Binary Representation Research Articles

Learning All-In Collaborative Multiview Binary Representation for Clustering.

Multiview clustering via binary representation has attracted intensive attention due to its effectiveness in handling large-scale multiple view data. However, these kind of clustering approaches usually ignore a very important potential high-order correlation in discrete representation learning. In this article, we propose a novel all-in collaborative multiview binary representation for clustering (AC-MVBC) framework, where multiview collaborative binary representation and clustering structure are learned in a joint manner. Specifically, using a new type of tensor low-rank constraint, the high-order collaborations, i.e., cross-view and inner view collaborations, can be effectively captured in our model. Moreover, by incorporating the Bregman discrepancy, the projective consistency among different views can be guaranteed to achieve a more powerful binary representation. An efficient optimization algorithm is also proposed to solve the objective function with fast convergence empirically. Experimental results on several challenge datasets demonstrate that the proposed method has achieved highly competent performance compared with the state-of-the-art multiview clustering (MVC) methods while maintaining low computational and memory requirements.

Mutation boosted salp swarm optimizer meets rough set theory: A novel approach to software defect detection

AbstractSoftware defect detection (SDD) is crucial to ensure the reliability of software systems and identify defects in classification. One of the key challenges in defect detection is to select more informative and relevant features from the vast pool of available software metrics. A novel approach is proposed in this paper that leverages mutation boosted salp swarm optimizer (MBSSO) and Rough Set Theory for feature selection (FS) in SDD. It efficiently explores the search space and incorporates a mutation boosting mechanism to overcome local optima. Rough Set Theory provides a formal framework for analyzing feature relevance and dependency, while MBSSO optimizes the search for the optimal feature subset. The proposed approach involves encoding the feature subsets using binary representation and designing a fitness function. The MBSSO algorithm explores the feature space, iteratively improving the feature subset based on the fitness function. The selected feature subset is then used to train a defect detection model using the Kernel Extreme Learning Machine (KELM) algorithm. The experimental validation is performed by Project Repository for Software Engineering (PROMISE) dataset and compared the performance of proposed approach against other FS methods and baselines. The experimental validation demonstrates that the proposed approach achieves superior performance and selected feature subset improves the accuracy and efficiency. This research contributes to the advancement of SDD by providing an effective and efficient FS technique using MBSSO and Rough Set Theory.

LISA Galactic Binaries with Astrometry from Gaia DR3

Galactic compact binaries with orbital periods shorter than a few hours emit detectable gravitational waves (GWs) at low frequencies. Their GW signals can be detected with the future Laser Interferometer Space Antenna (LISA). Crucially, they may be useful in the early months of the mission operation in helping to validate LISA's performance in comparison to prelaunch expectations. We present an updated list of 55 candidate LISA-detectable binaries with measured properties, for which we derive distances based on Gaia Data Release 3 astrometry. Based on the known properties from electromagnetic observations, we predict the LISA detectability after 1, 3, 6, and 48 months using Bayesian analysis methods. We distinguish between verification and detectable binaries as being detectable after 3 and 48 months, respectively. We find 18 verification binaries and 22 detectable sources, which triples the number of known LISA binaries over the last few years. These include detached double white dwarfs, AM CVn binaries, one ultracompact X-ray binary, and two hot subdwarf binaries. We find that across this sample the GW amplitude is expected to be measured to ≈10% on average, while the inclination is expected to be determined with ≈15° precision. For detectable binaries, these average errors increase to ≈50% and ≈40°, respectively.

Probing Supermassive Black Hole Seed Scenarios with Gravitational-wave Measurements

The process whereby the supermassive black holes (SMBHs) populating the centers of galaxies have been assembled remains to be established, with the relative importance of seeds provided by collapsed Population III stars, black holes formed in nuclear star clusters via repeated mergers, or direct collapses of protogalactic disks yet to be determined. In this paper we study the prospects for casting light on this issue by future measurements of gravitational waves emitted during the inspirals and mergers of pairs of intermediate-mass black holes (IMBHs), discussing in particular the roles of prospective measurements by LISA and the proposed atom interferometers AION and AEDGE. We find that the expected number of detectable IMBH binaries is O(100) for LISA and AEDGE and O(10) for AION in low-mass seeds scenarios and goes down to O(10) for LISA and below one for AEDGE and AION in high-mass seed scenarios. This allows all of these observatories to probe the parameters of the seed model, in particular, if at least a fraction of the SMBHs arises from a low-mass seed population. We also show that the measurement accuracy of the binary parameters is, in general, best for AEDGE, which sees very precisely the merger of the binary.

