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The implementation security of post-quantum cryptography (PQC) algorithms has emerged as a critical concern with the PQC standardization process reaching its end. In a side-channel-assisted chosen-ciphertext attack, the attacker builds linear inequalities on secret key components and uses the belief propagation (BP) algorithm to solve. The number of inequalities leverages the query complexity of the attack, so the fewer the better. In this paper, we use the PQC standard algorithm CRYSTALS-Kyber as a study case to construct bilateral inequalities on key variables with substantially narrower intervals using a side-channel-assisted oracle. For KYBER512, KYBER768, and KYBER1024, the average Shannon entropy carried by such inequality is improved from the previous 0.6094, 0.4734, and 0.8544 to 0.6418, 0.4777, and 1.2007. The number of such inequalities required to recover the key utilizing the BP algorithm for KYBER512 and KYBER1024 is reduced by 5.32% and 40.53% in theory and experimentally the reduction is even better. The query complexity is reduced by 43%, 37%, and 48% for KYBER512, 768, and 1024 assuming reasonably perfect reliability. Furthermore, we introduce a strategy aimed at further refining the interval of inequalities. Diving into the BP algorithm, we discover a measure metric named JSD (Jensen-Shannon distance)-metric that can gauge the tightness of an inequality. We then develop a machine learning-based strategy to utilize the JSD-metrics to contract boundaries of inequalities even with fewer inequalities given, thus improving the entropy carried by the system of linear inequalities. This contraction strategy is at the algorithmic level and has the potential to be employed in all attacks endeavoring to establish a system of inequalities concerning key variables. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we investigate the performance of message-passing algorithms for the weighted min–max flow (WMMF) problem which was introduced by Ichimori et al. (1980). WMMF was well studied in combinational optimization, as it provides important applications in time transportation problem and the storage management problem. We develop a message-passing algorithm called min–max belief propagation (BP) for determining the optimal solution of WMMF. As the main result of this paper, we prove that for a digraph of size n , BP converges to the optimal solution within O (n 3) time after O (n) iterations if the optimal solution of the underlying min–max flow problem instance is unique. To the best of our knowledge, the fastest polynomial time algorithm for WMMF runs in essentially O (n 6) time among the known algorithms, where n is the number of vertices. On the other hand, it is one of a very few instances where BP is proved correct with fully-polynomial running time. [ABSTRACT FROM AUTHOR]

A hardware architecture is presented to decode (N, K) polar codes based on a low-density parity-check code-like decoding method. By applying suitable pruning techniques to the dense graph of the polar code, the decoder architectures are optimized using fewer check nodes (CN) and variable nodes (VN). Pipelining is introduced in the CN and VN architectures, reducing the critical path delay. Latency is reduced further by a fully parallelized, singlestage architecture compared with the log N stages in the conventional belief propagation (BP) decoder. The designed decoder for short-to-intermediate code lengths was implemented using the Virtex-7 field-programmable gate array (FPGA). It achieved a throughput of 2.44 Gbps, which is four times and 1.4 times higher than those of the fast-simplified successive cancellation and combinational decoders, respectively. The proposed decoder for the (1024, 512) polar code yielded a negligible bit error rate of 10^-4 at 2.7 Eb/No (dB). It converged faster than the BP decoding scheme on a dense paritycheck matrix. Moreover, the proposed decoder is also implemented using the Xilinx ultra-scale FPGA and verified with the fifth generation new radio physical downlink control channel specification. The superior errorcorrecting performance and better hardware efficiency makes our decoder a suitable alternative to the successive cancellation list decoders used in 5G wireless communication.

