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Abstract A new image encryption algorithm based on the triple chaotic maps is proposed to deal with the issues of inadequate security and low encryption efficiency. Coloured images consist of three linked channels used in the scheme. This method uses different keys to break the correlations between adjacent pixels in each channel. The triple chaotic maps are Lorenz, 2D‐Logistic, and Henon. First, the plain image is split into RGB channels to encrypt each channel separately. Second, the triple chaotic maps generate two groups of keys. The first group of keys performs a pixel permutation, resulting in scrambled channels used as input for the following step. Finally, the second group of keys is used to diffuse the scrambled channels independently, resulting in diffused channels, which are then merged to obtain a cipher image. The triple chaotic maps of different orders generate the cipher image with great unpredictability and security. The security is evaluated using various measures. The results demonstrated a high level of security attained by successfully encrypting coloured images. Recent encryption algorithms are compared in terms of entropy, correlation coefficients, and attack robustness. The proposed method provided outstanding security and outperformed existing image encryption algorithms.

The digital age introduced the Digital Ecological Niche (DEN), revolutionizing human interactions. The advent of Digital History (DHy) has marked a methodological shift in historical studies, tracing its roots to Babbage and Lovelace’s 19th-century work on “coding” as a foundational communication process, fostering a new interaction paradigm between humans and machines, termed “person2persons2machines.” This evolution, through digitization and informatization, builds upon ancient coding practices but was significantly advanced by Babbage and Lovelace’s contributions to mathematical linguistic systems, laying the groundwork for Computer Science. This field, central to 20th-century mainframe interaction through programming languages and formalization, situates Digital History within a broader historical context. Here, coding and mathematical methodologies empower historians with advanced technologies for historical data preservation and analysis. Nonetheless, the extent to which computation and Turing machines can fully understand and interpret history remains a subject of debate.

Abstract Recently, a novel low‐cost coding digital receiving array based on machine learning (ML‐CDRA) has been proposed to reduce the required radio frequency channels in modern wireless systems. The spatial sensitivity of ML‐CDRA is studied which describes the spatial accumulation gain in different directions. It is demonstrated that the spatial sensitivity is determined by the encoding network, decoding network, and beamforming criterion. To obtain the desired spatial sensitivity, a spatial sensitivity synthesis method is proposed based on the alternate projection by optimising the encoding network with the constraint of amplitude‐phase quantisation. Simulation results show that the proposed method can significantly improve the spatial sensitivity of ML‐CDRA. Furthermore, in the directions of interest, the spatial accumulation gain of ML‐CDRA can exceed the full‐channel digital receiving array.

Abstract Computational models and eye-tracking research suggest that encoding variability accounts for the reduced recognition of targets (A) when paired with non-corresponding lures (B′) relative to corresponding lures (A′). The current study examined whether neural activity during learning coincided with subsequent performance on the forced-choice Mnemonic Similarity Task (MST). Event-related potential responses were collected during encoding while young adults completed A–B′ and A–A′ trials of the forced-choice MST. Consistent with previous research, performance was lower on A–B′ trials than A–A′ trials. The subsequent memory effect was not significant for the A–A′ test format. However, for A–B′ trials, we observed a significant Accuracy × Stimulus interaction 1000–1200 ms poststimulus onset across frontal and fronto-central electrodes. As hypothesized, subsequently correct A–B′ trials were associated with a larger amplitude response at encoding to the target (A) than the original version of the non-corresponding lure (B). However, subsequently incorrect trials were associated with a larger amplitude response to the non-corresponding lure (B) than the target stimulus (A). These findings provide additional support for the effect of encoding variability on mnemonic discrimination.

This article discusses one-sided screen discourse and the properties of encoding and decoding in it. The relevance of the research is conditioned by the dominance of artificially created screen information environment in modern communication and its increasing influence on the formation of public opinion, including through the distortion of information. The aim of this article is to describe the communication that takes place in one-way screen discourse in terms of encoding and decoding, as well as to identify the influence of these processes on information exchange. The source of material of the study was the detective series “Voskresensky” (8 episodes, 1 season), a total of 432 min were analysed. Screen discourse, being a part of mass communication, is inextricably linked to the activity and “sociality” of the communicative process. In this regard, this study adopts the activity concept and also implements a special discursive-semiotic approach to the organisation of screen discourse. Interpretation, analysis, synthesis, descriptive method, method of contextual analysis, functional-pragmatic methods were chosen as research methods. In the course of the research the following results were found. Information exchange in screen discourse can be characterised as: free, disequilibrium and mediated. Engineering and constructive activity is its main support. When encoding a screen message, the addresser, using a variety of codes according to his own intention, introduces variants and various possibilities into it, allowing the addressee to make a meaningful choice among them providing freedom of interpretation. The addressee, proceeding from the external expression - the meaningful structures embedded in the screen “speech” - reconstructs the process of creating the screen message, and relying on his knowledge of the “language” of screenness, through this external expression goes outwards to the content and meanings. The dialectical unity of symmetry and asymmetry is one of the main characteristics of information exchange in one-sided screen discourse. Symmetry is responsible for the ease of understanding the broadcast information, while asymmetry is responsible for endless semiosis. This combination makes it voluminous and dynamic.

Melodies are full of repeated patterns of pitch, interval, and rhythm. It has been suggested that these repeated patterns aid the listener in creating an efficient encoding; this raises the possibility that compositional practice might have evolved to facilitate this process. I propose three specific hypotheses about compositional practice: 1) Repeated intervallic patterns tend to be metrically parallel, with each instance of the pattern falling at the same position in relation to the metrical structure; 2) Purely intervallic repetitions tend to be confined to short distances (longer-distance repetitions tend to involve repetition of scale-degrees as well); 3) Repeated intervallic patterns tend to involve multiple intervals rather than single ones. In each case, I explain how such a compositional strategy might facilitate efficient encoding. Corpus analyses of classical themes and European folk songs find support for all three hypotheses.

