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We consider the problem of communicating over a channel that randomly “tears” the message block into small pieces of different sizes and shuffles them. For the binary torn-paper channel with block length $n$ and pieces of length ${\mathrm{ Geometric}}(p_{n})$ , we characterize the capacity as $C = e^{-\alpha }$ , where $\alpha = \lim _{n\to \infty } p_{n} \log n$. Our results show that the case of ${\mathrm{ Geometric}}(p_{n})$ -length fragments and the case of deterministic length- $(1/p_{n})$ fragments are qualitatively different and, surprisingly, the capacity of the former is larger. Intuitively, this is due to the fact that, in the random fragments case, large fragments are sometimes observed, which boosts the capacity. [ABSTRACT FROM AUTHOR]

The article introduces a small bit of data for Romanticists' consideration, a collection of seventeen commonplace books kept from 1814 to 1817. It explores how Mary and Sarah Leigh and their cousin Maria Leigh used their commonplace books as archives of shared intimacy. The strategies the Leigh sisters used to encode embodied data include linking immaterial ideas with the materiality of the notebook and paratext that teaches how to read the verse in the context of the sisters' lived experience.

A precoding technique based on polar codes and probabilistic shaping is introduced for dirty paper coding. Two variants of the precoding use multi-level shaping and sign-bit shaping in one dimension. The decoder uses multi-stage successive-cancellation list decoding with list-passing across the bit levels. The approach achieves approximately the same frame error rates as polar codes with multi-level shaping over standard additive white Gaussian noise channels at a block length of 256 symbols and with different amplitude shift keying (ASK) constellations. [ABSTRACT FROM AUTHOR]

Affective computing (AC), a field that bridges the gap between human affect and computational technology, has witnessed remarkable technical advancement. However, theoretical underpinnings of affective computing are rarely discussed and reviewed. This paper provides a thorough conceptual analysis of the literature to understand theoretical questions essential to affective computing and current answers. Inspired by emotion theories, we proposed the function-component-representation (FCR) framework to organize different conceptions of affect along three dimensions that each address an important question: function of affect (why compute affect), component of affect (how to compute affect), and representation of affect (what affect to compute). We coded each paper by its underlying conception of affect and found preferences towards affect detection, behavioral component, and categorical representation. We also observed coupling of certain conceptions. For example, papers using the behavioral component tend to adopt the categorical representation, whereas papers using the physiological component tend to adopt the dimensional representation. The FCR framework is not only the first attempt to organize different theoretical perspectives in a systematic and quantitative way, but also a blueprint to help conceptualize an AC project and pinpoint new possibilities. Future work may explore how the identified frequencies of FCR framework combinations may be applied in practice. [ABSTRACT FROM AUTHOR]

[Display omitted] • Photoreversible color switching systems have been integrated by reducing agent of triethanolamine, catalyst of β-FeOOH nanorods. • With high switching rate, high reversibility (>10 cycle), the new system could be broad use in rewritable paper. • The rewritable paper is highly applicable as self-erasing rewritable media for printing. • The rewritable paper can be applied for a data encoding and reading strategy. Nowadays, photo reversible color switching systems (PCSS) are always limited by some requirements, such as good stability, low toxicity, fast light response, long cycling performance and low production cost, therefore, it is a huge challenge to develop such a system that integrates beneficial features. Herein, a new type of PCSS have been demonstrated, which integrated reducing agent of triethanolamine (TEOA), catalyst of β-FeOOH nanorods and the redox driven color conversion characteristics of redox dyes. The system has the advantages of high switching rate, high reversibility (>10 cycles), wavelength selective response, safety and less light damage, which can be widely used in rewritable paper. As-prepared rewritable paper has high contrast, high resolution, suitable printing time and good reversibility, which is in line with the environmental protection concept of green printing. Rewritable paper is a kind of self-erasable rewritable medium which is highly suitable for printing. Environmental protection film has the advantages of low cost, convenient preparation and recycling. It is expected to replace the traditional writing, printing paper, existing systems and make a big step forward to practical application. Even more surprising, it also can be applied for a data encoding and reading strategy. [ABSTRACT FROM AUTHOR]

The diversity, infinity, and nonuniform description of human motion make it challenging for computers to understand human activities. To explore and reuse captured human motion data, this work defines a more comprehensive hierarchical theoretical model of human motion and proposes a standard human posture encoding scheme. We construct a domain knowledge graph (DKG) named the human motion encoding knowledge graph (HME-KG) based on posture codes and action labels. Community detection, similarity analysis, and centrality analysis are used to explore the potential value of motion data. This paper conducts an evaluation and visualization of HME-KG. [ABSTRACT FROM AUTHOR]

