Code Error Research Articles

Probability of Error for Optimal Codes in a Reconfigurable Intelligent Surface Aided URLLC System

Probability of Error for Optimal Codes in a Reconfigurable Intelligent Surface Aided URLLC System

Placement Sensor Modification of Photo Interrupter and ATDC Sensor on the Minolta Bizhub-421 Photocopy Machine

Minolta photocopiers produce very good and reliable photocopier quality during their lifetime with the condition that the toner must be replaced with the original. If it is replaced by refilling, then refilling can only be done an average of three times. Meanwhile, replacement by refilling is needed by small and medium business units (SMEs) in an effort to save costs. Recharging that exceeds the average three times will cause the Minolta photocopier to have an error with code C032. Therefore, this paper was conducted to increase the frequency of the toner refill process as a solution for the Minolta Bizhub 421 machine to minimize losses due to interference with code C032. So, the machine can be refilled repeatedly without interruption, as well as adding error information via SMS using an Arduino microcontroller. Method was used experimental of placement sensor modification of photo interrupter and ATDC (After Top Dead Center) sensor. The results showed that after modifying the sensor placement, the number of refills could be increased to an average usage of toner exceeding 12 refills before the error code C032 occurred. This was because of the opto sensor was covered by toner powder and with an indicator that controlled the toner rotation it had reached set point. Then, with SIM 800L, which sends SMS and miss call notifications to users with a success rate of 98% and 2% error. Therefore, it helps users of the Minolta Bizhub 421 photocopier can more easily supervise the toner tube.

Azure DevOps The Next Era of Application Lifecycle Management

The software development, testing, and deployment process is a series of actions that are carried out in order to achieve a particular result. highly manual process, resulting in a significant number of code errors and bugs. Development and operations (Support) teams can often be out of sync, slowing software delivery and disappointing business stakeholders. DevOps creates efficiency across all tasks involved in software development, deployment, and maintenance. Connecting development with operations leads to increased visibility, more accurate requirements, improved communication, and faster time to Industry. Azure DevOps supports a joint culture and set of multiple steps and processes that bring together the development team, managers, and contributors to build software quickly. It allows the software industry to improve software at a faster pace than they can with traditional software development approaches. Compared with ADO Server, ADO Services offers the following added benefits: • Easy for server management. • Fastest access to greatest features. • Improved faster connectivity for remote users. • A transition from capital expenditures (like servers) to operational expenditures (subscriptions). DevOps is becoming a software industry standard for multiple businesses. According to a report issued by Capgemini, 61% of businesses either adopted DevOps or planned to do so during 2018. Statistics like this demonstrate that DevOps is a necessary part of your business plan if you expect to respond quickly to the market's demands, improve your business’s time-to-market, and keep your software solutions updated regularly. DevOps creates efficiency across all tasks involved in software development, deployment, and maintenance. Connecting development with operations leads to increased visibility, more accurate requirements, improved communication, and faster time to Industry.

A scalable and fast artificial neural network syndrome decoder for surface codes

Surface code error correction offers a highly promising pathway to achieve scalable fault-tolerant quantum computing. When operated as stabilizer codes, surface code computations consist of a syndrome decoding step where measured stabilizer operators are used to determine appropriate corrections for errors in physical qubits. Decoding algorithms have undergone substantial development, with recent work incorporating machine learning (ML) techniques. Despite promising initial results, the ML-based syndrome decoders are still limited to small scale demonstrations with low latency and are incapable of handling surface codes with boundary conditions and various shapes needed for lattice surgery and braiding. Here, we report the development of an artificial neural network (ANN) based scalable and fast syndrome decoder capable of decoding surface codes of arbitrary shape and size with data qubits suffering from the depolarizing error model. Based on rigorous training over 50 million random quantum error instances, our ANN decoder is shown to work with code distances exceeding 1000 (more than 4 million physical qubits), which is the largest ML-based decoder demonstration to-date. The established ANN decoder demonstrates an execution time in principle independent of code distance, implying that its implementation on dedicated hardware could potentially offer surface code decoding times of O(μsec), commensurate with the experimentally realisable qubit coherence times. With the anticipated scale-up of quantum processors within the next decade, their augmentation with a fast and scalable syndrome decoder such as developed in our work is expected to play a decisive role towards experimental implementation of fault-tolerant quantum information processing.

An Empirical Study of Adoption of ChatGPT for Bug Fixing among Professional Developers

ChatGPT is a powerful tool that assists software engineers in identifying and rectifying errors in code. One of its primary advantages is its ability to engage in natural language conversation with humans, which allows it to collaborate more closely with engineers in improving and optimizing the code. However, despite its potential advantages, software developers do not always utilize ChatGPT as a tool for bug fixing. In this study, we aim to examine the factors that influence the adoption of ChatGPT for bug fixing among professional software developers, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) theory. To accomplish this, we conducted 50 semi-structured interviews with professional software developers and other stakeholders. Our findings indicate that the performance expectancy and effort expectancy of professional software developers, as well as social influence, facilitating conditions, data security, and trust are the key factors of adoption. These findings suggest that understanding these factors can be critical in promoting the adoption and use of ChatGPT in the software development industry.

