Decoding Method Research Articles

Adaptive HD-sEMG decomposition: towards robust real-time decoding of neural drive.

Objective. Neural interfacing via decomposition of high-density surface electromyography (HD-sEMG) should be robust to signal non-stationarities incurred by changes in joint pose and contraction intensity.Approach. We present an adaptive real-time motor unit decoding algorithm and test it on HD-sEMG collected from the extensor carpi radialis brevis during isometric contractions over a range of wrist angles and contraction intensities. The performance of the algorithm was verified using high-confidence benchmark decompositions derived from concurrently recorded intramuscular electromyography.Main results. In trials where contraction conditions between the initialization and testing data differed, the adaptive decoding algorithm maintained significantly higher decoding accuracies when compared to static decoding methods.Significance. Using "gold standard" verification techniques, we demonstrate the limitations of filter re-use decoding methods and show the necessity of parameter adaptation to achieve robust neural decoding.

Improved genetic algorithm for solving the total weight tardiness job shop scheduling problem

An improved genetic algorithm is proposed for the Job Shop Scheduling Problem with Minimum Total Weight Tardiness (JSSP/TWT). In the proposed improved genetic algorithm, a decoding method based on the Minimum Local Tardiness (MLT) rule of the job is proposed by using the commonly used chromosome coding method of job numbering, and a chromosome recombination operator based on the decoding of the MLT rule is added to the basic genetic algorithm flow. As a way to enhance the quality of the initialized population, a non-delay scheduling combined with heuristic rules for population initialization. and a PiMX (Precedence in Machine crossover) crossover operator based on the priority of processing on the machine is designed. Comparison experiments of simulation scheduling under different algorithm configurations are conducted for randomly generated larger scale JSSP/TWT. Statistical analysis of the experimental evidence indicates that the genetic algorithm based on the above three improvements exhibits significantly superior performance for JSSP/TWT solving: faster convergence and better scheduling solutions can be obtained.

Several Efficient Alternative Solutions for RS Codes in RAID 6

The RAID-6 specification requires a storage system with multiple storage devices to be able to endure the failure of any two parities. RAID-6 can be implemented using a variety of erasure coding methods, but each has drawbacks, like low fault tolerance, bad writing performance low decoding speed, and so on. In this paper, we research a variety of materials, explore RAID 6 disks to comprehend their purpose and prerequisites, and understand how to decode RS codes. In order to help people find a decoding method that is more appropriate to their own systems and actual needs, we research and compare numerous high-quality alternating decoding techniques, and the RAID 6 Liberation codes and Row-Diagonal Parity emerge as the winners. We introduce their decoding methods briefly and list their advantages and disadvantages in this paper for people to request based on their needs. Finally, we compare different decoding methods, propose practical application scenarios and future development directions for these more efficient decoding methods, and also point out their existing shortcomings.

Affective Prompt-Tuning-Based Language Model for Semantic-Based Emotional Text Generation

The large language models based on transformers have shown strong text generation ability. However, due to the need for significant computing resources, little work has been done to generate emotional text using language models such as GPT-2. To address this issue, the authors proposed an affective prompt-tuning-based language model (APT-LM) equipped with an affective decoding (AD) method, aiming to enhance emotional text generation with limited computing resources. In detail, the proposed model incorporates the emotional attributes into the soft prompt by using the NRC emotion intensity lexicon and updates the additional parameters while freezing the language model. Then, it steers the generation toward a given emotion by calculating the cosine distance between the affective soft prompt and the candidate tokens generated by the language model. Experimental results show that the proposed APT-LM model significantly improves emotional text generation and achieves competitive performance on sentence fluency compared to baseline models across automatic evaluation and human evaluation.

