Problem Of Decoding Research Articles

MODRED: A code-based non-interactive key exchange protocol

How to construct a non-interactive key exchange (NIKE) protocol based on coding theory is an opening problem. In this paper, we propose the first code-based NIKE protocol MODRED, whose security relies on the hardness of the Restricted Syndrome Decoding (R-SD) problem and its variant. A non-interactive error reconciliation mechanism is presented and applied to MODRED, which enables the two sides of communication to “approximately agree” on the shared key. This is of great significance because it may lead to a more generic framework construction. Furthermore, our proposal is competitive and practical. We provide an initial choice of parameters for MODRED, tailored to 120-bit post-quantum security level, yielding the public keys of less than 1.2 MBs.

Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks

The exponential growth and proliferation of wireless devices for different wireless applications have led to the emergence of cognitive radio network (CRN) for optimal utilization of scarce spectrum resources. However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. In this paper, a 3-state HMM spectrum occupancy and prediction technique in CRNs is proposed. The transmission, emission and initial state probabilities of the proposed 3-state HMM parameters were derived based on the three canonical problems associated with HMM. The evaluation, decoding and learning problems were solved using Forward algorithm, Viterbi algorithm and the Baum-Welch algorithm, respectively. The performance of the proposed 3-state HMM spectrum prediction technique was evaluated using prediction accuracy, probability of detection and spectrum utilization efficiency. The simulation results obtained revealed that the 3-state HMM outperformed the 2-state HMM spectrum prediction technique by 24.1% in prediction accuracy.

Multi-Bottleneck progressive propulsion network for medical image semantic segmentation with integrated macro-micro dual-stage feature enhancement and refinement

Semantic segmentation can assist doctors to locate key lesion areas intelligently, rapidly and with high precision in medical imaging, which is of great clinical significance for the diagnosis and rehabilitation of patients in various medical fields such as oncology and neurology. Existing segmentation networks face accuracy bottlenecks because enriched information at the decoding end can’t disperse over multiple layers of semantic depth. This is compounded by challenges like complex anatomical structures and varying imaging modalities. In this study, we propose a Multi-Bottleneck Progressive Semantic Segmentation Network (MBP-SSNet), which is essentially a new Baseline based on our proposal: Multi-Bottleneck based Progressive Propulsion Feature Guidance Network (MB-PPFG Net). Its Multi-Bottleneck solves the problem of coding and decoding multiplexed transmission of enriched information, and the propulsive transmission alleviates the problem of insufficient semantic fusion. In addition, under the macro–micro processing idea, MacroFocus Pre-Bottleneck Feature Enhancer (MPBFE) is proposed at the codec intersection in order to better enhance the image contextual semantic dependency association; while MicroDetail Post- Decoding Feature Refiner (MPDFR) is proposed at the tail of the network for fine-grained polishing. In the experiment, the MBP-SSNet was compared with 17 other models across 7 datasets, including diverse medical imaging scenarios, involving metric exploration, image comparison, process validation, and statistical analysis. Concurrently, 22 types of ablation analyses were conducted on it, along with detailed discussions on two critical issues. The experimental results demonstrate that MBP-SSNet achieves an average performance improvement of 0.6809 % in medical image segmentation compared to the optimal benchmark model. The official web address of MBP-SSNet is at: https://github.com/YF-W/MBP-SSNet.

Post-quantum secure fully-dynamic logarithmic-size deniable group signature in code-based setting

<p style='text-indent:20px;'>Since its introduction by Chaum and Heyst, <i>group signature</i> has been one of the most active areas of cryptographic research with numerous applications to computer security and privacy. Group signature permits the members of a group to sign a document on behalf of the entire group keeping signer's identity secret and enabling disclosure of the signer's identity if required. In this work, we present the first code-based fully-dynamic group signature scheme which allows group members to join or leave the group at any point of time. We employ a code-based updatable <i>Merkle-tree accumulator</i> in our design to achieve logarithmic-size signature and utilize randomized <i>Niederreiter</i> encryption to trace the identity of the signer. More positively, we equipped our scheme with <i>deniability</i> characteristic whereby the tracing authority can furnish evidence showing that a given member is not the signer of a particular signature. Our scheme satisfies the security requirements of <i>anonymity</i>, <i>non-frameability</i>, <i>traceability</i> and <i>tracing-soundness</i> in the random oracle model under the hardness of generic decoding problem. We emphasize that our scheme provides full-dynamicity, features deniability in contrast to the existing code-based group signature schemes and works favourably in terms of signature size, group public key size and secret key size.</p>