Data Integrity Enhancement with Interleaved Hsiao Code and CRC Error Detection and Correction – A Brief Review

Data Integrity Enhancement with Interleaved Hsiao Code and CRC Error Detection and Correction – A Brief Review

Promoting accuracy in low-magnification histopathology grading: With augmentation and multi-dilation model

Advances in artificial intelligence have facilitated the automated grading of histopathology slides. Yet, the magnification of whole slide scanners (WSS) has restrained the accuracy of patch-based grading. In this work, we found that augmentation can significantly promote grading performance under this issue, even when the data volume is large (>140 K). With augmentation and a multi-dilation model, the CovXNet, we yielded a Balanced Accuracy of 92.13%, which is the current highest for the Breast Histopathology Dataset (40X magnification) also the first time both sensitivity and specificity >90%. However, in this focused grading task, augmentation only improves models with high invariance (the CovXNet and BCA-CNN). Pre-trained ResNet has lower invariance in this task, but fine-tuning can significantly improve both accuracy and invariance. For the CropNet attention model, adapting with max pooling but not augmentation offers promotions. Additionally, this work also found two types of common errors in high-starred codes, when using random.shuffle for data-label composited array, or the integrated shuffle function of ImageDataGenerator, which fake a higher accuracy by masking class 0 as class 1. Using Sklearn.shuffle instead is safer. All codes are available on our GitHub.

DeepSHARQ: hybrid error coding using deep learning

Cyber-physical systems operate under changing environments and on resource-constrained devices. Communication in these environments must use hybrid error coding, as pure pro- or reactive schemes cannot always fulfill application demands or have suboptimal performance. However, finding optimal coding configurations that fulfill application constraints—e.g., tolerate loss and delay—under changing channel conditions is a computationally challenging task. Recently, the systems community has started addressing these sorts of problems using hybrid decomposed solutions, i.e., algorithmic approaches for well-understood formalized parts of the problem and learning-based approaches for parts that must be estimated (either for reasons of uncertainty or computational intractability). For DeepSHARQ, we revisit our own recent work and limit the learning problem to block length prediction, the major contributor to inference time (and its variation) when searching for hybrid error coding configurations. The remaining parameters are found algorithmically, and hence we make individual contributions with respect to finding close-to-optimal coding configurations in both of these areas—combining them into a hybrid solution. DeepSHARQ applies block length regularization in order to reduce the neural networks in comparison to purely learning-based solutions. The hybrid solution is nearly optimal concerning the channel efficiency of coding configurations it generates, as it is trained so deviations from the optimum are upper bound by a configurable percentage. In addition, DeepSHARQ is capable of reacting to channel changes in real time, thereby enabling cyber-physical systems even on resource-constrained platforms. Tightly integrating algorithmic and learning-based approaches allows DeepSHARQ to react to channel changes faster and with a more predictable time than solutions that rely only on either of the two approaches.

Efficient Linear and Affine Codes for Correcting Insertions/Deletions

This paper studies linear and affine error-correcting codes for correcting synchronization errors such as insertions and deletions. We call such codes linear/affine insdel codes. Linear codes that can correct even a single deletion are limited to having an information rate at most (achieved by the trivial two fold repetition code). Previously, it was (erroneously) reported that more generally no nontrivial linear codes correcting deletions exist, i.e., that the -fold repetition codes and its rate of are basically optimal for any . We disprove this and show the existence of binary linear codes of length and rate just below capable of correcting insertions and deletions. This identifies rate as a sharp threshold for recovery from deletions for linear codes and reopens the quest for a better understanding of the capabilities of linear codes for correcting insertions/deletions. We prove novel outer bounds and existential inner bounds for the rate vs. (edit) distance trade-off of linear insdel codes. We complement our existential results with an efficient synchronization-string-based transformation that converts any asymptotically good linear code for Hamming errors into an asymptotically good linear code for insdel errors. Last, we show that the -rate limitation does not hold for affine codes by giving an explicit affine code of rate which can efficiently correct a constant fraction of insdel errors.

