Binary Representation Research Articles

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.

GraphMoCo: A graph momentum contrast model for large-scale binary function representation learning

In the field of cybersecurity, the ability to compute similarity scores at the function level for binary code is of utmost importance. Considering that a single binary file may contain an extensive amount of functions, an effective learning framework must exhibit both high accuracy and efficiency when handling substantial volumes of data. Nonetheless, conventional methods encounter several limitations. Firstly, accurately annotating different pairs of functions with appropriate labels poses a significant challenge, thereby making it difficult to employ supervised learning methods without risk of overtraining. Secondly, while SOTA models often rely on pre-trained encoders or fine-grained graph comparison techniques, these approaches suffer from drawbacks related to time and memory consumption. Thirdly, the momentum update algorithm utilized in graph-based contrastive learning models can result in information leakage. Surprisingly, none of the existing articles address this issue. This research focuses on addressing the challenges associated with large-scale Binary Code Similarity Detection (BCSD). To overcome the aforementioned problems, we propose GraphMoCo: a graph momentum contrast model that leverages multimodal structural information for efficient binary function representation learning on a large scale. We adopt an unsupervised learning strategy. Our approach eliminates the need for manual labeling. By leveraging the intrinsic structural information at multiple levels of the binary code, our model could achieve higher accuracy with a simple CNN-based model. By introducing the preshuffle mechanism, the issue of information leakage in graph momentum update algorithm is mitigated. The evaluation results indicate that GraphMoCo exhibits superior performance compared to SOTA approaches in the function pair search task, showing an average improvement of 7% on AUC, and 10% on MRR and Recall@1. Furthermore, GraphMoCo achieves a MAP of 0.93 on the more challenging dataset 2, which comprises a larger function pool. In a real-world scenario, specifically in known vulnerability searching, GraphMoCo achieves a MRR that surpasses existing SOTA models by 5%.

New Programmable LFSR Counters with Automatic Encoding and State Extension

An efficient method for detecting the end of a count of a linear feedback shift register (LFSR) is presented. We show how this detector can be used to extend a maximum-length LFSR sequence by a number of states up to one less than the degree of the polynomial. In addition, a new algorithm is proposed to encode binary sequence values into their corresponding LFSR sequence states. Based on this algorithm and an alternative second method, two novel programmable, full-range, high-speed LFSR counter designs are proposed. The time complexity of the conversion algorithms ranges from quadratic to exponential time in the degree of the underlying polynomial. The proposed counter solutions are fully synchronous and can be implemented with standard cells, enabling easy portability across technologies. As the levels of logic are independent of the counter size, high scalability is facilitated. For evaluation, all the designs, including the recreated state-of-the-art solution, have been implemented on an FPGA, and also simulated targeting TSMC’s N3 CMOS process node for a wide range of counter sizes. The results confirm the superiority of the proposed solutions over the state of the art in terms of frequency, area and power efficiency as the counter is scaled up.

FPGA realization of an image encryption system using the DCSK-CDMA technique

This paper describes a four-wing chaotic oscillator-based DCSK-CDMA modulation technique for image encryption and decryption. The system consists of a transmission module for the encrypted and modulated color matrices, and a reception module for the decrypted and demodulated original data. Among our main contributions is the integration for the first time in the state of the art of DCSK chaotic modulation techniques with the CDMA communication scheme, as well as the implementation of our DCSK and CDMA algorithms in VHDL language for Xilinx FPGA cards. The DCSK-CDMA technique enables demodulation and decryption without any loss in the original data. Other contributions arising from this investigation are the encryption process implemented using DCSK-CDMA techniques based on a four-wing chaotic oscillator and the realization on the FPGA of a chaos-based communications system for the secure transmission of images of grayscale and RGB formats using DCSK-CDMA techniques. The system architecture in this work was designed using fixed-point binary arithmetic, with the application of the 4th order Runge–Kutta numerical method for the four-wing chaotic oscillator. The analysis of the correlation coefficients between the original and encrypted information indicates the values of 5.367×10−4 and −2.205×10−7 for grayscale and RGB images, respectively, with a full recovery of the original information. The results of simulations in MATLAB/Simulink coincide with the implementation of the complete system in VHDL on the Xilinx Artix-7 AC701 board.

