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Brain–computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI. [ABSTRACT FROM AUTHOR]

This paper attempts to highlight the decoding capabilities of MDP convolutional codes over the erasure channel by defining them as discrete linear dynamical systems, with which the controllability property and the observability characteristics of linear system theory can be applied, in particular those of output observability, easily described using matrix language. Those are viewed against the decoding capabilities of MDS block codes over the same channel. Not only is the time complexity better but the decoding capabilities are also increased with this approach because convolutional codes are more flexible in handling variable-length data streams than block codes, where they are fixed-length and less adaptable to varying data lengths without padding or other adjustments. [ABSTRACT FROM AUTHOR]

This paper presents a new theory and model of the teaching of decoding, reading and writing. The first part of the paper reviews a selection of influential models of learning to read and write that to varying degrees have been used as the basis for approaches to teaching, including the Simple View of Reading. As well as noting some strengths of the models in relation to children's learning, limitations are identified in terms of their applicability as models of teaching. The second part of the paper presents seven components that are central to teaching reading and writing derived from social, cultural and cognitive research and theory. Explanations for the relevance of the components are offered, and seminal and more recent research that underpin them summarised. The final part of the paper introduces a new theory and model of teaching, The Double Helix of Reading and Writing. It is argued that this model provides a rationale for a balanced approach to teaching, and an alternative to synthetic phonics. [ABSTRACT FROM AUTHOR]

Introduction: Articulography and functional neuroimaging are two major tools for studying the neurobiology of speech production. Until now, however, it has generally not been feasible to use both in the same experimental setup because of technical incompatibilities between the two methodologies. Methods: Here we describe results from a novel articulography system dubbed Magneto-articulography for the Assessment of Speech Kinematics (MASK), which is technically compatible with magnetoencephalography (MEG) brain scanning systems. In the present paper we describe our methodological and analytic approach for extracting brain motor activities related to key kinematic and coordination event parameters derived from time-registered MASK tracking measurements. Data were collected from 10 healthy adults with tracking coils on the tongue, lips, and jaw. Analyses targeted the gestural landmarks of reiterated utterances/ipa/and/api/, produced at normal and faster rates. Results: The results show that (1) Speech sensorimotor cortex can be reliably located in peri-rolandic regions of the left hemisphere; (2) mu (8-12 Hz) and beta band (13-30 Hz) neuromotor oscillations are present in the speech signals and contain information structures that are independent of those present in higherfrequency bands; and (3) hypotheses concerning the information content of speech motor rhythms can be systematically evaluated with multivariate pattern analytic techniques. Discussion: These results show that MASK provides the capability, for deriving subject-specific articulatory parameters, based on well-established and robust motor control parameters, in the same experimental setup as the MEG brain recordings and in temporal and spatial co-register with the brain data. The analytic approach described here provides new capabilities for testing hypotheses concerning the types of kinematic information that are encoded and processed within specific components of the speech neuromotor system. [ABSTRACT FROM AUTHOR]

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In general, the problem of building optimal convolutional codes under a certain criteria is hard, especially when size field restrictions are applied. In this paper, we confront the challenge of constructing an optimal 2D convolutional code when communicating over an erasure channel. We propose a general construction method for these codes. Specifically, we provide an optimal construction where the decoding method presented in the bibliography is considered. [ABSTRACT FROM AUTHOR]

This paper investigates the linguistic interdependence of Grade 3 children studying in government primary schools in northern Nigeria who are learning to read in Hausa (L1) and English (L2) simultaneously. There are few studies in the African context that consider linguistic interdependence and the bidirectional influences of literacy skills in multilingual contexts. A total of 2328 Grade 3 children were tested on their Hausa and English letter sound knowledge (phonemes) and reading decoding skills (word) after participating in a two-year English structured reading intervention programme as part of their school day. In Grade 4, these children will become English immersion learners, with English becoming the medium of instruction. Carrying out bivariate correlations, we find a large and strongly positively significant correlation between L1 and L2 test scores. Concerning bidirectionality, a feedback path model illustrates that the L1 word score predicts the L2 word score and vice versa. Multi-level modelling is then used to consider the variation in test scores. Almost two thirds of the variation in the word score is attributable to the pupil level and one third to the school level. The Hausa word score is significantly predicted through Hausa sound and English word score. English word score is significantly predicted through Hausa word and English sound score. The findings have implications for language policy and classroom instruction, showing the importance of cross-language transfer between reading skills. The overall results support bidirectionality and linguistic interdependence. [ABSTRACT FROM AUTHOR]

Abstract Brain–computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI.

