Digit Strings Research Articles

Handwritten digit and Roman string recognition using gated CNN with federated learning

Handwritten digit and Roman string recognition using gated CNN with federated learning

Age Impacts Speech-in-Noise Recognition Differently for Nonnative and Native Listeners.

The purpose of this study was to explore potential differences in suprathreshold auditory function among native and nonnative speakers of English as a function of age. Retrospective analyses were performed on three large data sets containing suprathreshold auditory tests completed by 5,572 participants who were self-identified native and nonnative speakers of English between the ages of 18-65 years, including a binaural tone detection test, a digit identification test, and a sentence recognition test. The analyses show a significant interaction between increasing age and participant group on tests involving speech-based stimuli (digit strings, sentences) but not on the binaural tone detection test. For both speech tests, differences in speech recognition emerged between groups during early adulthood, and increasing age had a more negative impact on word recognition for nonnative compared to native participants. Age-related declines in performance were 2.9 times faster for digit strings and 3.3 times faster for sentences for nonnative participants compared to native participants. This set of analyses extends the existing literature by examining interactions between aging and self-identified native English speaker status in several auditory domains in a cohort of adults spanning young adulthood through middle age. The finding that older nonnative English speakers in this age cohort may have greater-than-expected deficits on speech-in-noise perception may have clinical implications on how these individuals should be diagnosed and treated for hearing difficulties.

On usage of an end-to-end deep neural architecture for handwritten digit string recognition

On usage of an end-to-end deep neural architecture for handwritten digit string recognition

Evidence that proactive distractor suppression does not require attentional resources.

Does the suppression of irrelevant visual features require attentional resources? McDonald et al. (2023, Psychonomic Bulletin & Review, 30, 224-234) proposed that suppression processes are unavailable while a person is busy performing another task. They reported the absence of the PD (believed to index suppression) when two tasks were presented close together in time. We looked for converging evidence using established behavior measures of suppression. Following McDonald et al., our participants performed a rapid serial visual presentation (RSVP) task followed by a search task. For the RSVP task, participants determined whether the target digit 4 or 6 appeared within a string of other digits. The search display appeared at a lag of 2 or 8 digits after the RSVP target. Participants searched for a yellow target circle amongst nine background circles, which included a uniquely colored distractor for some trials. The main question was whether distractor suppression would occur at Lag 2, while attentional resources were still processing the RSVP target. Suppression was assessed using the capture-probe paradigm. On 30% of trials, probe letters appeared inside the colored circles and participants reported those letters. Probe recall accuracy was lower at locations with distractor colors than those with neutral colors (the baseline), suggesting proactive suppression. Critically, this difference in probe recall accuracy was similar at Lag 2 and Lag 8, suggesting that the ability to proactively suppress distractors remains intact while dual-tasking. We argue that although reactive suppression likely requires attentional resources, proactive suppression-an implicit process-does not.

Top-Down Number Reading: Language Affects the Visual Identification of Digit Strings.

Reading numbers aloud involves visual processes that analyze the digit string and verbal processes that produce the number words. Cognitive models of number reading assume that information flows from the visual input to the verbal production processes-a feed-forward processing mode in which the verbal production depends on the visual input but not vice versa. Here, I show that information flows also in the opposite direction, from verbal production to the visual input processes. Participants read aloud briefly presented multi-digit strings in Hebrew, in which the order of words is congruent with the order of digits (21 = twenty-and-one), and in Arabic, in which the ones word precedes the tens word (one-and-twenty). The error-by-digit-position curve was affected by language: relative to Hebrew, in Arabic the error rate was slightly lower for the unit digit and slightly higher for the decade digit, indicating that in Arabic the unit digit was processed earlier and the decade digit later, in accord with the Arabic word order. This language-dependent processing order originated in the visual level and was not a verbal confound, because it persisted even when I controlled for the serial position of the decade/unit word in the verbal number by using numbers with 0 (two hundred three/two hundred thirty). I conclude that the visual analyzer's digit scanning order, decade-first or unit-first, is not fixed but affected by the language in which the number is produced-a top-down, verbal-to-visual information flow.

