Code Modulation Research Articles

Integrating session recording and eye-tracking: development and evaluation of a Chrome extension for user behavior analysis

The subject of this article is the development, implementation, and evaluation of a Chrome extension designed to record and replay web sessions using integrated eye-tracking data. The goal is to develop a tool that enhances user interaction analysis by combining session recordings with eye-tracking capabilities. The tasks to be solved are as follows: create a functional Chrome extension that utilizes rrweb for session recording and WebGazer.js for eye tracking; implement features, such as session recording, replay with eye-tracking data overlays, and session management; and provide options for exporting and importing recorded sessions. The methods used are: architectural modeling using UML diagrams to design the system architecture, software engineering techniques for developing the extension, integration testing to ensure the smooth operation of combined features, and data preprocessing techniques to prevent redundancy and reduce noise in eye-tracking data. Additionally, a structured user study with detailed questionnaires combining both Likert-scale questions and open-ended responses and feedback analysis were conducted to evaluate usability and gather feedback. The following results were obtained: the extension was successfully developed and evaluated with 25 participants aged 18–35 years in a controlled environment. High usability ratings were obtained, with an average score of 4.5 out of 5 for the session recording, replay, and session management features. However, the eye-tracking feature received a lower rating of 3.8 out of 5 due to occasional inaccuracies in the eye-tracking data. The qualitative feedback indicated the usefulness of the eye-tracking feature and highlighted the need for improved data accuracy. Conclusions. The scientific novelty of this study lies in the integration of session recording and eye tracking within a Chrome extension, which represents a novel and comprehensive tool for user interaction analysis on the web. The tool's ability to capture both behavioral data and visual attention without requiring website code modifications is particularly valuable for researchers, marketing specialists, UI/UX designers, and product developers. The usability study and feedback analysis provided a clear direction for future improvements, including enhancing the eye-tracking accuracy and integrating advanced analytics and customizable reporting options. Future work will also explore the integration of machine learning algorithms to automatically analyze recorded data to provide deeper insights and actionable recommendations.

Enhancing Sound Source Localization via False Negative Elimination.

Sound source localization aims to localize objects emitting the sound in visual scenes. Recent works obtaining impressive results typically rely on contrastive learning. However, the common practice of randomly sampling negatives in prior arts can lead to the false negative issue, where the sounds semantically similar to visual instance are sampled as negatives and incorrectly pushed away from the visual anchor/query. As a result, this misalignment of audio and visual features could yield inferior performance. To address this issue, we propose a novel audio-visual learning framework which is instantiated with two individual learning schemes: self-supervised predictive learning (SSPL) and semantic-aware contrastive learning (SACL). SSPL explores image-audio positive pairs alone to discover semantically coherent similarities between audio and visual features, while a predictive coding module for feature alignment is introduced to facilitate the positive-only learning. In this regard SSPL acts as a negative-free method to eliminate false negatives. By contrast, SACL is designed to compact visual features and remove false negatives, providing reliable visual anchor and audio negatives for contrast. Different from SSPL, SACL releases the potential of audio-visual contrastive learning, offering an effective alternative to achieve the same goal. Comprehensive experiments demonstrate the superiority of our approach over the state-of-the-arts. Furthermore, we highlight the versatility of the learned representation by extending the approach to audio-visual event classification and object detection tasks. Code and models are available at: https://github.com/zjsong/SACL.

Hierarchical differences in the encoding of amplitude modulation in the subcortical auditory system of awake nonhuman primates.

