Binary Strings Research Articles

Topological optimization of the DenseNet with pretrained-weights inheritance and genetic channel selection

Convolutional neural networks (CNNs) have been successfully applied in many computer vision applications [1], especially in image classification tasks, where most of the structures have been designed manually. With the aid of skip connection and dense connection, the depths of the models are becoming “deeper” and the filters of layers are getting “wider” in order to tackle the challenge of large-scale datasets. However, large-scale models in convolutional layers become inefficient due to the redundant channels from input feature maps. In this paper, we aim to automatically optimize the topology of the DenseNet, in which unnecessary convolutional kernels are reduced. To achieve this, we present a training pipeline that generates the network structure using a genetic algorithm. We first propose two encoding methods that can represent the structure of the model using a fixed-length binary string. A three-step based evolutionary process consisting of selection, crossover, and mutation is proposed to optimize the structure. We also present a pretrained weight inheritance method which can largely reduce the total time consumption of the genetic process. Experimental results have demonstrated that our proposed model can achieve comparable accuracy to the state-of-the-art models, across a wide range of image recognition and classification datasets, whilst significantly reducing the number of parameters.

Secure Digital Databases using Watermarking based on English-Character Attributes

Introduction:The single space and the double space (DS). In this procedure, an image is used to watermark a digital database, where the image bytes are divided into binary strings that block the text attributes of the selected database, we proposed an algorithm to defend against four common database attacks.Objective:Perform the watermark is Embedding and makes extraction of the watermark. We also describe the principal of the Embedding and extraction the watermark.Methods:The procedure to extract the watermark does not require knowledge of the original database that does not carry the same watermark. This feature is extremely important because it allows the discovery of a watermark in a copy of the original database, regardless of the subsequent updates to the asset. The extraction procedure is a direct reflection of the procedure used to embed the watermark is six steps.Results:Using new algorithm ability to develop a database watermark that would make it difficult for an attacker to remove or change the watermark without discovering the value of the object. To be judged effective, the database algorithm had to be able to create a strong enough watermark that could sustain the security of the database in the face of the following four types of attack: deletion of a sub-dataset, addition of a sub-dataset.Conclusion:The performance of the proposed algorithm was assessed in respect of its ability to defend the database against four common attacks for all tuples selection.

In‐Memory Hamming Weight Calculation in a 1T1R Memristive Array

AbstractIn‐memory computing enabled by advanced nonvolatile memory technologies, such as memristors and memristive devices, emerges as a promising approach to accelerate certain data‐intensive algorithms, and thus outperforms the von Neumann computing in terms of processing latency and energy efficiency. In this work, an efficient method to calculate the Hamming weight (HW) of a binary string in a one‐transistor‐one‐resistor (1T1R) memristive array is proposed, which can be beneficial for various computation tasks. Specifically, the target string is converted to a voltage vector and multiplies with an “all‐1” string pre‐stored in the resistance of the row, which equals to a binary matrix multiplication or AND logic operation. The in situ stored HW calculation result is then read out through a current accumulation operation. As a proof‐of‐concept demonstration, 4 bit and 8 bit HW calculation is successfully implemented in experiment and simulation, respectively. In addition, the influence of the resistance variation on the calculation correctness is discussed. This work broadens the application range of using emerging nonvolatile memories for classical information processing in hardware level with high efficiency.

A Note on the Probability of Rectangles for Correlated Binary Strings

Consider two sequences of n independent and identically distributed fair coin tosses, X = (X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , . . . , X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) and Y = (Y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , . . . , Y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ), which are ρ-correlated for each j, i.e. P[X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> = Y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> ] = 1+ρ/2 .We study the question of how large (small) the probability P[X ∈ A, Y ∈ B] can be among all sets A, B ⊂ {0, 1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> of a given cardinality. For sets |A|, |B| = Θ(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ) it is well known that the largest (smallest) probability is approximately attained by concentric (anti-concentric) Hamming balls, and this can be proved via the hypercontractive inequality (reverse hypercontractivity). Here we consider the case of |A|, |B| = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Θ</sup> (n). By applying a recent extension of the hypercontractive inequality of Polyanskiy-Samorodnitsky (J. Functional Analysis, 2019), we show that Hamming balls of the same size approximately maximize P[X ∈ A, Y ∈ B] in the regime of p → 1. We also prove a similar tight lower bound, i.e. show that for p → 0 the pair of opposite Hamming balls approximately minimizes the probability P[X ∈ A, Y ∈ B].

