As we look around, we observe objects and their inter-relations embedded in 4.pi steradian space; consider relations and processes as objects too. We especially note that the description of objects is constructed of information. We further notice the apparent realism of the information, it is undeniable, concrete, and non-probabilistic regardless of whether or not the objects exist. Since information does not interact physically, yet undeniably apparent, it must have a non-falsifiable existence in reality. The causal function in nature must be directly responsible for this reality. The regularity in causal function of physical systems dictates that a resultant state S of a system must causally depend on the reality of precursor states of interacting systems within limits of causal function. Therefore, S intrinsically must correlate with the information of relation among precursor states, which is expressible quantitatively as -- disjunction of conjunctions of values (semantics) of states within limits of reality that may result in S (Singh 2018). For the same causal dependence, S must also correlate with this expression on the values of correlation of respective states in each configuration. The second level correlation is organizable in modular hierarchy to give rise to arbitrary structured and abstract semantics. Conjunction creates a configuration within limits of specificity, whereas disjunction of such conjunctions gives rise to abstraction of structure and relation. Hence, we have a quantitative mechanism of information processing in addition to its ontological basis. If the brain can process semantic values of information, there must exist a formal mechanism.

Security is one of major issues in WSNs as poor security disrupts the entire network and can have a significant effect on data transmission. So, the focus of the current research is to develop a secure Z-MAC protocol along with the implementation of elliptic curve encryption and IHOP mechanism. Basic Z-MAC protocol and secure Z-MAC protocol were compared in terms of the stages and performance while they were introduced with blackhole, flooding, and DDOS attacks. It was found that secure Z-MAC protocol ensures high security and performance even when the network is introduced with variety of attacks. It was also observed that delay in packets was minimal when secure Z-MAC protocol was implemented in the network.

Shannon entropy (H) and Mutual Information (MI) are practically estimated using first-order statistics as it is easier and convenient. However, the estimated first-order H and MI values could be grossly incorrect as we shall demonstrate with 3 carefully designed short linearly independent sequences X1, X2 and X3. These are carefully constructed to take values from the set {0, +1, -1} such that the time stamps of zeroes exactly coincide. Being linearly independent, the pairwise correlation coefficients are zeros. X2 and X3 are cyclic permutations of each other (X1 is unrelated other than zero values being at identical locations). The estimated pair-wise first-order MI values turn out to be the same for all the 3 pairs, thus showing the inability of MI to capture the additional mutual dependence between X2 and X3. After puncturing the zero-values from all the sequences (along with the time stamps) the first-order MIs turn out to be zeros for all pairs - wrongly implying that the sequences are independent, whereas X2 and X3 continue to be cyclic permutations of each other (and hence completely dependent). Compression-complexity measures such as the Effort-To-Compress complexity measure can correctly capture the non-linear dependencies in this case. First-order estimation of H and MI is thus fraught with danger in practical applications, especially on short data lengths and in such situations, it is preferable to employ compression-complexity measures.

High-recall Information Retrieval systems tackle challenging tasks that require the finding of (nearly) all the relevant documents in a collection of documents. Electronic discovery (eDiscovery) and systematic review systems are probably the most important applications of such systems where the search for relevant information with limited resources, such as time and money, is necessary.

In this field, Technology-Assisted Review (TAR) systems use a kind of human-in-the-loop approach where, starting from an initial query of the user, ranking algorithms are continuously trained according to the relevance feedback from the user until a substantial number of the relevant documents are identified. This approach, named Continuous Active Learning (CAL), is more effective and more efficient than traditional e-discovery and systematic review practices, which typically consist of a mix of keyword search and manual review of the search results.

In this work, we aim to study the effectiveness of query variation approaches that work in parallel with the explicit relevance feedback of the users during a search session. In particular, we want to predict when to stop the search for relevant documents in terms of the cost/benefits of the amount of missing information compared to the effort of continuing to search. We evaluate the approaches on standard Information Retrieval test collections provided by the Conference and Labs Evaluation Forum (CLEF) and the Text Retrieval Conference (TREC).