Supervision and early warning of abnormal data in Internet of Things based on unsupervised attention learning

With the continuous development of artificial intelligence and big data, more and more communication protocols have appeared in the network connection. Because the network protocol content specification should not need to obtain the content of the common protocol by any means, it faces different attacks, which increases the complexity of network security detection to an unlimited extent. The absolute security analysis of the content of the relevant data protocol is not too limited. Based on the content of the unknown binary number protocol in the Internet of Things, this paper does the reverse research. To address the inefficiency, learning is focused on the inverse aspect of the content processing logic of the protocol for unsupervised execution attention. This paper proposes a method based on the name of the Dirichlet process mixture model normal field and the unknown protocol of the Internet of Things. It also establishes the behavioral criteria that process the logical inference least squares method to infer the content of the target protocol. Based on the original external characteristics of abnormal data in the Internet of Things, the neural network model of attention mechanism and the model checking of variational self-encoder are chosen. An innovative multi-dimensional spatial neural network model with a centralized control mechanism is taken to extract the spatial relevant information from the original external features, which are regarded as the intermediate external features. The intermediate extrinsic features are input to a variational self-encoder for computation and reconstruction. The experimental results verify that the time required by the proposed model is 45% less than that of the traditional structure. The flow processing improves by 36.7% compared with the optimization of the parameter control function. The specific method only relies on the original external characteristics of the Internet of Things traffic and can achieve better data anomaly detection and classification under the condition of not significantly improving the detection time. The abnormal data detection scheme of the Internet of Things designed in this paper not only fills the detection gap of the current mainstream Internet of Things system which lacks an autonomous supervision mechanism, but also effectively blocks external network intrusion and further improves the management authority verification system. It plays an indispensable role in the long-term stable operation of the Internet of Things platform.

Generating pseudo-random numbers with a Brownian system

We use a deterministic dynamical system that simulates a Brownian movement over time to produce pseudo-random sequences of binary numbers. We show that the used Brownian system has two positive Lyapunov exponents, this characteristic is different of a chaotic system that has only one maximum positive Lyapunov exponent. The implementation of the random number generator uses fixed point arithmetic with numbers with 5 bits for the integer part and 58 bits for the fractional part. The eight least significant bits for each variable of the 3rd order Brownian system are concatenated to form the sequences of random bits. The generated sequences pass all the NIST and TestU01 statistical randomness tests. Furthermore, a hardware design in FPGA of the proposed pseudo-random generator is presented with a throughput equal to 1085.16 Mb/s. For the best of our knowledge, this is the first time a Brownian system is used as the core of a PRNG.

Optimization and Beyond

This paper will be concerned with hard choices—that is, choice situations where an agent cannot make a rationally justified choice. Specifically, this paper asks: if an agent cannot optimize in a given situation, are they facing a hard choice? A pair of claims are defended in light of this question. First, situations where an agent cannot optimize because of incompleteness of the binary preference or value relation constitute a hard choice. Second, situations where agents cannot optimize because the binary preference or value relation violates acyclicity do not constitute a hard choice.

Novel real number representations in Ising machines and performance evaluation: Combinatorial random number sum and constant division.

Quantum annealing machines are next-generation computers for solving combinatorial optimization problems. Although physical simulations are one of the most promising applications of quantum annealing machines, a method how to embed the target problem into the machines has not been developed except for certain simple examples. In this study, we focus on a method of representing real numbers using binary variables, or quantum bits. One of the most important problems for conducting physical simulation by quantum annealing machines is how to represent the real number with quantum bits. The variables in physical simulations are often represented by real numbers but real numbers must be represented by a combination of binary variables in quantum annealing, such as quadratic unconstrained binary optimization (QUBO). Conventionally, real numbers have been represented by assigning each digit of their binary number representation to a binary variable. Considering the classical annealing point of view, we noticed that when real numbers are represented in binary numbers, there are numbers that can only be reached by inverting several bits simultaneously under the restriction of not increasing a given Hamiltonian, which makes the optimization very difficult. In this work, we propose three new types of real number representation and compared these representations under the problem of solving linear equations. As a result, we found experimentally that the accuracy of the solution varies significantly depending on how the real numbers are represented. We also found that the most appropriate representation depends on the size and difficulty of the problem to be solved and that these differences show a consistent trend for two annealing solvers. Finally, we explain the reasons for these differences using simple models, the minimum required number of simultaneous bit flips, one-way probabilistic bit-flip energy minimization, and simulation of ideal quantum annealing machine.