Elusive hot stripped helium stars in the Galaxy

Context. Stellar evolution theory predicts the existence of helium-core remnants of the primary components of intermediate-mass close binaries that lost most of their hydrogen-helium envelopes due to the mass exchange. These remnants are expected to be observed as hot helium-rich stars with masses of (1−7) M⊙, located in the area of the Hertzsprung-Russell diagram between OB subdwarfs and Wolf-Rayet stars. While several thousands of such stripped helium stars are expected to exist in the Galaxy, none of them have been identified so far. Aims. We aim to provide comprehensive predictions of the numbers and fundamental properties of stripped helium stars and their binary companions in the Galaxy. This is a necessary first step to guide observations, to enable a comparison between binary evolution models and realistic stellar populations, and to determine the feedback parameters of stripped helium stars in the Galaxy. Methods. We expanded the previously considered space of parameters describing close binary systems producing stripped helium stars and applied a population synthesis based on a grid of evolutionary models computed by the code MESA, using a spin-dependent algorithm for the treatment of mass and angular momentum loss from the system. Results. We show that the number of Galactic binaries hosting (1−7) M⊙ He-stars is ≃20 000 and that it steeply declines with an increase in the He-star mass (≃3000 with mass ≳2 M⊙). The decisive factor that defines the low number of stripped He-stars is runaway mass-loss after Roche lobe overflow (RLOF) by primary components of the binaries, which leads to the formation of common envelopes and the further merger of components. This effect strongly restricts the initial ranges of masses of components of the progenitors of stripped stars and orbital periods. In addition, stripped helium stars are much less numerous than expected, since a significant fraction of binaries in which the primaries have masses less than (5−7) M⊙ produce subdwarfs with masses ≲1 M⊙. Our calculations show that the overwhelming majority of helium stars reside in binaries with an early-type companion star and can be identified neither by the UV excess nor by emission features. The large periods of a significant fraction of binaries hosting stripped stars (≳several hundred days) also hamper their discovery.

Gender and the Disruption of Patriarchy in the Crime Stories of Mariana Enríquez's Things We Lost in the Fire

While Mariana Enríquez is most recognised as a writer of Gothic horror, this article explores issues of gender and power in crime stories from her collection Things We Lost in the Fire: ‘The Dirty Boy’, ‘Under the Black Water’ and ‘Things We Lost in the Fire’. Enríquez aligns herself with feminist variants of the crime genre by centring women's concerns and voices, breaking down binary and hierarchical representations of gender and sexuality and illustrating the struggles women and marginalised groups face in Argentina under patriarchy. Discussed as well are the implications of Enríquez's feminist project in contemporary crime fiction.

Toward Pixel-Level Precision for Binary Super-Resolution With Mixed Binary Representation.

Binary neural network (BNN) is an effective method for reducing model computational and memory cost, which has achieved much progress in the super-resolution (SR) field. However, there is still a noticeable performance gap between a binary SR network and its full-precision counterpart. Considering that the information density in quantization features is far lower than full-precision features, we aim to improve the precision of quantization features to produce rich-enough output activations for SR task. First, we make several observations that a multibit value could be approximated by multiple 1-bit values, and the computation power of binary convolution could be improved by approximating the multibit convolution process. Then, we propose a mixed binary representation set to approximate multibit activations, which is effective in compensating the quantization precision loss. Finally, we present a new precision-driven binary convolution (PDBC) module, which increases the convolution precision and protects image detail information without extra computation. Compared with normal binary convolution, our method could largely reduce the information loss caused by binarization. In experiments, our methods consistently show superior performance over the baseline models and can surpass state-of-the-art methods in terms of peak signal to noise ratio (PSNR) and visual quality.