This paper explores the idea of condensation transcription—which is defined as the reduction of lengthy collections of lexical elements or mathematical descriptions into short forms—as the point where mathematics and language learning cross-influence one another notably. To elaborate, condensation transcription is defined as the linguistic ability and procedure that permits the reconstruction and compacting of larger lexical sets into more manageable forms while maintaining the basic meanings of those sets. Such a phenomenon occurs when mathematical concepts are rewritten from page-long descriptions to figures and numerical entities, and when literary passages are compressed into their main ideas. Formulation of the problem. Word problems, main idea identifications, and essay writing are some of the most dreaded topics of study in mathematics and language. In order to suggest improvements in these challenging areas within the education of both disciplines, this paper’s research emphasizes the significance of condensation transcription and makes readers aware of it by examining lexical elements in language and mathematical entities, pinning their origins, and explaining what learning strategies can be extracted from language that could be useful in mathematics learning, and vice versa. Materials and methods. The resources used in this investigation include a comprehensive dictionary entry, a word problem, literary text passages, and written explorations of mathematical concepts, all of which are dissected through the implementation of condensation transcription’s conventional procedures, which are termed ‘analysis,’ ‘coding,’ and ‘decoding.’ This procedure is necessary to show, first-hand, how condensation transcription works and how it is applied, as well as what can be gleaned from its functions and applications. Results. The results point to condensation transcription being a basic concept in mathematics and language that powers mathematical learning through comprehension of language, and reversely. Conclusions. As a whole, this paper underscores the importance of having the knowledge of condensation transcription in language and mathematics. By recognizing the role of condensation transcription as a foundational language process, practitioners in both language and mathematics could make some of the most difficult concepts in both disciplines easier for students to grasp.

Polar codes have emerged as a focal point in the field of error-correcting codes, owing to their remarkable capacity-achieving characteristics and their relevance in various modern communication systems. The basic successive cancellation (SC) approach is not optimal to use in terms of the trade-off between performance and decoding complexity. SC-Creeper algorithm performs better with about the same low complexity as the SC version of the algorithm. However, the SC-Creeper algorithm did not have the ability to use the candidate list as a measure to improve performance and refine the search for the true codeword. To compare with successive cancellation list (SCL) approach and the ability to use more computing memory, the SCL-Creeper method was developed, using two additional lists. This method can also be used as a development of Fano algorithms for polar codes (mainly, Fano decoding in polar decoding does not use lists). This paper addresses the challenge of computational complexity in polar code decoding by integrating a list structure with the SC-Creeper algorithm. Building on prior research that introduced the concept of SC-Creeper, the study focuses on enhancing error correction performance while mitigating computational burden. The first chapters describe the polar encoding process and basic decoding technologies, then discuss the basic Creeper algorithm. In the following chapters, the authors describe a modified version of the two-list Creeper approach (that is, the SCL-Creeper version of the algorithm). Extensive simulations and numerical analysis presented in the paper underscore the tangible advantages of this novel decoding strategy. Leveraging the basic list algorithm, renowned for its superior error correction capabilities, the research explores the integration of Creeper to systematically prune unnecessary decoding paths. The resulting SCL-Creeper hybrid approach aims to strike a balance between error correction efficiency and computational complexity. Finally, the optimal selection of parameters for the SCL-Creeper approach and future directions in the research of the list version of the fast Creeper algorithm are discussed.

