Code Patterns Research Articles

Using JavaScript on 5G networks to improve real-time communication through WebRTC

The introduction of 5G networks has heralded a new era of connectivity marked by record-breaking download speeds and near-zero latency. This essay explains how JavaScript may be used to take advantage of 5G networks' hefty boost to WebRTC's real-time communication capabilities. Web Real-Time Communication (WebRTC) has long been the platform of choice for developers aiming to build serverless, real-time communication apps on the web. WebRTC allows users to directly transfer audio, video, and general data between users, eliminating the need for any middleware. However, delays and lost connections plagued previous network generations, creating less-than-ideal user experiences. The arrival of 5G presents a golden chance to rethink WebRTC's potential applications. Developers may build communication solutions that are more responsive and robust than ever before by making use of 5G's high bandwidth, low latency, and better dependability. Key methods and code patterns in JavaScript are outlined for maximizing 5G's potential in WebRTC. We examine the benefits of adopting 5G as the backbone for WebRTC apps, including faster connection times, higher-quality video, and more data throughput. We also provide empirical data that shows considerable improvements in real-time video and audio communications concerning delay and buffering. Together, JavaScript, 5G, and WebRTC form a formidable trinity, poised to transform the state of the art in real-time communication completely. As 5G networks become more widespread, organizations and developers should be ready to take advantage of them by making communication apps that are quick and efficient but also immersive and seamless

Working memory performance is tied to stimulus complexity

Working memory is the cognitive capability to maintain and process information over short periods. Behavioral and computational studies have shown that visual information is associated with working memory performance. However, the underlying neural correlates remain unknown. To identify how visual information affects working memory performance, we conducted behavioral experiments in pigeons (Columba livia) and single unit recordings in the avian prefrontal analog, the nidopallium caudolaterale (NCL). Complex pictures featuring luminance, spatial and color information, were associated with higher working memory performance compared to uniform gray pictures in conjunction with distinct neural coding patterns. For complex pictures, we found a multiplexed neuronal code displaying visual and value-related features that switched to a representation of the upcoming choice during a delay period. When processing gray stimuli, NCL neurons did not multiplex and exclusively represented the choice already during stimulus presentation and throughout the delay period. The prolonged representation possibly resulted in a decay of the memory trace ultimately leading to a decrease in performance. In conclusion, we found that high stimulus complexity is associated with neuronal multiplexing of the working memory representation possibly allowing a facilitated read-out of the neural code resulting in enhancement of working memory performance.

A mixed method comparison of stigma toward autism and schizophrenia and effects of person-first versus identity-first language.

While stigma toward autistic individuals has been well documented, less is known about how autism is perceived relative to other stigmatized disabilities. As a highly stigmatized condition with similar social cognitive features to autism, schizophrenia may offer a useful comparison for stigma. Previous studies have found that autistic people may be perceived more favorably than those with schizophrenia, but little is known about the underlying volitional thoughts that contribute to differences in how these conditions are perceived. The present study utilizes a mixed-methods approach, allowing for a detailed understanding of how young adults perceive different diagnostic labels. 533 college undergraduates completed questionnaires reflecting their perceptions of one of eight diagnostic labels: four related to autism (autism, autistic, autism spectrum disorder, or Asperger's), two related to schizophrenia (schizophrenia or schizophrenic), and two related to an unspecified clinical condition (clinical diagnosis or clinical disorder). Participants also completed an open-ended question regarding their thoughts about, and exposure to, these labels. Responses were compared across broader diagnostic categories (autism, schizophrenia, general clinical condition), with thematic analysis used to assess the broader themes occurring within the open-ended text. While perceptions did not differ significantly for person-first and identity-first language within labels, several differences were apparent across labels. Specifically, quantitative results indicated greater prejudice towards autism and schizophrenia than the generic clinical condition, with schizophrenia associated with more perceived fear and danger, as well as an increased preference for social distance, compared to autism. Patterns in initial codes differed across diagnostic labels, with greater variation in responses about autism than responses about schizophrenia or the general clinical condition. While participants described a range of attitudes toward autism (patronizing, exclusionary, and accepting) and schizophrenia (fear, prejudice, and empathy), they refrained from describing their attitudes toward the general clinical label, highlighting the centrality of a cohesive group identity for the development of stigma. Finally, participants reported a number of misconceptions about autism and schizophrenia, with many believing features such as savant syndrome to be core characteristics of the conditions. These findings offer a more detailed account of how non-autistic individuals view autism and may therefore aid in the development of targeted programs to improve attitudes toward autism.

