Binary Codes Research Articles

A new ultra-compact communications system across air-sea water based on ELF magnetic mechanical antenna

Abstract In this paper, a long-range trans-air-seawater medium communication system is proposed based on a small extremely low-frequency (ELF) mechanical antenna. We analyze the propagation characteristics of ELF electromagnetic waves in seawater and determine the paths of ELF electromagnetic waves propagating across domains. Equivalent models of various mechanical antennas are analyzed based on their operating principles. Based on the equivalent model, the transmission principle of mechanical antenna in transmitting across air-sea medium is analyzed, and finally the mechanical antenna with permanent magnet working horizontally is identified as the optimal choice. The above results are also verified by simulation. Experimental and simulation results verify the good performance of a horizontally operating permanent magnet mechanical antenna in transmitting across air-sea medium. The ELF-band transmission communication across air-sea medium based on binary code modulation is realized, and the farthest transmission distance can reach 200m in the experiment. Based on this system, transmission in air was also tested up to a distance of 1.5 km. The ELF transmission across air-sea medium based on a permanent magnet mechanical antenna can greatly facilitate cross-domain teleportation and ocean exploration.

Syntactic–Semantic Detection of Clone-Caused Vulnerabilities in the IoT Devices

This paper addresses the problem of IoT security caused by code cloning when developing a massive variety of different smart devices. A clone detection method is proposed to identify clone-caused vulnerabilities in IoT software. A hybrid solution combines syntactic and semantic analyses of the code. Based on the recovered code, an attributed abstract syntax tree is constructed for each code fragment. All nodes of the commonly used abstract syntax tree are proposed to be weighted with semantic attribute vectors. Each attributed tree is then encoded as a semantic vector using a Deep Graph Neural Network. Two graph networks are combined into a Siamese neural model, allowing training to generate semantic vectors and compare vector pairs within each training epoch. Semantic analysis is also applied to clones with low similarity metric values. This allows one to correct the similarity decision in the case of incorrect matching of functions at the syntactic level. To automate the search for clones, the BinDiff algorithm is added in the first stage to accurately select clone candidates. This has a positive impact on the ability to apply the proposed method to large sets of binary code. In an experimental study, the developed method—compared to BinDiff, Gemini, and Asteria tools—has demonstrated the highest efficiency.

Llasm: Naming Functions in Binaries by Fusing Encoder-only and Decoder-only LLMs

Predicting function names in stripped binaries, which requires succinctly summarizing semantics of binary code in natural languages, is a crucial but challenging task. Recently, many machine learning based solutions have been proposed. However, they have poor generalizability, i.e., fail to handle unseen binaries. To advance the state of the art, we present llasm ( L arge AS sembly L anguage M odel), a novel framework which fuses encoder-only and decoder-only LLMs for function name prediction. It refines encoder-only models to preserve more binary information and learn better binary representations. Then it adopts a novel architecture to project the encoding to the input space of a decoder-only natural language model, which enables it to have better capability of inferring general knowledge and better generalizability. We have evaluated llasm in the BinaryCorp and Debin datasets. llasm outperforms the state-of-the-art function name prediction tools by up to 19.9%, 40.7%, and 36.5% in precision, recall, and F1 score, with significantly better generalizability in unseen binaries. Our case studies further demonstrate the practical use cases of llasm in analyzing real-world malware, showing the usefulness of function name prediction.

Development of a codebook for the narrative analysis of in-hospital trauma interviews of patients following stroke.

Given their sudden onset and life-threatening consequences, strokes and transient ischemic attacks (TIAs) can trigger posttraumatic stress disorder (PTSD). To gain a deeper understanding of the potential influence of factors in patients' descriptions of these medical events on PTSD, we conducted a standardized trauma interview with a convenience sample of patients hospitalized for suspected stroke/TIA (N = 98) to assess the details and emotional experience of the stroke/TIA event. Three researchers reviewed the interviews and the research literature on risk and protective factors for PTSD. From this analysis, a codebook with descriptions, examples, and scoring protocols for eight Likert scale, two categorical, and four binary codes was developed. Upon demonstrating sufficient interrater reliability, the research team scored all narratives. Three superordinate themes were identified in the analysis: distress (e.g., fear, helplessness), potential protective factors (e.g., positive expectancies, concern for loved ones), and level of detail (e.g., somatic detail, emotional detail). Differences in perceptions, themes, and expectations emerged in the narratives, indicating a wide range of responses following stroke/TIA. Additionally, patient age was negatively correlated with scores for the fear, r = -.34, p <.001, and negative consequences, r = -.24, p =.018, codes and positively associated with the likelihood of having positive expectancies, OR = 1.05, 95% CI [1.00, 1.10], p =.039. These findings provide a more comprehensive understanding of how patients reflect on their experiences post-stroke/TIA and can inform future research on the contributions of trauma narrative characteristics and emotional responses to PTSD risk.