This paper investigates how Hebrew is taught and perceived at American part-time Jewish schools, based on surveys of 519 school directors, classroom observations, and 376 surveys of parents, students, teachers, and clergy. We found misalignment of rationales and goals for Hebrew education among these stakeholders. Most schools emphasize Liturgical and Biblical Hebrew decoding and recitation without comprehension for ritual participation, especially b'nai mitzvah. Many schools infuse Hebrew, and a small percentage teach Modern Hebrew conversation. Findings are discussed in historical context and compared to language education and metalinguistic communities in immigrant and indigenous groups. [ABSTRACT FROM AUTHOR]

Introduction: Cognitive models have proposed that behavioral tasks can be categorized along at least three dimensions: the sensory-motor modality of the information, its representational format (e.g., location vs. identity), and the cognitive processes that transform it (e.g., response selection). Moreover, we can quickly and flexibly encode, represent, or manipulate information along any of these dimensions. How is this flexibility in encoding such information implemented in the cerebral cortex? Methods: To address this question, we devised a series of functional magnetic resonance imaging (fMRI) experiments in each of which participants performed two distinct tasks that diered along one of the three dimensions. Results: Using multivariate pattern analysis of the fMRI data, we were able to decode between tasks along at least one task dimension within each of the cortical regions activated by these tasks. Moreover, the multiple demand network, a system of brain regions previously associated with flexible task encoding, was largely composed of closely juxtaposed sets of voxels that were specialized along each of the three tested task dimensions. Discussion: These results suggest that flexible task encoding is primarily achieved by the juxtaposition of specialized representations processing each task dimension in the multiple demand network [ABSTRACT FROM AUTHOR]

This study used a meta-analytic structural equation modeling approach to build extended versions of the simple view of reading (SVR) model in second and foreign language (SFL) learning contexts (i.e., SVR-SFL). Based on the correlation coefficients derived from primary studies, we replicated and integrated two previous extended meta-analytic SVR models, that is, Quinn and Wagner's (2018) model with an English-speaking population with a cognitive factor and Peng et al.'s (2021) model with Chinese-speaking readers with metalinguistic skills. A total of 180 independent samples (N = 36,235) obtained from 152 empirical studies in SFL contexts were included in our meta-analytic structural equation model. The results revealed that the collected data successfully replicated H. Lee et al.'s (2022) SVR model in SL contexts (SV2R) in terms of overall model fit and moderation effects; in addition, the results confirmed that the data fit well with the extended SVR-SFL models. [ABSTRACT FROM AUTHOR]

While the exertion of mental effort improves performance on cognitive tasks, the neural mechanisms by which motivational factors impact cognition remain unknown. Here, we used fMRI to test how changes in cognitive effort, induced by changes in task difficulty, impact neural representations of working memory (WM). Participants (both sexes) were precued whether WM difficulty would be hard or easy. We hypothesized that hard trials demanded more effort as a later decision required finer mnemonic precision. Behaviorally, pupil size was larger and response times were slower on hard compared with easy trials suggesting our manipulation of effort succeeded. Neurally, we observed robust persistent activity during delay periods in the prefrontal cortex (PFC), especially during hard trials. Yet, details of the memoranda could not be decoded from patterns in prefrontal activity. In the patterns of activity in the visual cortex, however, we found strong decoding of memorized targets, where accuracy was higher on hard trials. To potentially link these across-region effects, we hypothesized that effort, carried by persistent activity in the PFC, impacts the quality ofWM representations encoded in the visual cortex. Indeed, we found that the amplitude of delay period activity in the frontal cortex predicted decoded accuracy in the visual cortex on a trial-wise basis. These results indicate that effort-related feedback signals sculpt population activity in the visual cortex, improving mnemonic fidelity. [ABSTRACT FROM AUTHOR]

The 3-D TLC NAND flash memory significantly increases the storage capacity but makes data more prone to errors. To address the reliability problem, Polar Code is one coding method that can reach the channel capacity (Arikan 2009). However, the 3-D NAND flash memory channel is uncertain due to the variation of retention time and program/erase(P/E) cycle. So it is difficult to construct a Polar Code in the NAND flash memory. This letter proposes a new Polar Code construction method called dynamic merge Monte Carlo (DMMC). DMMC uses the Monte Carlo method to obtain the optimal Polar Code construction and then reduces the space overhead by combining similar construction so that it can be used in flash memory systems. Compared with the conventional method, the uncorrectable bit error rate (UBER) of the proposed method can reduce by up to 90.5%, and the read latency can reduce by more than 50%. [ABSTRACT FROM AUTHOR]