A tree-based model with branch parallel decoding for handwritten mathematical expression recognition

Handwritten mathematical expression recognition (HMER) is a challenging task in the field of computer vision due to the complex two-dimensional spatial structure and diverse handwriting styles of mathematical expressions (MEs). Recent mainstream approach treats MEs as objects with tree structures, modeled by sequence decoders or tree decoders. These decoders recognize the symbols and relationships between symbols in MEs in depth-first order, resulting in long decoding steps that can harm their performance, particularly for MEs with complex structures. In this paper, we propose a novel tree-based model with branch parallel decoding for HMER, which parses the structures of ME trees by explicitly predicting the relationships between symbols. In addition, a query constructing module is proposed to assist the decoder in decoding the branches of ME trees in parallel, thus reducing the number of decoding time steps and alleviating the problem of long sequence attention decoding. As a result, our model outperforms existing models on three widely-used benchmarks and demonstrates significant improvements in HMER performance.

A method for recovering linear block codes over an arbitrary finite field from sets of distorted code words

The article is devoted to one of the practically important problems of information security and cryptanalysis, which consists in recovering an unknown linear block code over an arbitrary field from a set of distorted code words. This is a hard computational problem, and the known problem-solving methods are proposed only for codes over the field of two elements and are based on the algorithms for searching words of small weight in (undistorted) linear block codes.
 The main result of the article is a method for solving the problem posed, which differs in essence from the known ones and consists in recovering the desired code by solving the LPN (Learning Parity with Noise) problem, namely, recovering the solutions of systems of linear equations with distorted right-hand sides and a random equally probable matrix of coefficients over specified field. The LPN problem is well known from the Theory of Computational Algorithms and Cryptanalysis. It is equivalent to the problem of random linear block code decoding, and the security of many modern post-quantum cryptosystems are based on its hardness.
 The proposed method provides an opportunity to apply a wider class of algorithms for recovering linear block codes in comparison with the previously known methods, in particular, algorithms like BKW and also the low weight words search algorithms in co-sets of linear block codes. Moreover, in contrast to previously known ones, the complexity of the proposed method depends linearly on the length of the required code (and increases with increasing of its dimension according to which algorithm for the LPN problem-solving is applied). Thus, the basic parameter determined the complexity of recovering a linear block code is its dimension (not its length), which, in principle, makes it possible to speed up known algorithms for recovering linear block codes from a set of corrupted code words.

Multimanned disassembly line balancing optimization considering walking workers and task evaluation indicators

Dynamic multimanned disassembly is a viable option for disassembly lines handling products with multiple attributes. This study proposes a multimanned disassembly line balancing problem with walking workers (MWW-DLBP). An experimental study investigates the worker disassembly fatigue attribute, and a novel evaluation index for muscle fatigue is introduced. The value attribute is evaluated using the AHP method. A mathematical model is developed with the aim of minimizing the number of workstations, workers, and worker fatigue indicators. To efficiently solve the DLBP problem, a SSA-DE algorithm is proposed. The SSA-DE algorithm adopts the SSA algorithm framework and utilizes a simplified single-layer decoding method for the double-layer decoding problem. Additionally, it uses the random strategy of the DE algorithm for optimization. Large-scale case studies verify the performance of SSA-DE. Finally, attribute evaluation indicators are applied to dismantle waste cars and the optimal solution is obtained.