Sinusoidal amplitude modulation (SAM) is a key feature of complex sounds. Although psychophysical studies have characterized SAM perception, and neurophysiological studies in anesthetized animals report a transformation from the cochlear nucleus' (CN; brainstem) temporal code to the inferior colliculus' (IC; midbrain's) rate code, none have used awake animals or nonhuman primates to compare CN and IC's coding strategies to modulation-frequency perception. To address this, we recorded single-unit responses and compared derived neurometric measures in the CN and IC to psychometric measures of modulation frequency (MF) discrimination in macaques. IC and CN neurons often exhibited tuned responses to SAM in rate and spike-timing measures of modulation coding. Neurometric thresholds spanned a large range (2-200 Hz ΔMF). The lowest 40% of IC thresholds were less than or equal to psychometric thresholds, regardless of which code was used, whereas CN thresholds were greater than psychometric thresholds. Discrimination at 10-20 Hz could be explained by indiscriminately pooling 30 units in either structure, whereas discrimination at higher MFs was best explained by more selective pooling. This suggests that pooled CN activity was sufficient for AM discrimination. Psychometric and neurometric thresholds decreased as stimulus duration increased, but IC and CN thresholds were higher and more variable than behavior at short durations. This slower subcortical temporal integration compared with behavior was consistent with a drift diffusion model that reproduced individual differences in performance and can constrain future neurophysiological studies of temporal integration. These measures provide an account of AM perception at the neurophysiological, computational, and behavioral levels.NEW & NOTEWORTHY In everyday environments, the brain is tasked with extracting information from sound envelopes, which involves both sensory encoding and perceptual decision-making. Different neural codes for envelope representation have been characterized in midbrain and cortex, but studies of brainstem nuclei such as the cochlear nucleus (CN) have usually been conducted under anesthesia in nonprimate species. Here, we found that subcortical activity in awake monkeys and a biologically plausible perceptual decision-making model accounted for sound envelope discrimination behavior.

Simulation of Groundwater Dissolved Organic Carbon in Yufu River Basin during Artificial Recharge: Improving through the SWAT-MODFLOW-RT3D Reaction Module

To keep groundwater levels stable, Jinan’s government has implemented several water management measures. However, considerable volumes of dissolved organic carbon (DOC) can enter groundwater via water exchange, impacting groundwater stability. In this study, a SWAT-MODFLOW-RT3D model designed specifically for the Yufu River Basin is developed, and part of the code of the RT3D module is modified to simulate changes in DOC concentrations in groundwater under different artificial recharge scenarios. The ultimate objective is to offer valuable insights into the effective management of water resources in the designated study region. The modified SWAT-MODFLOW-RT3D model simulates the variations of DOC concentration in groundwater under three artificial recharge scenarios, which are (a) recharged by Yellow River water; (b) recharged by Yangtze River water; and (c) recharged by Yangtze River and Yellow River water. The study shows that the main source of groundwater DOC in the basin is exogenous water. The distribution of DOC concentration in groundwater in the basin shows obvious spatial variations due to the influence of infiltration of surface water. The area near the upstream riverbank is the earliest to be affected. With the prolongation of the artificial recharge period, the DOC concentration in groundwater gradually rises from upstream to downstream, and from both sides of the riverbank to the surrounding area. By 2030, the maximum level of DOC in the basin will exceed 6.20 mg/l. The Yellow River water recharge scenario provides more groundwater recharge and less DOC input than the other two scenarios. The findings of this study indicate that particularly when recharge water supplies are enhanced with organic carbon, DOC concentrations in groundwater may alter dramatically during artificial recharge. This coupled modeling analysis is critical for assessing the impact of recharge water on groundwater quality to guide subsequent recharge programs.

LC-NeRF: Local Controllable Face Generation in Neural Radiance Field.

3D face generation has achieved high visual quality and 3D consistency thanks to the development of neural radiance fields (NeRF). However, these methods model the whole face as a neural radiance field, which limits the controllability of the local regions. In other words, previous methods struggle to independently control local regions, such as the mouth, nose, and hair. To improve local controllability in NeRF-based face generation, we propose LC-NeRF, which is composed of a Local Region Generators Module (LRGM) and a Spatial-Aware Fusion Module (SAFM), allowing for geometry and texture control of local facial regions. The LRGM models different facial regions as independent neural radiance fields and the SAFM is responsible for merging multiple independent neural radiance fields into a complete representation. Finally, LC-NeRF enables the modification of the latent code associated with each individual generator, thereby allowing precise control over the corresponding local region. Qualitative and quantitative evaluations show that our method provides better local controllability than state-of-the-art 3D-aware face generation methods. A perception study reveals that our method outperforms existing state-of-the-art methods in terms of image quality, face consistency, and editing effects. Furthermore, our method exhibits favorable performance in downstream tasks, including real image editing and text-driven facial image editing.