Abstract In spiking neural networks (SNNs), artificial sensor neurons are crucial for converting real‐world analog information into encoded spikes. However, existing SNNs face challenges due to the inefficient implementation of input sensor neurons. Here, this study proposes an SNN‐compatible spike mode sensor, designed to directly convert analog current signals into real‐time encoded spikes, feeding the SNN concurrently. The input sensor neuron is realized using a stable neuron circuit employing a threshold switching (TS) memristor and secondary order RC block. This design enables time delay‐free spike firing, operates at low voltage, and offers a wide signal sensing range. Furthermore, this study presents an expression delineating the relationship between the pulse emission properties of the circuit and the parameters of its components, laying the basis for circuit components design and development. Analytical analysis confirms the sensor's efficacy in implementing rate‐based and time‐to‐first spike encoding schemes. Integrating the sensor into SNNs as the input layer for image training and recognition tasks yields an impressive accuracy of 87.58% on the MNIST dataset, showcasing its applicability as a crucial interface between the physical world and the SNN framework.

Abstract Machine learning is a valuable tool that can accelerate the discovery and design of materials occupying combinatorial chemical spaces. However, the prerequisite need for vast amounts of training data can be prohibitive when significant resources are needed to characterize or simulate candidate structures. Recent results have shown that structure-free encoding of complex materials, based entirely on chemical compositions, can overcome this impediment and perform well in unsupervised learning tasks. In this study, we extend this exploration to supervised classification, and show how structure-free encoding can accurately predict classes of material compounds for battery applications without time consuming measurement of bonding networks, lattices or densities. Scientific contribution The comprehensive evaluation of structure-free encodings of complex materials in classification tasks, including binary and multi-class separation, inclusive of three classifiers based on different logic function, is measured four metrics and learning curves. The encoding is applied to two data sets from computational and experimental sources, and the outcomes visualised using 5 approaches to confirms the suitability and superiority of Mendeleev encoding. These methods are general and accessible using source software, to provide simple, intuitive and interpretable materials informatics outcomes to accelerate materials design.

Abstract This article proposes a cooperative jamming (CJ) architecture based on the coded signal of interest (SOI). The CJ is superimposed on the coded SOI directly at the transmitter, and the authorized user reconstructs the received signal and cancels the CJ based on the prior knowledge of the coding information, then different radiation patterns after cancellation are created for SOI and CJ, which effectively cancels CJ while retaining SOI. Eavesdropper, however, is unaware of the coding information and will cause significant loss to the SOI while cancelling the CJ, therefore unable to achieve eavesdropping on communication signals. Numerical results verify the feasibility of the proposed method.

Abstract The introduction of quantum communications, enabled by advancements in quantum computing, is expected to play a significant role in the field of communications. Inherent properties of quantum objects, such as superposition and entanglement, have the potential to provide novel solutions to overcome the challenges encountered by classical communication systems in bandwidth‐intensive applications such as media transmission. This research explores the performance of a quantum communication system in image transmission using quantum superposition and investigates its performance using a simple quantum channel model. With an increase in channel noise, there are significant gains in the rate distortion performance of images transmitted over the quantum channel compared to an ideal classical channel. This novel attempt at constructing a quantum communication‐based image transmission system indicates the potential of the approach to be applied to satisfy the ever‐increasing demands of high‐quality media transmission applications.

A recent development in deep learning techniques has attracted attention to the decoding and classification of electroencephalogram (EEG) signals. Despite several efforts to utilize different features in EEG signals, a significant research challenge is using time-dependent features in combination with local and global features. Several attempts have been made to remodel the deep learning convolution neural networks (CNNs) to capture time-dependency information. These features are usually either handcrafted features, such as power ratios, or splitting data into smaller-sized windows related to specific properties, such as a peak at 300 ms. However, these approaches partially solve the problem but simultaneously hinder CNNs' capability to learn from unknown information that might be present in the data. Other approaches, like recurrent neural networks, are very suitable for learning time-dependent information from EEG signals in the presence of unrelated sequential data. To solve this, we have proposed an encoding kernel (EnK), a novel time-encoding approach, which uniquely introduces time decomposition information during the vertical convolution operation in CNNs. The encoded information lets CNNs learn time-dependent features in addition to local and global features. We performed extensive experiments on several EEG data sets—physical human-robot collaborations, P300 visual-evoked potentials, motor imagery, movement-related cortical potentials, and the Dataset for Emotion Analysis Using Physiological Signals. The EnK outperforms the state of the art with an up to 6.5% reduction in mean squared error (MSE) and a 9.5% improvement in F1-scores compared to the average for all data sets together compared to base models. These results support our approach and show a high potential to improve the performance of physiological and non-physiological data. Moreover, the EnK can be applied to virtually any deep learning architecture with minimal effort.

Gamma oscillations nested in a theta rhythm are observed in the hippocampus, where are assumed to play a role in sequential episodic memory, i.e., memorization and retrieval of events that unfold in time. In this work, we present an original neurocomputational model based on neural masses, which simulates the encoding of sequences of events in the hippocampus and subsequent retrieval by exploiting the theta-gamma code. The model is based on a three-layer structure in which individual Units oscillate with a gamma rhythm and code for individual features of an episode. The first layer (working memory in the prefrontal cortex) maintains a cue in memory until a new signal is presented. The second layer (CA3 cells) implements an auto-associative memory, exploiting excitatory and inhibitory plastic synapses to recover an entire episode from a single feature. Units in this layer are disinhibited by a theta rhythm from an external source (septum or Papez circuit). The third layer (CA1 cells) implements a hetero-associative net with the previous layer, able to recover a sequence of episodes from the first one. During an encoding phase, simulating high-acetylcholine levels, the network is trained with Hebbian (synchronizing) and anti-Hebbian (desynchronizing) rules. During retrieval (low-acetylcholine), the network can correctly recover sequences from an initial cue using gamma oscillations nested inside the theta rhythm. Moreover, in high noise, the network isolated from the environment simulates a mind-wandering condition, randomly replicating previous sequences. Interestingly, in a state simulating sleep, with increased noise and reduced synapses, the network can “dream” by creatively combining sequences, exploiting features shared by different episodes. Finally, an irrational behavior (erroneous superimposition of features in various episodes, like “delusion”) occurs after pathological-like reduction in fast inhibitory synapses. The model can represent a straightforward and innovative tool to help mechanistically understand the theta-gamma code in different mental states.