Investigating the Effect of Persian Phonemic Synthesis Program in Cochlear Implant Users with Phoneme Processing Dificulities

Background and Aim: Children with severe to profound sensory-neural hearing loss who use a hearing aid or a Cochlear Implant (CI) are likely to have Decoding (DEC) problems. Various studies recommend auditory processing training to improve auditory processing difficulties in CI users. The aim of the present study was to evaluate phoneme processing difficulties in CI users and to investigate the efficacy of the Persian version of the Phonemic Synthesis Program (P-PSP) in improving phonological abilities in this population. Methods: A total of 28 prelingually hearing-impaired children aged 8–12 who underwent unilateral (right ear) cochlear implantation were included in this study. They were divided into experimental and control groups. The P-PSP was implemented on the experimental group, and the results were analyzed over three phases: baseline, intervention, and follow- up. Results: The present study’s results indicate that P-PSP training is suitable for CI children, particularly for the DEC subcategory. Moreover, the Persian Phonemic Synthesis Test (P-PST) results significantly improved after the intervention phase compared to the baseline evaluations (p<0.05). There was no significant difference between the intervention and follow-up phases’ results (p>0.05), indicating no recurrence of auditory processing difficulties after discontinuing training. Conclusion: Based on the study’s findings, the Persian version of PSP enhances phonological processing abilities in CI users. Therefore, the use of auditory phonological training in post- implantation rehabilitation programs appears to be highly important. Keywords: Auditory processing; decoding disorder; cochlear implant; auditory training

Commitment Capacity of Classical-Quantum Channels

We study commitment scheme for classical-quantum channels. To accomplish this we define various notions of commitment capacity for these channels and prove matching upper and lower bound on it in terms of the conditional entropy. Our achievability (lower bound) proof is quantum generalisation of the work of one of the authors (arXiv:2103.11548) which studied the problem of secure list decoding and its application to bit-string commitment. The techniques we use in the proof of converse (upper bound) is similar in spirit to the techniques introduced by Winter, Nascimento and Imai (Cryptography and Coding 2003) to prove upper bound on the commitment capacity of classical channels. However, generalisation of this technique to the quantum case is not so straightforward and requires some new constructions, which can be of independent interest.

Unsupervised neural decoding of signals recorded by thin-film electrode arrays implanted in muscles using autoencoding with a physiologically derived optimisation criterion

We present an autoencoder that learns the discharge times of individual motor units (MU) by processing multichannel electromyogram (EMG) recordings obtained by electrode arrays implanted in muscles, thereby providing a neural interface with the spinal cord. To this end, after preprocessing of the EMG via convolutive sphering, the encoder is constrained to apply an orthogonal transformation to the spatial data and the optimisation criterion enforces a temporal sparsity constraint. These constraints are based on the theoretical modelling of EMG generation. This decomposition method was evaluated on both simulated and experimental signals. For simulated signals, with a ratio of 1.5 between observations and sources, the average detection accuracy was 94% for up to 60 sources for SNR 20 dB. Moreover, the detection accuracy did not significantly decrease when decreasing the SNR to values as low as 0 dB. Experimental signals were collected in humans with thin-film invasive electrodes implanted in the tibialis anterior muscle. The results showed an accuracy on experimental data >90%. Moreover, the proposed method outperformed a state-of-the-art blind source separation approach in terms of the number of reliably detected motor units. In conclusion, we have proposed the first fully unsupervised neural network approach to the problem of neural decoding of intramuscular EMG time series by translating the available theoretical knowledge on the signal properties into an autoencoding architecture tailored to the decomposition problem.