Task sub-type states decoding via group deep bidirectional recurrent neural network

Decoding brain states under different cognitive tasks from functional magnetic resonance imaging (fMRI) data has attracted great attention in the neuroimaging filed. However, the well-known temporal dependency in fMRI sequences has not been fully exploited in existing studies, due to the limited temporal-modeling capacity of the backbone machine learning algorithms and rigid training sample organization strategies upon which the brain decoding methods are built. To address these limitations, we propose a novel method for fine-grain brain state decoding, namely, group deep bidirectional recurrent neural network (Group-DBRNN) model. We first propose a training sample organization strategy that consists of a group-task sample generation module and a multiple-scale random fragment strategy (MRFS) module to collect training samples that contain rich task-relevant brain activity contrast (i.e., the comparison of neural activity patterns between different tasks) and maintain the temporal dependency. We then develop a novel decoding model by replacing the unidirectional RNNs that are widely used in existing brain state decoding studies with bidirectional stacked RNNs to better capture the temporal dependency, and by introducing a multi-task interaction layer (MTIL) module to effectively model the task-relevant brain activity contrast. Our experimental results on the Human Connectome Project task fMRI dataset (7 tasks consisting of 23 task sub-type states) show that the proposed model achieves an average decoding accuracy of 94.7% over the 23 fine-grain sub-type states. Meanwhile, our extensive interpretations of the intermediate features learned in the proposed model via visualizations and quantitative assessments of their discriminability and inter-subject alignment evidence that the proposed model can effectively capture the temporal dependency and task-relevant contrast.

New Golay decoding method using auto-encoder and OSD

In this study, we explore the potential of leveraging machine learning techniques, specifically auto-encoders (AE), for the decoding of linear block codes. Our findings suggest that this approach can outperform the conventional ordered statistics decoding (OSD) method, especially in a Rayleigh fading channel environment. We have rigorously trained the AE under both additive white Gaussian noise and Rayleigh fading channel conditions to ensure robustness in its performance. The output of the AE is combined with the received vector in a suitable manner to perform OSD. Through our experiments, we demonstrate that this proposed decoding approach yields better results than the conventional OSD method in Rayleigh fading channel when we used (23,12) Golay code.

Evidence for Multiscale Multiplexed Representation of Visual Features in EEG.

Distinct neural processes such as sensory and memory processes are often encoded over distinct timescales of neural activations. Animal studies have shown that this multiscale coding strategy is also implemented for individual components of a single process, such as individual features of a multifeature stimulus in sensory coding. However, the generalizability of this encoding strategy to the human brain has remained unclear. We asked if individual features of visual stimuli were encoded over distinct timescales. We applied a multiscale time-resolved decoding method to electroencephalography (EEG) collected from human subjects presented with grating visual stimuli to estimate the timescale of individual stimulus features. We observed that the orientation and color of the stimuli were encoded in shorter timescales, whereas spatial frequency and the contrast of the same stimuli were encoded in longer timescales. The stimulus features appeared in temporally overlapping windows along the trial supporting a multiplexed coding strategy. These results provide evidence for a multiplexed, multiscale coding strategy in the human visual system.

A modified memetic algorithm with multi-operation precise joint movement neighbourhood structure for the assembly job shop scheduling problem

This paper presents an adaptive memetic algorithm based on a new neighbourhood structure (AMA) for solving the assembly job shop scheduling problem, with the aim of minimising the maximum completion time (makespan). To utilise the knowledge of problem, a theoretical analysis is conducted to explore the criteria for feasible and effective movement of operations under assembly constraints, and a multi-operation precise joint movement neighbourhood structure is proposed accordingly. In the AMA, to ensure the feasibility of solutions during the evolution process, a feasible encoding mechanism based on the constraint degree of operations is designed, a greedy active decoding method as well as feasible crossover operation based on independent operation chains are designed specifically for this encoding method. To avoid premature convergence of the population, a population update operator with diversity adaptive control is proposed. Finally, by comparing the results with five state-of-the-art algorithms, the superiority of AMA in terms of solution quality and stability is verified, particularly with the update of known optimal solutions for 11 instances.