DNA-based storage has been a promising technique of data storage, due to its high density and long duration. During synthesizing and sequencing of DNA storage, edit errors including insertions, deletions and substitutions are introduced inevitably. An effective way to reduce the error probability is to limit the content of G and C in DNA sequences to around 50%, which is called GC-balanced. To deal with edit errors, DNA sequences are also expected to have error-correcting capabilities. In this paper, GC globally balanced and GC locally balanced error-correcting codes are explicitly constructed, respectively. Inspired by repetition codes, the proposed codes are able to correct multiple edit errors. Furthermore, an efficient decoding algorithm applied for both codes is derived when only one kind of edit error occur. [ABSTRACT FROM AUTHOR]

The connected digital environment generates massive amounts of data that must be protected from unauthorized disclosure. Technological advancements and the creative techniques attackers use can exacerbate data security breaches. Security protocols and cryptographic algorithms are the two components of information security, the last being information security's basis and fundamental technology. The cryptographic conception will improve and assure security while also protecting network resources that are exchanged. The goal of this study is to demonstrate the few encryption algorithms that are used to encrypt data on an unsecured network. This research presents a new hybrid encryption strategy for encrypting data. MRDAES encrypts data using Rivest, Shamir, Adleman (RSA), Diffie Hellman (DH), and the Advanced Encryption System. The data will be encoded in two stages: the first by producing the encoded data using the XOR function, and the second by using AES to encode data and obtain the final result to be shared with intended reception with high-security complexity compared to other methods in terms of performance and avalanche effect. The proposed study examines the three most widely used cryptographic symmetric and asymmetric algorithms, RSA, Diffie-Hellman, and AES, and how they operate and impact security and complexity when integrated into a unified hybrid (MRDAES) computation with the proper adjustments. The study also presents an approach for increasing data security while minimizing the number of mathematical equations and obtaining high-security complexity while slightly increasing execution time with chosen large random prime numbers. The results indicate that the new hybrid technique (MRDAES) has more efficient performance and avalanche effect inputs with a minor increase in execution time. The proposed approach MRDAES produced more advanced complexity than classical RSA existing methods and others. According to avalanche effect data, the proposed technique provides better results with a higher percentage than existing algorithms on modified RSA Random Bit Insertion Algorithm (RBMRSA) and classical Rivest, Shamir, and Adleman (RSA). The proposed approach enhances the avalanche effect by 88% compared to 48% achieved by an RBMRSA, with a 40% increase and 87.80% compared to RSA. [ABSTRACT FROM AUTHOR]

Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multi region recordings in mice, and (4) decoding position from hippocampal recordings in rats. For each setting, we characterize the overconfidence, and we describe a possible method to correct miscalibration post hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain–machine interfaces that more accurately reflect confidence levels when identifying external variables. [ABSTRACT FROM AUTHOR]

One potential application of forensic "brain reading" is to test whether a suspect has previously experienced a crime scene. Here, we investigated whether it is possible to decode real life autobiographic exposure to spatial locations using fMRI. In the first session, participants visited four out of eight possible rooms on a university campus. During a subsequent scanning session, subjects passively viewed pictures and videos from these eight possible rooms (four old, four novel) without giving any responses. A multivariate searchlight analysis was employed that trained a classifier to distinguish between "seen" versus "unseen" stimuli from a subset of six rooms. We found that bilateral precuneus encoded information that can be used to distinguish between previously seen and unseen rooms and that also generalized to the two stimuli left out from training. We conclude that activity in bilateral precuneus is associated with the memory of previously visited rooms, irrespective of the identity of the room, thus supporting a parietal contribution to episodic memory for spatial locations. Importantly, we could decode whether a room was visited in real life without the need of explicit judgments about the rooms. This suggests that recognition is an automatic response that can be decoded from fMRI data, thus potentially supporting forensic applications of concealed information tests for crime scene recognition. [ABSTRACT FROM AUTHOR]

