Decoding Procedure Research Articles

A C++ application programming interface for co-evolutionary biased random-key genetic algorithms for solution and scenario generation

ABSTRACT This paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic algorithm, it involves two types of populations that are mutually impacted by the fitness calculations. In the solution population, high-quality solutions evolve, representing first-stage decisions evaluated by their performance in the face of the scenario population. The scenario population ultimately generates a diverse set of scenarios regarding their impact on the solutions. This application allows the straightforward implementation of this algorithm, where the user needs only to define the problem-dependent decoding procedure and may adjust the risk profile of the decision-maker. This paper presents the co-evolutionary algorithm and structures the interface. We also present some experiments that validate the impact of relevant features of the application.

Behavioral validation of novel high resolution attention decoding method from multi-units & local field potentials

The ability to access brain information in real-time is crucial both for a better understanding of cognitive functions and for the development of therapeutic applications based on brain-machine interfaces. Great success has been achieved in the field of neural motor prosthesis. Progress is still needed in the real-time decoding of higher-order cognitive processes such as covert attention. Recently, we showed that we can track the location of the attentional spotlight using classification methods applied to prefrontal multi-unit activity (MUA) in the non-human primates. Importantly, we demonstrated that the decoded (x,y) attentional spotlight parametrically correlates with the behavior of the monkeys thus validating our decoding of attention. We also demonstrate that this spotlight is extremely dynamic. Here, in order to get closer to non-invasive decoding applications, we extend our previous work to local field potential signals (LFP). Specifically, we achieve, for the first time, high decoding accuracy of the (x,y) location of the attentional spotlight from prefrontal LFP signals, to a degree comparable to that achieved from MUA signals, and we show that this LFP content is predictive of behavior. This LFP attention-related information is maximal in the gamma band (30–250 Hz), peaking between 60 to 120 Hz. In addition, we introduce a novel two-step decoding procedure based on the labelling of maximally attention-informative trials during the decoding procedure. This procedure strongly improves the correlation between our real-time MUA and LFP based decoding and behavioral performance, thus further refining the functional relevance of this real-time decoding of the (x,y) locus of attention. This improvement is more marked for LFP signals than for MUA signals. Overall, this study demonstrates that the attentional spotlight can be accessed from LFP frequency content, in real-time, and can be used to drive high-information content cognitive brain-machine interfaces for the development of new therapeutic strategies.

Numerically Stable Polynomially Coded Computing

We study the numerical stability of polynomial based encoding methods, which has emerged to be a powerful class of techniques for providing straggler and fault tolerance in the area of coded computing. Our contributions are as follows: 1)We construct new codes for matrix multiplication that achieve the same fault/straggler tolerance as the previously constructed MatDot Codes and Polynomial Codes.2)We show that the condition number of every m ×m sub-matrix of an m ×n, n ≥ m Chebyshev-Vandermonde matrix, evaluated on the n-point Chebyshev grid, grows as O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(n-m)</sup> ) for n > m.3)By specializing our orthogonal polynomial based constructions to Chebyshev polynomials, and using our condition number bound for Chebyshev-Vandermonde matrices, we construct new numerically stable techniques for coded matrix multiplication. We empirically demonstrate that our constructions have significantly lower numerical errors compared to previous approaches which involve inversion of Vandermonde matrices. We generalize our constructions to explore the trade-off between computation/communication and fault-tolerance.4)We propose a numerically stable specialization of Lagrange coded computing. Our approach involves the choice of evaluation points and a suitable decoding procedure. Our approach is demonstrated empirically to have lower numerical errors as compared to standard methods.

Efficient Beamforming Training and Channel Estimation for Millimeter Wave OFDM Systems

We study the problem of downlink beamforming training and channel estimation for millimeter wave (mmWave) OFDM systems, where a hybrid analog and digital beamforming structure is employed at the transmitter (i.e., base station) and an omni-directional antenna or an antenna array is used at the receiver (i.e., user). To efficiently probe the channel, we form multiple directional beams simultaneously at the transmitter and steer them towards different directions. The objective is to devise the beam training sequence and develop an efficient algorithm to estimate the channel. By exploiting the sparse scattering nature of mmWave channels, the above problem is formulated as one of sparse encoding and signal recovery, which involves finding a sparse sensing matrix to compress the sparse channel and an efficient channel estimation algorithm to recover the sparse channel from compressive measurements. In this article, we propose a sparse bipartite graph code-based algorithm, where a set of bipartite graphs are employed to encode the sparse channel and a simple decoding procedure that relies on the presence of a No-Multiton-graph (NM-graph) is used to reconstruct the sparse channel. Theoretical analysis shows that our proposed method can help achieve a substantial training overhead reduction. Simulations are provided to show the effectiveness of the proposed algorithm and its performance advantage over compressed sensing-based methods.