Photoacoustic imaging creates light-induced ultrasonic signals to provide valuable information on internal body structures and tissue morphology non-invasively. A multi-aperture photoacoustic imaging (MP-PAI) system is an improvement over conventional photoacoustic imaging (PAI) systems in terms of resolution, contrast, and field of view. Previously, a prototype MP-PAI system was introduced based on multiple capacitive micromachined ultrasound transducers (CMUTs) with shared channels, such that each element in a CMUT shares its channel with its counterpart in other CMUTs. The system uses the biasing voltages of the CMUTs to switch between them and multiplex the received signals in time. Notwithstanding all the enhancements, the signal-to-noise ratio (SNR) remains limited in PAI. To address this issue, we are proposing a multi-aperture encoding scheme (MAES) to further increase the SNR in a multi-aperture PAI system. The proposed method involves receiving signals with multiple CMUTs simultaneously based on an encoding matrix, instead of switching between individual CMUTs. As a result, shared channels contain a superposition of signals, which are later recovered by applying a decoding matrix. Here, an analytical model for computing SNR with an arbitrary encoding sequence is presented, and the method is validated through numerical simulations and in an experimental study. Bipolar and unipolar encoding sequences were considered for the experiments. The numerical results show, in comparison to conventional MP-PAI, that MAES will obtain an SNR gain of 5.8 and 8.8 dB for S-sequence and truncated Hadamard encodings, respectively, when using 15 transducers. In experiments, three transducers are encoded by S-sequences and show 1.5 dB improvement in SNR over conventional MP-PAI method, which aligns well with the analytical model.

The study of genre preferences of different doctors by leveraging their handled computed tomography (CT) images from multiple hospital databases offers a compelling field of AI-based medical treatment, especially in terms of deciphering and categorizing different diseases and the diseased regions. This research introduces a technique to categorize a large number of medical images into defined groups or “circles” based on common genre preferences, such as “heart diseases” or “osteoarthritis”. Our findings underscore two primary factors: 1) the necessity for an adaptable genre model that can adjust to varied visual characteristics depending on the database, and 2) the inconsistency in the volume of CT images per hospital, with some possessing very few image collection for some particular diseases (for example, a heart hospital has very few joint fracture CT images). To tackle these issues, we propose a regularized latent probabilistic model to depict each doctor’s genre-relevant features as a distribution within a latent manifold space. We subsequently develop a graph that reflects the genre similarities among different doctors. Employing an advanced technique for densely connected graph discovery, we are able to cluster doctors with similar genre preferences into circles. Our experimental outcomes, based on a review of CT image datasets from over 6000 hospitals, validate the effectiveness of our method in precisely distinguishing doctors/users between different genre types. Based on this, various applications can be enhanced.

The Internet of Things (IoT) ecosystem presents substantial challenges in terms of privacy and security, rendering it an attractive target for malicious actors. In this context, the literature review highlights the ongoing difficulty in addressing privacy and security through a unified mechanism owing to the heterogeneous nature of IoT devices, their dynamic behavior, and the continual advancement of intelligent hacking tools. Hence, encoding techniques have been considered from the perspective of privacy in Artificial Intelligence (AI) models. To overcome these challenges, this study introduces an integrated single mechanism for privacy and security in the IoT. In order to safeguard sensitive data during AI model training, a novel privacy mechanism called Replacement Encoding (RE) is proposed. This mechanism ensures the camouflage of sensitive information while preserving the integrity and utility of trained models. Additionally, this approach provides automated preprocessing, enhancing the performance of AI models. Message packet features were derived, extracted, and analyzed from the CICIoT2023 dataset (PCAP files) using Wireshark. The proposed replacement encoding scheme is integrated with AI classifiers to detect attacks, achieving an accuracy of 88.94% and 86.61% for the Random Forest (RF) and Deep Neural Network (DNN) models, respectively, utilizing 100 features. These results are compared to accuracies of 90.16% and 94.81% for the same models with up to 15 features using genetic algorithm-based correlation features. Finally, the proposed RF mechanism demonstrates its utility across multiple domains, including privacy preservation, automated data preprocessing, and protection of sensitive user data in Generative Pre-Training Transformer (GPT) applications as well as AI models.

An eye diagram, a critical metric in signal integrity analysis for high-speed interconnects such as packages, interposer, and printed circuit boards (PCBs), is generated by superposition of the received waveform. Obtaining an eye diagram is time-consuming, thus signal integrity analysis is inefficient. This article reviews that have been proposed to overcome this limitation. The statistical eye diagram provides a probability distribution depending on a sampling time and voltage, therefore it can be expanded to other metrics, such as the bit-error rate and shmoo plot. This article introduces previous research on statistical eye diagrams applied to complementary metal-oxide-semiconductors (CMOSs), noise, and high-speed systems. The methods applied to CMOSs include asymmetry between the P/NMOS transistors and the nonlinearity of the CMOS. The methods applied to noise include signal and power noise. The methods applied to high-speed systems include equalizers, signaling, encoding, linear feedback shift register, and error correction code.

Abstract Digitalized natural history collections serve as vital ecological and evolutionary research resources. Specimen retrieval based on morphological features allows for the rapid acquisition of similar specimens from these collections, aiding in maximizing the utilization of their resources and catering to the requirements of related research. However, achieving this objective requires effective feature extraction and representation techniques. We developed a phenotype encoding network (PENet), a deep learning‐based model that combines hashing methods to automatically extract and encode discriminative features into hash codes. We evaluated the performance of PENet on six data sets, including a newly constructed beetle data set (6566 images), which covers over 60% of the genera within the six subfamilies of Scarabaeidae. Phenotype encoding network showed high performance in feature extraction and image retrieval, allowing users to input an image of a specimen and efficiently retrieve all specimens with similar morphology. Two visualization methods, t‐SNE and Grad‐CAM, were used to evaluate the representation ability of the hash codes. Additionally, by using the hash codes generated from PENet, a phenetic distance tree was constructed based on the beetle data set. The result indicated that the hash codes could reveal the phenetic distances and relationships among categories to a certain extent. PENet provides an automatic and efficient method to extract and represent morphological discriminative features. The generated hash code can be used as a low‐dimensional carrier of these features, enabling efficient specimen retrieval. Moreover, the distance information carried by these hash codes suggests their potential in systematics, deserving further exploration.

