance with the advent of CAD for embedded systems. A key phase in such designs is partitioning the specification into hardware and software implementation sets. The problem being combinatorically explosive, several greedy search algorithms have been proposed for hardware software partitioning. In this paper, we model the hardware software partitioning problem as a Constraint Satisfaction Problem (CSP), and present a genetic algorithm based approach to solve the CSP in order to obtain the partitioning solution.

Microfinance in Africa; Experience and Lessons From Selected African Countries

Abstract: Based on the experience of selected countries, this paper offers a critical presentation of the development of the microfinance sector in Africa. The paper supports the view that microfinance institutions, especially those engaged in full financial intermediation, complement effectively the banking sector in extending financial services and successfully draw on the rich experience of community-based development and preexisting informal methods of financial intermediation in Africa. Growing linkages between microfinance institutions and the banking system and the dissemination of good practices by nongovernment organizations contribute to the sound development of the sector, supported by regulation and supervision by local authorities.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Thinking fast and slow.

Abstract: In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

Multivariate Density Estimation

Abstract: Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.