Inundative practice for screening siRNA management candidates against a notorious predatory beetle using olfactory silencing

Calosoma maximoviczi, a predatory pest beetle, poses a significant threat to wild silk farm production due to its predation on wild silkworms. Given the coexistence of this species with beneficial silkworms in the farm orchards, chemical pesticides are not an ideal solution for controlling its population. In this study, we employed a comprehensive multi-target RNA interference (RNAi) approach to disrupt the olfactory perception of C. maximoviczi through independently silencing 16 odorant receptors (ORs) in the respective genders. Specifically, gene-specific siRNAs were designed to target a panel of ORs, allowing us to investigate the specific interactions between odorant receptors and ligands within this species. Our investigation led to identifying four candidate siOR groups that effectively disrupted the beetle's olfactory tracking of various odorant ligands associated with different trophic levels. Furthermore, we observed sex-specific differences in innate RNAi responses reflected by subsequent gene expression, physiological and behavioral consequences, underscoring the complexity of olfactory signaling and emphasizing the significance of considering species/sex-specific traits when implementing pest control measures. These findings advance our understanding of olfactory coding patterns in C. maximoviczi beetles and establish a foundation for future research in the field of pest management strategies.

Phase-domain modulated hybrid phase-shifting structured light based efficient 3D measurement

Phase-shifting profilometry (PSP) measurement technology remains a research hotspot in 3D imaging. However, existing PSP methods require projecting additional patterns, which limits measurement space, or involves compressing fringe amplitudes to eliminate phase ambiguity. These practices result in reduced measurement efficiency and robustness. To overcome these challenges, we present a novel phase-domain modulated hybrid phase-shifting (PDM-HPS) structured light 3D measurement approach to solve the phase error of existing spatial-domain modulated phase-shifting (SDM-PS) methods without imposing additional pattern or space constraints. The method integrates phase-shifting speckle and phase-shifting fringe methodologies to create a hybrid phase-shifting coding pattern. In the decoding phase, the offset speckle phase is detected and corrected by the low-precision phase extracted in the frequency domain. The separation of binary speckle and high-precision wrapped phase is realized. Ultimately, an efficient binary speckle-aided stereo phase matching process is employed to achieve unambiguous phase unwrapping. We test the PDM-HPS method in multiple complex real-world scenarios using binocular structured light systems, and compare its accuracy and efficiency with three existing advanced methods. The qualitative and quantitative results show that the proposed method can achieve the measurement accuracy similar to that of the multi-frequency (MF) method by using only 1/3 projection patterns.

Scorpion-inspired bionic gait activity location and recognition smart home system

Vibration-based contactless technologies are arising for indoor person location and gait activity recognition applications, which can overcome issues such as privacy breaches and wearing discomfort with traditional methods to satisfy the critical need for health and safety monitoring in smart home. However, simultaneous location and recognition of occupant gait activity using information-starved vibration signals remains challenging due to its less characterization of event details. In nature, scorpions use a 3/1 neuron coding pattern to enrich vibration information by enhancing sensory differences from eight vibration sensilla embedded in their metatarsal ends of each walking leg, thus exhibiting high accuracy in locating moving prey. Inspired by this, we exploit a coding pattern to make vibration signal features richer for discrimination, while designing a bionic gait activity location and recognition system in which the data acquisition device is built by imitating scorpion vibration sensilla array distribution. To verify the feasibility of the system, we collect vibration signals produced by different people with varying gait activities (walking, jogging, running, and stepping) in an actual scene, and a trained hidden Markov model is employed to identify the activity of the current location target. The average estimated error and the relative estimated error of position estimation are calculated to be 0.3517 m and 8.79 %, respectively. The average recall and average precision of gait activity recognition are 95.83 % and 95.84 %, respectively. Our system is proven to simultaneously locate and recognize occupant gait activities, which can meet the needs including smart home, and health care monitoring.