Protograph LDPC Code and Shaped Index Modulation Design for Multi-Mode OAM Systems

Protograph LDPC Code and Shaped Index Modulation Design for Multi-Mode OAM Systems

Resolving Range Ambiguity Using Transmit Coding Modulation and Multi-Pulse Processing in FPC-PA Radar

Resolving Range Ambiguity Using Transmit Coding Modulation and Multi-Pulse Processing in FPC-PA Radar

A large dataset for VEP based brain-computer interfaces employing narrow-band code modulation and frequency-phase modulation

A large dataset for VEP based brain-computer interfaces employing narrow-band code modulation and frequency-phase modulation

A Waveform Design for Integrated Radar and Jamming Based on Smart Modulation and Complementary Coding

A Waveform Design for Integrated Radar and Jamming Based on Smart Modulation and Complementary Coding

A machine-learning augmented mixed method for simulating α particle transport in inertial confinement fusion

The plasma heating by the α particle transport is a main self-heating source in inertial confinement fusion (ICF) that determines the capsule implosion performance. Due to the high energy of α particle and the high temperature in the ICF capsule hot spot, significant non-equilibrium effect exists and the continuum mechanics breaks down. For the numerical simulation of implosion and charged particle transport, the Boltzmann equation needs to be solved to capture the kinetic effects. However, the 7-dimensional Boltzmann equation, the highly frequent Coulomb collision, and the multi-folded integral stopping power formulation greatly limits the computational efficiency and challenges the computational power. To overcome the high computational cost of high-frequent Coulomb collisions, we propose a mixed collision model according to which the collisions are categorized into low-frequent large-angle collisions and high frequent small-angle grazing collisions. The large-angle collision process is precisely solved based on the Coulomb cross-section. For the highly frequent small-angle grazing, a statistic model is constructed with second-order accuracy in time. The mixed collision model reduces the computational cost of scattering calculation by two orders of magnitude. For the multi-folded integral stopping power formulation, a neural network is used to improve computational efficiency. Based on the proposed algorithm, we develop one-dimensional to three-dimensional module code to directly solve the α particle transport Boltzmann equation. The α transport module code is integrated into the multi-physics LARED-S program. The MC version ICF software is verified by a simulation study of the N210207 and N191110 experiment.

A Developed Polar Coded Generalized Frequency Division Multiplexing Based on Bit-Interleaved Coded Modulation

Due to the tremendous development in the wireless communication field and the wide requirements of current generations, such as high data rates, extremely low power consumption, and high performance, it is necessary to think about techniques and methods that present improvement to meet the growing requirements. The introduction of the Generalized Frequency Division Multiplexing (GFDM) technique was one of the proposed methods; it is one of the candidate schemes for the current generations and beyond. GFDM was presented as an improved method over other multiplexing techniques, as it provided an improvement in the field of Out Of Band (OOB) and Peak Average Power Ratio (PAPR). At the same time, it provided a lower performance in BER with respect to SNR. In this paper, methods of enhancement were presented based on Matlab to improve the performance of the GFDM system, which was improved by adding a coding system based on Polar Coding (PC). The proposed method is presented to improve the performance of GFDM by using the Genetic Algorithm (GA) to improve the performance of the Pulse shaping Filter (PSF) in GFDM. The channel estimation is accomplished by means of the LS method, and the Artificial Neural Network (ANN) based on feed-forward backpropagation is used to enhance the estimation process. The proposed Genetic Algorithm Artificial Neural Network Polar Code Interleaver GFDM (GNPI-GFDM) presents an enhancement over the traditional GFDM of more than 3.5dB at BER=10-4over the Rayleigh fading channel

6G communication systems: concept, trends, physical level technologies

A review and analysis of scientific papers published to date on various aspects of 6G is presented. The emphasis is on physical layer technologies, since they are the ones that largely form the basis and determine the potential of the future system. These include multiple access, multi-user multi-antenna systems, reconfigurable reflective surfaces, and modulation and channel coding techniques. In addition, promising application scenarios, new services and services, predicted requirements, concepts of 6G network architecture, the role of artificial intelligence and machine learning, as well as new frequency ranges are considered.

