Code Error Research Articles

Adaptive coding of pain prediction error in the anterior insula.

Understanding the mechanisms behind the influence of expectation and context on pain perception is crucial for improving analgesic treatments. Prediction error (PE) signals how much a noxious stimulus deviates from expectation and is therefore crucial for our understanding of pain perception. It is thought that the brain engages in 'adaptive coding' of pain PE, such that sensitivity to unexpected outcomes is modulated by contextual information. While there is behavioural evidence that pain is coded adaptively, and evidence that reward PE signals are coded adaptively, controversy remains regarding the underlying neural mechanism of adaptively-coded pain PEs. A cued-pain task was performed by 19 healthy adults while undergoing FMRI scanning. BOLD responses to the task were tested using an axiomatic approach to identify areas that may code pain PE adaptively. The left dorsal anterior insula demonstrated a pattern of response consistent with adaptively-coded pain PE. Signals from this area were sensitive to both predicted pain magnitudes on the instigation of expectation, and the unexpectedness of pain delivery. Crucially however, the response at pain delivery was consistent with the local context of the pain stimulation, rather than the absolute magnitude of delivered pain, a pattern suggestive of an adaptively-coded PE signal. The study advances our understanding of the neural basis of pain prediction. Alongside existing evidence that the periaqueductal grey codes pain PE and the posterior insula codes pain magnitude, the results highlight a distinct contribution of the left dorsal anterior insula in the processing of pain. Although there is behavioural evidence that pain is coded adaptively, the neural mechanisms serving this process are not well understood. This study used functional MRI to provide the first evidence that the left dorsal anterior insula, an area associated with aversive learning, responds to pain in a manner consistent with the adaptive coding of pain prediction error. This study aids our understanding of the neural basis of subjective pain representation, and thus can contribute to the advancement of analgesic treatments.

A robust mixed error coding method based on nonconvex sparse representation

Linear representation based methods have been extensively applied in image recognition, especially for those with noise, illumination changes, and occlusions. However, most existing methods assume a specific distribution for image noise estimation, which is intractable to handle complex variations. Besides, they usually use convex norm to describe the noise sparse and low-rank property, and it is a biased approximation. To address these problems, we propose a novel nonconvex regularized robust mixed error coding (NRRM) method, which uses mixed norms from both 1D and 2D perspectives to model the complex image noise without convex relaxation. In specific, we use weighted ℓ2-norm based robust coding to characterize the sparse noise in images, and weighted matrix nuclear norm to characterize the low-rank noise. Compared with traditional regression approaches, our method can more fine-grained and accurate to capture noise and alleviate its negative influence for robust recognition. Besides, we constrain the representation component in a group-wise manner to weigh the roles of different classes. The NRRM model is solved efficiently by adopting an alternating direction method of multipliers (ADMM) algorithm. Comprehensive experiments on some benchmark face image databases validate the superiority of NRRM over several state-of-the-art linear representation based methods.

Rigour and reproducibility in perinatal and paediatric epidemiologic research using big data.

Rigour and reproducibility in perinatal and paediatric epidemiologic research using big data.

Predictive Coding and Internal Error Correction in Speech Production.

Speech production involves the careful orchestration of sophisticated systems, yet overt speech errors rarely occur under naturalistic conditions. The present functional magnetic resonance imaging study sought neural evidence for internal error detection and correction by leveraging a tongue twister paradigm that induces the potential for speech errors while excluding any overt errors from analysis. Previous work using the same paradigm in the context of silently articulated and imagined speech production tasks has demonstrated forward predictive signals in auditory cortex during speech and presented suggestive evidence of internal error correction in left posterior middle temporal gyrus (pMTG) on the basis that this area tended toward showing a stronger response when potential speech errors are biased toward nonwords compared to words (Okada et al., 2018). The present study built on this prior work by attempting to replicate the forward prediction and lexicality effects in nearly twice as many participants but introduced novel stimuli designed to further tax internal error correction and detection mechanisms by biasing speech errors toward taboo words. The forward prediction effect was replicated. While no evidence was found for a significant difference in brain response as a function of lexical status of the potential speech error, biasing potential errors toward taboo words elicited significantly greater response in left pMTG than biasing errors toward (neutral) words. Other brain areas showed preferential response for taboo words as well but responded below baseline and were less likely to reflect language processing as indicated by a decoding analysis, implicating left pMTG in internal error correction.