Navigation system performance degrades significantly in complex environments. It is important to analyze satellite visibility through 3D terrain modelling and separate the satellite signals propagated by NLOS to suppress the NLOS error. However, the traditional 3D terrain modelling visibility analysis method based on the pure terrain cover angle is only suitable for determining the visibility of GNSS satellites and may incorrectly separate LOS propagate measurement signals from members with low relative ranges and elevation angles under air–ground swarm conditions. To this end, this paper proposes a belief-propagating cooperative navigation method based on air–ground visibility analysis, which avoids mistakenly separating close-range LOS cooperative navigation signals by simultaneously considering the distances, elevation angles, and azimuths of the signal sources relative to the air–ground swarm members. The simulation shows that the cooperative navigation NLOS identification method based on air–ground visibility analysis proposed in this paper can more accurately realize the separation of NLOS signals under cooperative conditions than the traditional pure angular 3D terrain modelling visibility analysis method can, and the localization error of the members to be assisted is significantly reduced. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A hardware architecture is presented to decode (N, K) polar codes based on a low‐density parity‐check code‐like decoding method. By applying suitable pruning techniques to the dense graph of the polar code, the decoder architectures are optimized using fewer check nodes (CN) and variable nodes (VN). Pipelining is introduced in the CN and VN architectures, reducing the critical path delay. Latency is reduced further by a fully parallelized, single‐stage architecture compared with the log N stages in the conventional belief propagation (BP) decoder. The designed decoder for short‐to‐intermediate code lengths was implemented using the Virtex‐7 field‐programmable gate array (FPGA). It achieved a throughput of 2.44 Gbps, which is four times and 1.4 times higher than those of the fast‐simplified successive cancellation and combinational decoders, respectively. The proposed decoder for the (1024, 512) polar code yielded a negligible bit error rate of 10−4 at 2.7 Eb/No (dB). It converged faster than the BP decoding scheme on a dense parity‐check matrix. Moreover, the proposed decoder is also implemented using the Xilinx ultra‐scale FPGA and verified with the fifth generation new radio physical downlink control channel specification. The superior error‐correcting performance and better hardware efficiency makes our decoder a suitable alternative to the successive cancellation list decoders used in 5G wireless communication. [ABSTRACT FROM AUTHOR]

Joint probabilistic data association (JPDA) filter methods and multiple hypothesis tracking (MHT) methods are widely used for multitarget tracking (MTT). However, they are known to exhibit undesirable behavior in tracking scenarios with targets in close proximity: JPDA filter methods suffer from the track coalescence effect, i.e., the estimated tracks of targets in close proximity tend to merge and can become indistinguishable, while MHT methods suffer from an opposite effect known as track repulsion, i.e., the estimated tracks of targets in close proximity tend to repel each other in the sense that their separation is larger than the actual distance between the targets. In this paper, we review the JPDA filter and MHT methods and discuss the track coalescence and track repulsion effects. We also consider a more recent methodology for MTT that is based on the belief propagation (BP) algorithm. We argue that BP-based MTT does not exhibit track repulsion because it is not based on maximum a posteriori estimation, and that it exhibits significantly reduced track coalescence because certain properties of the BP messages related to data association encourage separation of target state estimates. Our theoretical arguments are confirmed by numerical results for four representative simulation scenarios.

Belief propagation is one of low latency decoding algorithms for polar codes but it requires relatively high decoding complexity due to its inherent iterative decoding nature. To remedy the problem, early stopping belief propagation algorithm was proposed. Unfortunately, it requires additional complexity to test stopping criterion at each end of decoder iterations. In this paper, we propose a low complexity early stopping belief propagation decoder for polar codes which requires lower complexities not only for stopping criterion test but also for decoding by reducing the number of message updates within the decoder without any performance loss.

Factorization reduces computational complexity, and is therefore an important tool in statistical machine learning of high dimensional systems. Conventional molecular modeling, including molecular dynamics and Monte Carlo simulations of molecular systems, is a large research field based on approximate factorization of molecular interactions. Recently, the local distribution theory was proposed to factorize joint distribution of a given molecular system into trainable local distributions. Belief propagation algorithms are a family of exact factorization algorithms for (junction) trees, and are extended to approximate loopy belief propagation algorithms for graphs with loops. Despite the fact that factorization of probability distribution is the common foundation, computational research in molecular systems and machine learning studies utilizing belief propagation algorithms have been carried out independently with respective track of algorithm development. The connection and differences among these factorization algorithms are briefly presented in this perspective, with the hope to intrigue further development of factorization algorithms for physical modeling of complex molecular systems.