Motor decoding from the posterior parietal cortex using deep neural networks

Objective. Motor decoding is crucial to translate the neural activity for brain-computer interfaces (BCIs) and provides information on how motor states are encoded in the brain. Deep neural networks (DNNs) are emerging as promising neural decoders. Nevertheless, it is still unclear how different DNNs perform in different motor decoding problems and scenarios, and which network could be a good candidate for invasive BCIs. Approach. Fully-connected, convolutional, and recurrent neural networks (FCNNs, CNNs, RNNs) were designed and applied to decode motor states from neurons recorded from V6A area in the posterior parietal cortex (PPC) of macaques. Three motor tasks were considered, involving reaching and reach-to-grasping (the latter under two illumination conditions). DNNs decoded nine reaching endpoints in 3D space or five grip types using a sliding window approach within the trial course. To evaluate decoders simulating a broad variety of scenarios, the performance was also analyzed while artificially reducing the number of recorded neurons and trials, and while performing transfer learning from one task to another. Finally, the accuracy time course was used to analyze V6A motor encoding. Main results. DNNs outperformed a classic Naïve Bayes classifier, and CNNs additionally outperformed XGBoost and Support Vector Machine classifiers across the motor decoding problems. CNNs resulted the top-performing DNNs when using less neurons and trials, and task-to-task transfer learning improved performance especially in the low data regime. Lastly, V6A neurons encoded reaching and reach-to-grasping properties even from action planning, with the encoding of grip properties occurring later, closer to movement execution, and appearing weaker in darkness. Significance. Results suggest that CNNs are effective candidates to realize neural decoders for invasive BCIs in humans from PPC recordings also reducing BCI calibration times (transfer learning), and that a CNN-based data-driven analysis may provide insights about the encoding properties and the functional roles of brain regions.

An application of a qd‐type discrete hungry Lotka–Volterra equation over finite fields to a decoding problem

AbstractIn this paper, the decoding problem for multiple Bose–Chaudhuri–Hocquenghem (BCH)‐Goppa codes over the same finite field is investigated. A new iterative decoding algorithm is proposed based on the quotient difference (qd)‐type discrete hungry Lotka–Volterra equation over finite fields. Compared with certain existing algorithms, the proposed algorithm manifests its advantage in computational complexity. A few of examples are presented to demonstrate its efficiency.

Case Study: COVID-19 Brain Fog or Auditory Processing Disorder?

Case Study: COVID-19 Brain Fog or Auditory Processing Disorder?

BiTSRS: A Bi-Decoder Transformer Segmentor for High-Spatial-Resolution Remote Sensing Images

Semantic segmentation of high-spatial-resolution (HSR) remote sensing (RS) images has been extensively studied, and most of the existing methods are based on convolutional neural network (CNN) models. However, the CNN is regarded to have less power in global representation modeling. In the past few years, methods using transformer have attracted increasing attention and generate improved results in semantic segmentation of natural images, owing to their powerful ability in global information acquisition. Nevertheless, these transformer-based methods exhibit limited performance in semantic segmentation of RS images, probably because of the lack of comprehensive understanding in the feature decoding process. In this paper, a novel transformer-based model named the bi-decoder transformer segmentor for remote sensing (BiTSRS) is proposed, aiming at alleviating the problem of flexible feature decoding, through a bi-decoder design for semantic segmentation of RS images. In the proposed BiTSRS, the Swin transformer is adopted as encoder to take both global and local representations into consideration, and a unique design module (ITM) is designed to deal with the limitation of input size for Swin transformer. Furthermore, BiTSRS adopts a bi-decoder structure consisting of a Dilated-Uper decoder and a fully deformable convolutional network (FDCN) module embedded with focal loss, with which it is capable of decoding a wide range of features and local detail deformations. Both ablation experiments and comparison experiments were conducted on three representative RS images datasets. The ablation analysis demonstrates the contributions of specifically designed modules in the proposed BiTSRS to performance improvement. The comparison experimental results illustrate that the proposed BiTSRS clearly outperforms some state-of-the-art semantic segmentation methods.

Deviant Arabic Transliterations of Foreign Shop Names in Saudi Arabia and Decoding Problems Among Shoppers