Methods for decoding cortical gradients of functional connectivity

Abstract Macroscale gradients have emerged as a central principle for understanding functional brain organization. Previous studies have demonstrated that a principal gradient of connectivity in the human brain exists, with unimodal primary sensorimotor regions situated at one end and transmodal regions associated with the default mode network and representative of abstract functioning at the other. The functional significance and interpretation of macroscale gradients remains a central topic of discussion in the neuroimaging community, with some studies demonstrating that gradients may be described using meta-analytic functional decoding techniques. However, additional methodological development is necessary to fully leverage available meta-analytic methods and resources and quantitatively evaluate their relative performance. Here, we conducted a comprehensive series of analyses to investigate and improve the framework of data-driven, meta-analytic methods, thereby establishing a principled approach for gradient segmentation and functional decoding. We found that a two-segment solution determined by a k-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery database was the optimal combination of methods for decoding functional connectivity gradients. Finally, we proposed a method for decoding additional components of the gradient decomposition. The current work aims to provide recommendations on best practices and flexible methods for gradient-based functional decoding of fMRI data.

A Reinforcement-Learning-Based 3-D Estimation of Distribution Algorithm for Fuzzy Distributed Hybrid Flow-Shop Scheduling Considering On-Time-Delivery.

With the increasing level of mass-customization and globalization of competition, environmentally friendly production scheduling for distributed manufacturing considering customer satisfaction has received growing attention. Meanwhile, uncertain scheduling is becoming a force to be considered within intelligent manufacturing industries. However, little research has been found that surveyed the uncertain distributed scheduling considering both energy consumption and customer satisfaction. In this article, the fuzzy distributed hybrid flow-shop scheduling problem considering on-time delivery (FDHFSP-OTD) is addressed, and a 3-D estimation of distribution algorithm (EDA) with reinforcement learning (RL) is proposed to minimize the makespan and total energy consumption while maximizing delivery accuracy. First, two heuristics and a random method are designed and used cooperatively for initialization. Next, an EDA with a 3-D probability matrix is innovated to generate offspring. Then, a biased decoding method based on Q -learning is proposed to adjust the direction of evolution self-adaptively. Moreover, a local intensification strategy is employed for further enhancement of elite solutions. The effect of major parameters is analyzed and the best combination of values is determined through extensive experiments. The numerical results prove the effectiveness of each specially designed strategy and method, and the comparisons with existing algorithms demonstrate the high-potential of the 3D-EDA/RL in solving the FDHFSP-OTD.

An EEG motor imagery dataset for brain computer interface in acute stroke patients

The brain-computer interface (BCI) is a technology that involves direct communication with parts of the brain and has evolved rapidly in recent years; it has begun to be used in clinical practice, such as for patient rehabilitation. Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. We collected data from 50 acute stroke patients with wireless portable saline EEG devices during the performance of two tasks: 1) imagining right-handed movements and 2) imagining left-handed movements. The dataset consists of four types of data: 1) the motor imagery instructions, 2) raw recording data, 3) pre-processed data after removing artefacts and other manipulations, and 4) patient characteristics. This is the first open dataset to address left- and right-handed motor imagery in acute stroke patients. We believe that the dataset will be very helpful for analysing brain activation and designing decoding methods that are more applicable for acute stroke patients, which will greatly facilitate research in the field of motor imagery-BCI.

Ensemble Learning Method for the Continuous Decoding of Hand Joint Angles.

Human-machine interface technology is fundamentally constrained by the dexterity of motion decoding. Simultaneous and proportional control can greatly improve the flexibility and dexterity of smart prostheses. In this research, a new model using ensemble learning to solve the angle decoding problem is proposed. Ultimately, seven models for angle decoding from surface electromyography (sEMG) signals are designed. The kinematics of five angles of the metacarpophalangeal (MCP) joints are estimated using the sEMG recorded during functional tasks. The estimation performance was evaluated through the Pearson correlation coefficient (CC). In this research, the comprehensive model, which combines CatBoost and LightGBM, is the best model for this task, whose average CC value and RMSE are 0.897 and 7.09. The mean of the CC and the mean of the RMSE for all the test scenarios of the subjects' dataset outperform the results of the Gaussian process model, with significant differences. Moreover, the research proposed a whole pipeline that uses ensemble learning to build a high-performance angle decoding system for the hand motion recognition task. Researchers or engineers in this field can quickly find the most suitable ensemble learning model for angle decoding through this process, with fewer parameters and fewer training data requirements than traditional deep learning models. In conclusion, the proposed ensemble learning approach has the potential for simultaneous and proportional control (SPC) of future hand prostheses.