BinDMO: a new Binary Dwarf Mongoose Optimization algorithm on based Z-shaped, U-shaped, and taper-shaped transfer functions for CEC-2017 benchmarks

Intelligent swarm optimization algorithms have become increasingly common due to their success in solving real-world problems. Dwarf Mongoose Optimization (DMO) algorithm is a newly proposed intelligent swarm optimization algorithm in recent years. It was developed for continuous optimization problem solutions in its original paper. But real-world problems are not always problems that take continuously variable values. Real-world problems are often problems with discrete variables. Therefore, heuristic algorithms proposed for continuous optimization problems need to be updated to solve discrete optimization problems. In this study, DMO has been updated for binary optimization problems and the Binary DMO (BinDMO) algorithm has been proposed. In binary optimization, the search space consists of binary variable values. Transfer functions are often used in the conversion of continuous variable values to binary variable values. In this study, twelve different transfer functions were used (four Z-shaped, four U-shaped, and four Taper-shaped). Thus, twelve different BinDMO variations were obtained (BinDMO1, BinDMO2, …, BinDMO12). The achievements of BinDMO variations were tested on thirteen different unimodal and multimodal classical benchmark functions. The effectiveness of population sizes on the effectiveness of BinDMO was also investigated. When the results were examined, it was determined that the most successful BinDMO variation was BinDMO1 (with Z1-shaped transfer function). The most successful BinDMO variation was compared with three different binary heuristic algorithms selected from the literature (SO, PDO, and AFT) on CEC-2017 benchmark functions. According to the average results, BinDMO was the most successful binary heuristic algorithm. This has proven that BinDMO can be chosen as an alternative algorithm for binary optimization problems.

LSB8 using DSF to Hide Secret Message

An efficient method of message steganography will be proposed, the method will use 8 bits from the speech sample binary value to hold one character from the secret message, thus the capacity hiding will equal the covering DSF size. Using lower LSBs of the speech sample binary value will not much affect the sample value, so the quality of the stego DSF will be excellent and the stego file will be closed to the covering file. The proposed method will add an extra security issues by using a private key, this key will be used to calculate the starting sample where to start characters hiding, it will determine the starting LSB to get the set of 8 LSBs, and it will be also used to generate eight element chaotic key, this key will be converted to 8 orders of LSBs to hide the bits of characters, these bit will be not consecutive. The PK will a long enough long provide a good key space capable to resist hacking attempt and the extracted message will be very sensitive to the selected values of the private key. The proposed method will provide enhancements in the quality, security, speed and sensitivity of message steganography, these enhancements will be proved based on the discussing of obtained experimental results.

CNN-optimized text recognition with binary embeddings for Arabic expiry date recognition

Recognizing Arabic dot-matrix digits is a challenging problem due to the unique characteristics of dot-matrix fonts, such as irregular dot spacing and varying dot sizes. This paper presents an approach for recognizing Arabic digits printed in dot matrix format. The proposed model is based on convolutional neural networks (CNN) that take the dot matrix as input and generate embeddings that are rounded to generate binary representations of the digits. The binary embeddings are then used to perform Optical Character Recognition (OCR) on the date images. To overcome the challenge of the limited availability of dotted Arabic expiration date images, we developed a True Type Font (TTF) for generating synthetic images of Arabic dot-matrix characters. The model was trained on a synthetic dataset of 3287 images and 658 synthetic images for testing, representing realistic expiration dates from 2019 to 2027 in the format of yyyy/mm/dd and yy/mm/dd. Our model achieved an accuracy of 98.94% on the expiry date recognition with Arabic dot matrix format using fewer parameters and less computational resources than traditional CNN-based models. By investigating and presenting our findings comprehensively, we aim to contribute substantially to the field of OCR and pave the way for advancements in Arabic dot-matrix character recognition. Our proposed approach is not limited to Arabic dot matrix digit recognition but can be also extended to text recognition tasks, such as text classification and sentiment analysis.