Featured Application: The model is primarily utilized for the task of entity relationship extraction during the construction process of an aquatic disease knowledge graph. Entity–relationship extraction plays a pivotal role in the construction of domain knowledge graphs. For the aquatic disease domain, however, this relationship extraction is a formidable task because of overlapping relationships, data specialization, limited feature fusion, and imbalanced data samples, which significantly weaken the extraction's performance. To tackle these challenges, this study leverages published books and aquatic disease websites as data sources to compile a text corpus, establish datasets, and then propose the Fd-CasBGRel model specifically tailored to the aquatic disease domain. The model uses the Casrel cascading binary tagging framework to address relationship overlap; utilizes task fine-tuning for better performance on aquatic disease data; trains on specialized aquatic disease corpora to improve adaptability; and integrates the BRC feature fusion module—which incorporates self-attention mechanisms, BiLSTM, relative position encoding, and conditional layer normalization—to leverage entity position and context for enhanced fusion. Further, it replaces the traditional cross-entropy loss function with the GHM loss function to mitigate category imbalance issues. The experimental results indicate that the F1 score of the Fd-CasBGRel on the aquatic disease dataset reached 84.71%, significantly outperforming several benchmark models. This model effectively addresses the challenges of ternary extraction's low performance caused by high data specialization, insufficient feature integration, and data imbalances. The model achieved the highest F1 score of 86.52% on the overlapping relationship category dataset, demonstrating its robust capability in extracting overlapping data. Furthermore, We also conducted comparative experiments on the publicly available dataset WebNLG, and the model in this paper obtained the best performance metrics compared to the rest of the comparative models, indicating that the model has good generalization ability. [ABSTRACT FROM AUTHOR]

The present paper investigates reflexives in Najdi Arabic (NA). We start by examining how the encoding of reflexivity in NA can be attained lexically, morphologically, and syntactically. We also investigate the argument structure of reflexive constructions in NA in accordance with Reinhart and Siloni’s (2005) bundling approach. Finally, possessive reflexives and their cross-linguistic distribution with definiteness marking are examined, providing empirical coverage to this area in NA. [ABSTRACT FROM AUTHOR]

With the urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA's potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation. [ABSTRACT FROM AUTHOR]

Effective clinical trials are necessary for understanding medical advances but early termination of trials can result in unnecessary waste of resources. Survival models can be used to predict survival probabilities in such trials. However, survival data from clinical trials are sparse, and DeepSurv cannot accurately capture their effective features, making the models weak in generalization and decreasing their prediction accuracy. In this paper, we propose a survival prediction model for clinical trial completion based on the combination of denoising autoencoder (DAE) and DeepSurv models. The DAE is used to obtain a robust representation of features by breaking the loop of raw features after autoencoder training, and then the robust features are provided to DeepSurv as input for training. The clinical trial dataset for training the model was obtained from the ClinicalTrials.gov dataset. A study of clinical trial completion in pregnant women was conducted in response to the fact that many current clinical trials exclude pregnant women. The experimental results showed that the denoising autoencoder and deep survival regression (DAE‐DSR) model was able to extract meaningful and robust features for survival analysis; the C‐index of the training and test datasets were 0.74 and 0.75 respectively. Compared with the Cox proportional hazards model and DeepSurv model, the survival analysis curves obtained by using DAE‐DSR model had more prominent features, and the model was more robust and performed better in actual prediction. [ABSTRACT FROM AUTHOR]

In this paper, we consider encoding strategies for the Z-channel with noiseless feedback. We analyze the combinatorial setting where the maximum number of errors inflicted by an adversary is proportional to the number of transmissions, which goes to infinity. Without feedback, it is known that the rate of optimal asymmetric-error-correcting codes for the error fraction $\tau \ge 1/4$ vanishes as the blocklength grows. In this paper, we give an efficient feedback encoding scheme with $n$ transmissions that achieves a positive rate for any fraction of errors $\tau < 1$ and $n\to \infty $. Additionally, we state an upper bound on the rate of asymptotically long feedback asymmetric error-correcting codes. [ABSTRACT FROM AUTHOR]