A novel method for spectrum sensing in cognitive radio networks using fractional GWO[sbnd]CS optimization

The co-channel interference and radio resources in a wireless communication system are often systematically managed by employing Radio Resource Management (RRM) strategies. The parameters of radio resources such as transmit power, data rate, error coding, and user allocation can be controlled via the RRM techniques whose main aim is to better exploit the limited radio frequency spectrum resources. The unallocated spectrum resources become spectrum holes and the effective utilization of the spectrum holes is made possible through cognitive radio by improving the network throughput. To enhance the effective energy utilization capacity of the spectrum holes by considering different sensing states, this paper integrates the grey wolf optimization (GWO) with the Cuckoo search (CS) algorithm to form the Fractional GWOCS Optimization model. The Fractional Optimization Model (FOM) allows the Secondary User (SU) for performing periodic sensing and data transfer. The main aim of the Fractional GWOCS Optimization model is to optimize different parameters like power spectral density (PSD), transmission power, and sensing bandwidth (SB). When computing energy efficiency concerning detection probability, SB, PSD, mutual optimization of PSD and SB, and convergence, the proposed methodology offers improved outcomes when compared to the existing techniques.

Fresh Approaches for Structured Text Programmable Logic Controllers Programs Verification

Programmable logic controllers (PLCs) are everywhere today and perform critical tasks in industries. They are considered as a key component for the Industry 4.0. Before they are put into operation, it is necessary to check the accuracy of the PLC programs. This verification operation can be performed using model checkers. This stage is often long and costly and requires a domain expert who can understand the system, as well as the different model checker tools able to verify the code implemented in the controller. Furthermore, this verification often requires a conversion of the PLC code into a language understood by a model checker which can influence the behavior of the observed PLC. Hence, there is a need to propose methods and tools which could be used by technicians and engineers. The aim of this paper is to propose methods that require little work to set up and are robust to program sizes used in Industry 4.0. This paper explores some fresh ideas for human-adapted PLC code verification. We present different methods to test codes in structured text (ST) compliant with the IEC 61131-3 standard. Hence, the first idea is to test the ST code that will be directly implemented on a controller. For that, we propose a method using the model checker UPPAAL which allows us to obtain exact results on short codes. Second, we propose verifying the generic properties that a PLC program must avoid: deadlocks, non-accessible states and fugitive states or actions. To solve combinatory explosion problems encountered with the UPPAAL software, the third proposition consists of using relational databases. The same verification as previously followed can be obtained, but the search time is longer. The fourth and last proposal is to process the ST code with a neural network composed of long short-term memory layers (LSTM) to quickly determine the validity of the code. This method could give an approximation of code errors in a few seconds. The different proposed methods are supported with several examples.

Efficient color code decoders in d≥2 dimensions from toric code decoders

We introduce an efficient decoder of the color code in d≥2 dimensions, the Restriction Decoder, which uses any d-dimensional toric code decoder combined with a local lifting procedure to find a recovery operation. We prove that the Restriction Decoder successfully corrects errors in the color code if and only if the corresponding toric code decoding succeeds. We also numerically estimate the Restriction Decoder threshold for the color code in two and three dimensions against the bit-flip and phase-flip noise with perfect syndrome extraction. We report that the 2D color code threshold p2D≈10.2% on the square-octagon lattice is on a par with the toric code threshold on the square lattice.

Encryption Methods and Algorithms Based on Domestic Standards in Open-Source Operating Systems

The paper describes the principles and methods underlying the creation of an application in secure operating systems, which provides reliable data encryption. The research aims to analyze and indicate the specifics of encryption methods and algorithms based on domestic standards in open-source operating systems. Cryptanalysis was used in the article, as this avoids vulnerabilities identified in previously created implementations. In the article, the authors draw attention to the fact that 7-Zip uses CBC encryption (concatenation of encrypted text blocks), but the Counter Mode is supported. The same support was provided in the encrypt implementation. Since the key expansion function initially fills the special array created by p7zip with round keys using a unique property of the domestic standard, only one round encryption function was created (performed both during encryption and decryption). This method is also used in various modes. In many cases, initialization time deviations depending on the selected mode are insignificant. The created cryptographic module was tested to meet the domestic standard, which contains several test cases. It was confirmed during the tests that the created module implements the algorithm of the domestic standard. The article shows a way to implement a fairly convenient graphical interface for accessing the cryptographic module, which enables the user not to call the command line and remember the sequence and types of parameters passed to p7zip. This implementation also takes into account the verification of the correctness of decryption and the reading of other error codes.