Evaluation of classical correlation functions from 2/3D images on CPU and GPU architectures: Introducing CorrelationFunctions.jl

Correlation functions are becoming one of the major tools for quantification of structural information that is usually represented as 2D or 3D images. In this paper we introduce ▪ open-source package developed in Julia and capable of computing all classical correlation functions based on imaging input data. Images include both binary and multi-phase representations. Our code is capable of evaluating two-point probability S2, phase cross-correlation ρij, cluster C2, lineal-path L2, surface-surface Fss, surface-void Fsv, pore-size P and chord-length p distribution functions on both CPU and GPU architectures. Where possible, we presented two types of computations: full correlation map (correlations of each point with other points on the image, that also allows obtaining ensemble averaged CF) and directional correlation functions (currently in major orthogonal and diagonal directions). Such an implementation allowed for the first time to assemble a completely free solution to evaluate correlation functions under any operating system with well documented application programming interface (API). Our package includes automatic tests against analytical solutions that are described in the paper. We measured execution times for all CPU and GPU implementations and as a rule of thumb full correlation maps on GPU are faster than other methods. However, full maps require more RAM and, thus, are limited to available RAM resources. On the other hand, directional CFs are memory efficient and can be evaluated for huge datasets – this way they are the first candidates for structural data compression of feature extraction. The package itself is available through Julia package ecosystem and on GitHub, the latter source also contains documentation and additional helpful resources such as tutorials. We believe that a single powerful computational tool such as ▪ presented in this paper will significantly facilitate the usage of correlation functions in numerous areas of structural description and research of porous materials, as well as in machine learning applications. We also present some examples as applied to ceramic, soil composite and oil-bearing rock samples based on their 3D X-ray tomography and 2D scanning electron microscope images. Finally, we conclude our paper with discussion of possible ways to further improve presented computational framework. Program summaryProgram Title: CorrelationFunctions.jlCPC Library link to program files:https://doi.org/10.17632/6gb9gfm3dw.1Developer's repository link:https://github.com/fatimp/CorrelationFunctions.jlLicensing provisions: MITProgramming language: JuliaSupplementary material: Numerous Jupiter notebooks with examples are available on the GitHub pageNature of problem: Correlation functions are invaluable universal statistical descriptors of structures used in numerous scientific fields such as astronomy, material science, rock and soil physics, hydrology and biology, to name just a handful of examples. While computational approaches are available in the literature for some functions, they are fragmented and are usually implemented in proprietary interpreted languages for CPU architecture alone.Solution method: We contribute an open source and cross-platform solution with well documented API for computation of all classical correlation functions from both 2D and 3D images on CPU and GPU architectures. The package computes correlation functions using two approaches: computation of correlation maps and computation along predefined directions. These two approaches can be thought of as an execution time - memory trade-off, but the choice may also depend on the application. The computations are based on a) fast Fourier transform with preprocessing steps such as cluster labeling or edge detection, and b) linear scan approach to evaluate correlation functions along predefined directions. Where justified, the algorithms can be executed on both CPU and GPU which results in high execution speed on modern hardware.

A 131-year evidence of more extreme and higher total amount of hourly precipitation in Hong Kong

Based on the observations of hourly precipitation for 131 years from Hong Kong Observatory Headquarters, this study examined the long-term changes in the characteristics of hourly precipitation extremes in terms of intensity, total precipitation amount, duration, and frequency. Results show that the hourly precipitation extremes have significantly intensified by 29%–38% from 1885 to 2022. The 131-year observations evidence that the more extreme the hourly precipitation is (i.e. higher percentiles), the faster the increasing rate it has. Specifically, the magnitudes of hourly precipitation with the 95th, 97.5th, 99th, and 99.9th percentiles increased by rates of 0.03 mm, 0.05 mm, 0.07 mm, and 0.12 mm per year, respectively. Through the secular trend analysis, we found that only the maximum intensity of extreme precipitation events (i.e. events with maximum intensity exceeding the 95th percentiles) shows a significant increasing trend during 1885–2022, while the trends in the total precipitation amount, duration, and mean intensity are not significant. However, by comparing the percentile bin values between three sub-periods of the 131-year record, we found a significant rise over time in the total precipitation amount, mean intensity, and maximum intensity of extreme precipitation events with different intensities (i.e. 95th, 96th, 97th, 98th, and 99th percentiles), while the change in the duration is not significant. The analysis of the frequency of precipitation events shows significant increases in the proportion of extreme precipitation events during 1885–2022. The observations of 53 stations across Hong Kong from 1986 to 2022 show significant intensification and increasing frequency in the hourly precipitation extremes in most areas of Hong Kong. Meanwhile, the precipitation duration shows a decreasing tendency, which may explain the insignificant changes in the total precipitation amount. These findings provide important insights into the longer-term variations in the characteristics of hourly precipitation extremes.