Fake news can generate memory distortions and influence people's behavior. Within the framework of the great debates, the tendency to generate false memories from fake news seems to be modulated by the ideological alignment of each individual. This effect has been observed mainly around issues involving large sectors of society, but little is known about its impact on smaller-scale discussions focused on more specific populations. In this work we examine the formation of false memories from fake news in the debate between psychological currents in Argentina. For this, 326 individuals aligned to psychoanalysis (PSA) or Evidence-Based Practices (EBP) observed a series of news (12 true and 8 fabricated). The EBP group remembered or believed more fake news that damaged PSA. They also remembered with greater precision the statements of the news that harmed their own school, than those referring to others. These results could be understood as the product of an imbalance in the commitment between the different parties, since the group that proposes the paradigm shift (EBP) exhibited a congruence effect, while the group whose orientation is hegemonic in this field (PSA) did not show any effect of ideological alignment. The fact that the congruence effect is manifested to some extent in settings as relevant as the education of mental health professionals, highlights the need to move towards more careful practices in the consumption and production of media.

The following is a commentary of David Temperley's article on melodic pattern repetition and encoding. The article advances our understanding of patterns that could foster efficient encoding of melodies in Western tonal music. The research has room to grow in the theoretical context and provides opportunities for experimental work to test its predictions.

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.

To navigate their environment, insects need to keep track of their orientation. Previous work has shown that insects encode their head direction as a sinusoidal activity pattern around a ring of neurons arranged in an eight-column structure. However, it is unclear whether this sinusoidal encoding of head direction is just an evolutionary coincidence or if it offers a particular functional advantage. To address this question, we establish the basic mathematical requirements for direction encoding and show that it can be performed by many circuits, all with different activity patterns. Among these activity patterns, we prove that the sinusoidal one is the most noise-resilient, but only when coupled with a sinusoidal connectivity pattern between the encoding neurons. We compare this predicted optimal connectivity pattern with anatomical data from the head direction circuits of the locust and the fruit fly, finding that our theory agrees with experimental evidence. Furthermore, we demonstrate that our predicted circuit can emerge using Hebbian plasticity, implying that the neural connectivity does not need to be explicitly encoded in the genetic program of the insect but rather can emerge during development. Finally, we illustrate that in our theory, the consistent presence of the eight-column organisation of head direction circuits across multiple insect species is not a chance artefact but instead can be explained by basic evolutionary principles.

Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or ‘iconic’ memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.

DNA steganography is a technique for securely transmitting important data using DNA sequences. It involves encrypting and hiding messages within DNA sequences to prevent unauthorized access and decoding of sensitive information. Biometric systems, such as fingerprinting and iris scanning, are used for individual recognition. Since biometric information cannot be changed if compromised, it is essential to ensure its security. This research aims to develop a secure technique that combines steganography and cryptography to protect fingerprint images during communication while maintaining confidentiality. The technique converts fingerprint images into binary data, encrypts them, and embeds them into the DNA sequence. It utilizes the Feistel network encryption process, along with a mathematical function and an insertion technique for hiding the data. The proposed method offers a low probability of being cracked, a high number of hiding positions, and efficient execution times. Four randomly chosen keys are used for hiding and decoding, providing a large key space and enhanced key sensitivity. The technique undergoes evaluation using the NIST statistical test suite and is compared with other research papers. It demonstrates resilience against various attacks, including known-plaintext and chosen-plaintext attacks. To enhance security, random ambiguous bits are introduced at random locations in the fingerprint image, increasing noise. However, it is important to note that this technique is limited to hiding small images within DNA sequences and cannot handle video, audio, or large images.

The promoter is a key DNA sequence whose primary function is to control the initiation time and the degree of expression of gene transcription. Accurate identification of promoters is essential for understanding gene expression studies. Traditional sequencing techniques for identifying promoters are costly and time-consuming. Therefore, the development of computational methods to identify promoters has become critical. Since deep learning methods show great potential in identifying promoters, this study proposes a new promoter prediction model, called iPro2L-DG. The iPro2L-DG predictor, based on an improved Densely Connected Convolutional Network (DenseNet) and a Global Attention Mechanism (GAM), is constructed to achieve the prediction of promoters. The promoter sequences are combined feature encoding using C2 encoding and nucleotide chemical property (NCP) encoding. An improved DenseNet extracts advanced feature information from the combined feature encoding. GAM evaluates the importance of advanced feature information in terms of channel and spatial dimensions, and finally uses a Full Connect Neural Network (FNN) to derive prediction probabilities. The experimental results showed that the accuracy of iPro2L-DG in the first layer (promoter identification) was 94.10% with Matthews correlation coefficient value of 0.8833. In the second layer (promoter strength prediction), the accuracy was 89.42% with Matthews correlation coefficient value of 0.7915. The iPro2L-DG predictor significantly outperforms other existing predictors in promoter identification and promoter strength prediction. Therefore, our proposed model iPro2L-DG is the most advanced promoter prediction tool. The source code of the iPro2L-DG model can be found in https://github.com/leirufeng/iPro2L-DG.

Abstract Accurate segmentation of the skin lesion region is crucial for diagnosing and screening skin diseases. However, skin lesion segmentation is challenging due to the indistinguishable boundaries of the lesion region, irregular shapes and hair interference. To settle the above issues, we propose a Multi‐scale Depthwise Separable Convolutional Neural Network for skin lesion segmentation named MDSC‐Net. Specifically, a novel Multi‐scale Depthwise Separable Residual Convolution Module is employed in skip connection, conveying more detailed features to the decoder. To compensate for the loss of spatial location information in down‐sampling, we propose a novel Spatial Adaption Module. Furthermore, we propose a Multi‐scale Decoding Fusion Module in the decoder to capture contextual information. We have performed extensive experiments to verify the effectiveness and robustness of the proposed network on three public benchmark skin lesion segmentation datasets and one public benchmark polyp segmentation dataset, including ISIC‐2017, ISIC‐2018, PH2, and Kvasir‐SEG datasets. Experimental results consistently demonstrate the proposed MDSC‐Net achieves superior segmentation across five popularly used evaluation criteria. The proposed network reaches high‐performance skin lesion segmentation, and can provide important clues to help doctors diagnose and treat skin cancer early.