Efficient Decoding of Large-Scale Neural Population Responses With Gaussian-Process Multiclass Regression.

Neural decoding methods provide a powerful tool for quantifying the information content of neural population codes and the limits imposed by correlations in neural activity. However, standard decoding methods are prone to overfitting and scale poorly to high-dimensional settings. Here, we introduce a novel decoding method to overcome these limitations. Our approach, the gaussian process multiclass decoder (GPMD), is well suited to decoding a continuous low-dimensional variable from high-dimensional population activity and provides a platform for assessing the importance of correlations in neural population codes. The GPMD is a multinomial logistic regression model with a gaussian process prior over the decoding weights. The prior includes hyperparameters that govern the smoothness of each neuron's decoding weights, allowing automatic pruning of uninformative neurons during inference. We provide a variational inference method for fitting the GPMD to data, which scales to hundreds or thousands of neurons and performs well even in data sets with more neurons than trials. We apply the GPMD to recordings from primary visual cortex in three species: monkey, ferret, and mouse. Our decoder achieves state-of-the-art accuracy on all three data sets and substantially outperforms independent Bayesian decoding, showing that knowledge of the correlation structure is essential for optimal decoding in all three species.

A coarse-to-fine adaptive spatial–temporal graph convolution network with residuals for motor imagery decoding from the same limb

In the field of Brain Computer Interface (BCI) technology, Motor Imagery (MI) plays an important role as a paradigm. One of the primary focuses of this research area lies in exploring the MI of various upper limbs. Decoding MI signals from distinct joints within the same limb poses more intricate challenges in comparison to decoding MI signals originating from different upper limbs. In order to explore more efficient decoding methods, we propose a novel coarse-to-fine classification approach to investigate categorical decoding across three tasks, namely ‘rest’, ‘hand’, and ‘elbow’. This approach consists of two classification stages performed from coarse to fine. In the coarse classification stage, in order to capture the features of both resting state and the moving state in the temporal domain, the EEGNet network with temporal domain convolution is used to extract temporal domain features and classify the original samples into categories of ‘rest’ and ‘move’ (‘hand’, ‘elbow’). In the fine classification stage, the samples of ‘move’ category are segmented by time to form a graph sequence. Then, an adaptive spatial–temporal graph convolutional network with residuals is utilized to extract both temporal and spatial domains’ features from the graph sequence. The proposed algorithm has been validated experimentally on the MI-2 dataset and compared with contemporary methods. Its classification performance is quantified by the average accuracy which achieves a value of 72.21%. Extensive experimental results indicate that the novel coarse-to-fine classification approach is superior to the single classification approach.