Due to inter-modal effects hidden in multi-modalities and the impact of weak modalities on multi-modal entity alignment, a Multi-modal Entity Alignment Method with Inter-modal Enhancement (MEAIE) is proposed. This method introduces a unique modality called numerical modality in the modal aspect and applies a numerical feature encoder to encode it. In the feature embedding stage, this paper utilizes visual features to enhance entity relation representation and influence entity attribute weight distribution. Then, this paper introduces attention layers and contrastive learning to strengthen inter-modal effects and mitigate the impact of weak modalities. In order to evaluate the performance of the proposed method, experiments are conducted on three public datasets: FB15K, DB15K, and YG15K. By combining the datasets in pairs, compared with the current state-of-the-art multi-modal entity alignment models, the proposed model achieves a 2% and 3% improvement in Top-1 Hit Rate(Hit@1) and Mean Reciprocal Rank (MRR), demonstrating its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient design of Hamming (11, 7) encoder utilising Full Swing-Gate Diffusion Input (FS-GDI) approach in 65 nm technology nano-size node. The proposed design of Hamming codes aims to improve the power and area efficiency through reducing of transistors count by employing power-efficient logic style. Encoding circuits of Hamming code (11, 7) and (7, 4) are designed using the various traditional and proposed approaches. The amount of consumed power, delay time, Power Delay Product (PDP) and hardware simplicity are employed as a metrics for evaluating the efficiency of the proposed designs of encoding circuits. The simulation experiments are executed utilising Cadence Virtuoso simulator package. These experiments revealed that the proposed designs of Hamming encoding circuits achieve delay time reduction by 50.91% and 20% for Hamming codes (7, 4) and (11, 7), respectively. Also, hardware (H/W) simplicity and area efficiency of the circuits are improved by 50% compared to CMOS-based circuits. From the results analysis, the proposed FS-GDI based Hamming encoding circuits achieve efficient power and delay optimising. Hence, the power consumption, delay and area in communications systems and DSP circuits due to encoding process are reduced. The whole performance of DSP circuits can be more power/area efficient. [ABSTRACT FROM AUTHOR]

Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions. [ABSTRACT FROM AUTHOR]

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase the visual impression of fused images by improving the quality of infrared and visible light picture fusion. The network comprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder module utilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformer to achieve deep-level co-extraction of local and global features from the original picture. An edge enhancement module (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy is introduced to enhance the adaptive representation of information in various regions of the source image, thereby enhancing the contrast of the fused image. The encoder and the EEM module extract features, which are then combined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test the algorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preserves background and detail information in both infrared and visible images, yielding superior outcomes in subjective and objective evaluations. [ABSTRACT FROM AUTHOR]

The aim of this paper is to highlight the complexity and the central importance to academic achievement of taking and reviewing notes at third level. It is based on a review of international literature on the notetaking process between 1970 and 2023. The paper describes notetaking and reviewing as the method of encoding and externally storing new material, for the purpose of advancement in learning and attainment in assessment. It outlines research on the benefits of typed versus handwritten methods of notetaking. The overriding outcome demonstrates that taking notes, either by longhand or typing, produces superior results than not taking and reviewing notes. The remainder of the review focuses on the status of notetaking instruction in third level colleges and universities. It is observed that despite the centrality of notetaking to educational success, and the positive impact of instruction on taking notes, skills training and modelling are generally not taught or embedded in the curricula in tertiary education. Furthermore, the paper describes teaching strategies alongside linear and non-linear notetaking methods that have been shown to encourage students to take and revise notes which has, in turn, led to the enhancement of learning. The conclusion reviews the main points of the article and its limitations. A further review of literature on the examination of cognitive and metacognitive functions on notetaking would contribute to the understanding of how notetaking and revision operate to increase students' capacity for recall, comprehension, and knowledge. [ABSTRACT FROM AUTHOR]

This paper presents a pioneering 2D spatial Optical Code-Division Multiple Access (OCDMA) encoding system that exploits Mode Division Multiplexing (MDM) and Multicore Fiber (MCF) technologies. This innovative approach utilizes two spatial dimensions to enhance the performance and security of OCDMA systems. In the first dimension, we employ Hermite-Gaussian modes (HG00, HG01, HG11) to modulate each user's signal individually. This unique approach offers a robust means of data transmission while ensuring minimal interference among users. The second-dimension leverages MCF encoding, introducing two incoherent OCDMA codes: the Zero Cross Correlation (ZCC) code (λc=0) and the ZFD code (λc=1). These codes are thoughtfully designed and simulated, taking into account their cross-correlation properties to guarantee minimal interference and heightened data security. To assess the efficiency of this novel OCDMA encoding system, we implemented simulations with three active users using the Opti system software. At the transmitter end, each user's signal is modulated individually by their designated HG mode (HG00, HG01, HG11), resulting in separate channels. Subsequently, at the multicore fiber, each user's data is encoded with a unique code-word, and they are directed through specific core groups, ensuring data isolation and integrity. In this paper, the BER and eye pattern are examined with respect to different parameters such as data rate and distance. At a distance of 5 km and data rate of 10 Gbit/s, a BER value around 10-70 is achieved. [ABSTRACT FROM AUTHOR]