Background Self-construal influences the way people ascribe blame to victims, but it is not clear whether the same applies to harm do-ers, especially those in a position of authority. Participants and procedure We examined (N = 122, men n = 60) participants’ ascriptions of both blame and intentionality to harm doers (authority fig-ure versus peer) while priming self-construal (relational versus individual self). Using eye-tracking, we explored whether priming relational self, compared to individual self, affects the allocation of attention to faces versus objects. Results Although no effects of priming were found, the type of harm doer influenced the way people interpreted harmful social encounters. Participants attributed both greater intentionality and blame to peer than authority perpetrators. Also, in the case of peer perpetrators, blame ascription was higher than judgements of intentionality, which was the opposite pattern for authority perpetrators, where judgements of intentionality were greater than ascribed blame. In regard to encoding, par-ticipants independently of the type of harm doer looked significantly longer at faces than at objects in violent scenes. Conclusions Our results suggest the status of perpetrator influences judgements of harm independently of intrapersonal factors, such as primed self-construal. Moreover, people perceived as authority figures are not blamed for the hurtful action, despite attribut-ed intentionality.

The potential of neuromorphic (NM) solutions often lies in their low-SWaP (Size, Weight, and Power) capabilities, which often drive their application to domains that could benefit from this. Nevertheless, spiking neural networks (SNNs), with their inherent time-based nature, present an attractive alternative also for areas where data features are present in the time dimension, such as time series forecasting. Time series data, characterized by seasonality and trends, can benefit from the unique processing capabilities of SNNs, which offer a novel approach for this type of task. Additionally, time series data can serve as a benchmark for evaluating SNN performance, providing a valuable alternative to traditional datasets. However, the challenge lies in the real-valued nature of time series data, which is not inherently suited for SNN processing. In this work, we propose a novel spike-encoding mechanism and two loss functions to address this challenge. Our encoding system, inspired by NM event-based sensors, converts the derivative of a signal into spikes, enhancing interoperability with the NM technology and also making the data suitable for SNN processing. Our loss functions then optimize the learning of subsequent spikes by the SNN. We train a simple SNN using SLAYER as a learning rule and conduct experiments using two electricity load forecasting datasets. Our results demonstrate that SNNs can effectively learn from encoded data, and our proposed DecodingLoss function consistently outperforms SLAYER’s SpikeTime loss function. This underscores the potential of SNNs for time series forecasting and sets the stage for further research in this promising area of research.

The Latent Space Perspicacity and Interpretation Enhancement (LS-PIE) framework enhances dimensionality reduction methods for linear latent variable models (LVMs). This paper extends LS-PIE by introducing an optimal latent discovery strategy to automate identifying optimal latent dimensions and projections based on user-defined metrics. The latent condensing (LCON) method clusters and condenses an extensive latent space into a compact form. A new approach, latent expansion (LEXP), incrementally increases latent dimensions using a linear LVM to find an optimal compact space. This study compares these methods across multiple datasets, including a simple toy problem, mixed signals, ECG data, and simulated vibrational data. LEXP can accelerate the discovery of optimal latent spaces and may yield different compact spaces from LCON, depending on the LVM. This paper highlights the LS-PIE algorithm’s applications and compares LCON and LEXP in organising, ranking, and scoring latent components akin to principal component analysis or singular value decomposition. This paper shows clear improvements in the interpretability of the resulting latent representations allowing for clearer and more focused analysis.

This review critically examines the contributions of pupillometry to memory research, primarily focusing on its enhancement of our understanding of memory encoding and retrieval mechanisms mainly investigated with the recognition memory paradigm. The evidence supports a close link between pupil response and memory formation, notably influenced by the type of novelty detected. This proposal reconciles inconsistencies in the literature regarding pupil response patterns that may predict successful memory formation, and highlights important implications for encoding mechanisms. The review also discusses the pupil old/new effect and its significance in the context of recollection and in reflecting brain signals related to familiarity or novelty detection. Additionally, the capacity of pupil response to serve as a true memory signal and to distinguish between true and false memories is evaluated. The evidence provides insights into the nature of false memories and offers a novel understanding of the cognitive mechanisms involved in memory distortions. When integrated with rigorous experimental design, pupillometry can significantly refine theoretical models of memory encoding and retrieval. Furthermore, combining pupillometry with neuroimaging and pharmacological interventions is identified as a promising direction for future research.

Abstract The increasing development and utilisation of optical camera communications offer an excellent opportunity for the implementation of the smartphone‐to‐smartphone‐based visible light communications (S2SVLC) system. In this study, the authors experimentally demonstrate that the S2SVLC system can support high‐quality multimedia transmissions by the investigation of the mean square error estimation, peak signal‐to‐noise (PSNR) degradation, bit error rate (BER), and data rate achieved. The experimental analysis has been carried out for different typical practical background illuminations (i.e., normal illumination and dark condition), distances, tilting, and rotation angles. The PSNR and BER are determined for different frame sizes in the S2SVLC system to provide an insight of system performance. Those results indicate that the data transmission at different conditions significantly impacts the system's performance.

Abstract Face regions containing rich semantic information appear frequently in the videos. As the video resolution increase dramatically, the face regions will inevitably attract more attentions. This paper proposes a face perception based coding scheme to improve the visual quality of the face regions in UHD videos. A specially tailored face perception model is first utilized to precisely and quickly locate the face regions. Then, a face perception map is generated based on a hierarchical mapping algorithm. Finally, the face perception map is employed as a guidance to optimize the encoding process, including mode decision, block partition and bit allocation. The proposed method is implemented on HEVC to demonstrate the effectiveness. Experimental results on a set of 4K test sequences show that the proposed method can obviously improve the objective and subjective quality of the face regions, while causing only slight quality decline over the rest of the frame. Additionally, the computation required for mode decision and block partition is reduced, thereby saving encoding time cost.