Multilevel Stimuli-Responsive Smart "Sandwich" Label with Physical Unclonable Functions Bionic Wrinkles and Space-Selective Fluorescence Patterns.

With the increasing popularity of the internet, it brings convenience to lives while also increases security risks. Physical Unclonable Functions (PUFs) can generate random, unclonable, and unique identifiers using their inherent physical characteristics, which have broad prospects in anti-counterfeiting. Herein, inspired by the irregular tree bark fissures and random skin wrinkles found in nature, a method for creating complex micro-wrinkles with unclonable random patterns is proposed by simply stretching hydrogels. The random texture information contained in the micro-wrinkles is digitized into binary codes using an adaptive threshold algorithm. Additionally, a novel "sandwich" label with a multilevel intelligent anti-counterfeiting system is proposed. The first-level involves photoluminescence encryption with adjustable luminescence within visible light range and modulated luminescence at different excitation wavelengths; the second-level includes strain-related mechanical encryption, and the third-level consists of highly random and unclonable micro-wrinkles. The certification difficulty increases as the anti-counterfeiting grade increases, thereby enhancing label security. Furthermore, space-selective doping of rare earth metal-organic framework (RE-MOF) fluorescent materials in hydrogels is achieved through the use of screen-printing technology. The concept of novel multilevel smart anti-counterfeiting PUF labels will further enhance current levels of counterfeiting prevention.

Weights of Codewords in Random Binary Linear Codes and Their Correlation Properties

Weights of Codewords in Random Binary Linear Codes and Their Correlation Properties

UniBin: Assembly semantic-enhanced binary vulnerability detection without disassembly

The widespread reuse of open-source code amplifies the impact of vulnerabilities. Current vulnerability detection methods predominantly rely on binary code similarity comparisons, which involve disassembling to obtain assembly code or control flow graphs. These methods depend on specific disassembly tools and complex preprocessing, limiting their applicability and detection speed. This paper proposes UniBin, a vulnerability detection method based on the multi-layer Transformer encoder. By employing bidirectional LM, unidirectional LM, and sequence-to-sequence LM tasks on both binary and assembly code during the pre-training phase, UniBin learns richer semantic information from binary machine code, enabling efficient similarity comparison without disassembly and mitigating the limitations of disassembly. We cross-compile 55 widely used open-source C projects as datasets. After 52 hours of pre-training and 8 hours of fine-tuning, UniBin reaches an average accuracy of 98.3% in similarity detection across compilation conditions, outperforming the state-of-the-art method. For search tasks across optimization options with a pool size of 1000, the Recall@1 metric improves by 28.2% (from 67.9% to 87.1%). UniBin eliminates dependency on specific disassembly tools and improves end-to-end binary analysis speed by over 36%. In real-world vulnerability detection tasks, UniBin detects all vulnerability functions with the lowest false positive rate of 0.16%.

Pitfalls in Machine Learning for Computer Security

With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas. This development has influenced computer security, spawning a series of work on learning-based security systems, such as for malware detection, vulnerability discovery, and binary code analysis. Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance and render learning-based systems potentially unsuitable for security tasks and practical deployment. In this paper, we look at this problem with critical eyes. First, we identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We conduct a study of 30 papers from top-tier security conferences within the past 10 years, confirming that these pitfalls are widespread in the current security literature. In an empirical analysis, we further demonstrate how individual pitfalls can lead to unrealistic performance and interpretations, obstructing the understanding of the security problem at hand. As a remedy, we propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible. Furthermore, we identify open problems when applying machine learning in security and provide directions for further research.