List-Decoding with Double Samplers

We strengthen the notion of double samplers, first introduced by Dinur and Kaufman [``High dimensional expanders imply agreement expanders,” in Proc. 58th IEEE Symp. on Foundations of Comp. Science, IEEE, 2017, pp. 974--985], which are samplers with additional combinatorial properties, and whose existence we prove using high-dimensional expanders. The ABNNR code construction [N. Alon et al., IEEE Trans. Inform. Theory, 38 (1992), pp. 509--516] achieves large distance by starting with a base code $C$ with moderate distance, and then amplifying the distance using a sampler. We show that if the sampler is part of a larger double sampler, then the construction has an efficient list-decoding algorithm. Our algorithm works even if the ABNNR construction is not applied to a base code $C$ but rather to any string. In this case the resulting code is approximate-list-decodable, i.e., the output list contains an approximation to the original input. Our list-decoding algorithm works as follows: It uses a local voting scheme from which it constructs a unique games constraint graph. The constraint graph is an expander, so we can solve unique games efficiently. These solutions are the output of the list-decoder. This is a novel use of a unique games algorithm as a subroutine in a decoding procedure, as opposed to the more common situation in which unique games are used for demonstrating hardness results. Double samplers and high-dimensional expanders are akin to pseudorandom objects in their utility, but they greatly exceed random objects in their combinatorial properties. We believe that these objects hold significant potential for coding theoretic constructions and view this work as demonstrating the power of double samplers in this context.

MEANS OF VERBAL INFLUENCE IN ADVERTISING (ON THE MATERIAL OF ENGLISH-LANGUAGE MOVIE POSTERS) Movchan Diana,

The present paper deals with the study of tactics of implementing the strategy of language influence in English-language movie posters. It is considered that advertising movie posters are oriented on pragmatically focused impact on the potential viewers, stimulate their interest, persuade them, influence their emotions, feelings or rationality and create the algorithm of their behavior. Motivational strategies of proposing, reminding or encouraging actions in movie posters are implemented through the specifics of discursive techniques and tactics by selecting language tools at the structural, phonetic, lexical, grammatical and stylistic levels, their combinatorics and application of visual techniques. The research finds out, that the mechanism of verbal influence in movie posters includes the repertoire of following techniques and tactics: the presence of an effective advertising slogan, alliteration, assonance, word play, repetitions, omissions, graphic modifications of the text, symbolization, the use of imperatives and interrogative sentences, contrast, gradation. It is emphasized that movie posters, which are one of the most effective means of advertising film products, are of polycode nature, as they are created by combining verbal and nonverbal elements that interact and complement each other. An important role in the pragmatic orientation of movie posters belongs to the visual series, which activates the attention of the recipient and facilitates the procedure of perception and decoding of advertising. It is underlined that the main features of the language of advertising text of movie posters are general accessibility, social orientation and expressiveness. The language and style of presentation of each advertising message in movie posters are subject to the main idea and have a communicative and motivating nature

Joint Feature Optimization and Fusion for Compressed Action Recognition.

Recent methods including CoViAR and DMC-Net provide a new paradigm for action recognition since they are directly targeted at compressed videos (e.g., MPEG4 files). It avoids the cumbersome decoding procedure of traditional methods, and leverages the pre-encoded motion vectors and residuals in compressed videos to complete recognition efficiently. However, motion vectors and residuals are noisy, sparse and highly correlated information, which cannot be effectively exploited by plain and separated networks. To tackle these issues, we propose a joint feature optimization and fusion framework that better utilizes motion vectors and residuals in the following three aspects. (i) We model the feature optimization problem as a reconstruction process that represents features by a set of bases, and propose a joint feature optimization module that extracts bases in the both modalities. (ii) A low-rank non-local attention module, which combines the non-local operation with the low-rank constraint, is proposed to tackle the noise and sparsity problem during the feature reconstruction process. (iii) A lightweight feature fusion module and a self-adaptive knowledge distillation method are introduced, which use motion vectors and residuals to generate predictions similar to those from networks with optical flows. With these proposed components embedded in a baseline network, the proposed network not only achieves the state-of-the-art performance on HMDB-51 and UCF-101, but also maintains its advantage in computational complexity.