The Asymptotic Induced Matching Number of Hypergraphs: Balanced Binary Strings

We compute the asymptotic induced matching number of the $k$-partite $k$-uniform hypergraphs whose edges are the $k$-bit strings of Hamming weight $k/2$, for any large enough even number $k$. Our lower bound relies on the higher-order extension of the well-known Coppersmith–Winograd method from algebraic complexity theory, which was proven by Christandl, Vrana and Zuiddam. Our result is motivated by the study of the power of this method as well as of the power of the Strassen support functionals (which provide upper bounds on the asymptotic induced matching number), and the connections to questions in tensor theory, quantum information theory and theoretical computer science.Our proof relies on a new combinatorial inequality that may be of independent interest. This inequality concerns how many pairs of Boolean vectors of fixed Hamming weight can have their sum in a fixed subspace.

Efficient hash maps to {mathbb {G}}_2 on BLS curves

When a pairing e: {mathbb {G}}_1 times {mathbb {G}}_2 rightarrow {mathbb {G}}_{T}, on an elliptic curve E defined over a finite field {mathbb {F}}_q, is exploited for an identity-based protocol, there is often the need to hash binary strings into {mathbb {G}}_1 and {mathbb {G}}_2. Traditionally, if E admits a twist {tilde{E}} of order d, then {mathbb {G}}_1=E({mathbb {F}}_q) cap E[r], where r is a prime integer, and {mathbb {G}}_2={tilde{E}}({mathbb {F}}_{q^{k/d}}) cap {tilde{E}}[r], where k is the embedding degree of E w.r.t. r. The standard approach for hashing into {mathbb {G}}_2 is to map to a general point P in {tilde{E}}({mathbb {F}}_{q^{k/d}}) and then multiply it by the cofactor c=#{tilde{E}}({mathbb {F}}_{q^{k/d}})/r. Usually, the multiplication by c is computationally expensive. In order to speed up such a computation, two different methods—by Scott et al. (International conference on pairing-based cryptography. Springer, Berlin, pp 102–113, 2009) and by Fuentes-Castaneda et al. (International workshop on selected areas in cryptography)—have been proposed. In this paper we consider these two methods for BLS pairing-friendly curves having k in {12,24,30,42,48}, providing efficiency comparisons. When k=42,48, the application of Fuentes et al. method requires expensive computations which were infeasible for the computational power at our disposal. For these cases, we propose hashing maps that we obtained following Fuentes et al. idea.

Efficient implied alignment

BackgroundGiven a binary tree mathcal {T} of n leaves, each leaf labeled by a string of length at most k, and a binary string alignment function ⊗, an implied alignment can be generated to describe the alignment of a dynamic homology for mathcal {T}. This is done by first decorating each node of mathcal {T} with an alignment context using ⊗, in a post-order traversal, then, during a subsequent pre-order traversal, inferring on which edges insertion and deletion events occurred using those internal node decorations.ResultsPrevious descriptions of the implied alignment algorithm suggest a technique of “back-propagation” with time complexity mathcal {O}left (k^{2} * n^{2}right). Here we describe an implied alignment algorithm with complexity mathcal {O}left (k * n^{2}right). For well-behaved data, such as molecular sequences, the runtime approaches the best-case complexity of Ω(k∗n).ConclusionsThe reduction in the time complexity of the algorithm dramatically improves both its utility in generating multiple sequence alignments and its heuristic utility.

