A weighted group shuffled decoding for low-density parity-check codes

In this paper, we have developed several concepts such as the tree concept, the short cycle concept and the group shuffling concept of a propagation cycle to decrypt low-density parity-check (LDPC) codes. Thus, we proposed an algorithm based on group shuffling propagation where the probability of occurrence takes exponential form exponential factor appearance probability belief propagation-group shuffled belief propagation (EFAP-GSBP). This algorithm is used for wireless communication applications by providing improved decryption performance with low latency. To demonstrate the effectiveness of our suggested technique EFAP-GSBP, we ran numerous simulations that demonstrated that our algorithm is superior to the traditional BP/GSBP algorithm for decrypting LPDC codes in both regular and non-regular forms

Collective minds: social network topology shapes collective cognition

Human cognition is not solitary, it is shaped by collective learning and memory. Unlike swarms or herds, human social networks have diverse topologies, serving diverse modes of collective cognition and behaviour. Here, we review research that combines network structure with psychological and neural experiments and modelling to understand how the topology of social networks shapes collective cognition. First, we review graph-theoretical approaches to behavioural experiments on collective memory, belief propagation and problem solving. These results show that different topologies of communication networks synchronize or integrate knowledge differently, serving diverse collective goals. Second, we discuss neuroimaging studies showing that human brains encode the topology of one's larger social network and show similar neural patterns to neural patterns of our friends and community ties (e.g. when watching movies). Third, we discuss cognitive similarities between learning social and non-social topologies, e.g. in spatial and associative learning, as well as common brain regions involved in processing social and non-social topologies. Finally, we discuss recent machine learning approaches to collective communication and cooperation in multi-agent artificial networks. Combining network science with cognitive, neural and computational approaches empowers investigating how social structures shape collective cognition, which can in turn help design goal-directed social network topologies. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.

CIPC: A Change Impact Propagation Computing Based Technique for Microservice Regression Testing Prioritization

Regression testing is the optimal technique that can be used in each iteration of microservice systems. However, regression testing prioritization is the only main method that gives better results. These techniques directly involve the processes of artifacts, data acquisition, analysis, and maintenance. The microservice systems have input data, which are difficult to obtain and control, while such processes are of high costs with impractical design. This paper gives a detailed study on testing prioritization technique, which is referred to as CIPC. As there are dependencies between services from API gateway logs, a novel CIPC algorithm is proposed, which is based on belief propagation. There are some rules that are directly affected by service changes. Therefore, the higher execution order of test case prioritizes CIPC, which is based on impact changes. Multiobjective prioritization algorithm is based on heuristic searching, in which sequence test cases are done by coverage. By evaluating the effectiveness of CIPC, the empirical study presents five microservice systems and four different techniques. The results describe that CIPC has improved fault detection rate with acceptable time and cost. The technique is more practical than typical artifacts, which are based on increments of system scales.

Degenerate Quantum LDPC Codes With Good Finite Length Performance

We study the performance of medium-length quantum LDPC (QLDPC) codes in the depolarizing channel. Only degenerate codes with the maximal stabilizer weight much smaller than their minimum distance are considered. It is shown that with the help of OSD-like post-processing the performance of the standard belief propagation (BP) decoder on many QLDPC codes can be improved by several orders of magnitude. Using this new BP-OSD decoder we study the performance of several known classes of degenerate QLDPC codes including hypergraph product codes, hyperbicycle codes, homological product codes, and Haah's cubic codes. We also construct several interesting examples of short generalized bicycle codes. Some of them have an additional property that their syndromes are protected by small BCH codes, which may be useful for the fault-tolerant syndrome measurement. We also propose a new large family of QLDPC codes that contains the class of hypergraph product codes, where one of the used parity-check matrices is square. It is shown that in some cases such codes have better performance than hypergraph product codes. Finally, we demonstrate that the performance of the proposed BP-OSD decoder for some of the constructed codes is better than for a relatively large surface code decoded by a near-optimal decoder.