SONGS CONTINUATION GENERATION TECHNOLOGY BASED ON TEST GENERATION STRATEGIES, TEXTMINING AND LANGUAGE MODEL T5

Context. Pre-trained large language models are currently the driving force behind the development of not only NLP, but also deep learning systems in general. Model transformers are able to solve virtually all problems that currently exist, provided that certain requirements and training practices are met. In turn, words, sentences and texts are the basic and most important way of communication between intellectually developed beings. Of course, speech and texts are used to convey certain emotions, events, etc. One of the main ways of using language to describe experienced emotions is songs with lyrics. However, often due to the need to preserve rhyme and rhyming, the dimensions of verse lines, song structure, etc., artists have to use repetition of lines in the lyrics. In addition, the process of writing texts can be long.
 Objective of the study is to develop information technology for generating the continuation of song texts based on the T5 machine learning model with (SA, specific author) and without (NSA, non-specific author) consideration of the author's style.
 Method. Choosing a decoding strategy is important for the generation process. However, instead of favoring a particular strategy, the system will support multiple strategies. In particular, the following 8 strategies: Contrastive search, Top-p sampling, Top-k sampling, Multinomial sampling, Beam search, Diverse beam search, Greedy search, and Beam-search multinomial sampling.
 Results. A machine learning model was developed to generate the continuation of song lyrics using large language models, in particular the T5 model, to accelerate, complement and increase the flexibility of the songwriting process.
 Conclusions. The created model shows excellent results of generating the continuation of song texts on test data. Analysis of the raw data showed that the NSA model has less degrading results, while the SA model needs to balance the amount of text for each author. Several text metrics such as BLEU, RougeL and RougeN are calculated to quantitatively compare the results of the models and generation strategies. The value of the BLEU metric is the most variable, and its value varies significantly depending on the strategy. At the same time, Rouge metrics have less variability, a smaller range of values. For comparison, 8 different decoding methods for text generation, supported by the transformers library, were used. From all the results of the text comparison, it is clear that the metrically best method of song text generation is beam search and its variations, in particular beam sampling. Contrastive search usually outperformed the conventional greedy approach. The top-p and top-k methods are not clearly superior to each other, and in different situations gave different results.

Deep transfer learning-based SSVEP frequency domain decoding method

ObjectiveImproving the decoding accuracy and information transfer rate (ITR) of a steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) system and narrowing the inter-subject variance are key to the application of the SSVEP-BCI system. To this end, we proposed a deep transfer learning-based SSVEP frequency domain decoding method to improve the decoding performance. MethodsInput data representations with rich spatial and frequency domain features were extracted using filter bank and zero-padding-based fast Fourier transform techniques. A concise and efficient 3-dimensional convolutional neural network (3DCNN) model was designed for feature extraction and decoding of the input data. A transfer learning strategy was proposed to further improve the decoding accuracy and narrow the inter-subject variance. ResultsOur proposed 3DCNN achieves 89.35 % average classification accuracy and 173.02 bits/min ITR on the benchmark dataset with 1 s signal length. On our laboratory dataset, the average classification accuracy and ITR of 3DCNN reach 88.75 % and 120.33 bits/min, respectively, when the signal length is 0.6 s. ConclusionsIn this study, we experimentally confirmed the effectiveness and superiority of our proposed method for SSVEP decoding, which provides a promising decoding method for the application of SSVEP-BCI.

Integration of Motor Unit Filters for Enhanced Surface Electromyogram Decomposition During Varying Force Isometric Contraction.

Muscles generate varying levels of force by recruiting different numbers of motor units (MUs), and as the force increases, the number of recruited MUs gradually rises. However, current decoding methods encounter difficulties in maintaining a stable and consistent growth trend in MU numbers with increasing force. In some instances, an unexpected reduction in the number of MUs can even be observed as force intensifies. To address this issue, in this study, we propose an enhanced decoding method that adaptively reutilizes MU filters. Specifically, in addition to the normal decoding process, we introduced an additional procedure where MU filters are reused to initialize the algorithm. The MU filters are iterated and adapted to the new signals, aiming to decode motor units that were actually activated but cannot be identified due to heavy superimposition. We tested our method on both simulated and experimental surface electromyogram (sEMG) signals. We simulated isometric signals (10%-70%) with known MU firing patterns using experimentally recorded MU action potentials from forearm muscles and compared the decomposition results to two baseline approaches: convolution kernel compensation (CKC) and fast independent component analysis (fastICA). Our method increased the decoded MU number by a rate of 135.4% ± 62.5 % and 63.6% ± 20.2 % for CKC and fastICA, respectively, across different signal-to-noise ratios. The sensitivity and precision for MUs decomposed using the enhanced method remained at the same accuracy level (p <0.001) as those of normally decoded MUs. For the experimental signals, eight healthy subjects performed hand movements at five different force levels (10%-90%), during which sEMG signals were recorded and decomposed. The results indicate that the enhanced process increased the number of decoded MUs by 21.8% ± 10.9 % across all subjects. We discussed the possibility of fully capturing all activated motor units by appropriately reusing previously decoded MU filters and improving the balance of activated motor unit numbers across varying excitation levels.