Generation of Phase-Coded LFM Signals Based on Fourier Domain Mode-Locked Optoelectronic Oscillator

Phase-coded linearly frequency modulated (LFM) signals play essential roles in enhancing the performance of radar as well as exploring wireless communication. To generate phase-coded LFM signals, we propose to launch a phase-coded signal into an optoelectronic oscillator (OEO). When the OEO is Fourier domain mode-locked (FDML), a phase-coded LFM signal is generated. The center frequency, bandwidth, coding pattern, and bit rate of the generated signals can be flexibly adjusted. In the experiment, a phase-coded LFM microwave signal with a bandwidth of 5.0 GHz is generated, corresponding to a time bandwidth product (TBWP) of 2.8×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> . The center frequency and bandwidth of the generated signals are adjusted from 9.7 to 10.7 GHz and from 5.0 to 3.0 GHz, respectively. Meanwhile, different coding patterns and rates are also implemented, indicating that the phase-coded LFM signal can be used in wireless communications. The proposed approach can potentially be applied to multifunctional integration in radar.

Multiple description coding ghost imaging

Ghost imaging (GI) reveals its exceptional superiority over conventional cameras in a range of challenging scenarios such as weak illumination or special waveband. For high-performance GI, it is vital to obtain a sequence of high-fidelity bucket signals. However, measurements may suffer from distortion or loss in harsh environments. Here we present multiple description coding ghost imaging, which rests on illumination consisting of different coding patterns to address this challenge. Experimental results indicate that the proposed method is capable of producing satisfactory image even when the sequence of bucket signals is incomplete or highly distorted. This method provides an encouraging boost for GI in practical applications.

VulGAI: vulnerability detection based on graphs and images

Deep learning models have been widely used in the field of vulnerability detection. Deep learning-based vulnerability detection methods can automatically learn code patterns. Some methods consider processing codes as text sequences to achieve scalable vulnerability detection. They leverage natural language processing models to extract code features. These methods do not consider the code's semantic structure and treat code slices as text. Vulnerability detection methods based on graph structures and graph neural networks are more accurate than text-based methods. However, these methods lack scalability in practice. Both graph generation and graph neural network training are all time-consuming. We propose a vulnerability detection method based on graphs and images (VulGAI). VulGAI choose the more reasonable node centrality to generate the image. It can preserve program details and distinguish node importance from different perspectives. In addition, we design a more efficient CNN model, which reduces computational overhead and improves detection performance (Time and Accuracy). We implement VulGAI and evaluate six methods (VulDePecker, SySeVR, Devign, VulCNN, mVulPreter, and VulGAI) on 40,657 functions. Experimental results show that VulGAI achieves higher Accuracy, TPR, and F1-Score than the others. In addition, we compare VulGAI and VulCNN on 30270 real-world functions. VulGAI outperforms VulCNN by 1.48 times in the number of TP. VulGAI is about 3.9 times faster than VulCNN in detection time.

A novel phase-shifting segmented phase coding method for absolute phase retrieval

The phase-coding method is widely used during 3D measurement for attaining absolute phase retrieval. However, traditional the phase-coding method still suffers from low accuracy and insufficient reliability. To address these issues, a phase-shifting segmented phase coding method was proposed in this work for absolute phase retrieval in complex object measurements, which specifically improved upon the segmented method of phase coding. Coding patterns with different phase-shifting were obtained by embedding the codeword into the phase-shifting phase. These patterns were also combined and arranged to create novel segmented coding patterns. In addition, this segmented encoding can be self-connected based on its different phase-shifting information to obtain the complete fringe order, achieving thus absolute phase retrieval of over 36 codewords. To solve the problem of the periodic misalignment of the wrapped phase and fringe order, the complementary phase coding method was encoded by the bidirectional coding method, and the separation method of the bidirectional coding was improved. From experimental results, it was demonstrated that the proposed method has high measurement accuracy and reliability.