Quantum search algorithm for binary constant weight codes

A binary constant weight code is a type of error-correcting code with a wide range of applications. The problem of finding a binary constant weight code has long been studied as a combinatorial optimization problem in coding theory. In this paper, we propose a quantum search algorithm for binary constant weight codes. Specifically, the search problem is formulated as a polynomial binary optimization problem and Grover adaptive search is used for providing the quadratic speedup. Focusing on the inherent structure of the problem, we derive an upper bound on the minimum of the objective function value and a lower bound on the exact number of solutions. By exploiting these two bounds, we successfully reduced the constant overhead of the algorithm, although the overall query complexity remains exponential due to the NP-complete nature of the problem. In our algebraic analysis, it was found that this proposed algorithm is capable of reducing the number of required qubits, thus enhancing the feasibility. Additionally, our simulations demonstrated that it reduces the average number of classical iterations by 63% as well as the average number of total Grover rotations by 31%. The proposed approach may be useful for other quantum search algorithms and optimization problems.

Botnets Unveiled: A Comprehensive Survey on Evolving Threats and Defense Strategies

ABSTRACTBotnets have emerged as a significant internet security threat, comprising networks of compromised computers under the control of command and control (C&amp;C) servers. These malevolent entities enable a range of malicious activities, from denial of service (DoS) attacks to spam distribution and phishing. Each bot operates as a malicious binary code on vulnerable hosts, granting remote control to attackers who can harness the combined processing power of these compromised hosts for synchronized, highly destructive attacks while maintaining anonymity. This survey explores botnets and their evolution, covering aspects such as their life cycles, C&amp;C models, botnet communication protocols, detection methods, the unique environments botnets operate in, and strategies to evade detection tools. It analyzes research challenges and future directions related to botnets, with a particular focus on evasion and detection techniques, including methods like encryption and the use of covert channels for detection and the reinforcement of botnets. By reviewing existing research, the survey provides a comprehensive overview of botnets, from their origins to their evolving tactics, and evaluates how botnets evade detection and how to counteract their activities. Its primary goal is to inform the research community about the changing landscape of botnets and the challenges in combating these threats, offering guidance on addressing security concerns effectively through the highlighting of evasion and detection methods. The survey concludes by presenting future research directions, including using encryption and covert channels for detection and strategies to strengthen botnets. This aims to guide researchers in developing more robust security measures to combat botnets effectively.

Active logic modulator of terahertz metamaterial for future 6G communication system

A tunable G-shaped metamaterial (GSM) for the upcoming 6G communication system is presented. GSM device shows polarization-dependent characteristics with three or five resonances. The GSM unit cell is composed of two identical G-shaped structures on a silicon (Si) substrate. By utilizing micro-electro-mechanical system (MEMS) technology to change the vertical height (h) between the two G-shaped structures and the horizontal distance (g) between them or change the polarization angle (PA) of the incident light, the electromagnetic responses of GSM device can be switched three to five resonances. The tuning range of resonant frequency of the GSM device is up to 15 GHz by changing the g value, and the modulation depth can reach 93 % by changing PA and h values. This MEMS-based GSM device exhibits multi-resonance and polarization-dependent characteristics, offering an efficient and economical way to modulate terahertz (THz) signals. Moreover, the GSM device also exhibits good optical switching performance at PA = 0°, 30°, and 75° under the condition of h = 2 μm. These characteristics can be expressed as logic signals 'ON' and 'OFF' states, enabling their application in the logic operation function of the NAND gate and programmable two-bit binary digital coding metamaterials.

LPI radar waveform design based on complementary phase and discrete chaotic frequency joint coding

In order to reduce the probability of radar radiation signals being detected by enemy passive detection systems, this paper proposes a phase and frequency joint coding low intercept Radar waveform design method. Based on the linear frequency modulation signal, this method uses complementary binary code and chaotic sequence to phase encode the intra-pulse modulation. Code and frequency coding. The numerical simulation results show that the designed waveform exhibits pseudo-random characteristics in the time-frequency domain, and the low recognition performance is improved; The signal has an extremely low peak sidelobe level after matched filtering, showing excellent low intercept performance; its three-dimensional ambiguity function diagram presents an ideal “graph”. Fishing type”, with good distance, speed resolution and anti-interference characteristics.