The study aims to review the illustration methods of four top illustrators in Czechoslovakia. Its purpose is to understand their successful approaches and recognize the unique characteristics of their works, which convey personal ideas and thoughts. The study delves into the most important illustration techniques employed by these artists. The central research question seeks to uncover the expression methods and execution techniques used by these top illustrators to convey their ideas and emotions. Culture and customs play a pivotal role in an illustrator's work. No artist can create truly ingenious works divorced from the cultural context of their hometown. Their visual language becomes a reflection of their culture, and this authenticity contributes to the value of their creations. Sensitivity to children's perceptions of life, art, culture, and the future is essential for compassionate and impactful work. These illustrators are deeply committed to their young audience, shaping the future through their art. This research follows a fundamental descriptive-analytical approach, with data collection primarily based on library resources. [ABSTRACT FROM AUTHOR]

Our study examined the complex relationships among reading performance (decoding, comprehension) and language, visuo-spatial, and attentional control abilities in 115 Italian-speaking children. Latent profile analysis was used to identify distinct clusters of participants showcasing quantitative differences in decoding skills, including word, pseudo-word, text reading speed and accuracy. Then, we used this classification to investigate group differences in a variety of linguistic, working memory, and visuo-spatial tasks, as well as in reading comprehension skills, by means of multivariate and univariate tests. Our results reveal significant links between reading proficiency and several key factors: language skills, visuo-spatial abilities, and attentional control. These findings illuminate the nuanced impact of domain-general processes that govern a series of linguistic and visuo-perceptive subcomponents during reading tasks. Additionally, using dominance analysis, predictors of written text comprehension were identified. Our findings suggest that effective reading comprehension relies on a synergistic interplay of adequate reading speed, attentional control, working memory, and verbal fluency, accounting for 23% of the explained variance. This study highlights the multifaceted nature of reading proficiency and suggests that a broader perspective is necessary to fully understand reading development and support its improvement. [ABSTRACT FROM AUTHOR]

Background. Brain–machine interfaces (BMIs) offer users the ability to directly communicate with digital devices through neural signals decoded with machine learning (ML)-based algorithms. Spiking Neural Networks (SNNs) are a type of Artificial Neural Network (ANN) that operate on neural spikes instead of continuous scalar outputs. Compared to traditional ANNs, SNNs perform fewer computations, use less memory, and mimic biological neurons better. However, SNNs only retain information for short durations, limiting their ability to capture long-term dependencies in time-variant data. Here, we propose a novel spike-weighted SNN with spiking long short-term memory (swSNN-SLSTM) for a regression problem. Spike-weighting captures neuronal firing rate instead of membrane potential, and the SLSTM layer captures long-term dependencies. Methods. We compared the performance of various ML algorithms during decoding directional movements, using a dataset of microelectrode recordings from a macaque during a directional joystick task, and also an open-source dataset. We thus quantified how swSNN-SLSTM performed compared to existing ML models: an unscented Kalman filter, LSTM-based ANN, and membrane-based SNN techniques. Result. The proposed swSNN-SLSTM outperforms both the unscented Kalman filter, the LSTM-based ANN, and the membrane based SNN technique. This shows that incorporating SLSTM can better capture long-term dependencies within neural data. Also, our proposed swSNN-SLSTM algorithm shows promise in reducing power consumption and lowering heat dissipation in implanted BMIs. [ABSTRACT FROM AUTHOR]

In this article, we address the problem of 4D facial expressions generation. This is usually addressed by animating a neutral 3D face to reach an expression peak, and then get back to the neutral state. In the real world though, people show more complex expressions, and switch from one expression to another. We thus propose a new model that generates transitions between different expressions, and synthesizes long and composed 4D expressions. This involves three sub-problems: (1) modeling the temporal dynamics of expressions, (2) learning transitions between them, and (3) deforming a generic mesh. We propose to encode the temporal evolution of expressions using the motion of a set of 3D landmarks, that we learn to generate by training a manifold-valued GAN (Motion3DGAN). To allow the generation of composed expressions, this model accepts two labels encoding the starting and the ending expressions. The final sequence of meshes is generated by a Sparse2Dense mesh Decoder (S2D-Dec) that maps the landmark displacements to a dense, per-vertex displacement of a known mesh topology. By explicitly working with motion trajectories, the model is totally independent from the identity. Extensive experiments on five public datasets show that our proposed approach brings significant improvements with respect to previous solutions, while retaining good generalization to unseen data. [ABSTRACT FROM AUTHOR]

