Descriptional Complexity of Semi-Simple Splicing Systems

Splicing systems are generative mechanisms introduced by Tom Head in 1987 to model the biological process of DNA recombination. The computational engine of a splicing system is the “splicing operation”, a cut-and-paste binary string operation defined by a set of “splicing rules”, quadruples [Formula: see text] where [Formula: see text] are words over an alphabet [Formula: see text]. For two strings [Formula: see text] and [Formula: see text], applying the splicing rule [Formula: see text] produces the string [Formula: see text]. In this paper we focus on a particular type of splicing systems, called [Formula: see text] semi-simple splicing systems, [Formula: see text] and [Formula: see text], wherein all splicing rules [Formula: see text] have the property that the two strings in positions [Formula: see text] and [Formula: see text] in [Formula: see text] are singleton letters, while the other two strings are empty. The language generated by such a system consists of the set of words that are obtained starting from an initial set called “axiom set”, by iteratively applying the splicing rules to strings in the axiom set as well as to intermediately produced strings. We consider semi-simple splicing systems where the axiom set is a regular language, and investigate the descriptional complexity of such systems in terms of the size of the minimal deterministic finite automata that recognize the languages they generate.

Development of Combinational Circuits by Encoding on the Basis of Developmental Biology

The present work visualizes the evolution of primitive digital circuits as a development problem. The development of the digital circuit is implemented similar to the development of a human embryo from a single cell to the complete organism. The constituent parts making up a primitive digital circuit are encoded into binary strings. Each binary string is viewed as a cell, and several such cells are allowed to adhere and multiply before culminating into a developed organism. The binary string of the cell is further mapped to a particular attribute which defines the constituent of the complete digital circuit implemented. The present work illustrates the development of a 4-input combinational digital circuit. The development of 2-input majority function is illustrated, and the results are shown for the 2-input Ex-OR gate, 2-input majority function with 4 input variables, and a 2-to-1 multiplexer circuit. The development of the digital circuit resembles the development of an embryo in a living organism.

Improved ORB Algorithm Using Three-Patch Method and Local Gray Difference

This paper presents an improved Oriented Features from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) algorithm named ORB using three-patch and local gray difference (ORB-TPLGD). ORB takes a breakthrough in real-time aspect. However, subtle changes of the image may greatly affect its final binary description. In this paper, the feature description generation is focused. On one hand, instead of pixel patch pairs comparison method used in present ORB algorithm, a three-pixel patch group comparison method is adopted to generate the binary string. In each group, the gray value of the main patch is compared with that of the other two companion patches to determine the corresponding bit of the binary description. On the other hand, the present ORB algorithm simply uses the gray size comparison between pixel patch pairs, while ignoring the information of the gray difference value. In this paper, another binary string based on the gray difference information mentioned above is generated. Finally, the feature fusion method is adopted to combine the binary strings generated in the above two steps to generate a new feature description. Experiment results indicate that our improved ORB algorithm can achieve greater performance than ORB and some other related algorithms.

Prediction of Genome Sequences in Terms of Cellular Automata Expansion of Rule Based Logics

This paper proposes a novel concept called “Percentage Nucleotide Concentration of genomes” in terms of cellular automata evolutions of adjoints of Adenine, Thymine, Guanine, and Cytosine. The adjoints of the given a genome sequenceare the characteristic binary string sequences. For example, the adjoint of Adenine of a given genome sequence is a binary string consisting of 0’s and 1’s where 1’s corresponds to the presence of Adenine in the genome sequence. So, one can have four adjoint sequences of Adenine, Thymine, Guanine, and Cytosine corresponding to a given genome sequence. Onedimensional three neighborhood binary value cellular automata rules could be applied to an adjoint sequence and the desired number of evolutions obtained.These rules aredefined by linear Boolean functions and one can have 256 such linear Boolean functions. The analysis of genome sequences with predictive analytics gives a scope of getting the inherent properties of the genome. The predictive model suits the Nucleotide concentration and is computed for an adjoint sequence and its variation evaluated for its successive evolutions based on a cellular automaton rule.

Actin droplet machine

The actin droplet machine is a computer model of a three-dimensional network of actin bundles developed in a droplet of a physiological solution, which implements mappings of sets of binary strings. The actin bundle network is conductive to travelling excitations, i.e. impulses. The machine is interfaced with an arbitrary selected set of k electrodes through which stimuli, binary strings of length k represented by impulses generated on the electrodes, are applied and responses are recorded. The responses are recorded in a form of impulses and then converted to binary strings. The machine’s state is a binary string of length k : if there is an impulse recorded on the i th electrode, there is a ‘1’ in the i th position of the string, and ‘0’ otherwise. We present a design of the machine and analyse its state transition graphs. We envisage that actin droplet machines could form an elementary processor of future massive parallel computers made from biopolymers.

Modified honey encryption scheme for encoding natural language message

Conventional encryption schemes are susceptible to brute-force attacks. This is because bytes encode utf8 (or ASCII) characters. Consequently, an adversary that intercepts a ciphertext and tries to decrypt the message by brute-forcing with an incorrect key can filter out some of the combinations of the decrypted message by observing that some of the sequences are a combination of characters which are distributed non-uniformly and form no plausible meaning. Honey encryption (HE) scheme was proposed to curtail this vulnerability of conventional encryption by producing ciphertexts yielding valid-looking, uniformly distributed but fake plaintexts upon decryption with incorrect keys. However, the scheme works for only passwords and PINS. Its adaptation to support encoding natural language messages (e-mails, human-generated documents) has remained an open problem. Existing proposals to extend the scheme to support encoding natural language messages reveals fragments of the plaintext in the ciphertext, hence, its susceptibility to chosen ciphertext attacks (CCA). In this paper, we modify the HE schemes to support the encoding of natural language messages using Natural Language Processing techniques. Our main contribution was creating a structure that allowed a message to be encoded entirely in binary. As a result of this strategy, most binary string produces syntactically correct messages which will be generated to deceive an attacker who attempts to decrypt a ciphertext using incorrect keys. We evaluate the security of our proposed scheme.

Metric Dimension of Graph Join P2 and Pt

The following metric dimension of join two paths $P_2 + P_t$ is determined as follows. For every $k = 1, 2, 3, ...$ and $t = 2 + 5k$ or $t = 3 + 5k$, the dimension of $P_2 + P_t$ is $2 + 2k$ whereas for $t = 4 + 5k, t = 5(k+1)$ or $t = 1 + 5(k+1)$, the dimension is $3 + 2k$. In case $t \geq 7$, the dimension is determined by a chosen (maximal) ordered basis for $P_2 + P_t$ in which the integers 1, 2 are the two consecutive vertices of $P_2$ and the next integers $3, 4, ..., t + 2$ are the $t$ consecutive vertices of $P_t$. If $t \geq 10$, the ordered binary string contains repeated substrings of length 5. For $t 7$, the dimension is easily found using a computer search, or even just using hand computations.