There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data, but these techniques and algorithms cannot be used to protect data from an attacker. Cloud cryptography is the best way to transmit data in a secure and reliable format. Various researchers have developed various mechanisms to transfer data securely, which can convert data from readable to unreadable, but these algorithms are not sufficient to provide complete data security. Each algorithm has some data security issues. If some effective data protection techniques are used, the attacker will not be able to decipher the encrypted data, and even if the attacker tries to tamper with the data, the attacker will not have access to the original data. In this paper, various data security techniques are developed, which can be used to protect the data from attackers completely. First, a customized American Standard Code for Information Interchange (ASCII) table is developed. The value of each Index is defined in a customized ASCII table. When an attacker tries to decrypt the data, the attacker always tries to apply the predefined ASCII table on the Ciphertext, which in a way, can be helpful for the attacker to decrypt the data. After that, a radix 64-bit encryption mechanism is used, with the help of which the number of cipher data is doubled from the original data. When the number of cipher values is double the original data, the attacker tries to decrypt each value. Instead of getting the original data, the attacker gets such data that has no relation to the original data. After that, a Hill Matrix algorithm is created, with the help of which a key is generated that is used in the exact plain text for which it is created, and this Key cannot be used in any other plain text. The boundaries of each Hill text work up to that text. The techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorithms. Then, the Kasiski test is used to verify the validity of the proposed algorithm and found that, if the proposed algorithm is used for data encryption, so an attacker cannot break the proposed algorithm security using any technique or algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a single English text is taken as the research object, and the automatic extraction method of text summary is studied using data-driven method. This paper takes a single text as the research object, establishes the connection relationship between article sentences, and proposes a method of automatic extraction of text summary based on graph model and topic model. The method combines the text graph model, complex network theory, and LDA topic model to construct a sentence synthesis scoring function to calculate the text single-sentence weights and output the sentences within the text threshold in descending order as text summaries. The algorithm improves the readability of the text summary while providing enough information for the text summary. In this paper, we propose a BERT-based topic-aware text summarization model based on a neural topic model. The approach uses the potential topic embedding representation encoded by the neural topic model to match with the embedding representation of BERT to guide topic generation to meet the requirements of semantic representation of text and explores topic inference and summary generation jointly in an end-to-end manner through the transformer architecture to capture semantic features while modelling long-range dependencies by a self-attentive mechanism. In this paper, we propose improvements based on pretrained models on both extractive and generative algorithms, making them enhanced for global information memory. Combining the advantages of both algorithms, a new joint model is proposed, which makes it possible to generate summaries that are more consistent with the original topic and have a reduced repetition rate for evenly distributed article information. Comparative experiments were conducted on several datasets and small uniformly distributed private datasets were constructed. In several comparative experiments, the evaluation metrics were improved by up to 2.5 percentage points, proving the effectiveness of the method, and a prototype system for an automatic abstract generation was built to demonstrate the results. [ABSTRACT FROM AUTHOR]

The lack of data democratization and information leakage from trained models hinder the development and acceptance of robust deep learning-based healthcare solutions. This paper argues that irreversible data encoding can provide an effective solution to achieve data democratization without violating the privacy constraints imposed on healthcare data and clinical models. An ideal encoding framework transforms the data into a new space where it is imperceptible to a manual or computational inspection. However, encoded data should preserve the semantics of the original data such that deep learning models can be trained effectively. This paper hypothesizes the characteristics of the desired encoding framework and then exploits random projections and random quantum encoding to realize this framework for dense and longitudinal or time-series data. Experimental evaluation highlights that models trained on encoded time-series data effectively uphold the information bottleneck principle and hence, exhibit lesser information leakage from trained models. Healthcare data democratization is often hampered by privacy constraints governing the sensitive healthcare data. Here, the authors show that encoding healthcare data could be a potential solution for achieving healthcare democratization within the context of deep learning. [ABSTRACT FROM AUTHOR]