IntroductionThe main objective of this research is to explore the core cognitive mechanisms utilized by exceptionally skilled percussionists as they navigate complex rhythms. Our specific focus is on understanding the dynamic interactions among brain regions, respectively, related to externally directed cognition (EDC), internally directed cognition (IDC), and rhythm processing, defined as the neural correlates of rhythm processing (NCRP).MethodsThe research involved 26 participants each in the percussionist group (PG) and control group (CG), who underwent task-functional magnetic resonance imaging (fMRI) sessions focusing on rhythm encoding and synchronization. Comparative analyses were performed between the two groups under each of these conditions.ResultsRhythmic encoding showed decreased activity in EDC areas, specifically in the right calcarine cortex, left middle occipital gyrus, right fusiform gyrus, and left inferior parietal lobule, along with reduced NCRP activity in the left dorsal premotor, right sensorimotor cortex, and left superior parietal lobule. During rhythmic synchronization, there was increased activity in IDC areas, particularly in the default mode network, and in NCRP areas including the left inferior frontal gyrus and bilateral putamen. Conversely, EDC areas like the right dorsolateral prefrontal gyrus, right superior temporal gyrus, right middle occipital gyrus, and bilateral inferior parietal lobule showed decreased activity, as did NCRP areas including the bilateral dorsal premotor cortex, bilateral ventral insula, bilateral inferior frontal gyrus, and left superior parietal lobule.DiscussionPG’s rhythm encoding is characterized by reduced cognitive effort compared to CG, as evidenced by decreased activity in brain regions associated with EDC and the NCRP. Rhythmic synchronization reveals up-regulated IDC, down-regulated EDC involvement, and dynamic interplay among regions with the NCRP, suggesting that PG engages in both automatic and spontaneous processing simultaneously. These findings provide valuable insights into expert performance and present opportunities for improving music education.

This case study presents the Dictionnaire numérique de la Ferme générale (1664-1794), a digital dictionary of the General Farm created in the course of FermeGé, which is a project accredited by the Agence nationale de la recherche in 2021, from its conceptual modelling to its Web deployment. While the dictionary initially appeared to the group of historians writing the entries to be a simple object with an organic structure, the need to make the data available in a Web environment proved to be an essential step in structuring the information. This initial point in the dialogue between historians and engineers led us to encode the structure of the records and the named entities in TEI XML to add a semantic layer to the raw text. In the same perspective, processing the bibliography and sources systematically mentioned at the end of a record enabled these elements to be indexed for later processing. This phase gradually changed the project’s approach to the dictionary. From then on, the dictionary was no longer simply an organic work, but was gradually transformed into a hybrid object backed by a relational database. The aim of this article is to analyse the contribution of digital humanities and documentary engineering to the project.

This textometric analysis, performed with the TXM software, offers a re-reading of a Franco-Ontarian play where each character is characterized by the language register he uses. The analysis of the lexicon of Michel Ouellette’s play French Town (1994) highlights the peculiarities of the language of Cindy, both in the way she tries to express herself, in her vocabulary and in her way of expressing her emotions. Thus, the “calcul des spécificités” method reveals that the three lexical units with the greatest frequency relate to the character of Cindy and their analysis shows how she manages to express her emotions although she has great difficulties to say I.

Abstract An area‐ and energy‐efficient encoding method and encoder architecture are proposed for the quasi‐cyclic low‐density parity check (LDPC) in near‐earth applications recommended by the Consultant Committee for Space Data Systems. The proposed encoding method reduces the computational complexity using a switch network before bit XOR operations in vector–matrix multiplication. A fully parallel LDPC encoder is implemented on the Taiwan Semiconductor Manufacturing Company 90 nm complementary metal oxide semiconductor (CMOS) technology, achieving a throughput of 10.06 Tbps with the lower area and energy utilization.

Fault attacks impose a serious threat against the practical implementations of cryptographic algorithms. Statistical Ineffective Fault Attacks (SIFA), exploiting the dependency between the secret data and the fault propagation overcame many of the known countermeasures. Later, several countermeasures have been proposed to tackle this attack using error detection methods. However, the efficiency of the countermeasures, in part governed by the number of error checks, still remains a challenge. In this work, we propose a fault countermeasure, StaTI, based on threshold implementations and linear encoding techniques. The proposed countermeasure protects the implementations of cryptographic algorithms against both side-channel and fault adversaries in a non-combined attack setting. We present a new composable notion, stability, to protect a threshold implementation against a formal gate/register-faulting adversary. Stability ensures fault propagation, making a single error check of the output suffice. To illustrate the stability notion, first, we provide stable encodings of the XOR and AND gates. Then, we present techniques to encode threshold implementations of S-boxes, and provide stable encodings of some quadratic S-boxes together with their security and performance evaluation. Additionally, we propose general encoding techniques to transform a threshold implementation of any function (e.g., non-injective functions) to a stable one. We then provide an encoding technique to use in symmetric primitives which encodes state elements together significantly reducing the encoded state size. Finally, we used StaTI to implement a secure Keccak on FPGA and report on its efficiency.

The subject of research in the article are the video images compression and encryption processes during the critically important objects managing process. The goal is to develop a method for compressing video images based on floating positional coding with an uneven codegrams length to simultaneously ensure information reliability and confidentiality during its transmission with a given time delay. Objectives: analyzing existing approaches to ensuring the video images confidentiality; development a method for compressing video images based on floating positional coding with an uneven codegrams length; evaluate the developed method effectiveness. The methods used are: digital image processing methods, digital image compression methods, image encryption and scrambling methods, structural-combinatorial coding methods, statistical analysis methods. The following results are obtained. The technology of floating encoding of an uneven sequence of blocks is proposed. Code values are formed from elements of different video image blocks. For this, a scheme for linearizing an image point coordinates from its four-dimensional representation on a plane into a one-dimensional element coordinate in a vector has been developed. The four-dimensional element coordinate on the plane describes the image block coordinates and the coordinates of the element in this block. Code values are formed under conditions of control their binary representation's length. At the same time, coding is implemented for an indeterminate number of video image elements. The number of elements depends on the length of the code word. Accordingly, codegrams with an indeterminate length are formed. Their length depends on the service data values, generated during the encoding process. Service data acts as a key element. Conclusions. The one-stage polyadic image encoding method in a differentiated basis has been further improved. The developed encoding method provides image compression without information quality loss. The original images volume compression provides by 3–20 % better compared to the TIFF data presentation format and by 4–15 % compared to the PNG format. The overhead amount is less than 2.5 % of the entire codestream size.