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants—what we call "shared intelligence." This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world—also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales, that is, inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent's generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing—leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first—and key—step towards such an ecology. [ABSTRACT FROM AUTHOR]

Online auctions are increasingly used as a smart and efficient way to optimise the consumers' and sellers' utility. A recently active field of research is the detection of fraud in online auctions. One of the most difficult types of fraud to detect is collusive shill bidding, where multiple user accounts jointly drive up the bids in an auction. This paper revises the Collusive Shill Bidding Algorithm(CSBD) proposed by Majadi et al. (2019) to develop an algorithm that is applied to a data set from an online auction platform (TBAuctions). We find that our algorithm converges, that computation time can be significantly reduced by appropriate choice of parameters, and we identify Shill Bidding for this data set, although the accuracy of the algorithm cannot be tested because of lack of ground truth values for the data. The paper further discusses steps needed for application of the algorithm to (very) large data sets, using a multiple core server, which despite substantial reduction in computation time would still require too much time to foresee a rapid implementation in real-time. [ABSTRACT FROM AUTHOR]

The error correction performance of Belief Propagation (BP) decoding for polar codes is satisfactory compared with the Successive Cancellation (SC) decoding. Nevertheless, it has to complete a fixed number of iterations, which results in high computational complexity. This necessitates an intelligent identification of successful BP decoding for early termination of the decoding process to avoid unnecessary iterations and minimize the computational complexity of BP decoding. This paper proposes a hybrid technique that combines the “parity-check” with the “G-matrix” to reduce the computational complexity of BP decoder for polar codes. The proposed hybrid technique takes advantage of the parity-check to intelligently identify the valid codeword at an early stage and terminate the BP decoding process, which minimizes the overhead of the G-matrix and reduces the computational complexity of BP decoding. We explore a detailed mechanism incorporating the parity bits as outer code and prove that the proposed hybrid technique minimizes the computational complexity while preserving the BP error correction performance. Moreover, mathematical formulation for the proposed hybrid technique that minimizes the computation cost of the G-matrix is elaborated. The performance of the proposed hybrid technique is validated by comparing it with the state-of-the-art early stopping criteria for BP decoding. Simulation results show that the proposed hybrid technique reduces the iterations by about 90% of BP decoding in a high Signal-to-Noise Ratio (SNR) (i.e., 3.5 ∼ 4 ​dB), and approaches the error correction performance of G-matrix and conventional BP decoder for polar codes.

Consider a random network of static primary wireless sources and a co-located network of secondary wireless devices. The channel coefficients between the two networks are assumed to be known to the secondary users (SUs), e.g., using radio environment maps (REM). However, the operational state of the sources is unknown due to intermittency. In this paper, we study the performance of primary source detection by SUs using a message-passing algorithm. Additionally, we employ methods from statistical mechanics, in particular, the Replica approach, to obtain analytic results for the performance of such networks in the large system-size limit. We test the results through a large-scale simulation analysis, obtaining good agreement. The proposed method provides a simple way to evaluate the performance of the system and assess how it depends on the macroscopic parameters that characterize it, such as the average density of SUs and sources and the signal-to-noise ratio. The main contribution of this paper is the application of an algorithm that quantitatively predicts the parameter value region for which accurate and reliable detection of the operational state of the primary sources can be achieved in a fast and decentralized manner.

Navigation system performance degrades significantly in complex environments. It is important to analyze satellite visibility through 3D terrain modelling and separate the satellite signals propagated by NLOS to suppress the NLOS error. However, the traditional 3D terrain modelling visibility analysis method based on the pure terrain cover angle is only suitable for determining the visibility of GNSS satellites and may incorrectly separate LOS propagate measurement signals from members with low relative ranges and elevation angles under air–ground swarm conditions. To this end, this paper proposes a belief-propagating cooperative navigation method based on air–ground visibility analysis, which avoids mistakenly separating close-range LOS cooperative navigation signals by simultaneously considering the distances, elevation angles, and azimuths of the signal sources relative to the air–ground swarm members. The simulation shows that the cooperative navigation NLOS identification method based on air–ground visibility analysis proposed in this paper can more accurately realize the separation of NLOS signals under cooperative conditions than the traditional pure angular 3D terrain modelling visibility analysis method can, and the localization error of the members to be assisted is significantly reduced.