Iterative Soft Decoding Algorithm for DNA Storage Using Quality Score and Redecoding.

Ever since deoxyribonucleic acid (DNA) was considered as a next-generation data-storage medium, lots of research efforts have been made to correct errors occurred during the synthesis, storage, and sequencing processes using error correcting codes (ECCs). Previous works on recovering the data from the sequenced DNA pool with errors have utilized hard decoding algorithms based on a majority decision rule. To improve the correction capability of ECCs and robustness of the DNA storage system, we propose a new iterative soft decoding algorithm, where soft information is obtained from FASTQ files and channel statistics. In particular, we propose a new formula for log-likelihood ratio (LLR) calculation using quality scores (Q-scores) and a redecoding method which may be suitable for the error correction and detection in the DNA sequencing area. Based on the widely adopted encoding scheme of the fountain code structure proposed by Erlich et al., we use three different sets of sequenced data to show consistency for the performance evaluation. The proposed soft decoding algorithm gives 2.3% ∼ 7.0% improvement of the reading number reduction compared to the state-of-the-art decoding method and it is shown that it can deal with erroneous sequenced oligo reads with insertion and deletion errors.

РАЗРАБОТКА КОНЦЕПТУАЛЬНОЙ ОСНОВЫ ИНТЕЛЛЕКТУАЛЬНОГО ЗДАНИЯ НА ОСНОВЕ МЕТОДА ТРИАДИЧЕСКОЙ ДЕШИФРОВКИ: ЭВОЛЮЦИОННЫЙ ПОДХОД

The study focuses on the concept development of the architectural and structural design of intelligent (smart) buildings, which have been of increasing research interest in recent years. The hypothesis of the study was the assumption that smart building was an evolution of green building. Evolutionary approach, single-level and two-level triadic decoding of system-categorial methodology, general scientific and graphic methods helped us to solve the following tasks: 1) to identify the components of the "intellectual building" definition content and reflecting its essential features; 2) to structure the main components (engineering and technical equipment, personification of space, environmental friendliness and resource saving) in the sequence of their formation in the object. The definition and structuring of the main components of an intelligent building by the method of triadic decoding allowed us to expand the idea of its essence, gave a better insight into the conceptual foundations of architectural and construction design of intelligent buildings and outlined the preconditions for its further development.

Dynamic decoding and dual synthetic data for automatic correction of grammar in low-resource scenario.

Grammar error correction systems are pivotal in the field of natural language processing (NLP), with a primary focus on identifying and correcting the grammatical integrity of written text. This is crucial for both language learning and formal communication. Recently, neural machine translation (NMT) has emerged as a promising approach in high demand. However, this approach faces significant challenges, particularly the scarcity of training data and the complexity of grammar error correction (GEC), especially for low-resource languages such as Indonesian. To address these challenges, we propose InSpelPoS, a confusion method that combines two synthetic data generation methods: the Inverted Spellchecker and Patterns+POS. Furthermore, we introduce an adapted seq2seq framework equipped with a dynamic decoding method and state-of-the-art Transformer-based neural language models to enhance the accuracy and efficiency of GEC. The dynamic decoding method is capable of navigating the complexities of GEC and correcting a wide range of errors, including contextual and grammatical errors. The proposed model leverages the contextual information of words and sentences to generate a corrected output. To assess the effectiveness of our proposed framework, we conducted experiments using synthetic data and compared its performance with existing GEC systems. The results demonstrate a significant improvement in the accuracy of Indonesian GEC compared to existing methods.