Stable case BB/BC mass transfer to form GW190425-like massive binary neutron star mergers

On April 25, 2019, the LIGO-Virgo Collaboration discovered a gravitational-wave (GW) signal from a binary neutron star (BNS) merger, that is, GW190425. Due to the inferred large total mass, the origin of GW190425 remains unclear. Assuming GW190425 originated from the standard isolated binary evolution channel, its immediate progenitor is considered to be a close binary system, consisting of a He-rich star and a NS just after the common envelope phase. We aim to study the formation of GW190425 in a solar-like environment by using the detailed binary evolution code MESA We perform detailed stellar structure and binary evolution calculations that take into account mass loss, internal differential rotation, and tidal interactions between a He-rich star and a NS companion. We explore the parameter space of the initial binary properties, including initial NS and He-rich masses and initial orbital period. We find that the immediate post-common-envelope progenitor system, consisting of a primary $ NS and a secondary He-rich star with an initial mass of $ in a close binary with an initial period of $ days days $), that experiences stable Case BB/BC mass transfer (MT) during binary evolution, can reproduce the formation of GW190425-like BNS events. Our studies reveal that the secondary He-rich star of the GW190425's progenitor before its core collapse can be efficiently spun up through tidal interaction, finally remaining as a NS with rotational energy even reaching $ erg $, which is always much higher than the neutrino-driven energy of the supernova (SN) explosion. If the newborn secondary NS is a magnetar, we expect that GW190425 can be the remnant of a magnetar-driven SN, namely a magnetar-driven ultra-stripped SN, a superluminous SN, or a broad-line Type Ic SN. Our results show that GW190425 could be formed through the isolated binary evolution, which involves a stable Case BB/BC MT just after the common envelope phase. On top of that, we show the He-rich star can be tidally spun up, potentially forming a spinning magnetized NS (magnetar) during the second SN explosion.

Adaptive Measurement and Parameter Estimation for Low-SNR PRBC-PAM Signal Based on Adjusting Zero Value and Chaotic State Ratio

Accurately estimating the modulation parameters of pseudorandom binary code–pulse amplitude modulation (PRBC–PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, weak signal detection methods based on chaos theory can handle situations with low signal-to-noise ratio, but most of them are developed for simple sin/cos waveform and cannot face PRBC–PAM signals commonly used in ultra-low altitude performance equipment. To address the issue, this article proposes a novel adaptive detection and estimation method utilizing the in-depth analysis of the Duffing oscillator’s behaviour and output characteristics. Firstly, the short-time Fourier transform (STFT) is used for chaotic state identification and ternary processing. Then, two novel approaches are proposed, including the adjusting zero value (AZV) method and the chaotic state ratio (CSR) method. The proposed weak signal detection system exhibits unique capability to adaptively modify its internal periodic driving force frequency, thus altering the difference frequency to estimate the signal parameters effectively. Furthermore, the accuracy of the proposed method is substantiated in carrier frequency estimation under varying SNR conditions through extensive experiments, demonstrating that the method maintains high precision in carrier frequency estimation and a low bit error rate in both the pseudorandom sequence and carrier frequency, even at an SNR of −30 dB.

Mix Testing: Specifying and Testing ABI Compatibility of C/C++ Atomics Implementations

The correctness of complex software depends on the correctness of both the source code and the compilers that generate corresponding binary code. Compilers must do more than preserve the semantics of a single source file: they must ensure that generated binaries can be composed with other binaries to form a final executable. The compatibility of composition is ensured using an Application Binary Interface (ABI), which specifies details of calling conventions, exception handling, and so on. Unfortunately, there are no official ABIs for concurrent programs, so different atomics mappings, although correct in isolation, may induce bugs when composed. Indeed, today, mixing binaries generated by different compilers can lead to an erroneous resulting binary. We present mix testing : a new technique designed to find compiler bugs when the instructions of a C/C++ test are separately compiled for multiple compatible architectures and then mixed together. We define a class of compiler bugs, coined mixing bugs , that arise when parts of a program are compiled separately using different mappings from C/C++ atomic operations to assembly sequences. To demonstrate the generality of mix testing, we have designed and implemented a tool, atomic-mixer, which we have used: (a) to reproduce one existing non-mixing bug that state-of-the-art concurrency testing tools are limited to being able to find (showing that atomic-mixer at least meets the capabilities of these tools), and (b) to find four previously-unknown mixing bugs in LLVM and GCC, and one prospective mixing bug in mappings proposed for the Java Virtual Machine. Lastly, we have worked with engineers at Arm to specify, for the first time, an atomics ABI for Armv8, and have used atomic-mixer to validate the LLVM and GCC compilers against it.