MMV method: a new approach to compare protein sequences under binary representation

In the present work, a new form of descriptor using minimal moment vector (MMV) is introduced to compare protein sequences in the frequency domain under their component wise binary representations. From every sequence, 20 different binary component sequences are formed, each corresponding to 20 amino acids. Each such vector is now shifted from the time domain to the frequency domain by applying the Fast Fourier Transform (FFT). Next, the power spectrum calculated from the FFT values for each component sequence is so normalized that the sum of the components equals 1. The descriptor is defined as a 20-component vector composed of the 20 second-order minimal moments calculated from the normalized spectrum of the 20 component sequences. Once the descriptor is known, the distance matrix is created by applying the Euclidean Distance measure. The phylogenetic tree is generated by applying the unweighted pair group method with the arithmetic mean (UPGMA) algorithm using Molecular Evolutionary Genetics Analysis11 (MEGA11) software. In this work, the datasets used for similarity studies are 9 NADH dehydrogenase 5 (ND5), 12 Baculoviruses, 24 Transferrins (TF) proteins, and 50 Spike Protein of coronavirus. A qualitative measure using rationalized perception is used to compare the effectiveness of the proposed method. Quantitative measure based on symmetric distance (SD) is used to compare the phylogenetic trees of the present method with those obtained by other methods. It is observed that the phylogenetic trees generated by the proposed technique are at par with their known biological references, and they produce results better than those of the earlier methods. Communicated by Ramaswamy H. Sarma

Differences in nasalance scores obtained with different Nasometer headsets

ABSTRACT The goal of the present research study was to investigate possible differences in nasalance scores between different Nasometer headgears. Frequency response characteristics of microphone pairs in a Nasometer model 6200, a model 6450 and two model 6500 headsets were compared using long-term average spectra of white noise and multi-speaker babble signals. Prerecorded sound files from a male and a female speaker were used to record nasalance scores with the four Nasometer headsets and to calculate cumulative absolute differences within and between the headsets. The main outcome measures were the cumulative absolute differences between the decibel (dB) values in the frequency bins from 300 to 750 Hz for the nasal and oral channels of each microphone pair. Cumulative absolute differences between nasalance scores of repeated stimuli within and across Nasometer headsets were tabulated. Results showed that cumulative absolute differences for the frequency range 300–750 Hz were between 6.58 and 7.68 dB. Within headsets, 95.6% to 100% of measurements of all four Nasometer headsets were within 3 nasalance points, although test–retest differences of up to 6 nasalance points were found. Between headsets, 56.1% to 98.9% of measurements were within 3 nasalance points, with the single largest difference of 8 nasalance points. In conclusion, differences between repeated nasalance scores obtained with the same and different headsets were noted. Clinicians should allow a margin of error of ±6 to 8 nasalance points when interpreting scores from different Nasometer headsets.

Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs

Compact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two innovative data encoding techniques, known as QCrank and QBArt, which exhibit significant quantum parallelism via uniformly controlled rotation gates. The QCrank method encodes a series of real-valued data as rotations on data qubits, resulting in increased storage capacity. On the other hand, QBArt directly incorporates a binary representation of the data within the computational basis, requiring fewer quantum measurements and enabling well-established arithmetic operations on binary data. We showcase various applications of the proposed encoding methods for various data types. Notably, we demonstrate quantum algorithms for tasks such as DNA pattern matching, Hamming weight computation, complex value conjugation, and the retrieval of a binary image with 384 pixels, all executed on the Quantinuum trapped-ion QPU. Furthermore, we employ several cloud-accessible QPUs, including those from IBMQ and IonQ, to conduct supplementary benchmarking experiments.