Spin representations and binary numbers

Spin representations and binary numbers

The critical mass ratio for W UMa-type contact binary systems

Contact binaries are close binary systems in which both components fill their inner Roche lobes so that the stars are in direct contact, and in potential mass and energy exchange. The most common such systems of low mass are the so-called W UMa-type. In the last few years, there has been a growing interest of the astronomical community in stellar mergers, primarily due to the detection of gravitational waves (mergers of black holes and neutron stars), but also because of an alternative model for the type Ia supernovae (merger of two white dwarfs), which are again particularly important in cosmology where they played a significant role in the discovery of dark energy and the accelerated expansion of the Universe. In that sense, contact systems of W UMa type with extremely low mass ratio are especially interesting because there are indications that, in their case too, stars can merge and possibly form fast-rotating stars such as FC Com stars and the blue-stragglers, and (luminous) red novae such as V1309 Sco. Namely, the previous theoretical research has shown that in the cases when the orbital angular momentum of the system is only about three times larger than the rotational angular momentum of the primary, a tidal Darwin's instability occurs, the components can no longer remain in synchronous rotation, orbit continue to shrink fast, and they finally merge into a single star. The above stability condition for contact systems can be linked to a specific critical mass ratio below which we expect a system to be unstable. We give an overview of this condition and show how it can be used to identify potential mergers. Finally, we discuss a number of known extreme mass ratio binaries from the literature and consider prospects for future research on this topic.

Cascaded Thinning in Upscale and Downscale Representation for EEG Signal Processing.

Smoothing filters are widely used in EEG signal processing for noise removal while preserving signals' features. Inspired by our recent work on Upscale and Downscale Representation (UDR), this paper proposes a cascade arrangement of some effective image-processing techniques for signal filtering in the image domain. The UDR concept is to visualize EEG signals at an appropriate line width and convert it to a binary image. The smoothing process is then conducted by skeletonizing the signal object to a unit width and projecting it back to the time domain. Two successive UDRs could result in a better-smoothing performance, but their binary image conversion should be restricted. The process is computationally ineffective, especially at higher line width values. Cascaded Thinning UDR (CTUDR) is proposed, exploiting morphological operations to perform a two-stage upscale and downscale within one binary image representation. CTUDR is verified on a signal smoothing and classification task and compared with conventional techniques, such as the Moving Average, the Binomial, the Median, and the Savitzky Golay filters. Simulated EEG data with added white Gaussian noise is employed in the former, while cognitive conflict data obtained from a 3D object selection task is utilized in the latter. CTUDR outperforms its counterparts, scoring the best fitting error and correlation coefficient in signal smoothing while achieving the highest gain in Accuracy (0.7640%) and F-measure (0.7607%) when used as a smoothing filter for training data of EEGNet.

Are Changes in Crude Oil Prices Predictable?

The oil prices influence the transport sector, and subsequently the profitability of trade and the condition of countries’ economies. Therefore, the prediction of future price changes is of high importance. Said prices can be forecasted by macroeconomic or technical methods of analysis, yet both of those are often questioned. In the article, we present a method for a complex verification of the hypothesis that current prices are not influenced by historical changes in oil prices. To do so we applied an alternative and more precise representation of prices than the most commonly used candlestick representation, i.e. the binary temporal representation and utilized advanced statistical testing. In the paper, we also present the results of empirical research performed on the oil price quotations, with the use of advanced software in C++. The research confirms the sensibility of using technical and fundamental analysis.