Generation of large-scale continuous-variable cluster states multiplexed both in time and frequency domains.

A large-scale continuous variable (CV) cluster state is necessary in quantum information processing based on measurement-based quantum computing (MBQC). Specially, generating a large-scale CV cluster state multiplexed in a time domain is easier to implement and has strong scalability in experiment. Here one-dimensional (1D) large-scale dual-rail CV cluster states multiplexed both in time and frequency domains are parallelly generated, which can be further extended to a three-dimensional (3D) CV cluster state by combining two time-delay nondegenerate optical parametric amplification systems with beam-splitters. It is shown that the number of parallel arrays depends on the corresponding frequency comb lines, the partite number of each array can be very large (million), and the scale of the 3D cluster state can be ultra-large. In addition, the concrete quantum computing schemes of applying the generated 1D and 3D cluster states are also demonstrated. Our schemes may pave the way for fault-tolerant and topologically protected MBQC in hybrid domains, by further combining with efficient coding and quantum error correction.

Ventral pallidal regulation of motivated behaviors and reinforcement.

The interconnected nuclei of the ventral basal ganglia have long been identified as key regulators of motivated behavior, and dysfunction of this circuit is strongly implicated in mood and substance use disorders. The ventral pallidum (VP) is a central node of the ventral basal ganglia, and recent studies have revealed complex VP cellular heterogeneity and cell- and circuit-specific regulation of reward, aversion, motivation, and drug-seeking behaviors. Although the VP is canonically considered a relay and output structure for this circuit, emerging data indicate that the VP is a central hub in an extensive network for reward processing and the regulation of motivation that extends beyond classically defined basal ganglia borders. VP neurons respond temporally faster and show more advanced reward coding and prediction error processing than neurons in the upstream nucleus accumbens, and regulate the activity of the ventral mesencephalon dopamine system. This review will summarize recent findings in the literature and provide an update on the complex cellular heterogeneity and cell- and circuit-specific regulation of motivated behaviors and reinforcement by the VP with a specific focus on mood and substance use disorders. In addition, we will discuss mechanisms by which stress and drug exposure alter the functioning of the VP and produce susceptibility to neuropsychiatric disorders. Lastly, we will outline unanswered questions and identify future directions for studies necessary to further clarify the central role of VP neurons in the regulation of motivated behaviors. Significance: Research in the last decade has revealed a complex cell- and circuit-specific role for the VP in reward processing and the regulation of motivated behaviors. Novel insights obtained using cell- and circuit-specific interrogation strategies have led to a major shift in our understanding of this region. Here, we provide a comprehensive review of the VP in which we integrate novel findings with the existing literature and highlight the emerging role of the VP as a linchpin of the neural systems that regulate motivation, reward, and aversion. In addition, we discuss the dysfunction of the VP in animal models of neuropsychiatric disorders.

Symbol-Level Detection With Matched Non-Binary LDPC Codes for Position Errors in Racetrack Memories

In racetrack memory (RM), insertion and deletion (ID) errors occur owing to alignment faults; such errors are referred to as position errors (PEs). Recently, several coding schemes for PEs have been proposed based on binary low-density parity-check (BI-LDPC) codes with a joint bit-wise (BW) detection and decoding algorithm. Although these schemes yield promising asymptotic performance, they suffer from unsatisfactory finite-length performance in short block sizes. To address this problem, we propose a novel coding scheme based on non-binary LDPC (NB-LDPC) codes and symbol-wise (SW) detection. The factor graph of the proposed scheme avoids generating cycles between the detector and decoder graphs by leveraging RM’s special property that multiple ID errors are caused by PEs simultaneously, which enables the attainment of good finite-length performance while providing fast convergence. With the aim of providing further performance improvement, we also provide an SW extrinsic information transfer chart analysis to design NB-LDPC codes for the proposed scheme.

