Data modelling and data processing generated by human eye movements

Data modeling and data processing are important activities in any scientific research. This research focuses on the modeling of data and processing of data generated by a saccadometer. The approach used is based on the relational data model, but the processing and storage of the data is done with client datasets. The experiments were performed with 26 randomly selected files from a total of 264 experimental sessions. The data from each experimental session was stored in three different formats, respectively text, binary and extensible markup language (XML) based. The results showed that the text format and the binary format were the most compact. Several actions related to data processing were analyzed. Based on the results obtained, it was found that the two fastest actions are respectively loading data from a binary file and storing data into a binary file. In contrast, the two slowest actions were storing the data in XML format and loading the data from a text file, respectively. Also, one of the time-consuming operations turned out to be the conversion of data from text format to binary format. Moreover, the time required to perform this action does not depend in proportion on the number of records processed.

Design of True Random Number Generator Using Fingerprint as an Entropy Source and Its Implementation in S-Box

This paper deals with the design of a true random number generator (TRNG) using the fingerprint as an entropy source and its implementation in substitution box (S-box) of Advanced Encryption Standard (AES). Considering fingerprint as a unique and random arrangement of minutiae, these minutiae points are used as the source of entropy. The proposed design utilizes fewer resources minimizing hardware redundancy and enhancing the level of randomness. This TRNG has been designed and validated using Artix-7 FPGA. The data rate, speed and latency have been obtained as 40 Mbps, 5 Mbps and 305 ns, respectively. The generated random bit stream had also been sampled and converted to a binary format in MATLAB and tested through the National Institute of Standards and Technology (NIST) 800.22 statistical suite for validation. The proposed TRNG design pass efficiency achieved is more than 95% for a sample size of 10 binary sequences.

IDENTIFIKASI CIRI PENYAKIT COVID 19 MENGGUNAKAN METODE WAVELET DEUBECHIES-2

Coronavirus is a type of virus that can cause mild to severe illness. Transmission from animals to humans (zoonosis) and transmission from humans to humans is very limited. The main symptoms of Covid 19 are six, namely chills, chills, muscle aches, headaches, sore throats, and loss of sense of smell accompanied by a greater body temperature of 380C, Other symptoms such as skin rashes, dizziness and redness of the eyes. The incubation period is 2-14 days. This disease has become a pandemic, the number 1 cause of death in the world today. In this research, a process of identifying the characteristics of covid 19 will be carried out based on the appearance of lung X-ray images. There are 9 samples of lung X-ray images that will be identified by their characteristics. The image processing method used is the Wavelet Deubechies 2 (Wavelet DB2) method. The processing technique is by displaying images in binary format and displaying the values ​​of approximation energy, horizontal energy, vertical energy, diagonal energy and the detailed energy of each lung image. Of the 9 sample images tested there were 4 samples of healthy lung images and 5 samples of lung images infected with the covid virus 19. It turned out that the energy value of healthy lung images was greater than the energy value of covid lung images 19. The accuracy of the method DB2 wavelet in identifying the characteristics of covid lung images 19 about 78%.

The Mechanical Basis of Memory – the MeshCODE Theory

One of the major unsolved mysteries of biological science concerns the question of where and in what form information is stored in the brain. I propose that memory is stored in the brain in a mechanically encoded binary format written into the conformations of proteins found in the cell-extracellular matrix (ECM) adhesions that organise each and every synapse. The MeshCODE framework outlined here represents a unifying theory of data storage in animals, providing read-write storage of both dynamic and persistent information in a binary format. Mechanosensitive proteins that contain force-dependent switches can store information persistently, which can be written or updated using small changes in mechanical force. These mechanosensitive proteins, such as talin, scaffold each synapse, creating a meshwork of switches that together form a code, the so-called MeshCODE. Large signalling complexes assemble on these scaffolds as a function of the switch patterns and these complexes would both stabilise the patterns and coordinate synaptic regulators to dynamically tune synaptic activity. Synaptic transmission and action potential spike trains would operate the cytoskeletal machinery to write and update the synaptic MeshCODEs, thereby propagating this coding throughout the organism. Based on established biophysical principles, such a mechanical basis for memory would provide a physical location for data storage in the brain, with the binary patterns, encoded in the information-storing mechanosensitive molecules in the synaptic scaffolds, and the complexes that form on them, representing the physical location of engrams. Furthermore, the conversion and storage of sensory and temporal inputs into a binary format would constitute an addressable read-write memory system, supporting the view of the mind as an organic supercomputer.

Efficient Private Conjunctive Query Protocol Over Encrypted Data

Conjunctive queries play a key role in retrieving data from a database. In a database, a query containing many conditions in its predicate, connected by an “and/&/∧” operator, is called a conjunctive query. Retrieving the outcome of a conjunctive query from thousands of records is a heavy computational task. Private data access to an outsourced database is required to keep the database secure from adversaries; thus, private conjunctive queries (PCQs) are indispensable. Cheon, Kim, and Kim (CKK) proposed a PCQ protocol using search-and-compute circuits in which they used somewhat homomorphic encryption (SwHE) for their protocol security. As their protocol is far from being able to be used practically, we propose a practical batch private conjunctive query (BPCQ) protocol by applying a batch technique for processing conjunctive queries over an outsourced database, in which both database and queries are encoded in binary format. As a main technique in our protocol, we develop a new data-packing method to pack many data into a single polynomial with the batch technique. We further enhance the performances of the binary-encoded BPCQ protocol by replacing the binary encoding with N-ary encoding. Finally, we compare the performance to assess the results obtained by the binary-encoded BPCQ protocol and the N-ary-encoded BPCQ protocol.