Consider a random network of static primary wireless sources and a co-located network of secondary wireless devices. The channel coefficients between the two networks are assumed to be known to the secondary users (SUs), e.g., using radio environment maps (REM). However, the operational state of the sources is unknown due to intermittency. In this paper, we study the performance of primary source detection by SUs using a message-passing algorithm. Additionally, we employ methods from statistical mechanics, in particular, the Replica approach, to obtain analytic results for the performance of such networks in the large system-size limit. We test the results through a large-scale simulation analysis, obtaining good agreement. The proposed method provides a simple way to evaluate the performance of the system and assess how it depends on the macroscopic parameters that characterize it, such as the average density of SUs and sources and the signal-to-noise ratio. The main contribution of this paper is the application of an algorithm that quantitatively predicts the parameter value region for which accurate and reliable detection of the operational state of the primary sources can be achieved in a fast and decentralized manner. [ABSTRACT FROM AUTHOR]

This work presents a data-driven factor graph (FG) model designed to perform anchor-based positioning. The system makes use of the FG to compute the target position, given the distance measurements to the anchor node that know its own position.The aim was to design a hybrid structure (that involves data and modeling approaches) to address positioning models from a Bayesian point of view, customizing them for each technology and scenario. The weighted geometric dilution of precision (WGDOP) metric, which measures the effect on the positioning solution of distance error to the corresponding anchor node and network geometry of the anchor nodes, was taken into account. The presented algorithms were tested with simulated data and also with real-life data collected from IEEE 802.15.4-compliant sensor network nodes with a physical layer based on ultra-wide band (UWB) technology, in scenarios with one target node, three and four anchor nodes, and a time-of-arrival-based range technique. The results showed that the presented algorithm based on the FG technique provided better positioning results than the least squares-based algorithms and even UWB-based commercial systems in various scenarios, with different setups in terms of geometries and propagation conditions. [ABSTRACT FROM AUTHOR]

In order to solve the high latency problem of polar codes belief propagation decoding algorithm in the 5G and the dimension limitation problem of belief propagation decoding algorithm under deep learning, a multilayer perceptron belief propagation decoding (MLP-BP) algorithm based on partitioning idea is proposed. In this work, polar codes is decoded using neural networks in partitioning, and the right transfer message value of BP decoding algorithm is also set to complete the propagation process. Simulation results show that, compared with BP decoding algorithm, the proposed algorithm has better decoding performance, reducing the decoding latency, and it is also applicable to long polar codes. [ABSTRACT FROM AUTHOR]

Using stereo-imaging systems to collect 3D information is innovative and flexible for underwater exploration. The stereo-matching of underwater image pairs is a significant and challenging task due to the poor visibility and the complex underwater light field. In this paper, we propose a novel underwater stereo-matching algorithm based on belief propagation(BP). We design the energy function suitable to apply in the underwater scenes. Specifically, we use zero-based normalized cross-correlation and Hamming distance to form the data term that computes a measure of similarity between points of the binocular image pair and design the smoothness term based on the color metric to settle the discontinuity of the disparity map. Furthermore, we use bilateral filtering to gather the initial matching cost and propose a filling operation for the occlusion in the disparity map. Extensive experiments demonstrate the effectiveness of the proposed algorithm both on simulated UW-Middlebury dataset and real-world underwater images pairs [ABSTRACT FROM AUTHOR]