A Review of Diagnostic Methods for Hydraulically Powered Flight Control Actuation Systems

Aircraft systems are designed to perform functions that will aid the various missions of the aircraft. Their performance, when subjected to an unfamiliar condition of operation, imposes stress on them. The system components experience degradation due to fault which ultimately results in failure. Maintenance and monitoring mechanisms are put in place to ensure these systems are readily available when required. Thus, the sensing of parameters assists in providing conditions under which healthy and faulty scenarios can be indicated. To obtain parameter values, sensor data is processed, and the results are displayed so that the presence of faults may be known. Some faults are intermittent and incipient in nature. These are not discovered easily and can only be known through a display of unusual system performance by error code indication. Therefore, the assessed faults are transmitted to a maintenance crew by error codes. The results may be fault found (FF), no fault found (NFF), or cannot display (CND). However, the main classification of the faults and their origins may not be known in the system. This continues throughout the life cycle of the system or equipment. This paper reviews the diagnostic methods used for the hydraulically powered flight control actuation system (HPFCAS) of an aircraft and its interaction with other aircraft systems. The complexities of the subsystem’s integration are discussed, and different subsystems are identified. Approaches used for the diagnostics of faults, such as model-based, statistical mapping and classification, the use of algorithms, as well as parity checks are reviewed. These are integrated vehicle health management (IVHM) tools for systems diagnostics. The review shows that when a system is made up of several subsystems on the aircraft with dissimilar functions, the probability of fault existing in the system increases, as the subsystems are interconnected for resource sharing, space, and weight savings. Additionally, this review demonstrates that data-driven approaches for the fault diagnostics of components are good. However, they require large amounts of data for feature extraction. For a system such as the HPFCAS, flight-management data or aircraft maintenance records hold information on performance, health monitoring, diagnostics, and time scales during operation. These are needed for analysis. Here, a knowledge of training algorithms is used to interpret different fault scenarios from the record. Thus, such specific data are not readily available for use in a data-driven approach, since manufacturers, producers, and the end users of the system components or equipment do not readily distribute these verifiable data. This makes it difficult to perform diagnostics using a data-driven approach. In conclusion, this paper exposes the areas of interest, which constitute opportunities and challenges in the diagnostics and health monitoring of flight-control actuation systems on aircraft.

Clinical Coding Error With Potential Loss Of Hospital Income During Implementation Of The Indonesian Case Base Groups Casemix System

The diagnostic code is also the basis for determining the cost of health services. Diagnostic codes that are not qualified will cause losses to the hospital both financially and in policy making. This research method uses qualitative research methods. by using a case study approach (case study). The population which is used are medical record documents of inpatients at dr. Soeratno Gemolong Hospital in Trisemester 4 (October, November, December) in 2019, obtained a sample of 80 documents from a population of 804 documents. The sampling technique used is simple random sampling. The results of the study that the coding accuracy of the Casemix Indonesian Case Base Groups (INA-CBGs) system at Dr. Soeratno Gemolong Hospital Sragen Hospital there are 42 accurate diagnosis codes and 38 inaccurate diagnosis codes, while the inaccuracy of the diagnosis code is caused by the coding error of the main diagnostic code, coding error of the Secondary diagnosis code, coding error  Primary and secondary diagnosis code and coding error of the Diagnosis and procedure code. There is a difference between the claim rate from the inaccurate coding and the claim rate from the accurate coding, where the inaccurate coding claim rate generates revenue for the hospital of IDR. 22,816,048 whereas if the claim rate from the correct coding will result in an additional IDR. 41,221,848 for the hospital, so that due to the coding error, the hospital will experience a decrease in income or loss of IDR.18,405,800.

Modified Hamming Codes in Computing Devices Technical Diagnostic Systems

Previously unknown error detection characteristics by types and multiplicities in modified Hamming codes code words are established. The rules for constructing this modification of Hamming codes are described. Detailed characteristic tables of codes with data vectors lengths m = 4...16 are given. The considered code's error detection properties brief analysis is given. The results obtained in the study can be effectively used in the digital computing devices with controllable structures synthesis, as well as in the self-checking concurrent error-detection (CED) circuit synthesis using the Boolean complement method. The modified Hamming codes key property is the impossibility of 100 % double error detection in all code word bits when any single and double errors that occur only in data bits are detected. That is why it is recommended, when synthesizing a CED circuit using the Boolean complement method, to convert only those functions that describe the operating functions of the diagnostic object that will form the modified Hamming code control bits, and to separate the converted and non-convertible outputs into two groups of independent outputs. The experiment results with test combinational circuits presented in the article confirm the effectiveness of their use in the CED circuit synthesis by the Boolean complement method.