Random Access Channel Coding in the Finite Blocklength Regime

Consider a random access communication scenario over a channel whose operation is defined for any number of possible transmitters. As in the model recently introduced by Polyanskiy for the Multiple Access Channel (MAC) with a fixed, known number of transmitters, the channel is assumed to be invariant to permutations on its inputs, and all active transmitters employ identical encoders. Unlike the Polyanskiy model, in the proposed scenario, neither the transmitters nor the receiver knows which transmitters are active. We refer to this agnostic communication setup as the Random Access Channel (RAC). Scheduled feedback of a finite number of bits is used to synchronize the transmitters. The decoder is tasked with determining from the channel output the number of active transmitters, $k$, and their messages but not which transmitter sent which message. The decoding procedure occurs at a time $n_t$ depending on the decoder's estimate, $t$, of the number of active transmitters, $k$, thereby achieving a rate that varies with the number of active transmitters. Single-bit feedback at each time $n_i, i \leq t$, enables all transmitters to determine the end of one coding epoch and the start of the next. The central result of this work demonstrates the achievability on a RAC of performance that is first-order optimal for the MAC in operation during each coding epoch. While prior multiple access schemes for a fixed number of transmitters require $2^k - 1$ simultaneous threshold rules, the proposed scheme uses a single threshold rule and achieves the same dispersion.

On the decoding of 1-Fibonacci error correcting codes

The study of new error correcting codes has raised attention in the last years, especially because of their use in cryptosystems that are resistant to attacks running on quantum computers. In 2006, while leaving a more in-depth analysis for future research, Stakhov gave some interesting ideas on how to exploit Fibonacci numbers to derive an original error correcting code with a compact representation. In this paper, we provide an explicit formula to compute the redundancy of Stakhov codes, we identify some flows in the initial decoding procedure described by Stakhov, whose crucial point is to solve some non-trivial Diophantine equations, and provide a detailed discussion on how to avoid solving such equations in some cases and on how to detect and correct errors more efficiently.

Part of Speech Tagging Using Hidden Markov Models

Abstract In this paper, we present a wide range of models based on less adaptive and adaptive approaches for a PoS tagging system. These parameters for the adaptive approach are based on the n-gram of the Hidden Markov Model, evaluated for bigram and trigram, and based on three different types of decoding method, in this case forward, backward, and bidirectional. We used the Brown Corpus for the training and the testing phase. The bidirectional trigram model almost reaches state of the art accuracy but is disadvantaged by the decoding speed time while the backward trigram reaches almost the same results with a way better decoding speed time. By these results, we can conclude that the decoding procedure it’s way better when it evaluates the sentence from the last word to the first word and although the backward trigram model is very good, we still recommend the bidirectional trigram model when we want good precision on real data.

FPGA Implementation of Rate-Adaptable Prefix-Free Code Distribution Matching for Probabilistic Constellation Shaping

In this work, we implement a rate-adaptable (RA) prefix-free code distribution matching (PCDM) encoder in a field-programmable gate array (FPGA). The implemented RA-PCDM encoder supports a wide range of information rates from 1.6 to 4.8 bit/symbol with fine granularity of 0.2 bit/symbol in probabilistic constellation shaping (PCS) systems. The shaping performance of the implemented RA-PCDM is within 0.55 dB of the theoretical limit. Massively parallel encoding of RA-PCDM is demonstrated using a sliding window processor, in a universal architecture for all PCDM codebooks. Given that the procedure and complexity of RA-PCDM decoding is almost the same as those of RA-PCDM encoding, it is shown that the RA-PCDM uses substantially less hardware area than a traditional rate adaptation scheme based on low-density parity-check (LDPC) codes, while offering finer rate granularity and shaping gain. The RA-PCDM is therefore a practical PCS technology that enables high-throughput optical communications to approach channel capacity more closely at a lower cost than RA-LDPC.

Noisless coding of SAW RFID tags

The article is devoted to SAW RFID systems. The time-position code of the RFID tags is considered. It is proposed to use ReedSolomon codes to increase the noise immunity of the system. The cost of noise immunity is the degradation of information capacity. The coding procedure is optimized for the case when receiving of tag codes is carried out in a correlation way. The procedure for obtaining code using the Matlab is described in detail. The decoding procedure for the RFID tag codes based on the Berlekamp-Massey algorithm is described. Such decoding procedure allows reading codes and correcting decoding errors in real time. A comparative analysis of the probability of a tag code reading error with using and without using the proposed coding in condition of a Gaussian channel for a fixed signal-to-noise ratio. The problem of a more general case of error-correcting coding in SAW RFID systems is viewed.