Abstract Regression testing is required to assure the quality of each iteration of microservice systems. Test case selection is one of main techniques to optimize regression testing. Existing techniques mainly involve artifacts acquisition, processing and maintenance, thus hard to apply in microservice regression testing since it is difficult to obtain and process required artifacts from multiple development teams, which is normal in cases of microservice systems. This paper proposes a novel approach, namely MRTS-BP, which takes API gateway logs instead of artifacts as inputs. By mining service dependencies from API gateway logs, MRTS-BP analyzes service change impacts based on a propagation calculation, and selects test cases affected by changes based on impact degree values. To evaluate the effectiveness of MRTS-BP, empirical studies based on four real deployed systems are presented. Retest-all strategy and a regression testing selection approach based on control flow graphs called RTS-CFG are compared with MRTS-BP. The results show that, MRTS-BP can significantly reduce both the number of test cases and overall time cost while maintaining the fault detection capability of selected test suite, and that MRTS-BP can save more time cost than RTS-CFG with the similar safety and precision.

Due to the increasing channel capacity of multiple input multiple output (MIMO) systems, we herein propose a multilevel modulated chaotic encryption method to allow belief propagation (BP) decoding in massive MIMO systems. The proposed scheme was reconstructed based on a previous scheme: binary modulated chaotic encryption for allowing BP decoding. In the encryption method of the proposed scheme, constellation points in general multilevel modulation were encrypted by rotating them by an arbitrary angle using chaos rules. Numerical results showed that the proposed scheme has high performance with regard to the bit error ratio and security. Thus, the proposed scheme could be practical and may also increase the channel capacity of massive MIMO systems using chaotic encryption. [ABSTRACT FROM AUTHOR]

This paper proposes a centralized MTT method based on a state-of-the-art multi-sensor labeled multi-Bernoulli (LMB) filter in underwater multi-static networks with autonomous underwater vehicles (AUVs). The LMB filter can accurately extract the number of targets and trajectories from measurements affected by noise, missed detections, false alarms and port–starboard ambiguity. However, its complexity increases as the number of sensors increases. In addition, due to the time-varying underwater environment, AUV detection probabilities are time-varying, and their mismatches often lead to poor MTT performance. Consequently, we detail a robust multi-sensor LMB filter that estimates detection probabilities and multi-target states simultaneously in real time. Moreover, we derive an effective approximate form of the multi-sensor LMB filter using Kullback–Leibler divergence and develop an efficient belief propagation (BP) implementation of the multi-sensor LMB filter. Our method scales linearly with the number of AUVs, providing good scalability and low computational complexity. The proposed method demonstrates superior performance in underwater multi-AUV network MTT simulations. [ABSTRACT FROM AUTHOR]

Lightweight cryptography is an emerging field where designers are testing the limits of symmetric cryptography. We investigate the resistance against sidechannel attacks of a new class of lighter blockciphers, which use a classic substitution–permutation network with slow diffusion and many rounds. Among these ciphers, we focus on SKINNY, a primitive used up to the final round ofNIST’s recent lightweight standardisation effort. We show that the lack of diffusion in the key scheduler allows an attacker to combine leakage from the first and the last rounds, effectively pincering its target. Furthermore, the slow diffusion used by its partial key-absorption and linear layers enable, on both sides, to target S-Boxes from several rounds deep. As some of these S-boxes leak on the same part of the key, full key recovery exploiting all leakage requires a clever combining strategy. We introduce the use of cluster graph inference (an established tool from probabilistic graphical model theory) to enhance both unprofiled or profiled differential power analysis, enabling us to handle the increase of S-Boxes with their intertwined leakage. We evaluate the strength of our attack both in the Hamming weight model and against two implementations running on an STM32F303 ARM Cortex-M4 hosted on a ChipWhisperer target board, showing that our attack reduces the number of traces required to attack SKINNY by a factor of around 2.75.

In LWE-based KEMs, observed decryption errors leak information about the secret key in the form of equations or inequalities. Several practical fault attacks have already exploited such leakage by either directly applying a fault or enabling a chosen-ciphertext attack using a fault. When the leaked information is in the form of inequalities, the recovery of the secret key is not trivial. Recent methods use either statistical or algebraic methods (but not both), with some being able to handle incorrect information. Having in mind that integration of the side-channel information is a crucial part of several classes of implementation attacks on LWEbased schemes, it is an important question whether statistically processed information can be successfully integrated in lattice reduction algorithms. We answer this question positively by proposing an error-tolerant combination of statistical and algebraic methods that make use of the advantages of both approaches. The combination enables us to improve upon existing methods – we use both fewer inequalities and are more resistant to errors. We further provide precise security estimates based on the number of available inequalities. Our recovery method applies to several types of implementation attacks in which decryption errors are used in a chosen-ciphertext attack. We practically demonstrate the improved performance of our approach in a key-recovery attack against Kyber with fault-induced decryption errors.

