The emergency powers in Bangladesh: means for subversion of the rule of law?

The Constitution of Bangladesh empowers the President to proclaim an emergency on the actual or imminent ground of war or external aggression or internal disturbance. However, the insertion of ‘internal disturbance’ in the Constitution as a ground for invoking emergency has provided the executive with the opportunity to proclaim all the five emergencies in Bangladesh on this vague ground for purposes other than that of securing the life of the nation. Furthermore, in the absence of any effective constitutional mechanisms for scrutinising the exercise of emergency powers and a time limit on the continuation of a state of emergency, some of the proclamations of emergency continued even after the alleged threat posed to the life of the nation was over to perpetuate the survival of the party in power by repressing any political threat to the regime. This Article, therefore, recommends for insertion in the Constitution of Bangladesh detailed norms providing for legal limits on the wide power of the executive concerning the proclamation, administration and termination of emergency with a view to ensure that emergencies can no longer be resorted to as the effective means of discarding the rule of law.

Joint optimization of rule placement and traffic engineering for QoS provisioning in software defined network

Software-Defined Network (SDN) is a promising network paradigm that separates the control plane and data plane in the network. It has shown great advantages in simplifying network management such that new functions can be easily supported without physical access to the network switches. However, Ternary Content Addressable Memory (TCAM), as a critical hardware storing rules for high-speed packet processing in SDN-enabled devices, can be supplied to each device with very limited quantity because it is expensive and energy-consuming. To efficiently use TCAM resources, we propose a rule multiplexing scheme, in which the same set of rules deployed on each node apply to the whole flow of a session going through but towards different paths. Based on this scheme, we study the rule placement problem with the objective of minimizing rule space occupation for multiple unicast sessions under QoS constraints. We formulate the optimization problem jointly considering routing engineering and rule placement under both existing and our rule multiplexing schemes. Via an extensive review of the state-of-the-art work, to the best of our knowledge, we are the first to study the non-routing-rule placement problem. Finally, extensive simulations are conducted to show that our proposals significantly outperform existing solutions.

Personality differences and hotel web design study using targeted positive and negative association rule mining

As people have unique tastes, the way to satisfy a small group of targeted customers or to be generic to meet most people's preference has been a traditional question to many fashion designers and website developers. This study examined the relationship between individuals' personality differences and their web design preferences. Each individual's personality is represented by a combination of five traits, and 15 website design-related features are considered to test the users' preference. We introduced a data mining technique called targeted positive and negative association rule mining to analyze a dataset containing the survey results collected from undergraduate students. The results of this study not only suggest the importance of providing specific designs to attract individual customers, but also provide valuable input on the Big Five personality traits in their entirety.

Optimized fuzzy association rule mining for quantitative data

With the advance of computing and electronic technology, quantitative data, for example, continuous data (i.e., sequences of floating point numbers), become vital and have wide applications, such as for analysis of sensor data streams and financial data streams. However, existing association rule mining generally discover association rules from discrete variables, such as boolean data (`O' and `l') and categorical data (`sunny', `cloudy', `rainy', etc.) but very few deal with quantitative data. In this paper, a novel optimized fuzzy association rule mining (OFARM) method is proposed to mine association rules from quantitative data. The advantages of the proposed algorithm are in three folds: 1) propose a novel method to add the smoothness and flexibility of membership function for fuzzy sets; 2) optimize the fuzzy sets and their partition points with multiple objective functions after categorizing the quantitative data; and 3) design a two-level iteration to filter frequent-item-sets and fuzzy association-rules. The new method is verified by three different data sets, and the results have demonstrated the effectiveness and potentials of the developed scheme.