A digital watermarking method using image compression based on a fuzzy relational equation (ICF) is proposed. The method is based on least significant bit modification. If the coding system of ICF is not designed appropriately, the fuzzy relational equation will be unsolvable due to the watermarking (modification of compressed image). In order to avoid this problem, a condition for appropriate coding system design is represented in terms of the solvability degree of the fuzzy relational equation. Image compression and reconstruction experiments using 100 images (extracted from Corel Gallery) are performed, and it is confirmed that the signed image is indistinguishable from the unsigned one.
Images are uploaded to the Internet over time which makes concept drifting and distribution change in semantic classes unavoidable. Current hashing methods being trained using a given static database may not be suitable for nonstationary semantic image retrieval problems. Moreover, directly retraining a whole hash table to update knowledge coming from new arriving image data may not be efficient. Therefore, this paper proposes a new incremental hash-bit learning method. At the arrival of new data, hash bits are selected from both existing and newly trained hash bits by an iterative maximization of a 3-component objective function. This objective function is also used to weight selected hash bits to re-rank retrieved images for better semantic image retrieval results. The three components evaluate a hash bit in three different angles: 1) information preservation; 2) partition balancing; and 3) bit angular difference. The proposed method combines knowledge retained from previously trained hash bits and new semantic knowledge learned from the new data by training new hash bits. In comparison to table-based incremental hashing, the proposed method automatically adjusts the number of bits from old data and new data according to the concept drifting in the given data via the maximization of the objective function. Experimental results show that the proposed method outperforms existing stationary hashing methods, table-based incremental hashing, and online hashing methods in 15 different simulated nonstationary data environments.
The paradigm of Artificial Intelligence and Machine Learning has resulted in an amazingly diverse plethora of models operating in various environments and quite often exhibiting numerous successes. There is a growing spectrum of challenging application areas of high criticality where one has to meet a number of fundamental requirements. Those manifest evidently when Machine Learning constructs have to function autonomously and any decisions being rendered entail far reaching implications. The carefully crafted learning process has to result with advanced models. Along with the developed models, they have to come hand-in-hand with credibility measures that are crucial to assess an extent to which the results generated by such measures are meaningful, trustworthy and credible. The credibility of the Machine Learning models becomes of paramount importance given the nature of application domains. Autonomous systems including autonomous vehicles, user identification (both using audio and video channels), financial systems (calling for sound mechanisms to quantify risk levels) require the ML system making classification or prediction decisions some level of self-awareness. Among others, this translates to forming sound answers to the following crucial questions emerging within the design process: How much confidence could be associated with the result? Could any action /decision be taken on a basis of obtained result? Given the reported level of credibility, is there any other experimental evidence one could acquire to validate the decision? In this study, we advocate that a general way to achieve such goals is to engage the mechanism of Granular Computing; subsequently, the granularity endowing the results are sought as a vehicle use to quantify the credibility level. Sustainable (or green) Machine Learning gives rise to the agenda of knowledge reuse, namely exploring possibilities of potential reuse of the already designed models in a spectrum of current environments where computing overhead as one of the ways to contribute to the agenda of sustainable Machine Learning and discuss a crucial role of information granularity in this context.
Given the complexity and sophistication of many contemporary software systems, it is often difficult to gauge the effectiveness, maintainability, extensibility, and efficiency of their underlying software components. A strategy to evaluate the qualitative attributes of a system's components is to use software metrics as quantitative predictors. We present a fusion strategy that combines the predicted qualitative assessments from multiple classifiers with the anticipated outcome that the aggregated predictions are superior to any individual classifier prediction. Multiple linear classifiers are presented with different, randomly selected, subsets of software metrics. In this study, the software components are from a sophisticated biomedical data analysis system, while the external reference test is a thorough assessment of both complexity and maintainability, by a software architect, of each system component. The fuzzy integration results are compared against the best individual classifier operating on a software metric subset