32-bit software implementations become increasingly popular for embedded security applications. As a result, profiling 32-bit target intermediate values becomes increasingly needed to evaluate their side-channel security. This implies the need of statistical tools that can deal with long traces and large number of classes. While there are good options to solve these issues separately (e.g., linear regression and linear discriminant analysis), the current state of the art lacks efficient tools to solve them jointly. To the best of our knowledge, the best-known option is to fragment the profiling in smaller parts, which is suboptimal from the information theoretic viewpoint. In this paper, we therefore revisit regression-based linear discriminant analysis, which combines linear regression and linear discriminant analysis, and improve its efficiency so that it can be used for profiling long traces corresponding to 32-bit implementations. Besides introducing the optimizations needed for this purpose, we show how to use regression-based linear discriminant analysis in order to obtain efficient bounds for the perceived information, an information theoretic metric characterizing the security of an implementation against profiled attacks. We also combine this tool with optimizations of soft analytical side-channel attack that apply to bitslice implementations. We use these results to attack a 32-bit implementation of SAP instantiated with Ascon’s permutation, and show that breaking the initialization of its re-keying in one trace is feasible for determined adversaries.

The article discusses the challenge of finding an efficient decoder for quantum error correction codes for fault-tolerant experiments in quantum computing. The study aims to develop a better decoding scheme based on the flag-bridge fault tolerance experiment. The research compares two decoding algorithms, a deep neural network decoding scheme and a simple decoder, and a recurrent neural network decoding scheme based on the belief propagation algorithm variant MBP4 algorithm. The study improved the syndrome extraction circuit based on the flag-bridge method to meet the requirements of fault-tolerant experiments better. Two decoding schemes were studied, a combination of a deep neural network and a simple decoder and a recurrent neural network structure based on the MBP4 algorithm. The first scheme used neural networks to assist simple decoders in determining whether additional logical corrections need to be added. The second scheme used a recurrent neural network structure designed through the variant MBP4 algorithm, along with a post-processing method to pinpoint the error qubit position for decoding. Experimental results showed that the decoding scheme developed in the study improved the pseudo-threshold by 39.52% compared to the minimum-weight perfect matching decoder. The two decoders had thresholds of approximately 15.8% and 16.4%, respectively. The study’s findings suggest that the proposed decoding schemes could improve quantum error correction and fault-tolerant experiments in quantum computing.

Networks and network computations have become a primary mathematical tool for analyzing the structure of many kinds of complex systems, ranging from the Internet and transportation networks to biochemical interactions and social networks. A common task in network analysis is the calculation of quantities that reside on the nodes of a network, such as centrality measures, probabilities or model states. In this perspective article we discuss message passing methods, a family of techniques for performing such calculations, based on the propagation of information between the nodes of a network. We introduce the message passing approach with a series of examples, give some illustrative applications and results and discuss the deep connections between message passing and phase transitions in networks. We also point out some limitations of the message passing approach and describe some recently introduced methods that address these limitations. [ABSTRACT FROM AUTHOR]

Regression testing is required to assure the quality of each iteration of microservice systems. Test case selection is one of main techniques to optimize regression testing. Existing techniques mainly involve artifacts acquisition, processing and maintenance, thus hard to apply in microservice regression testing since it is difficult to obtain and process required artifacts from multiple development teams, which is normal in cases of microservice systems. This paper proposes a novel approach, namely MRTS-BP, which takes API gateway logs instead of artifacts as inputs. By mining service dependencies from API gateway logs, MRTS-BP analyzes service change impacts based on a propagation calculation, and selects test cases affected by changes based on impact degree values. To evaluate the effectiveness of MRTS-BP, empirical studies based on four real deployed systems are presented. Retest-all strategy and a regression testing selection approach based on control flow graphs called RTS-CFG are compared with MRTS-BP. The results show that, MRTS-BP can significantly reduce both the number of test cases and overall time cost while maintaining the fault detection capability of selected test suite, and that MRTS-BP can save more time cost than RTS-CFG with the similar safety and precision. [ABSTRACT FROM AUTHOR]