Meta Learning-Based MIMO Detectors: Design, Simulation, and Experimental Test

Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. However, existing NN-based MIMO detectors are difficult to be deployed in practical systems because of their slow convergence speed and low robustness in new environments. To address these issues systematically, we propose a receiver framework that enables efficient online training by leveraging the following simple observation: although NN parameters should adapt to channels, not all of them are channel-sensitive. In particular, we use a deep unfolded NN structure that represents iterative algorithms in signal detection and channel decoding modules as multi layer deep feed forward networks. An expectation propagation (EP) module, called EPNet, is established for signal detection by unfolding the EP algorithm and rendering the damping factors trainable. An unfolded turbo decoding module, called TurboNet, is used for channel decoding. This component decodes the turbo code, where trainable NN units are integrated into the traditional max-log-maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> decoding procedure. We demonstrate that TurboNet is robust for channels and requires only one off-line training. Therefore, only a few damping factors in EPNet must be re-optimized online. An online training mechanism based on meta learning is then developed. Here, the optimizer, which is implemented by long short-term memory NNs, is trained to update damping factors efficiently by using a small training set such that they can quickly adapt to new environments. Simulation results indicate that the proposed receiver significantly outperforms traditional receivers and that the online learning mechanism can quickly adapt to new environments. Furthermore, an over-the-air platform is presented to demonstrate the significant robustness of the proposed receiver in practical deployment.

Experimental Demonstration of Continuous Sensor Data Monitoring Using Neural Network-Based Optical Camera Communications

This technical paper presents an optical camera communication system by using data as input collected from a sensor that continuously garners humidity and temperature information from the environment. A small light-emitting diode (LED) and an LED array are used as a transmitter and a low-speed rolling-shutter camera as a receiver. A modulation scheme standardized in IEEE 802.15.7, referred to as variable pulse-width modulation, is used to encode the data bits. The proposed scheme is flicker-free in different frequencies and applicable in both static and mobile scenarios. A neural network is designed for LED detection and improving the bit-error-rate in mobile scenarios. A new method based on the region-wise comparison and an existing method is used to remove the interference and noise generated from the neighboring light sources, respectively. The decoding procedure is performed and analyzed in Python 3.7. A data rate of 1.02 kbps is achieved using the single LED, which is further augmented to 64 kbps by using an $8\times 8$ LED dot matrix.

An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants

Steelmaking and the continuous-casting (SCC) scheduling problem is a realistic hybrid flow shop scheduling problem with continuous-casting production at the last stage. This study considers the SCC scheduling problem with diverse products, which is a vital and difficult problem in steel plants. To tackle this problem, this study first presents the mixed-integer linear programming (MILP) model to minimize the objective of makespan. Then, an improved migrating birds optimization algorithm (IMBO) is proposed to tackle this considered NP-hard problem. In the proposed IMBO, several improvements are employed to achieve the proper balance between exploration and exploitation. Specifically, a two-level decoding procedure is designed to achieve feasible solutions; the simulated annealing-based acceptance criterion is employed to ensure the diversity of the population and help the algorithm to escape from being trapped in local optima; a competitive mechanism is developed to emphasize exploitation capacity by searching around the most promising solution space. The computational experiments demonstrate that the proposed IMBO obtains competing performance and it outperforms seven other implemented algorithms in the comparative study.

Cognitive-innovative University: Intension, Concepts, Definitions

The subject of the research is University. The aim is to form a brief and detailed definitions of the concept “cognitive innovation University”. The method is based on the categorical-system approach using the methods of traditional logic and the theory of dynamic information systems.The article deals with the problems of definition of the concept of “University” and its types in the conditions of formation of the digital knowledge economy, due to the imperfection of the methodology. The article provides an overview of the most widely used definitions of the University. It is shown that in most concepts the essential characteristics of the University are empirical, so they are reflected non-systemically. The application of categorical-system methodology using the methods of traditional logic and the theory of dynamic information systems as a methodology for the study of cognitive innovation University is justified in this paper. On the basis of the triadic research method and methods of traditional logic, a brief definition of the concept of “cognitive innovative University” as an object of the socio-economic system is proposed. During the procedure of sequential two-level triadic decoding of the basic concept, leading to obtain an exhaustive definition of the desired concept, a detailed definition of the concept “cognitive-innovative University” was presented.