Pyrolysis of Al-Based Metal-Organic Frameworks to Carbon Dot-Porous Al2 O3 Composites With Time-Dependent Phosphorescence Colors for Advanced Information Encryption.

Phosphorescent materials with time-dependent phosphorescence colors (TDPCs) have great potential in advanced optical applications. Synthesis of such materials is attractive but challenging. Here, a series of carbon dot-porous Al2 O3 composites exhibiting distinctive TDPC characteristics is prepared by high-temperature pyrolysis of Al-based metal-organic frameworks NH2 -MIL-101(Al). The composite synthesized at 700 °C (CDs@Al2 O3 -700) shows an obvious change in phosphorescence color from blue to green after removing the excitation light of 280nm. Photophysical analysis reveals that two emission centers in CDs, namely carbon core and surface states, are responsible for the short-lived blue phosphorescence (96ms) and long-lived green phosphorescence (911ms), respectively. The combination of blue and green phosphorescence with different decay rates triggering the interesting TDPC phenomenon. CDs@Al2 O3 -700 has a significantly high phosphorescence quantum yield of up to 41.7% and possesses an excellent optical stability against water, organic solvents, and strong oxidants, which benefits from the multi-confinement of CDs by the porous Al2 O3 matrix through rigid network, strong space constraint, and stable covalent bonding. Based on the TDPC property, multilevel coding patterns composed of CDs@Al2 O3 are successfully fabricated for advanced dynamic information encryption.

Improved structured light system based on generative adversarial networks for highly-reflective surface measurement

Gray code pattern structured light projection technology is widely used in industrial inspection due to its good robustness and anti-noise performance. Gray code pattern technology projects a sequence of encoded fringe patterns with black and white strips onto the scanned object in order to measure its height distribution. However, if the scanned object has strong specular reflection properties, the acquired encoded fringe images tend to miss significant amounts of local area information. As a result, the measured three-dimensional point clouds contain many missing points, and hence the measurement accuracy is severely degraded. To address this problem, the present study proposes a novel fringe-inpainting system based on a generative adversarial network framework, to repair the fringe features in the regions of the scanned surface in which the local information is lost. The performance of the proposed fringe-inpainting system is compared with that of several other advanced highly-reflective surface measurement technologies reported in the literature. The experimental results show that the proposed method significantly outperforms these techniques and yields an excellent encoded fringe inpainting for all of the considered objects.

Time-slicing high dynamic range 3D imaging.

Fringe projection profilometry (FPP) has been widely used in 3D measurement due to its high precision and non-contact properties. Nevertheless, it still faces great challenges in measuring scenes with complex reflectivity, in which the dynamic range of the reflected light field of the scene is significantly higher than that of the image detector. In this paper, we propose a time-slicing strategy for high dynamics range 3D imaging by projecting a series of sinusoidal fringe patterns with short and equal length exposure time and performing the fusion of different numbers of short exposure images according to the local gray-value distribution of the images. Moreover, to further improve the measurement efficiency, we realize phase unwrapping using complementary Gray code patterns, which are binary and insensitive to the image sensor's nonlinear response to the reflected light from the scene under test. Experiments are conducted to demonstrate the feasibility and efficiency of the proposed method.

Life as a Guide to Its Own Origins

The origin of life entails a continuum from simple prebiotic chemistry to cells with genes and molecular machines. Using life as a guide to this continuum, we consider how selection could promote increased complexity before the emergence of genes. Structured, far-from-equilibrium environments such as hydrothermal systems drive the reaction between CO2 and H2 to form organics that self-organize into protocells. CO2 fixation within protocells generates a reaction network with a topology that prefigures the universal core of metabolism. Positive feedback loops amplify flux through this network, giving a metabolic heredity that promotes growth. Patterns in the genetic code show that genes and proteins arose through direct biophysical interactions between amino acids and nucleotides in this protometabolic network. Random genetic sequences template nonrandom peptides, producing selectable function in growing protocells. This context-dependent emergence of information gives rise seamlessly to an autotrophic last universal common ancestor.