It is known the belief propagation variants of linear codes can be readily unrolled as neural networks, after assigning learnable weights on the message‐passing edges. Contrary to the conventional top‐down training process, where the distillation occurs in the form of pruning or sharing when downsizing model is required, a new bottom‐up design methodology to augment performance of the raw min‐sum decoder of LDPC codes is proposed, by introducing incrementally a few parameters in the specific positions of corresponding neural network. Then a novel postprocessing method, devised to further improve performance, can cope with decoding failures effectively. In the training process, a simplified scheme of generating training data is presented via exploiting an approximation to the targeted mixture density, and it is found the evaluation of trained parameters converges after sufficient iterations, indicating its generality with an arbitrary designated number of iterations. Lastly, an extensive simulation of three codes carried on the AWGN or Rayleigh fading channels demonstrates the design reaches a good tradeoff of low‐complexity and comparable decoding performance. [ABSTRACT FROM AUTHOR]

In this paper, a joint group shuffled scheduling decoding (JGSSD) algorithm for a joint source-channel coding (JSCC) scheme based on double low-density parity-check (D-LDPC) codes is presented. The proposed algorithm considers the D-LDPC coding structure as a whole and applies shuffled scheduling to each group; the grouping relies on the types or the length of the variable nodes (VNs). By comparison, the conventional shuffled scheduling decoding algorithm can be regarded as a special case of this proposed algorithm. A novel joint extrinsic information transfer (JEXIT) algorithm for the D-LDPC codes system with the JGSSD algorithm is proposed, by which the source and channel decoding are calculated with different grouping strategies to analyze the effects of the grouping strategy. Simulation results and comparisons verify the superiority of the JGSSD algorithm, which can adaptively trade off the decoding performance, complexity and latency. [ABSTRACT FROM AUTHOR]

In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications. They have resemblance to Probabilistic Graphical Models (PGMs), but break free from some limitations of PGMs. By aiming to provide expressive methods for representation learning instead of computing marginals or most likely configurations, GNNs provide flexibility in the choice of information flowing rules while maintaining good performance. Despite their success and inspirations, they lack efficient ways to represent and learn higher-order relations among variables/nodes. More expressive higher-order GNNs which operate on k-tuples of nodes need increased computational resources in order to process higher-order tensors. We propose Factor Graph Neural Networks (FGNNs) to effectively capture higher-order relations for inference and learning. To do so, we first derive an efficient approximate Sum-Product loopy belief propagation inference algorithm for discrete higher-order PGMs. We then neuralize the novel message passing scheme into a Factor Graph Neural Network (FGNN) module by allowing richer representations of the message update rules; this facilitates both efficient inference and powerful end-to-end learning. We further show that with a suitable choice of message aggregation operators, our FGNN is also able to represent Max-Product belief propagation, providing a single family of architecture that can represent both Max and Sum-Product loopy belief propagation. Our extensive experimental evaluation on synthetic as well as real datasets demonstrates the potential of the proposed model. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of multitarget tracking using a network of sensing agents with unknown positions. Agents have to both localize themselves in the sensor network and, at the same time, perform multitarget tracking in the presence of clutter and miss detection. These two problems are jointly resolved using a holistic and centralized approach where graph theory is used to describe the statistical relationships among agent states, target states, and observations. A scalable message passing scheme, based on the sum-product algorithm, enables to efficiently approximate the marginal posterior distributions of both agent and target states. The proposed method is general enough to accommodate a full multistatic network configuration, with multiple transmitters and receivers. Numerical simulations show superior performance of the proposed joint approach with respect to the case in which cooperative self-localization and multitarget tracking are performed separately, as the former manages to extract valuable information from targets. Lastly, data acquired in 2018 by the NATO Science and Technology Organization (STO) Centre for Maritime Research and Experimentation (CMRE) through a network of autonomous underwater vehicles demonstrates the effectiveness of the approach in a practical application.