Abstract Existing traffic signal control systems present many limitations including fixed signal timing schemes, insufficient efficiency and flexibility, and difficulty in adapting to the changing traffic flows. In recent years, the development of deep reinforcement learning (RL) has shown great research potential and application prospects. This paper proposes an intersection signal control method based on the proximal policy optimization (PPO) method. Specifically, this paper uses the value vector representation method of traffic characteristics to encode the traffic state and then feeds the state encoding into the long short‐term memory (LSTM) network to obtain the signal phase output. Finally, to obtain the optimal signal control strategy, the PPO algorithm is utilized to train the neural network and adjust the signal phase. The proposed algorithm is benchmarked against other classic RL and adaptive signal control schemes in an experimental environment based on the traffic simulation software simulation of urban mobility (SUMO). Experimental results showed that the proposed algorithm greatly improves traffic efficiency. Specifically, the mean velocity of the vehicles increases by 45.09%, and the mean occupancy rate of each lane, the length of the longest jams during each step, and the mean halting duration dropped by 21.38%, 25.86%, and 12.94%, respectively.

Abstract Background Prior research has shown that memory for action sentences is stronger when stimuli are enacted during encoding than simply listened to: the so‐called enactment effect. The goal of the present study was to explore how writing during encoding—through handwriting and through keyboarding—fares compared with enacting, in supporting memory recall. Methods One hundred Norwegian high school students (64 girls, 36 boys) aged 16–21 years (M = 17.1) participated in the study. Four lists of verb–noun sentences with 12 sentences in each list were presented in four encoding conditions: (i) motor enactment, (ii) verbal listening, (iii) handwriting, and (iv) keyboarding. Results Results revealed a significant main effect of encoding condition, with the best memory gained in the enactment condition. Regarding writing, results showed that handwriting and keyboarding during encoding produced the lowest recall in comparison with the enactment and verbal listening conditions. Conclusion These results thus provide additional support for the enactment effect. While there has been much discussion on the relative benefits of handwriting versus keyboarding on student performance, both seemed to be equally poor strategies for the particular learning task explored here, potentially through increased cognitive load.

Abstract The advent of cloud computing, 5G, and artificial intelligence technology has spurred the exponential growth of big data and the need for data compression. To alleviate the CPU burden, computational engines for data compression integrated into network cards and storage devices, giving rise to computational storage. However, computational storage faces several challenges, such as limited memory resources for large hash tables, data dependency for parallel encoding and decoding, and stringent requirements for bandwidth, latency, and power efficiency. Here, an application‐specific integrated circuit‐based LZ77 solution that aims to address these challenges is proposed. Our solution reduces the hash table resource consumption and the bandwidth fluctuations, while achieving high compression ratios. The solution is implemented using Taiwan Semiconductor Manufacturing Company Limited 12 nm technology and demonstrate that a single LZ77 engine can achieve a 4 GB/s throughput at 1 GHz clock frequency. By using four engines, a computational storage drive to achieve 16 GB/s compression bandwidth is enabled. The solution achieves a comparable compression ratio as the standard LZ77 algorithm with a negligible compression ratio loss of 5%, balancing efficiency and effectiveness.

In this article, we discuss the ways in which Digital Humanities projects organise their resources, and argue for the need to collaborate with specialised services from other institutions for specific tasks, just as research publishers do. More specifically, we argue that libraries have a new central role to play in the publication of research data in repositories. Using the TextGrid Repository (TGR) as an example, we present its main components, its history, its main features, and discuss its advantages and disadvantages in comparison with similar repositories and technologies. We then present its current development within the Text+ consortium, both in the form of new features but also in the integration of existing corpora, making it interesting not only for German literature (as has been the case until now), but also for other traditions, especially Spanish literature.

Abstract One of the most important characteristics of all error control codes (ECCs) is the complexity of the encoding/decoding algorithms. Today, there are many ECCs that can correct multiple bit errors, but at the price of high encoding/decoding complexity. Among the rare exceptions are integer ECCs (IECCs), whose serial encoding/decoding algorithms run in O(n) time, where n is the codeword length. In this article, we show that IECCs can be encoded/decoded even faster, that is, that their parallel encoding/decoding algorithms have O(log2n) time complexity.

IntroductionOur brain is bombarded by a diverse range of visual stimuli, which are converted into corresponding neuronal responses and processed throughout the visual system. The neural activity patterns that result from these external stimuli vary depending on the object or scene being observed, but they also change as a result of internal or behavioural states. This raises the question of to what extent it is possible to predict the presented visual stimuli from neural activity across behavioural states, and how this varies in different brain regions.MethodsTo address this question, we assessed the computational capacity of decoders to extract visual information in awake behaving mice, by analysing publicly available standardised datasets from the Allen Brain Institute. We evaluated how natural movie frames can be distinguished based on the activity of units recorded in distinct brain regions and under different behavioural states. This analysis revealed the spectrum of visual information present in different brain regions in response to binary and multiclass classification tasks.ResultsVisual cortical areas showed highest classification accuracies, followed by thalamic and midbrain regions, with hippocampal regions showing close to chance accuracy. In addition, we found that behavioural variability led to a decrease in decoding accuracy, whereby large behavioural changes between train and test sessions reduced the classification performance of the decoders. A generalised linear model analysis suggested that this deterioration in classification might be due to an independent modulation of neural activity by stimulus and behaviour. Finally, we reconstructed the natural movie frames from optimal linear classifiers, and observed a strong similarity between reconstructed and actual movie frames. However, the similarity was significantly higher when the decoders were trained and tested on sessions with similar behavioural states.ConclusionOur analysis provides a systematic assessment of visual coding in the mouse brain, and sheds light on the spectrum of visual information present across brain areas and behavioural states.