Residue Number System (RNS) Sequence Comparison and Mutation (Error) Analysis

The genetic code may be generated using the Residue Number System (RNS). This achievement demonstrates the viability of protein synthesis. The genetic codes occupy RNS digits and enable sequence comparison as well as further analysis of biological flaws known as mutations. In this research work two mRNA reading frames (strings) are considered as strings of RNS digits. Triplets of these residue number digits constitutes an amino acids and chains of these triplets make up polypeptides, proteins. These digits are compared and single digits errors introduced, these errors mimic biological errors (mutations). The types of errors (mutations) that are manifested between the two reading frames, original and mutant, are detected and reported.

Handwritten Digit Recognition using Machine Learning with Python

Handwritten Digit Recognition is one of the essentially significant issues in pattern recognition applications. The main purpose of this project is to build an automatic handwritten digit recognition method for the recognition of handwritten digit strings. This paper proposes a simple convolution neural network approach to handwritten digit recognition. Convolutional Neural Network model is implemented using MNIST dataset. This dataset consists 60,000 small square 28×28pixel grayscale images of handwritten single digits between 0 and 9. The applications of digit recognition include postal mail sorting, check processing, form data entry, etc. The core of the issue exists in the capacity to foster a proficient calculation that can perceive manually written digits and which is put together by clients by the method of a scanner, tablet, and other computerized gadgets.

Deconfounding serial recall: Response timing and the overarching role of grouping.

We used the timing of serial recall in several situations to reveal important aspects of recall groupings that participants construct and the reasons those groupings occur. We examined the timing of responses in the recall of digit strings within two published experiments. Cowan, Saults, Elliott, and Moreno (2002) examined memory for nine-item lists in a way that deconfounded the presentation modality, input versus output serial position (using a varied starting point of recall), memory load from items not yet recalled (using whole vs. partial recall), and the presence or absence of the temporal grouping of the lists into triads. Accuracy was strikingly different in the two modalities, with grouping drastically changing recall of acoustic lists but with little difference between grouped versus ungrouped visual lists. Despite the modality difference in recall, the present timing results show that participants imposed a similar grouped structure in recall for both modalities. When errors were made, the original grouping structure still was maintained in the output timing. Cowan et al. (2005, Experiment 1) presented ungrouped acoustic lists of varying lengths in a span task. In this case, there was never a grouping structure presented to the participant and the accuracy data did not reveal evidence of grouping, but we show clear grouping in the timing structure. This grouping structure varied with both the list length and an individual's span. Timing in both experiments shows that a grouping structure is imposed on responses in a manner that is dependent on several factors not present in the stimuli. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Connecting slow solutions to nested recurrences with linear recurrent sequences

Labeled infinite trees provide combinatorial interpretations for many integer sequences generated by nested recurrence relations. Typically, such sequences are monotone increasing. Several of these sequences also have straightforward descriptions in terms of how often each value in the sequence occurs. In this paper, we generalize the most classical examples to a larger family of sequences parametrized by linear recurrence relations. Each of our sequences can be constructed in three different ways: via a nested recurrence relation, from labeled infinite trees, or by using Zeckendorf-like strings of digits to describe its frequency sequence. We conclude the paper by discussing the asymptotic behaviors of our sequences.

A tape-reading molecular ratchet.

Cells process information in a manner reminiscent of a Turing machine1, autonomously reading data from molecular tapes and translating it into outputs2,3. Randomly processive macrocyclic catalysts that can derivatise threaded polymers have been described4,5, as have rotaxanes that transfer building blocks in sequence from a molecular strand to a growing oligomer6-10. However, synthetic small-molecule machines that can read and/or write information stored on artificial molecular tapes remain elusive11-13. Here we report on a molecular ratchet in which a crown ether (the 'reading head') is pumped from solution onto an encoded molecular strand (the 'tape') by a pulse14,15 of chemical fuel16. Further fuel pulses transport the macrocycle through a series of compartments of the tape via an energy ratchet14,17-22 mechanism, before releasing it back to bulk off the other end of the strand. During its directional transport, the crown ether changes conformation according to the stereochemistry of binding sites along the way. This allows the sequence of stereochemical information programmed into the tape to be read out as a string of digits in a non-destructive manner through a changing circular dichroism response. The concept is exemplified by the reading of molecular tapes with strings of balanced ternary digits ('trits'23), -1,0,+1 and -1,0,-1. The small-molecule ratchet is a finite-state automaton: a special case24 of a Turing machine that moves in one direction through a string-encoded state sequence, giving outputs dependent on the occupied machine state25,26. It opens the way for the reading-and ultimately writing-of information using the powered directional movement of artificial nanomachines along molecular tapes.