With the development of technology, communication channels are increasingly experiencing burst faults of various forms caused by noise elements. To get around this, an appropriate encoding and decoding mechanism should be designed while taking into account things like redundancy, memory usage, efficiency, etc. Motivated by these facts, in this paper, we present a class of integer codes capable of correct asymmetric solid burst errors. In addition to the theoretical foundations, the paper also derives the expressions for the probabilities of incorrect and correct decoding for the proposed codes. Lastly, we compare the proposed codes with other similar codes in terms of code rate and memory consumption. [ABSTRACT FROM AUTHOR]

A bilinear map whose domain and target sets are identical is called a self-bilinear map. Original self-bilinear maps are defined over cyclic groups. Since the map itself reveals information about the underlying cyclic group, the Decisional Diffie–Hellman Problem (DDH) and the computational Diffie–Hellman (CDH) problem may be solved easily in some specific groups. This brings a lot of limitations to constructing secure self-bilinear schemes. As a compromise, a self-bilinear map with auxiliary information was proposed in CRYPTO'2014. In this paper, we construct this weak variant of a self-bilinear map from generic sets and indistinguishable obfuscation. These sets should own several properties. A new notion, One Way Encoding System (OWES), is proposed to summarize these properties. The new Encoding Division Problem (EDP) is defined to complete the security proof. The OWES can be built by making use of one level of graded encoding systems (GES). To construct a concrete self-bilinear map scheme, Garg, Gentry, and Halvei(GGH13) GES is adopted in our work. Even though the security of GGH13 was recently broken by Hu et al., their algorithm does not threaten our applications. At the end of this paper, some further considerations for the EDP for concrete construction are given to improve the confidence that EDP is indeed hard. [ABSTRACT FROM AUTHOR]

Situated visualization is an emerging concept within visualization, in which data is visualized in situ, where it is relevant to people. The concept has gained interest from multiple research communities, including visualization, human-computer interaction (HCI) and augmented reality. This has led to a range of explorations and applications of the concept, however, this early work has focused on the operational aspect of situatedness leading to inconsistent adoption of the concept and terminology. First, we contribute a literature survey in which we analyze 44 papers that explicitly use the term “situated visualization” to provide an overview of the research area, how it defines situated visualization, common application areas and technology used, as well as type of data and type of visualizations. Our survey shows that research on situated visualization has focused on technology-centric approaches that foreground a spatial understanding of situatedness. Secondly, we contribute five perspectives on situatedness (space, time, place, activity, and community) that together expand on the prevalent notion of situatedness in the corpus. We draw from six case studies and prior theoretical developments in HCI. Each perspective develops a generative way of looking at and working with situatedness in design and research. We outline future directions, including considering technology, material and aesthetics, leveraging the perspectives for design, and methods for stronger engagement with target audiences. We conclude with opportunities to consolidate situated visualization research. [ABSTRACT FROM AUTHOR]

In this paper, a simple yet effective network pruning framework is proposed to simultaneously address the problems of pruning indicator, pruning ratio, and efficiency constraint. This paper argues that the pruning decision should depend on the convolutional weights, and thus proposes novel weight-dependent gates (W-Gates) to learn the information from filter weights and obtain binary gates to prune or keep the filters automatically. To prune the network under efficiency constraints, a switchable Efficiency Module is constructed to predict the hardware latency or FLOPs of candidate pruned networks. Combined with the proposed Efficiency Module, W-Gates can perform filter pruning in an efficiency-aware manner and achieve a compact network with a better accuracy-efficiency trade-off. We have demonstrated the effectiveness of the proposed method on ResNet34, ResNet50, and MobileNet V2, respectively achieving up to 1.33/1.28/1.1 higher Top-1 accuracy with lower hardware latency on ImageNet. Compared with state-of-the-art methods, W-Gates also achieves superior performance. [ABSTRACT FROM AUTHOR]

This paper presents the overview and rationale behind the Decoder-Side Depth Estimation (DSDE) mode of the MPEG Immersive Video (MIV) standard, using the Geometry Absent profile, for efficient compression of immersive multiview video. A MIV bitstream generated by an encoder operating in the DSDE mode does not include depth maps. It only contains the information required to reconstruct them in the client or in the cloud: decoded views and metadata. The paper explains the technical details and techniques supported by this novel MIV DSDE mode. The description additionally includes the specification on Geometry Assistance Supplemental Enhancement Information which helps to reduce the complexity of depth estimation, when performed in the cloud or at the decoder side. The depth estimation in MIV is a non-normative part of the decoding process, therefore, any method can be used to compute the depth maps. This paper lists a set of requirements for depth estimation, induced by the specific characteristics of the DSDE. The depth estimation reference software, continuously and collaboratively developed with MIV to meet these requirements, is presented in this paper. Several original experimental results are presented. The efficiency of the DSDE is compared to two MIV profiles. The combined non-transmission of depth maps and efficient coding of textures enabled by the DSDE leads to efficient compression and rendering quality improvement compared to the usual encoder-side depth estimation. Moreover, results of the first evaluation of state-of-the-art multiview depth estimators in the DSDE context, including machine learning techniques, are presented. [ABSTRACT FROM AUTHOR]