SedSVD: Statement-level software vulnerability detection based on Relational Graph Convolutional Network with subgraph embedding

Current deep-learning based vulnerability detection methods have been proven more automatic and correct to a certain extent, nonetheless, they are limited to detect at function-level or file-level, which can hinder software developers from acquiring more detailed information and conducting more targeted repairs. Graph-based detection methods have shown dominant performance over others. Unfortunately, the information they reveal has not been fully utilized. We design SedSVD (Subgraph embedding driven Statement-level Vulnerability Detection) with two objectives: (i) to better utilize the information the code-related graphs can reflect; (ii) to detect vulnerabilities at a finer-grained level. In our work, we propose a novel graph-based detection framework that embeds graphs at subgraph-level to realize statement-level detection. It first leverages Code Property Graph (CPG) to learn both semantic and syntactic information from source code, and then selects several center nodes (code elements) in CPG to build their subgraphs. After embedding each subgraph with its nodes and edges, we apply Relational Graph Convolutional Network (RGCN) to process different edges differently. A Multi-Layer Perceptron (MLP) layer is further added to ensure its prediction performance. We conduct our experiments on C/C++ projects from NVD and SARD. Experimental results show that SedSVD achieves 95.15% in F1-measure which proves our work to be more effective. Our work detects at a finer-grained level and achieves higher F1-measure than existing state-of-art vulnerability detection techniques. Besides, we provide a more detailed detection report pointing the specific error code elements within statements.

Omission responses in local field potentials in rat auditory cortex

BackgroundNon-invasive recordings of gross neural activity in humans often show responses to omitted stimuli in steady trains of identical stimuli. This has been taken as evidence for the neural coding of prediction or prediction error. However, evidence for such omission responses from invasive recordings of cellular-scale responses in animal models is scarce. Here, we sought to characterise omission responses using extracellular recordings in the auditory cortex of anaesthetised rats. We profiled omission responses across local field potentials (LFP), analogue multiunit activity (AMUA), and single/multi-unit spiking activity, using stimuli that were fixed-rate trains of acoustic noise bursts where 5% of bursts were randomly omitted.ResultsSignificant omission responses were observed in LFP and AMUA signals, but not in spiking activity. These omission responses had a lower amplitude and longer latency than burst-evoked sensory responses, and omission response amplitude increased as a function of the number of preceding bursts.ConclusionsTogether, our findings show that omission responses are most robustly observed in LFP and AMUA signals (relative to spiking activity). This has implications for models of cortical processing that require many neurons to encode prediction errors in their spike output.

What issues are data scientists talking about? Identification of current data science issues using semantic content analysis of Q&A communities.

Because of the growing involvement of communities from various disciplines, data science is constantly evolving and gaining popularity. The growing interest in data science-based services and applications presents numerous challenges for their development. Therefore, data scientists frequently turn to various forums, particularly domain-specific Q&A websites, to solve difficulties. These websites evolve into data science knowledge repositories over time. Analysis of such repositories can provide valuable insights into the applications, topics, trends, and challenges of data science. In this article, we investigated what data scientists are asking by analyzing all posts to date on DSSE, a data science-focused Q&A website. To discover main topics embedded in data science discussions, we used latent Dirichlet allocation (LDA), a probabilistic approach for topic modeling. As a result of this analysis, 18 main topics were identified that demonstrate the current interests and issues in data science. We then examined the topics' popularity and difficulty. In addition, we identified the most commonly used tasks, techniques, and tools in data science. As a result, "Model Training", "Machine Learning", and "Neural Networks" emerged as the most prominent topics. Also, "Data Manipulation", "Coding Errors", and "Tools" were identified as the most viewed (most popular) topics. On the other hand, the most difficult topics were identified as "Time Series", "Computer Vision", and "Recommendation Systems". Our findings have significant implications for many data science stakeholders who are striving to advance data-driven architectures, concepts, tools, and techniques.

A Flexible Code Review Framework for Combining Defect Detection and Review Comments

Defects and errors in code are different in that they are not detected by editors or compilers but pose a potential risk to software operation. For safety-critical software such as airborne software, the code review process is necessary to ensure the proper operation of software applications and even an aircraft. The traditional manual review method can no longer meet the current needs with the dramatic increase in code sizes and variety. To this end, we propose Deep Reviewer, a general and flexible code review framework that automatically detects code defects and correlates the review comments of the defects. The framework first preprocesses the data using several methods, including the proposed D2U flow. Then, features are extracted and matched by the detector, which contains a pair of twin LSTM models, one for code defect type detection and the other for review comment retrieval. Finally, the review comment output function is implemented based on the masks generated by the code defect types. The method is validated using a large public dataset, SARD. For the binary-classification task, the test results of the proposed are 98.68% and 98.67% in terms of precision and F1 score, respectively. For the multi-classification task, the proposed framework shows a significant advantage over other methods.

Qualitative Analysis on Costs and Benefits of Adopting a Cloud-Based Accounting Information System: A Case Study of Rural Banks in Ghana

The accounting and recording approach have been impacted by the expansion of the internet and the advancement of information technology. Cloud accounting is a result of the use of cloud computing technology. Cloud computing is growing in acceptance, and more companies are using cloud-based software to boost productivity and gain a number of other advantages. The purpose of this study was to learn more about the advantages and difficulties of implementing a cloud-based accounting information system. The study, which employed a qualitative approach, discovered that cloud accounting enhanced operations compared to using in-house software, increased accessibility of the banks' services, and sped up customer turnaround time. High maintenance costs, network failure, trouble deciphering reported error codes, insufficient technical help, and system failure during an update are a few of the difficulties.