We report on a school-based randomized control trial study comparing two morphological interventions with untaught controls: one focusing on direct instruction targeting print morphological decoding (direct decoding condition) and the other on inquiry-focused pedagogy using oral morphological analysis (inquiry-analysis condition). We identified 63 Grade 3 children with below-average morphological awareness following screening (from N = 163). This sub-sample showed average pseudoword decoding but poor language and word reading abilities. Following a 13-week supplemental intervention randomized within the 63 children, results showed a statistically significant main effect of intervention on standardized reading vocabulary measures at immediate post-test in the direct decoding condition. Pre-test morphological awareness moderated reading vocabulary effects for the untaught control group. Statistically significant moderation of growth in sentence comprehension at post- by pre-test morphological awareness was also evident in the inquiry-analysis condition. Universal screening for below-average morphological awareness followed by inquiry-based or direct instruction interventions focusing on the meaning dimensions of morphemes may be modestly efficacious for supporting reading vocabulary and sentence comprehension in such at risk learners, potentially aiding school-wide literacy improvement. [ABSTRACT FROM AUTHOR]

Letter knowledge and word identification are key skills for reading and spelling. Letter knowledge facilitates the application of sublexical letter-sound mappings to decode words. With reading experience, word identification becomes a key lexical skill to support decoding. In transparent orthographies, however, letter knowledge might be an enduring predictor of decoding and spelling, even in children with some reading experience. This study investigated the association of children's sublexical (letter knowledge) and lexical skills (word identification and vocabulary) with word decoding and spelling accuracy in Spanish, which is a transparent orthography. The sample consisted of 117 Spanish-speaking children, aged 8 to 10. Results revealed that (1) letter knowledge and word identification were independently associated with childre's word spelling; (2) word identification was uniquely associated with word decoding; and (3) children's vocabulary level was associated with word identification. The implications of these findings were examined within the framework of reading models and the characteristics of a transparent orthography. [ABSTRACT FROM AUTHOR]

This letter proposes an augmented scheme of LT codes to improve the decoding success rate. The method involves substituting the 1‐s in full‐degree columns of the generator matrix of conventional LT codes with binary extension field elements and simultaneously increasing the ratio of full‐degree columns in the ideal soliton distribution. For non‐full‐degree columns, we retain the nonzero elements as 1‐s in the conventional LT codes generator matrix to preserve computational efficiency. Compared to conventional LT codes, the proposed method enhances the linear independence of the generator matrix, leading to a higher decoding success rate with minimal data packets. Experimental results demonstrate the effectiveness of the method at improving the performance of LT codes, with close to 100% decoding success rate achieved with around 5% data redundancy. [ABSTRACT FROM AUTHOR]

Polynomial based secret sharing is the art of protecting information and a tool used in areas where a secret has to be distributed among multiple parties. In this method, secret is encrypted into noisy shares and transferred to participants in the group. Decoding of secret is possible only when sufficient number of authorized members in the group, stack their respective shares. In this article, polynomial based secret sharing for audio is proposed that computes a checksum to identify the dishonest participant and also the audio shares generated are of smaller dimension. To achieve this, a polynomial function is defined by considering amplitude values and random values as coefficients. Inclusion of random value as the coefficient of higher degree term in the polynomial makes the audio shares noisy and does not provide any information about the secret. This reduces the overhead of performing preprocessing on original audio before shares are generated. In addition, our proposed method can be used for multi secret sharing. Proposed scheme also facilitates identification of dishonest participant before reconstruction of secret through a trusted entity called combiner. [ABSTRACT FROM AUTHOR]