Fourier PlenOctrees have shown to be an efficient representation for real-time rendering of dynamic neural radiance fields (NeRF). Despite its many advantages, this method suffers from artifacts introduced by the involved compression when combining it with recent state-of-the-art techniques for training the static per-frame NeRF models. In this paper, we perform an in-depth analysis of these artifacts and leverage the resulting insights to propose an improved representation. In particular, we present a novel density encoding that adapts the Fourier-based compression to the characteristics of the transfer function used by the underlying volume rendering procedure and leads to a substantial reduction of artifacts in the dynamic model. We demonstrate the effectiveness of our enhanced Fourier PlenOctrees in the scope of quantitative and qualitative evaluations on synthetic and real-world scenes. [ABSTRACT FROM AUTHOR]

Reaction systems are a formal model for computational processing in which reactions operate on sets of entities (molecules) providing a framework for dealing with qualitative aspects of biochemical systems. This paper is concerned with reaction systems in which entities can have discrete concentrations, and so reactions operate on multisets rather than sets of entities. The resulting framework allows one to deal with quantitative aspects of reaction systems, and a bespoke linear-time temporal logic allows one to express and verify a wide range of key behavioural system properties. In practical applications, a reaction system with discrete concentrations may only be partially specified, and the possibility of an effective automated calculation of the missing details provides an attractive design approach. With this idea in mind, the current paper discusses parametric reaction systems with parameters representing unknown parts of hypothetical reactions. The main result is a method aimed at replacing the parameters in such a way that the resulting reaction system operating in a specified external environment satisfies a given temporal logic formula.This paper provides an encoding of parametric reaction systems in smt, and outlines a synthesis procedure based on bounded model checking for solving the synthesis problem. It also reports on the initial experimental results demonstrating the feasibility of the novel synthesis method. [ABSTRACT FROM AUTHOR]

Countries are managed based on accurate and precise laws. Enacting appropriate and timely laws can cause national progress. Each law is a textual term that is added to the set of existing laws after passing a process with the approval of the assembly. In the review of each new law, the relevant laws are extracted and analyzed among the set of existing laws. This paper presents a new solution for extracting the relevant rules for a term from an existing set of rules using semantic similarity and deep learning techniques based on the BERT model. The proposed method encodes sentences or paragraphs of text in a fixed-length vector (dense vector space). Thereafter, the vectors are utilized to evaluate and score the semantic similarity of the sentences with the cosine distance measurement scale. In the proposed method, the machine can understand the meaning and concept of the sentences by using the BERT model coding method. The BERT model considers the position of the entities in the sentences. Then the semantic similarities of documents, calculating the degree of similarity between their documents with a subject, and detecting their semantic similarity are done. The results obtained from the test dataset indicated the precision and accuracy of the method in detecting semantic similarities of legal documents related to the Islamic Consultative Assembly of Iran, as well as the precision and accuracy of performance above 90%. [ABSTRACT FROM AUTHOR]

The fifth generation New Radio vehicle-to-everything (5G NR-V2X) has higher requirements for delay and reliability, which brings challenges to the physical layer signal processing. Mobile edge computing (MEC) can provide larger capacity data storage and more efficient computation through localization of on-board unit (OBU) and next generation Node B (gNB) services. This paper combines the channel estimation of New Radio vehicle-to-infrastructure (NR-V2I) communication baseband signal processing with MEC and designs an intelligent channel estimation framework. In the MEC server, this paper proposes a channel estimation algorithm based on deep learning. This algorithm uses a one-dimensional convolutional neural network (1D CNN) to complete frequency-domain interpolation and conditional recurrent unit (CRU) for time-domain state prediction. Additional velocity coding vector and multipath coding vector track changes in the environment, and accurately train channel data in different mobile environments. System simulation and analysis show that the proposed algorithm improves the channel estimation accuracy, reduces the bit error rate, and enhances the robustness compared with the representative channel estimation algorithms for NR-V2I. [ABSTRACT FROM AUTHOR]