In this paper, an empirical study shows that positive tests containing multiple defectives are unlikely to provide effective messages in belief propagation (BP) for non-adaptive group testing. Thus, an objective function is proposed to measure the effectiveness of messages over edges, especially in the low-noise region. The maximization of the objective function allows us to optimize the pool size for BP. Simulation results show that the error performance of BP in the low-noise region is significantly improved by our pool size optimization.

The use of estimation techniques on stochastic models to solve control problems is an emerging paradigm that falls under the rubric of Active Inference (AI) and Control as Inference (CAI). In this work, we use probability propagation on factor graphs to show that various algorithms proposed in the literature can be seen as specific composition rules in a factor graph. We show how this unified approach, presented both in probability space and in log of the probability space, provides a very general framework that includes the Sum-product, the Max-product, Dynamic programming and mixed Reward/Entropy criteria-based algorithms. The framework also expands algorithmic design options that lead to new smoother or sharper policy distributions. We propose original recursions such as: a generalized Sum/Max-product algorithm, a Smooth Dynamic programming algorithm and a modified versions of the Reward/Entropy algorithm. The discussion is carried over with reference to a path planning problem where the recursions that arise from various cost functions, although they may appear similar in scope, bear noticeable differences. We provide a comprehensive table of composition rules and a comparison through simulations, first on a synthetic small grid with a single goal with obstacles, and then on a grid extrapolated from a real-world scene with multiple goals and a semantic map.

Channel coding technique is a fundamental building block in any modern communication system to realize reliable, fast, and secure data transmission. At the same time, it is a challenging and crucial task, as the data transmission happens in a channel where noise, fading, and other impairments are present. The Low-Density Parity-Check (LDPC) codes give substantial results close to the Shannon limit when the complexity and processing delay time are unlimited. In this paper, the performance of the LDPC decoding with four algorithms was investigated. The investigated four algorithms were Belief Propagation (BP), Layered Belief Propagation (LBP), Normalized min-sum (NMS), and Offset min-sum (OMS). These algorithms were examined for code rates ranging from 1/3 to 9/10 and message block lengths (64, 512, 1024, and 5120) bits. The simulation results revealed the flexibility of these decoders in supporting these code rates and block lengths, which enables their usage in a wide range of applications and scenarios for fifth-generation (5G) wireless communication. In addition, the effect of the maximum number of decoding iterations on the error correction performance was investigated, and a gain of 5.6 dB can be obtained by using 32 decoding iterations at BER=2*10-3 instead of one decoding iteration. The results showed that the decoders performed better for longer message blocks than for short message blocks, and less power was required for transmitting longer messages. Finally, the comparison results of their performance in terms of bit error rate (BER) under the same conditions showed a gain of 0.8 dB using LBP at BER= 10-5 compared with the NMS decoding algorithm.

The previous multi-frame version of the generalized labeled multi-Bernoulli model (MF-GLMB) only accounts for standard measurement models. It is not suitable for application in the detection and tracking of multiple weak targets (low signal-to-noise ratio) due to the measurement information loss. In this paper, we introduce a MF-GLMB model that formally incorporates a track-before-detect scheme for point targets using an image sensor model. Furthermore, a belief propagation algorithm is adopted to approximately calculate the marginal association probabilities of the multi-target posterior density. In this formulation, an MF-GLMB model based on the track-before-detect measurement model (MF-GLMB-TBD smoothing) enables multi-target posterior recursion for multi-target state estimation. By taking the entire history of the state estimation into account, MF-GLMB-TBD smoothing achieves superior performance in estimation precision compared with the corresponding GLMB-TBD filter. The simulation results demonstrate that the performance of the proposed algorithm is comparable to or better than that of the Gibbs sampler-based version. [ABSTRACT FROM AUTHOR]