An Order-Preserving Encryption Scheme Based on Weighted Random Interval Division for Ciphertext Comparison in Wearable Systems.

With the rapid development of wearable devices with various sensors, massive sensing data for health management have been generated. This causes a potential revolution in medical treatments, diagnosis, and prediction. However, due to the privacy risks of health data aggregation, data comparative analysis under privacy protection faces challenges. Order-preserving encryption is an effective scheme to achieve private data retrieval and comparison, but the existing order-preserving encryption algorithms are mainly aimed at either integer data or single characters. It is urgent to build a lightweight order-preserving encryption scheme that supports multiple types of data such as integer, floating number, and string. In view of the above problems, this paper proposes an order-preserving encryption scheme (WRID-OPES) based on weighted random interval division (WRID). WRID-OPES converts all kinds of data into hexadecimal number strings and calculates the frequency and weight of each hexadecimal number. The plaintext digital string is blocked and recombined, and each block is encrypted using WRID algorithm according to the weight of each hexadecimal digit. Our schemes can realize the order-preserving encryption of multiple types of data and achieve indistinguishability under ordered selection plaintext attack (IND-OCPA) security in static data sets. Security analysis and experiments show that our scheme can resist attacks using exhaustive methods and statistical methods and has linear encryption time and small ciphertext expansion rate.

Detecting the Same Pattern in Choreography Balinese Dance Using Convolutional Neural Network and Analysis Suffix Tree

The Balinese dances that are popular today were created by maestros who have existed since time immemorial. To develop the dances made by the existing maestro, one must know the characteristics of each dance based on the motion used. The help of digital image processing and string algorithm analysis methods will help to determine the characteristics of a dance. The algorithm used for dance analysis is the Suffix Tree, where the suffix tree is one of the algorithms that can be used to find patterns from input strings. The string to be analyzed is a series of codes performed by the classifier. The classifier used is Convolutional Neural Network. This method uses an image as its input, which will later perform convolution operations and perform a full-connected layer. The results were obtained using the Convolutional Neural Network method with Alexnet architecture as the classification and confusion matrix to calculate the level of accuracy of the test set, the best accuracy for the head is by using parameter learning rate 0.001, epoch 150, and RGB color space obtained 95% accuracy, 88% precision, 78% recall, and 82% f1-score. For the full body, using a learning rate of 0.01, epoch 150, and RGB color space, the accuracy is 85%, precision is 79%, recall is 64%, and f1-score is 69%. For the legs, using a learning rate of 0.001, epoch 150, and RGB color space, the accuracy is 92%, precision is 84%, recall is 59%, and f1-score is 65%. The results of the suffix tree analysis between codes that use ground truth and classification results have similar values, although the results of the movement patterns obtained by the suffix tree algorithm have not varied, which is dominated by class A because class A is the dominant class in each dance.

Transcoding counts: Longitudinal contribution of number writing to arithmetic in different languages