This study aimed to analyze a sample of Arabic transliterations of foreign shop names in Saudi Arabia; to identify the inaccuracies, and inconsistencies in the Arabic transliterations; the causes of the Arabic inconsistent and erroneous transliterations; whether shoppers (who do not know English) can decode and pronounce a sample Arabic transliterations of shop names; and the factors that should be taken into consideration in transliterating foreign shop names to Arabic. Results of the data analysis showed that 52% of the inaccurate transliterations are in the vowels and diphthongs; 36% are inaccurate and inconsistent transliterations of consonants. In 11.6% of the shop names, ch was reduced to sh; and /g/ was transliterated in 3 ways. 13% of the consonant transliteration deviations occurred in words containing a silent letter. In 6%, the Arabic vowel was double. In 8%, compound shop names were spelled together (blended) in the Arabic transliteration; In 7%, foreign names from French, Italian, Japanese, and Turkish were transliterated as they are pronounced in English, not in the original language. Findings also showed variant spellings of the same name shop names especially from French, Italian and Turkish (Parfois بارفواه بافويس; Spring سبرينق سبرينج; Semit Sarai سميت/سيميت; Chocolate شوكلت/شوكليت). Results of a decoding test given to a sample of college students and faculty who have a low proficiency level in English showed that the subjects could decode shop names such as ماكدونالدر، بيرغر كينج، هارديز، كوستا، ساربكس.. نسكافيه بيرجر كينج، تويز ار اص، but had difficulty decoding , دولس قوستو, أدفنتورا, جارليشوز لاونج, أميريكان إيجل أوتفترز, أكسسوريز, بايليس, باربكيوتونايت, لولو سلبريت, ذا تشيلدرنز بليس and other. The students and faculty had difficulty decoding Arabic transliterations because they are not familiar with the shop name in the foreign language, and because of the absence of short vowels in the transliteration, which makes it difficult to pronounce the transliterated names correctly. Deviant transliterations are attributed to transliterators who are non-native speakers of Arabic, English and other foreign languages. Recommendations for accurate Arabic transliterations of foreign shop names in Saudi Arabia are given.

Proofs of retrievability with tag outsourcing based on Goppa codes

In cloud storage, because the data owner loses the physical control of the data, the data may be tampered with or deleted. Although, it has been proposed to adopt provable data possession (PDP) or proofs of retrievability (POR) mechanism to ensure the integrity of cloud storage data. However, at present, most PDP/POR schemes are based on traditional cryptographic mechanisms and cannot resist quantum computer attacks. For this reason, the first POR scheme based on coding mechanism (BC-POR) is proposed in this paper. The scheme is constructed based on the difficulty assumptions of 2-regular word syndrome decoding (2-RWSD) problem and Goppa code distinguishing problem. Moreover, considering the low computing power of lightweight users, this paper adopts an audit scheme that supports the outsourcing of data tag calculation on the client side, that is, the calculation of data tag generation is outsourced to a third-party institution for execution. First of all, this scheme can prevent third-party institution from obtaining the real content of the data in the process of calculation tags and realize the privacy protection of user data. Secondly, the scheme uses a FSB hash function to generate a decodable syndrome, and this algorithm does not require iterative operations during the tagging process, thereby reducing the computational overhead of the tag. Finally, the provable security method is used to prove the security of the proposed scheme, and the performance of the proposed audit scheme is evaluated to prove the effectiveness of the scheme.

Smooth Projective Hash Function From Codes and its Applications

Nowadays, Smooth Projective Hash Functions (SPHFs) play an important role in constructing cryptographic tools such as secure Password-based Authenticated Key Exchange (PAKE) protocol in the standard model, oblivious transfer, and zero-knowledge proofs. Specifically, in this article, we focus on constructing PAKE protocol; that is, a kind of key exchange protocol which needs only a low entropy password to produce a cryptographically strong shared session key. In spite of relatively good progress of SPHFs in applications, it seems there has been little effort to build them upon quantum-resistant assumptions such as lattice-based cryptography and code-based cryptography to make them secure against quantum computer attacks. More precisely, there are two proposals based on lattice assumptions that utilize the SPHFs to construct PAKE secured in standard model. Considering quantum-resistant assumptions is less than straightforward and needs some relaxations. In this article, we introduce two new Approximate SPHF (ASPHFs) from error-correcting codes. Upon designing ASPHF, we can construct two efficient PAKE protocols. The security of our protocols could be proved based on the hardness of bounded decoding (BD) problem and learning with parity (LPN) problem in the standard model.