Deep learning techniques for change detection have undergone rapid development in the past few years. However, it is still a challenge how to reduce massive network parameters and sufficiently fuse bitemporal image features to improve detection accuracy. Therefore, this work proposes a novel and lightweight network based on feature interleaved fusion and bistage decoding (FFBDNet) for change detection. In the encoding stage, considering the application problems caused by a large number of network parameters, we use the more efficient EfficientNet as the backbone to extract the bitemporal image features based on Siamese architecture. To fuse the bitemporal image features and reduce interference from surrounding objects, we propose a feature interleaved fusion module, which can interleave the shared feature information and the difference variance feature information. During the decoding stage, the fused features are split into two groups, and a novel bistage decoding framework is proposed to generate the accuracy change map gradually. Extensive experiments and ablation studies are validated on three public change detection datasets: WHU-CD, LEVIR-CD, and SYSU-CD datasets. Compared to state-of-the-art methods, the experimental results demonstrate that the proposed FFBDNet produces a better balance between performance and model parameters. Specifically, the F1 values obtained for these three datasets are 93.27%, 91.11%, and 80.10%, respectively, and the model parameters of the network are just 2.85 M. [ABSTRACT FROM AUTHOR]

This article addresses the problem of building segmentation for rural areas with high-resolution remote sensing images. Due to the irregular spatial distribution of rural buildings, it is often challenging to perform pixel-wise dense prediction to entire areas like the usual segmentation task to extract buildings. Specifically, we present a multilevel decoding network model that classifies the input image on the patch and image levels according to the distribution of buildings. A scene head module is used to identify scenes defined as patches that contain buildings. Depending on the scene classification results, a decode gate is taken to determine the level of prediction. This hierarchical extraction strategy reduces the amount of inference time. Experiments on our constructed rural building dataset with large-scale images validate the high efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Computing-in-memory (CIM) is a promising candidate for highly energy-efficient neural networks, alleviating the well-known bottleneck in Von Neumann architecture. MRAM has garnered significant attention in the CIM field, providing advantages in terms of non-volatility, high speed, and endurance. However, most existing MRAM-CIM primarily support low-precision operations, which poses a challenge in fulfilling the requirements of complex neural network models for high inference accuracy. To resolve this dilemma, an in-memory Booth Multiplier is proposed with the aim of enhancing the energy efficiency of neural networks performing multi-bit multiply-and-accumulate (MAC) operations. The MRAM array stores the multiplicand, while the multiplier is encoded by a Booth encoder into corresponding control signals, which perform negation and shift operations, reducing half of the partial products and accelerating the overall processing. Simulation results demonstrate at least a 17.3% improvement in energy efficiency compared to the previous in-SRAM counterpart in 8-bit multiplication. [ABSTRACT FROM AUTHOR]

Quantum computers have the potential to provide exponential speedups over their classical counterparts. Quantum principles are being applied to fields such as communications, information processing, and artificial intelligence to achieve quantum advantage. However, quantum bits are extremely noisy and prone to decoherence. Thus, keeping the qubits error free is extremely important toward reliable quantum computing. Quantum error correcting codes have been studied for several decades and methods have been proposed to import classical error correcting codes to the quantum domain. Along with the exploration into novel and more efficient quantum error correction codes, it is also essential to design circuits for practical realization of these codes. This article serves as a tutorial on designing and simulating quantum encoder and decoder circuits for stabilizer codes. We first describe Shor’s 9-qubit code which was the first quantum error correcting code. We discuss the stabilizer formalism along with the design of encoding and decoding circuits for stabilizer codes such as the five-qubit code and Steane code. We also design nearest neighbor compliant circuits for the above codes. The circuits were simulated and verified using IBM Qiskit. [ABSTRACT FROM AUTHOR]

Every age group and generation uses different lingo, jargon, and forms of communication in casual conversations. These lingos could be introduced by the fashions, significant events, radio shows, movies, TV shows, songs, and literature of their time, among many other devices. Additionally, technology has changed some words, adding new ones that are more inventive and simpler to type. The study aimed to analyze the linguistic meaning of "Sana All" used by the Grade 11 Senior High School students in Cotabato City National High School - Annex (Buaya-Buaya Site). Colaizzi's phenomenological method of inquiry was employed in this study. The researcher utilized the Focal Group Discussion (FGD) in gathering data. The study's findings revealed that the phrase "Sana All" is used by student participants in Social Media, School, Home, and the Real World. The phrase connotes admiration, appreciation, wishing for something to happen, and strengthening relationships with others. Various meanings were generated depending on the communication setting. The researcher concluded that students use the phrase "Sana All" in various communication settings. They use this phrase when wishing, admiring, appreciating, and strengthening relationships with others. Finally, the researcher produced a linguistic concept on the teaching strategies on pragmatics and semantics where the teacher can adopt the trendy phrase to be included when teaching the linguistic concept of pragmatics and semantics. [ABSTRACT FROM AUTHOR]