The Impact of GloVe and Word2Vec Word-Embedding Technologies on Bug Localization with Convolutional Neural Network

In the field of software engineering, software quality assurance faces many challenges, including overcoming the problem of identifying errors in the source code. Finding the location of the error in the source code is a very important process, as is taking advantage of the semantic information available in the bug reports and the source code to find the similarities between them, using modern techniques called word embedding. This study aims to demonstrate how GloVe and Doc2Vec word-embedding technologies affect bug localization accuracy and performance. Therefore, this study proposes to adapt DeepLoc by using GloVe embedding techniques to process the source code instead of Word2vec and using Word2vec embedding techniques to process the bug report instead of Sent2Vec. AspectJ represents the large dataset, which contains many bug reports, while SWT's small dataset contains fewer bug reports. Experimental results show that the improved DeepLoc on SWT achieves 0.60 and 0.72 MAP and MRR, respectively. While the improved DeepLoc on AspectJ achieves 0.17 and 0.27 MAP and MRR, respectively. The results of the improved DeepLoc should be compared using two advanced models from previous studies: DeepLoc, DeepLocator.

Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients.

This paper presents a lossless image compression method with a fast decoding time and flexible adjustment of coder parameters affecting its implementation complexity. A comparison of several approaches for computing non-MMSE prediction coefficients with different levels of complexity was made. The data modeling stage of the proposed codec was based on linear (calculated by the non-MMSE method) and non-linear (complemented by a context-dependent constant component removal block) predictions. Prediction error coding uses a two-stage compression: an adaptive Golomb code and a binary arithmetic code. The proposed solution results in 30% shorter decoding times and a lower bit average than competing solutions (by 7.9% relative to the popular JPEG-LS codec).

Improved reference-frame-independent quantum key distribution: erratum.

We present an erratum to our Letter [Opt. Lett.47, 4219 (2022)10.1364/OL.470558]. This erratum corrects the error results of Figs. 2 and 4 due to the error in the simulation code. The corrections have no influence on the conclusions of the original Letter.

ANALYSIS OF NEWS VIOLATING THE JOURNALISTIC CODE OF ETHICS IN SOUTH SULAWESI ONLINE MEDIA IN REPORTING MURDER CASES

The journalistic code of ethics is a set of rules that must be applied by journalists and journalists as well as a guide on how to write good and correct news. According to researchers, there are still many violations of the journalistic code of ethics committed by journalists. The research method used is descriptive qualitative, namely analyzing documents broadly and thoroughly. Based on the research conducted, there are 20 articles that violate the code of ethics article 4 regarding sadistic acts. This case is found in murder cases, for example, like the words "slitting, mutilating, slashing, and whipping". This study aims to determine errors in the journalistic code of ethics in online media, especially on the Detik Sulsel online media portal. Researchers have conducted research on 20 news articles on murder cases in online media Detik Sulsel for the period August, September, October. The results of this study are that there are still violations of the journalistic code of ethics article 4 which causes errors in writing the news chronology of the murder case which is considered too sadistic.

Detection of location of errors in code Using Deep Learning Ensembling methods

Detection of location of errors in code Using Deep Learning Ensembling methods

Software reliability model for open-source software that considers the number of finite faults and dependent faults.

Software has become a vital factor in the fourth industrial revolution. Owing to the increase in demand for software products in various fields (big data, artificial intelligence, the Internet of Things, etc.), the software industry has expanded more than ever before. Therefore, software reliability has become very important, and efforts are being made to increase it. One of these efforts is the development of software reliability models (SRMs). SRMs have been studied for a long time as a model that predicts software reliability by using the number of software faults. Software failures can occur for several reasons, including independent software faults such as code errors and software hangs, as well as dependent cases where code errors lead to other software faults. Recently, due to the diversity of software operating environments, software faults are more likely to occur in a dependent manner, and, for this reason, they are likely to increase rapidly from the beginning and progress slowly to the maximum number thereafter. In addition, many large companies have focused on open-source software (OSS) development, and OSS is being developed by many users. In this study, we propose a new SRM that considers the number of finite faults and dependent faults, and examine the goodness-of-fit of a new SRM and other existing non-homogeneous Poisson process models based on the OSS datasets. Through numerical examples, the proposed model demonstrated a significantly better goodness-of-fit when compared to other existing models, and it also exhibited better results on the newly proposed integrated criteria.