Terminological system of the polycode text theory

The formation of the terminological system of the polycode text theory as part of the lexical system of the Russian language is the current interest of Russian scientists’ research. The purpose of the proposed study is to overview the existing terms describing a complex text that combines series of semiotically heterogeneous codes to express a common idea. The relevance of the research is explained by the need to create a unified system of terms to describe the polycode text. The material of the study are scientific works devoted to the polycode text; the authors give quotes from scientific works that have the greatest influence on the formation of the theory of polycode text. The study considers terminological pairs: polycode - creolized text; linear - nonlinear text; polycode - polymodal text; iconic - non-verbal components of a polycode text; primary - secondary polycode text; and a separate term “paragraphemic means of a polycode text.” Currently, the polycode text is implemented in various types of discourse. The largest number of scientific papers that the authors consider are composed on the material of academic, advertising, cinema and educational discourses. Interest in the polycode text as an object of scientific analysis, on the one hand, and as an instrument, on the other, is increasing in view of growing significance of digital technologies and the semiotic series accompanying them in modern communication. As the results of the study, the authors note the relevant aspects of the polycode text studies, which include the problem of decoding, the difficulties in translating the polycode text, the prospects of a comparative analysis of the polycode text on the material of several languages, and the social orientation of the polycode text.

Framing as a tool of influencer marketing in the management of marketing communications and brand representation

Topicality of the selected issue is due to the change in the orientation of brand communications in the information space from rational economic goals and values to behavioral economic ones, as well as the probability of incorrect decoding of the information message by the recipient of the information. The aim was to create a model of representative marketing communications transition from the formation of meanings to the formation of trends by using framing as an impact marketing tool. To accomplish the tasks set, the work uses the selection of general scientific and special methods and techniques of scientific research, such as the method of semantic, morphological, comparative and critical analysis, the ontology construction method, system-structural and functional approaches, simulation modeling, etc. The brand phenomenon is considered from the point of view of social constructivism, as a result of which seven principles of brand existence in the concept of communications with society have been identified. Two types of frameworks, which are used when building a narrative in the context of representative brand actions, have been substantiated theoretically. The methodology for using these frameworks is considered from the position of marketing, logical, communicative and social metrics included extensional and intensional equalities, as a result of which a model, that allows determining the transformation of brand communications into meanings or trends and its trendy potential, has been built. The following framing tools have been analyzed and adapted for marketing purposes: Overton Window, Hallin’s Spheres, Opinion Corridor, Spiral of Silence, Echo Chamber and Epistemic Bubble. Reframing is considered from the point of view of culture, namely the processes of inculturation. The difference of approaches has been revealed and a model of the transition of using framing tools from constructing meanings to constructing trends has been built. This model allows to minimize the problem of incorrect decoding of information during representative and communicative actions of the brand

Quantum Information Protection Scheme Based on Reinforcement Learning for Periodic Surface Codes

Quantum information transfer is an information processing technology with high speed and high entanglement with the help of quantum mechanics principles. To solve the problem of quantum information getting easily lost during transmission, we choose topological quantum error correction codes as the best candidate codes to improve the fidelity of quantum information. The stability of topological error correction codes brings great convenience to error correction. The quantum error correction codes represented by surface codes have produced very good effects in the error correction mechanism. In order to solve the problem of strong spatial correlation and optimal decoding of surface codes, we introduced a reinforcement learning decoder that can effectively characterize the spatial correlation of error correction codes. At the same time, we use a double-layer convolutional neural network model in the confrontation network to find a better error correction chain, and the generation network can approach the best correction model, to ensure that the discriminant network corrects more nontrivial errors. To improve the efficiency of error correction, we introduced a double-Q algorithm and ResNet network to increase the error correction success rate and training speed of the surface code. Compared with the previous MWPM 0.005 decoder threshold, the success rate has slightly improved, which can reach up to 0.0068 decoder threshold. By using the residual neural network architecture, we saved one-third of the training time and increased the training accuracy to about 96.6%. Using a better training model, we have successfully increased the decoder threshold from 0.0068 to 0.0085, and the depolarized noise model being used does not require a priori basic noise, so that the error correction efficiency of the entire model has slightly improved. Finally, the fidelity of the quantum information has successfully improved from 0.2423 to 0.7423 by using the error correction protection schemes.