Nowadays, cybersecurity has grown into a more significant and difficult sci-entific issue. The recognition of threats and attacks meant for knowledge and safety on the internet is growing harder to detect. Since cybersecurity guar-antees the privacy and security of data sent via the Internet, it is essential, while also providing protection against malicious attacks. Encrypt has grown into an answer that has become an essential element of information security systems. To ensure the security of shared data, including text, images, or videos, it is essential to employ various methods and strategies. This study delves into the prevalent cryptographic methods and algorithms utilized for prevention and stream encryption, examining their encoding techniques such as advanced encryption standard (AES), Blowfish, Twofish, Salsa20, and ChaCha20. The primary objective of this research is to identify the optimal times and throughputs (speeds) for data encryption and decryption processes. The methodology of this study involved selecting five distinct types of images to compare the outcomes of the techniques evaluated in this research. The as-sessment focused on processing time and speed parameters, examining visual encoding and decoding using Java as the primary platform. A comparative analysis of several symmetric key ciphers was performed, focusing on handling large datasets. Despite this limitation, comparing different images helped evaluate the techniques' novelty. The results showed that ChaCha20 had the best average time for both encryption and decryption, being over 50% faster than some other algorithms. However, the Twofish algorithm had lower throughput during testing. The paper concludes with findings and suggestions for future improvements.

IntroductionArticulography and functional neuroimaging are two major tools for studying the neurobiology of speech production. Until now, however, it has generally not been feasible to use both in the same experimental setup because of technical incompatibilities between the two methodologies.MethodsHere we describe results from a novel articulography system dubbed Magneto-articulography for the Assessment of Speech Kinematics (MASK), which is technically compatible with magnetoencephalography (MEG) brain scanning systems. In the present paper we describe our methodological and analytic approach for extracting brain motor activities related to key kinematic and coordination event parameters derived from time-registered MASK tracking measurements. Data were collected from 10 healthy adults with tracking coils on the tongue, lips, and jaw. Analyses targeted the gestural landmarks of reiterated utterances/ipa/ and /api/, produced at normal and faster rates.ResultsThe results show that (1) Speech sensorimotor cortex can be reliably located in peri-rolandic regions of the left hemisphere; (2) mu (8–12 Hz) and beta band (13–30 Hz) neuromotor oscillations are present in the speech signals and contain information structures that are independent of those present in higher-frequency bands; and (3) hypotheses concerning the information content of speech motor rhythms can be systematically evaluated with multivariate pattern analytic techniques.DiscussionThese results show that MASK provides the capability, for deriving subject-specific articulatory parameters, based on well-established and robust motor control parameters, in the same experimental setup as the MEG brain recordings and in temporal and spatial co-register with the brain data. The analytic approach described here provides new capabilities for testing hypotheses concerning the types of kinematic information that are encoded and processed within specific components of the speech neuromotor system.

This paper attempts to highlight the decoding capabilities of MDP convolutional codes over the erasure channel by defining them as discrete linear dynamical systems, with which the controllability property and the observability characteristics of linear system theory can be applied, in particular those of output observability, easily described using matrix language. Those are viewed against the decoding capabilities of MDS block codes over the same channel. Not only is the time complexity better but the decoding capabilities are also increased with this approach because convolutional codes are more flexible in handling variable-length data streams than block codes, where they are fixed-length and less adaptable to varying data lengths without padding or other adjustments.

In general, the problem of building optimal convolutional codes under a certain criteria is hard, especially when size field restrictions are applied. In this paper, we confront the challenge of constructing an optimal 2D convolutional code when communicating over an erasure channel. We propose a general construction method for these codes. Specifically, we provide an optimal construction where the decoding method presented in the bibliography is considered.