FEDRak: Federated Learning-Based Symmetric Code Statement Ranking Model for Software Fault Forecasting

Software Fault Forecasting (SFF) pertains to timely identifying sections in software projects that are prone to faults and may result in significant development expenses. Deep learning models have become widespread in software fault monitoring and management, and these models rely on the design metrics and the code pattern features for classifying the code as erroneous or safe. The proposed model works based on the collective formulation of the fault localization model, which acquires the model-specific metadata for building a global model that would perform software fault forecasting globally. The proposed model works by ranking the suspicious code blocks based on the symmetry of the semantic features of the erroneous code and the implementation code. The feature selection and scaling process is initially performed to precisely identify the features contributing to fault forecasting. The data extraction that is portrayed as the intermediate phase would assist in focusing on the code statements and ranking them based on the impact of the fault. A fine-tuned spectrum-based fault localization technique is used in ranking the statements. The FEDRak model facilitates ongoing adaptation in instances where there are adjustments in the feature contribution of data over time. The federated learning model would update the feature weights of the global model based on the weights synchronized by locally built fault forecasting approaches. FEDRak is statistically analyzed in relation to other contemporary techniques in fault localization in terms of metrics like sensitivity, specificity, accuracy, F1-score, and ROC curves. The proposed model’s performances are analyzed in terms of local and global models.

On the Integration of Machine Agents into Live Coding

Co-creation strategies for human–machine collaboration have recently been explored in various creative disciplines and more opportunities for human–machine collaborations are materialising. In this article, we outline how to augment musical live coding by considering how human live coders can effectively collaborate with a machine agent imbued with the ability to produce its own patterns of executable code. Using machine agents allows live coders to explore not-yet conceptualised patterns of code and supports them in asking new questions. We argue that to move away from scenarios where machine agents are used in a merely generative way, or only as creative impetus for the human, and towards a more collaborative relationship with the machine agent, consideration is needed for system designers around the aspects of reflection, aesthetics and evaluation. Furthermore, owing to live coding’s close relationship with exposing processes, using agents in such a way can be a useful manner to explore how to make artificial intelligence processes more open and explainable to an audience. Finally, some speculative futures of co-creative and artificially intelligent systems and what opportunities they might afford the live coder are discussed.

Specific neural coding of fMRI spiking neural network based on time coding

Specific neural coding is the key to achieving advanced cognitive function in a bio-brain, which can form an identifying coding pattern for external stimulation. The performance of specific neural coding depends extremely on brain-like models. However, the bio-interpretability of the topology of a brain-like model is still insufficient. The purpose of this paper is to investigate a more biological interpretative brain-like model verified by the performance of specific neural coding. In this study, we used the topology constrained by human brain functional magnetic resonance imaging (fMRI) to construct a new spiking neural network (SNN) as a brain-like model called fMRI-SNN. In the fMRI-SNN, the nodes are Izhikevich neuron models, and the edges are synaptic plasticity models with time-delay. Then, we investigated the specific neural coding of fMRI-SNN, and discussed its mechanism. Our results indicated that: (i) fMRI-SNN has obvious specific neural coding based on time coding for different external stimulations. Furthermore, our discussion on relevance analysis implies that the intrinsic element of specific neural coding is synaptic plasticity. (ii) The specific neural coding of fMRI-SNN outperforms that of scale-free SNN and small-world SNN. Furthermore, our discussion on dynamic topological characteristics implies that the network topology is an element that impacts the performance level of the specific neural coding. (iii) Taking a speech recognition task as a case study, the performance of fMRI-SNN outperforms that of scale-free SNN and small-world SNN in terms of speech recognition accuracy.