Visualization of the Evolutionary Path: an Influenza Case Study

Visualization of viral evolution is one of the essential tasks in bioinformatics, through which virologists characterize a virus. The fundamental visualization tool for such a task is constructing a dendrogram, also called the phylogenetic tree. In this paper, we propose the visualization and characterization of the evolutionary path, starting from the root to isolated virus in the leaf of the phylogenetic tree. The suggested approach constructs the sequences of inner nodes (ancestors) within the phylogenetic tree and uses one-hot-encoding to represent the genetic sequence in a binary format. By employing embedding methods, such as multi-dimensional scaling, we project the path into 2D and 3D spaces. The final visualization demonstrates the dynamic of viral evolution locally (for an individual strain) and globally (for all isolated viruses). The results suggest applications of our approach in: detecting earlier changes in the characteristics of strains; exploring emerging novel strains; modeling antigenic evolution; and study of evolution dynamics. All of these potential applications are critical in the fight against viruses.

The Mechanical Basis of Memory - the MeshCODE Theory

One of the major unsolved mysteries of biological science concerns the question of where and in what form information is stored in the brain. I propose that memory is stored in the brain in a mechanically encoded binary format written into the conformations of proteins found in the cell-extracellular matrix adhesions that organise each and every synapse. The MeshCODE framework outlined here represents a unifying theory of data storage in animals, providing read-write storage of both dynamic and persistent information in a binary format. Mechanosensitive proteins that contain force-dependent switches can store information persistently, which can be written or updated using small changes in mechanical force. These mechanosensitive proteins, such as talin, scaffold each synapse, creating a meshwork of switches that together form a code, the so-called MeshCODE. Large signalling complexes assemble on these scaffolds as a function of the switch patterns and these complexes would both stabilise the patterns and coordinate synaptic regulators to dynamically tune synaptic activity. Synaptic transmission and action potential spike trains would operate the cytoskeletal machinery to write and update the synaptic MeshCODEs, thereby propagating this coding throughout the organism. Based on established biophysical principles, such a mechanical basis for memory would provide a physical location for data storage in the brain, with the binary patterns, encoded in the information-storing mechanosensitive molecules in the synaptic scaffolds, and the complexes that form on them, representing the physical location of engrams. Furthermore, the conversion and storage of sensory and temporal inputs into a binary format would constitute an addressable read-write memory system, supporting the view of the mind as an organic supercomputer.

Internet since decades - but no globally searchable data

The usability of digital information on the internet strongly depends on its searchability. There are search engines for global language based text search, but language based information representation has only limited value, e.g. concerning reproducibility, comparability, precision. Searchable digital information can be much more. Any piece of digital information is a number sequence. We can define the binary format and value set resp. domain of every number online. Then universal information transport is possible by the online defined "Domain Vector" (DV) data structure: "UL plus number sequence". At this "UL" is an efficient link to the machine readable online definition of the number sequence. The UL also is global identifier of a certain kind of data. The online definition includes additional information, e.g. for similarity comparison of the number sequence. With this a universal numeric search engine can provide precise user defined worldwide search of DVs like in a globalized database. All users can participate. Together we can optimize online definitions and provide defined data as DVs. Initially it is necessary to build a first attractive online presence where users can provide online definitions. We should stay in contact to optimize together the common standard for machine readable online definitions and DVs.

Reconfigurable monitoring for telecommunication networks

This article addresses the monitoring problem of the telecommunication networks. We consider these networks as multilevel dynamic objects. It shows that reconfigurable systems are necessary for their monitoring process in real life. We implement the reconfiguration abilities of the systems through the synthesis of monitoring programs and their execution in the monitoring systems and on the end-user devices. This article presents a new method for the synthesis of monitoring programs and develops a new language to describe the monitoring programs. The programs are translated into binary format and executed by the virtual machines installed on the elements of the networks. We present an example of the program synthesis for real distributed networks monitoring at last.

halSynteny: a fast, easy-to-use conserved synteny block construction method for multiple whole-genome alignments

Background Large-scale sequencing projects provide high-quality full-genome data that can be used for reconstruction of chromosomal exchanges and rearrangements that disrupt conserved syntenic blocks. The highest resolution of cross-species homology can be obtained on the basis of whole-genome, reference-free alignments. Very large multiple alignments of full-genome sequence stored in a binary format demand an accurate and efficient computational approach for synteny block production. Findings halSynteny performs efficient processing of pairwise alignment blocks for any pair of genomes in the alignment. The tool is part of the HAL comparative genomics suite and is targeted to build synteny blocks for multi-hundred–way, reference-free vertebrate alignments built with the Cactus system. Conclusions halSynteny enables an accurate and rapid identification of synteny in multiple full-genome alignments. The method is implemented in C++11 as a component of the halTools software and released under MIT license. The package is available at https://github.com/ComparativeGenomicsToolkit/hal/.