The aspects of sum rate and fairness for dirty paper coding (DPC) based MIMO downlink communications are investigated in this paper. We first apply the $\ell _{1}$ -norm fairness measure to formulate the problem of fairness maximization for a given sum rate as an optimization problem. The problem is unfortunately nonconvex and cannot be efficiently solved. To overcome the difficulty, we invoke the uplink-downlink duality to transform the problem back and forth between uplink and downlink communications. An efficient, iterative waterfilling based algorithm is then proposed to yield achievable rates with the best possible fairness values. Simulation results show that the proposed approach offers an enormous gain in the achievable sum rates for a wide range of fairness values, when compared to the popular successive zero-forcing DPC-based and block diagonalization based coding schemes. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Road crack detection is an important indicator of road detection. In real life, it is very meaningful work to detect road cracks. With the rapid development of science and technology, especially computer science and technology, quite a lot of methods have been applied to crack detection. Traditional detection methods rely on manual identification, which is inefficient and prone to errors. In addition, the commonly used image processing methods are affected by many factors, such as illumination, road stains, etc., so the results are unstable. In the research on pavement crack detection, many research studies mainly focus on the recognition and classification of cracks, lacking the analysis of the specific characteristics of cracks, and the feature values of cracks cannot be measured. Starting from the deep learning method in computer science and technology, this paper proposes a road crack detection technology based on deep learning. It relies on a new deep dictionary learning and encoding network DDLCN, establishes a new activation function MeLU, and adopts a new differentiable calculation method. The technology relies on the traditional Mask-RCNN algorithm and is implemented after improvement. In the comparison of evaluation indicators, the values of recall, precision, and F1-score reflect certain superiority. Experiments show that the proposed method has good implementability and performance in road crack detection and crack feature measurement. [ABSTRACT FROM AUTHOR]

MPPM (Multipulse Position Modulation) is a pulse code technology that is widely used in optical communication and deep space communication due to its high energy efficiency. Combination code is a variation of MPPM and is widely used in MWD (Measurement While Drilling) telemetry systems. However, whether it is MPPM or combination code, it is difficult to establish a simple mapping relationship between bit symbols and pulse symbols. Therefore, an encoding table is usually used to realize the mapping between bit symbols and pulse symbols. With an increase in bit width, the storage complexity of the encoding table increases exponentially. This is unacceptable for underground transmitters working in harsh environments. This paper maps the code symbols of MPPM to a hyper-triangular region of a high-dimensional tensor and establishes a simple lookup table and lookup algorithm to achieve low-complexity mapping from bit symbols to MPPM code symbols. At the same time, this paper also provides a method for extending from MPPM code symbols to commonly used combination code symbols in MWD, thereby providing an efficient implementation path for large bit width combination code encoding at the underground end of MWD. [ABSTRACT FROM AUTHOR]

The proliferation of the Internet and the widespread adoption of mobile devices have given rise to an immense volume of real-time trajectory big data. However, a single computer and conventional databases with limited scalability struggle to manage this data effectively. During the process of visual rendering, issues such as page stuttering and subpar visual outcomes often arise. This paper, founded on a distributed architecture, introduces a multi-level time encoding method using "minutes", "hours", and "days" as fundamental units, achieving a storage model for trajectory data at multi-scale time. Furthermore, building upon an improved DBSCAN clustering algorithm and integrating it with the K-means clustering algorithm, a novel density-based partitioning clustering algorithm has been introduced, which incorporates road coefficients to circumvent architectural obstacles, successfully resolving page stuttering issues and significantly enhancing the quality of visualization. The results indicate the following: (1) when data is extracted using the units of "minutes", "hours", and "days", the retrieval efficiency of this model is 6.206 times, 12.475 times, and 18.634 times higher, respectively, compared to the retrieval efficiency of the original storage model. As the volume of retrieved data increases, the retrieval efficiency of the proposed storage model becomes increasingly superior to that of the original storage model. Under identical experimental conditions, this model's retrieval efficiency also outperforms the space–time-coded storage model; (2) Under a consistent rendering level, the clustered trajectory data, when compared to the unclustered raw data, has shown a 40% improvement in the loading speed of generating heat maps. There is an absence of page stuttering. Furthermore, the heat kernel phenomenon in the heat map was also resolved while enhancing the visualization rendering speed. [ABSTRACT FROM AUTHOR]