User-Silicon Entangled Mobile Identity Authentication

We explore mobile device touchscreen characteristics to build a user-device (UD) biometric physical unclonable function (PUF). Human user touchscreen interaction induces dynamic capacitive differences. Sensors detect current differences which are a function of both (1) a human biometric of how a shape is traced and (2) silicon foundry process transistor-level variability embedded in the touchscreen grid. This forms a physical function with input x defining a shape and output y abstracted from the measured current value stream. We argue and establish that this physical function has PUF attributes. Moreover, it provides a robust user-device biometric-based authentication mechanism. Authentication is based on geometric shapes (challenges) drawn on the touchscreen. Users trace them. The authentication layer creates a response abstract, and validates it against a user profile. Authentication accuracy is affected by the complexity of geometric shapes as well as the validation algorithm. We consider polyline shapes (simple gestures) and complex closed geometric shapes (complex gestures). Complex gestures offer higher response entropy, but are computationally less efficient with a slightly lower validation accuracy. Complex gestures achieve 99.6% accuracy compared with 100% for simple gestures. User profiles exhibit physical unclonable function (PUF) properties. Touchscreen gestures are quantized into binary strings. Gesture hamming distance is 60+ bits for 128-bit strings for different user-device profiles; it is 0 bits for the same profile. This demonstrates variability and reproducibility respectively. Montreal TestU01 tests binary string pseudorandom characteristics; the majority of tests pass showing pseudorandom number generator (PRG) characteristics.

The surprising little effectiveness of cooperative algorithms in parallel problem solving

Biological and cultural inspired optimization algorithms are nowadays part of the basic toolkit of a great many research domains. By mimicking processes in nature and animal societies, these general-purpose search algorithms promise to deliver optimal or near-optimal solutions using hardly any information on the optimization problems they are set to tackle. Here we study the performances of a cultural-inspired algorithm -- the imitative learning search -- as well as of asexual and sexual variants of evolutionary algorithms in finding the global maxima of NK-fitness landscapes. The main performance measure is the total number of agent updates required by the algorithms to find those global maxima and the baseline performance, which establishes the effectiveness of the cooperative algorithms, is set by the blind search in which the agents explore the problem space (binary strings) by flipping bits at random. We find that even for smooth landscapes that exhibit a single maximum, the evolutionary algorithms do not perform much better than the blind search due to the stochastic effects of the genetic roulette. The imitative learning is immune to this effect thanks to the deterministic choice of the fittest string in the population, which is used as a model for imitation. The tradeoff is that for rugged landscapes the imitative learning search is more prone to be trapped in local maxima than the evolutionary algorithms. In fact, in the case of rugged landscapes with a mild density of local maxima, the blind search either beats or matches the cooperative algorithms regardless of whether the task is to find the global maximum or to find the fittest state within a given runtime.

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.

Time for Change: Implementation of Aksentijevic-Gibson Complexity in Psychology

Given that complexity is critical for psychological processing, it is somewhat surprising that the field was dominated for a long time by probabilistic methods that focus on the quantitative aspects of the source/output. Although the more recent approaches based on the Minimum Description Length principle have produced interesting and useful models of psychological complexity, they have not directly defined the meaning and quantitative unit of complexity measurement. Contrasted to these mathematical approaches are various ad hoc measures based on different aspects of structure, which can work well but suffer from the same problem. The present manuscript is composed of two self-sufficient, yet related sections. In Section 1, we describe a complexity measure for binary strings which satisfies both these conditions (Aksentijevic–Gibson complexity; AG). We test the measure on a number of classic studies employing both short and long strings and draw attention to an important feature—a complexity profile—that could be of interest in modelling the psychological processing of structure as well as analysis of strings of any length. In Section 2 we discuss different factors affecting the complexity of visual form and showcase a 2D generalization of AG complexity. In addition, we provide algorithms in R that compute the AG complexity for binary strings and matrices and demonstrate their effectiveness on examples involving complexity judgments, symmetry perception, perceptual grouping, entropy, and elementary cellular automata. Finally, we enclose a repository of codes, data and stimuli for our example in order to facilitate experimentation and application of the measure in sciences outside psychology.