Number writing involves transcoding from number words (e.g., “thirty-two”) to written digit strings (32) and is an important unique predictor of arithmetic. The existing longitudinal evidence about the relation between transcoding and arithmetic is mostly language specific. In languages with number word inversion (e.g., German), the order of tens and units is transposed in spoken number words compared with Arabic numbers. This makes transcoding more challenging than in languages without number word inversion (e.g., English). In the current study, we aimed to understand whether the contribution of number writing to the development of arithmetic is similar in languages with and without number word inversion. German-speaking children (n = 166) and English-speaking children (n = 201) were followed over the first 3 years of primary school. In a series of multiple linear regressions, we tested whether number writing of multi-digit numbers was a significant unique predictor of arithmetic after controlling for well-known non-numerical predictors (nonverbal reasoning and working memory) and numerical predictors (symbolic and nonsymbolic magnitude comparison). Number writing in Grade 1 predicted arithmetic in Grades 1, 2, and 3 over and above the other predictors. Crucially, number writing performance was of comparable importance for arithmetic development in German- and English-speaking children. Our findings extend previous evidence by showing that transcoding predicts the development of arithmetic skills during the first 3 years of primary school in languages with and without number word inversion.

Aging effects on listening effort in cochlear-implant users

Cochlear-implant (CI) users often report that listening to speech can be exhausting. Greater listening effort can occur with a degraded acoustic signal even in individuals with normal hearing, but age-related changes in auditory processing and individual differences in cognitive abilities may exacerbate the effort required to process a degraded signal. Therefore, we examined the effects of aging and individual differences in cognitive abilities on listening effort (measured with pupillometry) in two experiments. In the first experiment, we measured how changes in signal degradation (spectral resolution) affect listening effort in younger and older adults with normal hearing recalling CI-simulated (vocoded) sentences. In the second experiment, we examined how memory load and cognitive abilities (individual differences in working memory scores) affect listening effort in CI users recalling digit strings. It was hypothesized that increased effort would be observed with greater signal degradation, memory load, and age, and that individual differences would relate to working memory scores. Preliminary results are consistent with the hypothesized effects. These results support the clinical relevance of listening effort in older CI users and provide motivation for development of clinical practices to reduce listening effort in this population.

Neural Acoustic-Phonetic Approach for Speaker Verification With Phonetic Attention Mask

Traditional acoustic-phonetic approach makes use of both spectral and phonetic information when comparing the voice of speakers. While phonetic units are not equally informative, the phonetic context of speech plays an important role in speaker verification (SV). In this paper, we propose a neural acoustic-phonetic approach that learns to dynamically assign differentiated weights to spectral features for SV. Such differentiated weights form a phonetic attention mask (PAM). The neural acoustic-phonetic framework consists of two training pipelines, one for SV and another for speech recognition. Through the PAM, we leverage the phonetic information for SV. We evaluate the proposed neural acoustic-phonetic framework on the RSR2015 database Part III corpus, that consists of random digit strings. We show that the proposed framework with PAM consistently outperforms baseline with an equal error rate reduction of 13.45% and 10.20% for female and male data, respectively.

Performance analysis of DP-MZM radio over fiber links against fiber impairments

Abstract In this research article, the radio over fiber (RoF) Architecture using dual-parallel Mach–Zehnder modulator (DP-MZM) is analyzed and simulated. The nonlinearity is the main issues which degrade the performance of the RoF links. Using DP-MZM modulators, the nonlinearity can be reducing in the significant amount by controlling the bias voltages. The DP-MZM consists of two MZM modulators in parallel form with the variations of extinction ratio (ER). The proposed RoF link operated with single tone frequency signal at 10 GHz and the optical signal is transmitted further on Single mode optical fiber. The electrical spectrum at the output of photodetector confirms the quality of received signal while having desired and undesired terms. The proposed structure is designed using Optical Simulator simulation Software to confirm and validate the proposed results. The simulation output shows that the RF power of DP-MZM based RoF link has been found a decrement of 6.95 dB corresponding to undesired components with respect to desired RF signal for the increment in fiber length from 10 to 30 km. The digital transmission RoF link model is proposed and optimized for fiber impairments for 2–30 km, using 10 Gbps digital data string. electrical spectrum analyzer shows that very good eye openings for different fiber impairments, its shoes that proposed RoF model is optimized and having good SNR.