Deep learning-based counter anti-forensic of GAN-based attack in HEVC compressed domain using coding pattern analysis

Double compression detection is one of the most popular techniques for the authentication of images and videos in the sphere of multimedia forensics. Several forensic methods have been suggested revealing video forgery, particularly high-efficiency video codec (HEVC) double compression detection. Even though forensic methods can effectively validate the integrity of videos, they become more intricate while practicing anti-forensic (AF) on manipulated videos. In this study, we present an innovative counter-anti-forensic (CAF) approach to detect anti-forensically misguided HEVC double compressed videos with the same coding parameters using coding pattern analysis. We mainly focus on employing generative adversarial networks (GANs) attacks to reconstruct artifacts that appear in compressed videos for deceiving forensic methods. In the proposed CAF method, we apply AF attacks twice in primary and secondary compressions and analyze the coding pattern changes to build a CAF method. We extract syntax information, and introduce new syntax map representations to improve distinguishability between single and double-compressed sequences. Additionally, we apply Gaussian and median filtering as AF attacks to show the robustness of the proposed method. The proposed deep learning-based CAF method shows better performance and can overwhelm state-of-the-art works.

Irreversibility of symbolic time series: A cautionary tale.

Many empirical time series are genuinely symbolic: Examples range from link activation patterns in network science, to DNA coding or firing patterns in neuroscience, to cryptography or combinatorics on words. In some other contexts, the underlying time series is actually real valued, and symbolization is applied subsequently, as in symbolic dynamics of chaotic systems. Among several time series quantifiers, time series irreversibility-the difference between forward and backward statistics in stationary time series-is of great relevance. However, the irreversible character of symbolized time series is not always equivalent to the one of the underlying real-valued signal, leading to some misconceptions and confusion on interpretability. Such confusion is even bigger for binary time series-a classical way to encode chaotic trajectories via symbolic dynamics. In this paper we aim to clarify some usual misconceptions and provide theoretical grounding for the practical analysis-and interpretation-of time irreversibility in symbolic time series. We outline sources of irreversibility in stationary symbolic sequences coming from frequency asymmetries of nonpalindromic pairs which we enumerate, and prove that binary time series cannot show any irreversibility based on words of length m<4, thus discussing the implications and sources of confusion. We also study irreversibility in the context of symbolic dynamics, and clarify why these can be reversible even when the underlying dynamical system is not, such as the case of the fully chaotic logistic map.

Research on smart contract vulnerability detection method based on domain features of solidity contracts and attention mechanism

The smart contract, a self-executing program on the blockchain, is key to programmable finance. However, the rise of smart contract use has also led to an increase in vulnerabilities that attract illegal activity from hackers. Traditional manual approaches for vulnerability detection, relying on domain experts, have limitations such as low automation and weak generalization. In this paper, we propose a deep learning approach that leverages domain-specific features and an attention mechanism to accurately detect vulnerabilities in smart contracts. Our approach reduces the reliance on manual input and enhances generalization by continuously learning code patterns of vulnerabilities, specifically detecting various types of vulnerabilities such as reentrancy, integer overflow, forced Ether injection, unchecked return value, denial of service, access control, short address attack, tx.origin, call stack overflow, timestamp dependency, random number dependency, and transaction order dependency vulnerabilities. In order to extract semantic information, we present a semantic distillation approach for detecting smart contract vulnerabilities. This approach involves using a syntax parser, Slither, to segment the code into smaller slices and word embedding to create a matrix for model training and prediction. Our experiments indicate that the BILSTM model is the best deep learning model for smart contract vulnerability detection task. We looked at how domain features and self-attentiveness mechanisms affected the ability to identify 12 different kinds of smart contract vulnerabilities. Our results show that by including domain features, we significantly increased the F1 values for 8 different types of vulnerabilities, with improvements ranging from 7.35% to 48.58%. The methods suggested in this study demonstrate a significant improvement in F1 scores ranging from 4.18% to 38.70% when compared to conventional detection tools like Oyente, Mythril, Osiris, Slither, Smartcheck, and Securify. This study provides developers with a more effective method of detecting smart contract vulnerabilities, assisting in the prevention of potential financial losses. This research provides developers with a more effective means of detecting smart contract vulnerabilities, thereby helping to prevent potential financial losses.