Implementation of a Hamming distance\u2013like genomic quantum classifier using inner products on ibmqx2 and ibmq_16_melbourne

Motivated by the problem of classifying individuals with a disease versus controls using a functional genomic attribute as input, we present relatively efficient general purpose inner product–based kernel classifiers to classify the test as a normal or disease sample. We encode each training sample as a string of 1 s (presence) and 0 s (absence) representing the attribute’s existence across ordered physical blocks of the subdivided genome. Having binary-valued features allows for highly efficient data encoding in the computational basis for classifiers relying on binary operations. Given that a natural distance between binary strings is Hamming distance, which shares properties with bit-string inner products, our two classifiers apply different inner product measures for classification. The active inner product (AIP) is a direct dot product–based classifier whereas the symmetric inner product (SIP) classifies upon scoring correspondingly matching genomic attributes. SIP is a strongly Hamming distance–based classifier generally applicable to binary attribute-matching problems whereas AIP has general applications as a simple dot product–based classifier. The classifiers implement an inner product between N = 2n dimension test and train vectors using n Fredkin gates while the training sets are respectively entangled with the class-label qubit, without use of an ancilla. Moreover, each training class can be composed of an arbitrary number m of samples that can be classically summed into one input string to effectively execute all test–train inner products simultaneously. Thus, our circuits require the same number of qubits for any number of training samples and are O(log {N}) in gate complexity after the states are prepared. Our classifiers were implemented on ibmqx2 (IBM-Q-team 2019b) and ibmq_16_melbourne (IBM-Q-team 2019a). The latter allowed encoding of 64 training features across the genome.

The complexity of clinically-normal sinus-rhythm ECGs is decreased in equine athletes with a diagnosis of paroxysmal atrial fibrillation

Equine athletes have a pattern of exercise which is analogous to human athletes and the cardiovascular risks in both species are similar. Both species have a propensity for atrial fibrillation (AF), which is challenging to detect by ECG analysis when in paroxysmal form. We hypothesised that the proarrhythmic background present between fibrillation episodes in paroxysmal AF (PAF) might be detectable by complexity analysis of apparently normal sinus-rhythm ECGs. In this retrospective study ECG recordings were obtained during routine clinical work from 82 healthy horses and from 10 horses with a diagnosis of PAF. Artefact-free 60-second strips of normal sinus-rhythm ECGs were converted to binary strings using threshold crossing, beat detection and a novel feature detection parsing algorithm. Complexity of the resulting binary strings was calculated using Lempel-Ziv (‘76 & ‘78) and Titchener complexity estimators. Dependence of Lempel-Ziv ‘76 and Titchener T-complexity on the heart rate in ECG strips obtained at low heart rates (25–60 bpm) and processed by the feature detection method was found to be significantly different in control animals and those diagnosed with PAF. This allows identification of horses with PAF from sinus-rhythm ECGs with high accuracy.

Comparison Analysis with Huffman Algorithm and Goldbach Codes Algorithm in File Compression Text Using the Method Exponential Comparison

With the development of technology at this time many people know about compression. In simple compression is a process to shrink the file from its original size. At this time compression applications that are often used are WinZip, WinRar, and 7-Zip, namely with the aim of compressing documents and saving space on memory or data transmission. Compressed data can be in the form of images, audio, video and text. The use of the Huffman algorithm and the Goldbach Codes algorithm in compressing text files is intended to provide enormous benefits in the sending and storage process and requires less memory space compared to uncompressed text. The algorithm starts by providing a string of inputs as input, how to produce an algorithm output in the form of a binary string or code that translates each input string, so that the string has a small number of bits compared to strings that are not compressed. Thus, the problem is how to obtain the code with sorted characters and frequency tables as input and shorter binary code as output. In applying the Huffman algorithm and the Goldbach Codes algorithm in compressing text files is very good, the results were not reduced from the original file or there was no reduction

Remote Sensing Image Registration Based on Improved KAZE and BRIEF Descriptor

Remote sensing image registration is still a challenging task owing to the significant influence of nonlinear differences between remote sensing images. To solve this problem, this paper proposes a novel approach with regard to feature-based remote sensing image registration. There are two key contributions: 1) we bring forward an improved strategy of composite nonlinear diffusion filtering according to the scale factors in multi-scale space and 2) we design a gradually decreasing resolution of multi-scale pyramid space. And a binary code string is served as feature descriptors to improve matching efficiency. Extensive experiments of different categories of remote image datasets on feature extraction and feature registration are performed. The experimental results demonstrate the superiority of our proposed scheme compared with other classical algorithms in terms of correct matching ratio, accuracy and computation efficiency.