Facilitating compliance with BIM ISO 19650 naming convention through automation

PurposeTo foster effective implementation of building information modelling (BIM), it is guided by standards and protocols that require files naming in a format, containing a string of letters and digits in a tightly defined manner, which is perceived to be time-consuming, error-prone and serves as a barrier to BIM adoption. This paper aims to present a BIM-based plug-in solution (Auto-BIMName) that facilitates automated naming in compliance with BIM standards.Design/methodology/approachThe Auto-BIMName portal has an information management system (IMS) for generating a master information delivery plan (MIDP), which serves as pre-requisites to effective file naming. Once the naming schema is implemented through text input controls for a project name, volume, level and number, the Revit plugin communicates with its IMS to fetch the name string or concatenate the string in line with the ISO 19650 convention, where the IMS is unused. The system was validated through a simulated collaborative project.FindingsSystem testing and evaluation confirmed that the Auto-BIMName will ease the process of file naming, thereby facilitating collaboration efficiency, naming consistency across project teams and lifecycle stages, ease of file naming, time-saving and inducement for BIM implementation, etc. By linking information from MIDP in the BIM execution plan, the platform enhanced information management processes and improved coordination across project teams and lifecycle stages.Originality/valueApart from demonstrating how the automated naming platform enhances project performance, information management and coordination, the paper provides a practical demonstration of how the construction industry will benefit from enhanced digitalisation and process automation.

Visual attention span and phonological skills in Chinese developmental dyslexia

BackgroundIt has been debated whether visual attention span deficit was independent from phonological deficit in alphabetic developmental dyslexia. Yet, this issue has never been examined in Chinese developmental dyslexia. AimThe aim of the present study was to concurrently investigate visual attention span deficit and phonological deficit in Chinese developmental dyslexia, and examine the relationship between them. MethodsA total of 45 Chinese dyslexic and 43 control children aged between 8 and 11 years old participated in this study. A visual one-back paradigm with both verbal stimuli (character and digit strings) and nonverbal stimuli (color dots and symbols) was employed for measuring visual attention span. Phonological skills were measured by three dimensions: phonological awareness, rapid automatized naming, and verbal short-term memory. ResultsChinese dyslexic children showed deficits in verbal visual attention span and all three dimensions of phonological skills, but not in nonverbal visual attention span. Phonological skills significantly contributed to explaining variance of reading skills and classifying dyslexic and control memberships. Almost all Chinese dyslexic participants who showed a deficit in visual attention span also showed a phonological deficit. ConclusionThe study suggests that visual attention span deficit is not independent from phonological deficit in Chinese developmental dyslexia.

Keep it simple: Identification of basic versus complex emotions in spoken language in individuals with autism spectrum disorder without intellectual disability: A meta-analysis study.

Daily functioning involves identifying emotions in spoken language, a fundamental aspect of social interactions. To date, there is inconsistent evidence in the literature on whether individuals with autism spectrum disorder without intellectual disability (ASD-without-ID) experience difficulties in identification of spoken emotions. We conducted a meta-analysis (literature search following the PRISMA guidelines), with 26 data sets (taken from 23 peer-reviewed journal articles) comparing individuals with ASD-without-ID (N= 614) and typically-developed (TD) controls (N= 640), from nine countries and in seven languages (published until February 2020). In our analyses there was no sufficient evidence to suggest that individuals with HF-ASD differ from matched controls in the identification of simple prosodic emotions (e.g., sadness, happiness). However, individuals with ASD-without-ID were found to perform significantly worse than controls in identification of complex prosodic emotions (e.g., envy and boredom). The level of the semantic content of the stimuli presented (e.g., sentences vs. strings of digits) was not found to have an impact on the results. In conclusion, the difference in findings between simple and complex emotions calls for a new-look on emotion processing in ASD-without-ID. Intervention programs may rely on the intact abilities of individuals with ASD-without-ID to process simple emotions and target improved performance with complex emotions. LAY SUMMARY: Individuals with autism spectrum disorder without intellectual disability (ASD-without-ID) do not differ from matched controls in the identification of simple prosodic emotions (e.g., sadness, happiness). However, they were found to perform significantly worse than controls in the identification of complex prosodic emotions (e.g., envy, boredom). This was found in a meta-analysis of 26 data sets with 1254 participants from nine countries and in seven languages. Intervention programs may rely on the intact abilities of individuals with ASD-without-ID to process simple emotions.

Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.