This paper proposes an event-triggered state estimation method for parameter-uncertain systems with a binary encoding transmission scheme. Firstly, a binary encoding transmission scheme is introduced between the state estimator and the system to improve the efficiency of network communication. Secondly, an event-triggering mechanism (ETM) is designed to ensure the accuracy of state estimation and reduce the computational burden of the state estimator. At the event-triggered moments, considering the uncertainty of the system, the binary encoding transmission scheme, and the ETM, a moving horizon estimator (MHER) is designed using the robust least squares optimization method to obtain optimal state estimation. At the no-event-triggered moments, the state estimation of the system is computed based on an open-loop state estimator (OLER). Furthermore, stability analysis showed that the state estimation error of the proposed method is bounded. Finally, the practical value of the proposed in this paper is confirmed through numerical simulation. [ABSTRACT FROM AUTHOR]

Recent advances in quantum computing and machine learning have brought about a promising intersection of these two fields, leading to the emergence of quantum machine learning (QML). However, the integration of quantum computing and machine learning poses several challenges. One of the prominent challenges lies in the presence of barren plateaus (BP) in QML algorithms, particularly in quantum neural networks (QNNs). Recent studies have successfully identified the fundamental causes underlying the existence of BP in QNNs. This paper presents a framework designed to explore the interplay of multiple factors contributing to the BP problem in quantum neural networks (QNNs), which poses a critical challenge for the practical applications of QML. We focus on the combined influence of data encoding, qubit entanglement, and ansatz expressibility in hybrid quantum neural networks (HQNNs) for multi-class classification tasks. Our framework aims to empirically analyze the joint impact of these factors on the training landscape of HQNNs. Our results show that the occurrence of the BP problem in HQNNs is contingent upon the expressibility of the underlying ansatz and the type of the adopted data encoding technique. Additionally, we observe that qubit entanglement also plays a role in exacerbating the BP problem. Leveraging various evaluation metrics for classification tasks, we systematically evaluate the performance of HQNNs and provide recommendations tailored to different constraint scenarios. Our findings emphasize the significance of our framework in addressing the practical success of QNNs. [ABSTRACT FROM AUTHOR]

Data Security in terms of file encryption is very crucial, every file nowadays is important as it contains either personal information or industrial data. Hybrid encryption is achieved through data transfer using a combination method of symmetric and asymmetric encryption algorithms. Users can safely send files through hybrid encryption. Asymmetric encryption slows down the encryption process when used alone so symmetric encryption is used for file encryption, advantages of both forms of encryption are utilized. The result is another layer of security with no extra burden to system performance. The idea of hybrid encryption is very simple. Instead of using just AES only to encrypt the file, we use AES to encrypt the file. Then to maintain the secrecy of the key, we encrypt the key using RSA. This discussed paper is a completely different approach which is used for securely storing files. This proposed scheme will also make sure the newly proposed model to have confidentiality and integrity mechanisms. [ABSTRACT FROM AUTHOR]

In general coding theory, we often assume that error is observed in transferring or storing encoded symbols, while the process of encoding itself is error-free. Motivated by recent applications of coding theory, in this paper, we consider the case where the process of encoding is distributed and prone to error. We introduce the problem of distributed encoding, comprised of a set of $K \in \mathbb {N}$ isolated source nodes and $N \in \mathbb {N}$ encoding nodes. Each source node has one symbol from a finite field, which is sent to each of the encoding nodes. Each encoding node stores an encoded symbol from the same field, as a function of the received symbols. However, some of the source nodes are controlled by the adversary and may send different symbols to different encoding nodes. Depending on the number of the adversarial nodes, denoted by $\beta \in \mathbb {N}$ , and the cardinality of the set of symbols that each one generates, denoted by $v \in \mathbb {N}$ , the process of decoding from the encoded symbols could be impossible. Assume that a decoder connects to an arbitrary subset of $t \in \mathbb {N}$ encoding nodes and wants to decode the symbols of the honest nodes correctly, without necessarily identifying the sets of honest and adversarial nodes. An important characteristic of a distributed encoding system is $t^{*} \in \mathbb {N}$ , the minimum of such $t$ , which is a function of $K$ , $N$ , $\beta $ , and $v$. In this paper, we study the distributed linear encoding system, i.e. one in which the encoding nodes use linear coding. We show that $t^{*}_{\textrm {Linear}}=K+2\beta (v-1)$ , if $N\ge K+2\beta (v-1)$ , and $t^{*}_{\textrm {Linear}}=N$ , if $N\le K+2\beta (v-1)$. In order to achieve $t^{*}_{\textrm {Linear}}$ , we use random linear coding and show that in any feasible solution that the decoder finds, the messages of the honest nodes are decoded correctly. In order to prove the converse of the fundamental limit, we show that when the adversary behaves in a particular way, it can always confuse the decoder between two feasible solutions that differ in the message of at least one honest node. [ABSTRACT FROM AUTHOR]