Coding Scheme for High-Order QAM Modulations in the Manhattan Metric

In this letter, a code construction in the Manhattan metric with a polynomial complexity algebraic decoding algorithm is proposed. It is shown that the code construction combined with a high-order QAM modulation enjoys a better code rate compared to a binary BCH code along with a competitive decoding procedure complexity. A performance gain from the use of the proposed code construction increases with the growth of an order of a QAM modulation. The code construction is mostly perspective in scenarios with high signal-to-noise ratios and decoding latency constraints, which are required for current and further generations of communications systems.

Multiple-rate error-correcting coding scheme

Error-correcting codes that can effectively encode and decode messages of distinct lengths while maintaining a constant blocklength are considered. It is known conventionally that a k-dimensional block code of length n defined over $$\texttt {GF}(q^{n})$$ is designed to encode a k-symbol user data in to an n-length codeword, resulting in a fixed-rate coding. In contrast, considering $$q=p^{\lambda }$$, this paper proposes two coding procedures (for the cases of $$\lambda =k$$ and $$\lambda =n$$) each deriving a multiple-rate code from existing channel codes defined over a composite field $$\texttt {GF}(q^{n})$$. Formally, the proposed coding schemes employ $$\lambda$$ codes $${\mathcal {C}}_{1}(\lambda , 1), {\mathcal {C}}_{2}(\lambda , 2), \ldots , {\mathcal {C}}_{\lambda }(\lambda , \lambda )$$ defined over $$\texttt {GF}(q)$$ to encode user messages of distinct lengths and incorporate variable-rate feature. Unlike traditional block codes, the derived multiple-rate codes of fixed blocklength n can be used to encode and decode user messages $$\mathbf{m}$$ of distinct lengths $$|\mathbf{m}| = 1, 2, \ldots , k, k+1, \ldots , kn$$, thereby supporting a range of information rates—inclusive of the code rates $$1/n^{2}, 2/n^{2},\ldots , k/n^{2}$$ and $$1/n, 2/n, \ldots , k/n$$ ! A simple decoding procedure to the derived multiple-rate code is also given; in that, orthogonal projectors are employed for the identification of encoded user messages of variable length.

Complexity of statistical attacks on QC‐LDPC code‐based cryptosystems

Public-key cryptosystems built on quasi-cyclic (QC) low-density parity-check and moderate-density parity-check codes are promising candidates for post-quantum cryptography, since they are characterised by compact keys and high algorithmic efficiency. The main issue with this kind of system is represented by the fact that, since the decoding procedure is probabilistic, it may leak information about the secret key. In this work, the authors study cryptanalysis procedures that aim at recovering the secret key by exploiting this fact. They identify the phenomenon that is at the basis of these procedures and show that the QC structure plays an important role in the success of these attacks. They use a graph analogy to study the complexity of these attacks, and show that their feasibility strongly depends on the QC structure. They also devise an approach to perform full cryptanalysis by combining an information set decoding algorithm with some partial knowledge about the structure of the secret key.

HELP: a sparse error locator polynomial for BCH codes

In 1990 Cooper suggested to use Groebner bases’ computations to decode cyclic codes and his idea gave rise to many research papers. In particular, as proved by Sala-Orsini, once defined the polynomial ring whose variables are the syndromes, the locations and the error values and considered the syndrome ideal, only one polynomial of a lexicographical Groebner basis for such ideal is necessary to decode (the general error locator polynomial, a.k.a. GELP). The decoding procedure only consists in evaluating this polynomial in the syndromes and computing its roots: the roots are indeed the error locations. A possible bottleneck in this procedure may be the evaluation part, since a priori the GELP may be dense. In this paper, focusing on binary cyclic codes with length $$n=2^m-1$$, correcting up to two errors, we give a Groebner-free, sparse analog of the GELP, the half error locator polynomial (HELP). In particular, we show that it is not necessary to compute the whole Groebner basis for getting such kind of locator polynomial and we construct the HELP, studying the quotient algebra of the polynomial ring modulo the syndrome ideal by a combinatorial point of view. The HELP turns out to be computable with quadratic complexity and it has linear growth in the length n of the code: $${\mathcal {O}}\left( \frac{n+1}{2}\right)$$.