MTL-TRANSFER: Leveraging Multi-task Learning and Transferred Knowledge for Improving Fault Localization and Program Repair

Fault localization (FL) and automated program repair (APR) are two main tasks of automatic software debugging. Compared with traditional methods, deep learning-based approaches have been demonstrated to achieve better performance in FL and APR tasks. However, the existing deep learning-based FL methods ignore the deep semantic features or only consider simple code representations. And for APR tasks, existing template-based APR methods are weak in selecting the correct fix templates for more effective program repair, which are also not able to synthesize patches via the embedded end-to-end code modification knowledge obtained by training models on large-scale bug-fix code pairs. Moreover, in most of FL and APR methods, the model designs and training phases are performed separately, leading to ineffective sharing of updated parameters and extracted knowledge during the training process. This limitation hinders the further improvement in the performance of FL and APR tasks. To solve the above problems, we propose a novel approach called MTL-TRANSFER, which leverages a multi-task learning strategy to extract deep semantic features and transferred knowledge from different perspectives. First, we construct a large-scale open-source bug datasets and implement 11 multi-task learning models for bug detection and patch generation sub-tasks on 11 commonly used bug types, as well as one multi-classifier to learn the relevant semantics for the subsequent fix template selection task. Second, an MLP-based ranking model is leveraged to fuse spectrum-based, mutation-based and semantic-based features to generate a sorted list of suspicious statements. Third, we combine the patches generated by the neural patch generation sub-task from the multi-task learning strategy with the optimized fix template selecting order gained from the multi-classifier mentioned above. Finally, the more accurate FL results, the optimized fix template selecting order, and the expanded patch candidates are combined together to further enhance the overall performance of APR tasks. Our extensive experiments on widely-used benchmark Defects4J show that MTL-TRANSFER outperforms all baselines in FL and APR tasks, proving the effectiveness of our approach. Compared with our previously proposed FL method TRANSFER-FL (which is also the state-of-the-art statement-level FL method), MTL-TRANSFER increases the faults hit by 8/11/12 on Top-1/3/5 metrics (92/159/183 in total). And on APR tasks, the number of successfully repaired bugs of MTL-TRANSFER under the perfect localization setting reaches 75, which is 8 more than our previous APR method TRANSFER-PR. Furthermore, another experiment to simulate the actual repair scenarios shows that MTL-TRANSFER can successfully repair 15 and 9 more bugs (56 in total) compared with TBar and TRANSFER, which demonstrates the effectiveness of the combination of our optimized FL and APR components.

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder.

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.

A Road Crack Segmentation Method Based on Transformer and Multi-Scale Feature Fusion

To ensure the safety of vehicle travel, the maintenance of road infrastructure has become increasingly critical, with efficient and accurate detection techniques for road cracks emerging as a key research focus in the industry. The development of deep learning technologies has shown tremendous potential in improving the efficiency of road crack detection. While convolutional neural networks have proven effective in most semantic segmentation tasks, overcoming their limitations in road crack segmentation remains a challenge. To address this, this paper proposes a novel road crack segmentation network that leverages the powerful spatial feature modeling capabilities of Swin Transformer and the Encoder–Decoder architecture of DeepLabv3+. Additionally, the incorporation of a multi-scale coding module and attention mechanism enhances the network’s ability to densely fuse multi-scale features and expand the receptive field, thereby improving the integration of information from feature maps. Performance comparisons with current mainstream semantic segmentation models on crack datasets demonstrate that the proposed model achieves the best results, with an MIoU of 81.06%, Precision of 79.95%, and F1-score of 77.56%. The experimental results further highlight the model’s superior ability in identifying complex and irregular cracks and extracting contours, providing guidance for future applications in this field.