Towards the sociology of pets—Social functions of animals in contemporary societies observed from systems theoretical perspective

AbstractThis paper advocates for introducing sociology of pets as a particular field of research as the response of ever growing social relevance of pets in contemporary societies. Though other theoretical approaches are suitable for tackling these complex issues, the article offers viewpoint from Luhmann's social systems theory. Based on Luhmann's writings on ecological communication, the article departs from the system/environment distinction and conceives pets as being part of a society's environment that attained resonance of a social system. Further, entry of pets into a social system is interpreted through differentiation theorem arguing that pets perform highly relevant social functions for all functional subsystems of a society by contributing to their autopoietic reproduction. This is primarily accomplished through communication about pets, emerging specific semantics, usage of binary codes specific for different subsystems, correspondence to their mediums, programs and functions. The major part of work is presenting relations of pets to 10 functional subsystems of a society, as this delineates scope of empirical phenomena of potential interest for sociology of pets. These processes might be observed at systemic, organizational and interaction levels. Despite possessing multiple merits, also, certain limits of Luhmann's account of pets are unfolded, such as incapability to hear authentic voices of animals, but rather distorted semantic codes emerging from consciousness of human psychic systems, afterwards structurally coupled with communication as a medium of social systems. At this point, social systems theory might be complemented by theory of interspecies communication, as taking into consideration other theoretical approaches anchored at ontologies pertinent to include non‐human agents is invocated. The tentative outcome of this introductory discussion is that apparent process of anthro‐morphosization of animals is taking place while the question of relation between humans–humans in the context of building just societies of the 21st century still remains open.

Wasm-R3: Record-Reduce-Replay for Realistic and Standalone WebAssembly Benchmarks

WebAssembly (Wasm for short) brings a new, powerful capability to the web as well as Edge, IoT, and embedded systems. Wasm is a portable, compact binary code format with high performance and robust sandboxing properties. As Wasm applications grow in size and importance, the complex performance characteristics of diverse Wasm engines demand robust, representative benchmarks for proper tuning. Stopgap benchmark suites, such as PolyBenchC and libsodium, continue to be used in the literature, though they are known to be unrepresentative. Porting of more complex suites remains difficult because Wasm lacks many system APIs and extracting real-world Wasm benchmarks from the web is difficult due to complex host interactions. To address this challenge, we introduce Wasm-R3, the first record and replay technique for Wasm. Wasm-R3 transparently injects instrumentation into Wasm modules to record an execution trace from inside the module, then reduces the execution trace via several optimizations, and finally produces a replay module that is executable standalone without any host environment-on any engine. The benchmarks created by our approach are (i) realistic, because the approach records real-world web applications, (ii) faithful to the original execution, because the replay benchmark includes the unmodified original code, only adding emulation of host interactions, and (iii) standalone, because the replay benchmarks run on any engine. Applying Wasm-R3 to web-based Wasm applications in the wild demonstrates the correctness of our approach as well as the effectiveness of our optimizations, which reduce the recorded traces by 99.53% and the size of the replay benchmark by 9.98%. We release the resulting benchmark suite of 27 applications, called Wasm-R3-Bench, to the community, to inspire a new generation of realistic and standalone Wasm benchmarks.

Infinite Families of Few Weight Optimal Binary Linear Codes From Multivariable Functions

Infinite Families of Few Weight Optimal Binary Linear Codes From Multivariable Functions

Research on the application technology of expert knowledge graph based on genetic search algorithm

Abstract The present study investigates the application of a genetic search algorithm on an expert knowledge graph. By employing binary coding for candidate experts in the expert database, the matching evaluation function is utilized to define the environmental fitness index, thereby optimizing the selection of expert groups. Experimental results demonstrate that the GES algorithm exhibits a superior matching degree and reduced time consumption when addressing science and technology item-matching requirements in review work. In comparison with the traditional BFS algorithm and GIS algorithm, the GES algorithm showcases enhanced potential for application and stability when confronted with large datasets. This paper further analyzes the impact of factors such as the total number of experts, number of fields, and number of scientific and technological projects on algorithm performance, confirming that adjustment of algorithm parameters can effectively enhance matching degree while reducing time consumption, thus providing technical support for practical implementation.

Binary Codes for Correcting Two Edits

Binary Codes for Correcting Two Edits