Accelerating Binary String Comparisons with a Scalable, Streaming-Based System Architecture Based on FPGAs

This paper is concerned with Field Programmable Gate Arrays (FPGA)-based systems for energy-efficient high-throughput string comparison. Modern applications which involve comparisons across large data sets—such as large sequence sets in molecular biology—are by their nature computationally intensive. In this work, we present a scalable FPGA-based system architecture to accelerate the comparison of binary strings. The current architecture supports arbitrary lengths in the range 16 to 2048-bit, covering a wide range of possible applications. In our example application, we consider DNA sequences embedded in a binary vector space through Locality Sensitive Hashing (LSH) one of several possible encodings that enable us to avoid more costly character-based operations. Here the resulting encoding is a 512-bit binary signature with comparisons based on the Hamming distance. In this approach, most of the load arises from the calculation of the O ( m ∗ n ) Hamming distances between the signatures, where m is the number of queries and n is the number of signatures contained in the database. Signature generation only needs to be performed once, and we do not consider it further, focusing instead on accelerating the signature comparisons. The proposed FPGA-based architecture is optimized for high-throughput using hundreds of computing elements, arranged in a systolic array. These core computing elements can be adapted to support other string comparison algorithms with little effort, while the other infrastructure stays the same. On a Xilinx Virtex UltraScale+ FPGA (XCVU9P-2), a peak throughput of 75.4 billion comparisons per second—of 512-bit signatures—was achieved, using a design with 384 parallel processing elements and a clock frequency of 200 MHz. This makes our FPGA design 86 times faster than a highly optimized CPU implementation. Compared to a GPU design, executed on an NVIDIA GTX1060, it performs nearly five times faster.

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.

Asymptotic Analysis of the kth Subword Complexity.

Patterns within strings enable us to extract vital information regarding a string’s randomness. Understanding whether a string is random (Showing no to little repetition in patterns) or periodic (showing repetitions in patterns) are described by a value that is called the kth Subword Complexity of the character string. By definition, the kth Subword Complexity is the number of distinct substrings of length k that appear in a given string. In this paper, we evaluate the expected value and the second factorial moment (followed by a corollary on the second moment) of the kth Subword Complexity for the binary strings over memory-less sources. We first take a combinatorial approach to derive a probability generating function for the number of occurrences of patterns in strings of finite length. This enables us to have an exact expression for the two moments in terms of patterns’ auto-correlation and correlation polynomials. We then investigate the asymptotic behavior for values of . In the proof, we compare the distribution of the kth Subword Complexity of binary strings to the distribution of distinct prefixes of independent strings stored in a trie. The methodology that we use involves complex analysis, analytical poissonization and depoissonization, the Mellin transform, and saddle point analysis.

Smartphone based iris recognition through optimized textural representation

Mobile devices have become ubiquitous nowadays and so is the need of secure access to these devices. Iris being the most reliable and hard-to-tamper biometric trait, can serve the aforementioned purpose. Iris recognition on mobile phones has become a significant and challenging task for the research community. With advancement in technology, it has now become feasible to use mobile devices’ in-built cameras to unlock the device through the user’s iris. This paper presents a convenient and efficient approach: optimal bit-transition codes (OBTC), for representing mobile iris images in a more distinctive manner. The approach is derived from the texture analysis property of 2D Gabor filters. Optimization of Gabor parameters is performed for iris images from two challenging mobile iris databases: MICHE I (which comprises of eye images acquired from three different smartphones: iPhone5, Galaxy S4 and Galaxy Tab2) and VISOB (which contains eye images acquired from iPhone5S, Samsung Note 4 and Oppo N1). After filtering, the image responses are converted to binary numbers and stored in concatenated vectors. Later, the concatenated vectors produce binary strings across the direction of concatenation and number of bit-transitions in these binary strings are encoded to form the complete feature vectors. A capacious experimentation is performed on the challenging MICHE I and VISOB iris databases. Comparison of the proposed approach with several state-of-the-art approaches clearly shows its expediency. More importantly, the proposed iris recognition approach performs at par with a commercial iris matcher, named VeriEye, which proves its usefulness.

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. One-dimensional 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.