A Novel OFDM Approach Using Error Correcting Codes and Continuous Phase Modulation for Underwater Acoustic Communication

A Novel OFDM Approach Using Error Correcting Codes and Continuous Phase Modulation for Underwater Acoustic Communication

PWD’S Learning App

The goal of the proposed project is to create a mobile application specifically designed to meet the educational needs of people with impairments, with an emphasis on pupils who are blind or deaf. The program consists of two modules: a Basics of Coding module for deaf users and an English Learning module for blind users. The English module includes gamification features like levels and streak systems to increase student engagement, a voice-controlled interface to enhance interaction, and customized learning algorithms to modify the complexity of the lessons based on performance. In contrast, the Coding module makes use of a visual user interface that is accessible to students who are deaf. It does this by teaching coding principles through visually appealing material and by integrating game-like elements and individualized learning algorithms. Prioritizing inclusiveness and accessibility, both modules guarantee a flawless mobile user experience. To improve the learning process, additional features like progress monitoring and user profiles are included. Based on user input, the program will be updated and improved continuously to ensure that it continues to be relevant and successful in fostering diversity and skill development in education. Keywords— PWD, Gamified Education, Hearing Listening and Blind problem.

Engineering Ribosomal Machinery for Noncanonical Amino Acid Incorporation.

The introduction of noncanonical amino acids into proteins has enabled researchers to modify fundamental physicochemical and functional properties of proteins. While the alteration of the genetic code, via the introduction of orthogonal aminoacyl-tRNA synthetase:tRNA pairs, has driven many of these efforts, the various components involved in the process of translation are important for the development of new genetic codes. In this review, we will focus on recent advances in engineering ribosomal machinery for noncanonical amino acid incorporation and genetic code modification. The engineering of the ribosome itself will be considered, as well as the many factors that interact closely with the ribosome, including both tRNAs and accessory factors, such as the all-important EF-Tu. Given the success of genome re-engineering efforts, future paths for radical alterations of the genetic code will require more expansive alterations in the translation machinery.

High-Level Programming of FPGA-Accelerated Systems with Parallel Patterns

As a result of frequency and power limitations, multi-core processors and accelerators are becoming more and more prevalent in today’s systems. To fully utilize such systems, heterogeneous parallel programming is needed, but this introduces new complexities to the development. High-level frameworks such as SkePU have been introduced to help alleviate these complexities. SkePU is a skeleton programming framework based on a set of programming constructs implementing computational parallel patterns, while presenting a sequential interface to the programmer. Using the various skeleton backends, SkePU programs can execute, without source code modification, on multiple types of hardware such as CPUs, GPUs, and clusters. This paper presents the design and implementation of a new backend for SkePU, adding support for FPGAs. We also evaluate the effect of FPGA-specific optimizations in the new backend and compare it with the existing GPU backend, where the actual devices used are of similar vintage and price point. For simple examples, we find that the FPGA-backend’s performance is similar to that of the existing backend for GPUs, while it falls behind in more complex tasks. Finally, some shortcomings in the backend are highlighted and discussed, along with potential solutions.

NetHD: Neurally Inspired Integration of Communication and Learning in Hyperspace

The 6G network, the next‐generation communication system, is envisaged to provide unprecedented experience through hyperconnectivity involving everything. The communication should hold artificial intelligence‐centric network infrastructures as interconnecting a swarm of machines. However, existing network systems use orthogonal modulation and costly error correction code; they are very sensitive to noise and rely on many processing layers. These schemes impose significant overhead on low‐power internet of things devices connected to noisy networks. Herein, a hyperdimensional network‐based system, called , is proposed, which enables robust and efficient data communication/learning. exploits a redundant and holographic representation of hyperdimensional computing (HDC) to design highly robust data modulation, enabling two functionalities on transmitted data: 1) an iterative decoding method that translates the vector back to the original data without error correction mechanisms, or 2) a native hyperdimensional learning technique on transmitted data with no need for costly data decoding. A hardware accelerator that supports both data decoding and hyperdimensional learning using a unified accelerator is also developed. The evaluation shows that provides a bit error rate comparable to that of state‐of‐the‐art modulation schemes while achieving 9.4 faster and 27.8 higher energy efficiency compared to state‐of‐the‐art deep learning systems.