Design and Comparison of 24-bit Three Operand Adders using Parallel Prefix method for Efficient Computations

Binary Three-operand adder serves as a foundation block used within security and Pseudo Random-Bit Generator (PRBG) systems. Binary Three-operand adder was designed using Carry Save Adder but this consumes more delay. Therefore, a Parallel Prefix Adder (PPA) method can be utilized for faster operation. The canonical types of PPA result in a lesser path delay of approximately O (log2 n). These adders can be designed for 8, 16, 24 or n bits. But this work is focused on developing a 24-bit three-operand adder that takes three 24-bit binary numbers as input and generates a 24-bit sum output and a carry using five different PPA methods The proposed summing circuits are operationalized with Hardware-Description-Language (HDL) using Verilog, and then subjected to synthesis using Field -Programmable Gate- Array (FPGA) Vertex 5. On comparing the proposed adders, it shows that the delay and the size occupied are significantly less in the Sklansky PPA. These faster three-operand adders can be utilized for three-operand multiplication in image processing applications and Internet of Things (IoT) security systems.

Factors that Influence Employee Early Retirement Decision Making in the HR Unit of PT. Telekomunikasi, Tbk Bandung

Companies must be careful in managing early retirement policies. In designing, implementing and administering it. If the company does not do it properly, problems will arise for the company. For example, many employees retire early while they are still needed by the company. This research is aimed at examining the extent to which these four factors can change employees' intentions in making decisions to retire early. Using the case study method of PT. Telekomunikasi Indonesia Bandung HR unit. The research method uses binary number calculations. For the instrument, a questionnaire was distributed to 50 respondents. The variables studied are differences in personal interests (difference variables) and organizational factors which significantly influence the intention to participate in an early retirement program. Meanwhile, opportunities in career paths and external factors have little influence on employees' intentions to take early retirement.

DSF Cryptography by Rotating Left the Samples Binary Values

A novel method of digital speech file cryptography will be proposed. The method will apply speech encryption-decryption at the bit level. The binary values of the speech samples will be shifted left to a defined number of rotating left digits. The private key will determine the length of the set of the bits to be rotated will be selected, the starting bit from where to start rotating and the number of rotating digits. The speech sample value will be represented in binary using 64_bits binary representation. Because of the fractional decimal value of the speech sample, changing the LSBs of the binary value may not affect the speech sample, so the starting bit for selecting the bits to be rotated will be investigated, and this bit will be used in the encryption-decryption process to get a damaged encrypted speech file. The private key will contains the starting bit; the bits set length and the number of rotating left digits, conditional selections of the private key value will be provided based on analyzing the quality of the encrypted speech files. The encryption and decryption process of the proposed method will be implemented in one round using a simple rotate left operation. The proposed method will not need key generation process; the values included in the private key will be directly used to apply speech encryption-decryption. The proposed method will be implemented using various speech files, the obtained results will be analyzed using quality, speed and sensitivity analysis to prove the efficiency of the proposed method.

Beyond good and evil: imagined nation in Hindi films

ABSTRACT The influence of Indian films on popular culture and the influence of dominant political ideology on Indian films are interspersed with dialectical variations. Although a binary representation of these subtleties remains elusive, while promoting ideological superstructures in society, ‘the relatively mixed, confused, incomplete, or inarticulate consciousness' is replaced with a ‘generalised system'. In practice, the tendencies governing public emotions during moments of crisis are employed as tropes in Bollywood films. However, the ontology of political underpinnings and subversive ideological possibilities that enmesh lived experiences remain smothered by an unproblematic representation of historical events. With this rationale, the paper analyses three Bollywood films Panipat (2019), Tanhaji (2020) and Samrat Prithviraj (2022). These films seem to auger the idea of Dharma and Ramrajya in the construction of a Nation catering to the populist notion of a safe and secure nation under just Hindu rule. The paper argues that these films cater to the framework of historiography promulgated by the colonial historians on one hand; and function as a contrapuntal response to the ideological moorings of the leaders of the All-India Muslim League. This objective is achieved by a stereotypical and binary projection of good and evil by glossing over the ‘mixed consciousness’.

Binary Numeration System with Alternating Signed Digits and Its Graph Theoretical Relationship

The binary number system is the basic number representation in computing. We can encode natural numbers with finite 0-1 sequences. The representation of natural numbers is based on this system. However, this poses problems and is technically not perfect. Several attempts have been made to handle integers (signed numbers). We mention only two: the balanced triple number system and the number system with base −2. Our paper introduces new possibilities. We also shed light on the graph theoretical background of the new number systems.