Memory encoding and retrieval are critical sub-processes of episodic memory. While the hippocampus is involved in both, less is known about its connectivity with the neocortex during memory processing in humans. This is partially due to variations in demands in common memory tasks, which inevitably recruit cognitive processes other than episodic memory. Conjunctive analysis of data from different tasks with the same core elements of encoding and retrieval can reduce the intrusion of patterns related to subsidiary perceptual and cognitive processing. Leveraging data from two large-scale functional resonance imaging studies with different episodic memory tasks (514 and 237 participants), we identified hippocampal-cortical networks active during memory tasks. Whole-brain functional connectivity maps were similar during resting state, encoding, and retrieval. Anterior and posterior hippocampus had distinct connectivity profiles, which were also stable across resting state and memory tasks. When contrasting encoding and retrieval connectivity, conjunctive encoding-related connectivity was sparse. During retrieval hippocampal connectivity was increased with areas known to be active during recollection, including medial prefrontal, inferior parietal, and parahippocampal cortices. This indicates that the stable functional connectivity of the hippocampus along its longitudinal axis is superposed by increased functional connectivity with the recollection network during retrieval, while auxiliary encoding connectivity likely reflects contextual factors.

To index or compare sequences efficiently, often k-mers, i.e., substrings of fixed length k, are used. For efficient indexing or storage, k-mers are often encoded as integers, e.g., applying some bijective mapping between all possible σk k-mers and the interval [0, σk −1], where σ is the alphabet size. In many applications, e.g., when the reading direction of a DNA-sequence is ambiguous, canonical k-mers are considered, i.e., the lexicographically smaller of a given k-mer and its reverse (or reverse complement) is chosen as a representative. In naive encodings, canonical k-mers are not evenly distributed within the interval [0, σk −1]. We present a minimal encoding of canonical k-mers on alphabets of arbitrary size, i.e., a mapping to the interval [0, σk/2−1]. The approach is introduced for canonicalization under reversal and extended to canonicalization under reverse complementation. We further present a space and time efficient bit-based implementation for the DNA alphabet.

The precise control of bite force and gape is vital for safe and effective breakdown and manipulation of food inside the oral cavity during feeding. Yet, the role of the orofacial sensorimotor cortex (OSMcx) in the control of bite force and gape is still largely unknown. The aim of this study was to elucidate how individual neurons and populations of neurons in multiple regions of OSMcx differentially encode bite force and static gape when subjects (Macaca mulatta) generated different levels of bite force at varying gapes. We examined neuronal activity recorded simultaneously from three microelectrode arrays implanted chronically in the primary motor (MIo), primary somatosensory (SIo), and cortical masticatory (CMA) areas of OSMcx. We used generalized linear models to evaluate encoding properties of individual neurons and utilized dimensionality reduction techniques to decompose population activity into components related to specific task parameters. Individual neurons encoded bite force more strongly than gape in all three OSMCx areas although bite force was a better predictor of spiking activity in MIo vs. SIo. Population activity differentiated between levels of bite force and gape while preserving task-independent temporal modulation across the behavioral trial. While activation patterns of neuronal populations were comparable across OSMCx areas, the total variance explained by task parameters was context-dependent and differed across areas. These findings suggest that the cortical control of static gape during biting may rely on computations at the population level whereas the strong encoding of bite force at the individual neuron level allows for the precise and rapid control of bite force.

Distributed Denial of Service (DDoS) attacks pose a significant threat to the security of networking systems, as they can cause widespread disruption and even bring down entire distributed systems platforms. In this paper, we propose an approach called the DEQSVC that leverages quantum machine learning techniques to detect DDoS attacks with high accuracy. The DEQSVC integrates the most efficient dimensionality reduction techniques, a robust feature map method, and an efficient kernel estimation technique to improve data encoding, learning process, and detection accuracy. To evaluate the performance of the proposed DEQSVC, we conducted simulations using the Qiskit platform and executed the approach on an IBM quantum computer. Our results demonstrate that the DEQSVC outperforms several benchmark algorithms commonly used in intrusion detection systems. Specifically, the DEQSVC achieves a detection accuracy of 99.49, indicating its effectiveness as a highly accurate and efficient method for detecting DDoS attacks.

Rumoh aceh is a Acehnese traditional house that is one of the products and artifacts of the Acehnese culture. Efforts to preserve rumoh aceh often focus on physical aspects or aspects of form that are seen directly. It is has led to the discovery of many new buildings that now adopt the architectural form of rumoh aceh without a more comprehensive elaboration and transformation process. A concerted effort is required to present rumoh aceh architecture in a modern context. Transformation is one of the methods in architectural design to be able to present new ideas that are creative and innovative. The transformation in this paper is obtained through an encoding process that employs a semiotic approach as a method for bridging the rumoh aceh architecture transformation process. The encoding process is carried out by reinterpreting the values of rumoh aceh architecture obtained from previous research. This paper aims to gain knowledge regarding the architectural design process that can present a creation that represents regional culture, in this case, to present rumoh aceh architecture in a modern context

Abstract One of the most common causes of incompleteness is missing data, which occurs when no data value for the variables in observation is stored. An adaptive approach model outperforming other numerical methods in the classification problem was developed using the class center-based Firefly algorithm by incorporating attribute correlations into the imputation process (C3FA). However, this model has not been tested on categorical data, which is essential in the preprocessing stage. Encoding is used to convert text or Boolean values in categorical data into numeric parameters, and the target encoding method is often utilized. This method uses target variable information to encode categorical data and it carries the risk of overfitting and inaccuracy within the infrequent categories. This study aims to use the smoothing target encoding (STE) method to perform the imputation process by combining C3FA and standard deviation (STD) and compare by several imputation methods. The results on the tic tac toe dataset showed that the proposed method (C3FA-STD) produced AUC, CA, F1-Score, precision, and recall values of 0.939, 0.882, 0.881, 0.881, and 0.882, respectively, based on the evaluation using the kNN classifier.

In this article we analyze the results obtained in a survey on the use of the Text Encoding Initiative for Digital Humanities projects in Spanish that was conducted in early 2022 within the project “Communicating the Text Encoding Initiative to a Multilingual User Community” funded by the A. Mellon Foundation. The main objectives of our analysis are to understand who conforms the TEI user community in the Spanish-speaking world or in Spanish text-related initiatives, what types of TEI-related academic training is offered today, how and for what purposes this type of text encoding is used, and what are the existing resources for teaching and learning.