Multi-level dynamic error coding for face recognition with a contaminated single sample per person

Single sample per person (SSPP) has always been a significantly challenging and practical problem for face recognition owing to limited information and facial variations. Although the existing holistic and local methods have achieved great success in face recognition with SSPP, their performances suffer a serious degradation when SSPP is accompanied by a contaminated gallery database. In this situation, the gallery set is usually disturbed by facial variations such as illumination, expression, and disguise. To solve this problem, we propose a novel method called multi-level dynamic error coding. First, a multi-level pyramid structure is constructed for holistic and local sparse representation, where gallery dictionary patches are extracted to build a local gallery dictionary and a variation dictionary is built by extracting the generic dataset patches to depict potential facial variations. Second, we further propose a scheme of dynamic error coding by constructing an error function at different levels to reduce the negative impact of variations. At last, the corrected holistic and local errors are fused to perform the classification. Experimental results on various benchmark data sets have demonstrated that our method has strong generalization ability to dictionary and is more robust against facial variations under SSPP.

Characterization of reading errors in languages with different orthographic regularity: an Italian–English comparison

The study examined whether a classification of errors based on Hendriks and Kolk’s (1997) proposal would effectively characterize the reading profile of children learning two orthographies varying for regularity, such as Italian and English. The study considered both an age-match and a grade-match comparison. Offline analysis of error production was carried out for two lists of stimuli: List 1 including regular words varying for frequency and matched non-words and List 2 including low-frequency words varying for regularity. In List 1, Italian-reading children made more multiple attempts characterized by a slow and progressive approach to the target (sounding-out behavior) than English-reading children, while the latter made relatively more word substitutions and non-word lexicalizations. As for List 2, Italian-reading children made relatively more multiple attempts and progressive approaches to the target compared to the English-reading children (with more sounding-out behaviors and syllabications), while the opposite occurred for phonological-visual errors, word substitutions, morphological, and semantic errors. Both groups showed a high proportion of phonological-visual and regularization errors (stress assignment in the case of Italian-reading children). Overall, the use of an error coding system specifically tuned to the characteristics of the orthographies investigated allowed a more comprehensive identification of reading difficulties which allowed the different strategies used by children of different languages to emerge more clearly (more reliance on sub-lexical routines in Italian readers and on lexical routines in English readers). These results call for more attention to error patterns in the identification of reading difficulties in children of different languages including those learning a transparent orthography where error analyses have largely been ignored.

From coding to clinical nurse specialist: how a review of coding practice enabled hysteroscopy nurse development

Clinical coding, the method by which departments are reimbursed for providing services to patients, is widely mispractised within the NHS. Improving clinical coding accuracy therefore offers an opportunity to increase departmental income, guide efficient resource allocation and enable staff development. The authors audited the clinical coding in outpatient hysteroscopy clinics at their institution and found that coding errors were both prevalent and correctable. By implementing simple changes in coding procedure, and without any additional administrative cost, they significantly improved coding accuracy and achieved an increase in total annual tariffs. Although not applicable in a block contract, this will become highly relevant in a restoration of the Payment by Results tariff system. Nurse development is a key objective of the NHS Long Term Plan but can be hindered by staff costs, which require departmental funding. In the authors' institution, improved clinical coding accuracy directly led to a departmental restructuring, funded the development of a new hysteroscopy nurse development and improved care delivery. Coding errors are not unique to the authors' trust, yet simple amendments led to meaningful changes. Therefore, careful auditing and implemented change are needed to raise national clinical coding standards, to enable clinical restructuring, staff development, and provide more efficient, patient-centred care.

Researching Electronic System of the SC-920 Control Circuit of the DDL-8700-7 Single Needle Sewing Machine and Application

The DDL-8700-7 is a single-needle electronic sewing machine that uses a servo motor to drive the main shaft. This sewing machine has advantages such as being noiseless, energy saving, automatic thread trimming, reverse feed, wiper, and lifting of the needle bar to the highest position when the machine stops... The paper presents the research results of the relationship between the mechanical, electrical, and electronic parts of the SC-920 control circuit in the DDL-8700-7 single-needle sewing machine; analyzing the operating principle of the SC-920 control circuit electronic system according to each control block. In this study, theoretical and experimental research methods were used to measure, test, and evaluate the parameters. The obtained theoretical and experimental research results contribute to building a theoretical scientific basis for making test methods and repairing circuits for some electrical and electronic errors in the error code list of SC-920, helping the repairer identify defects for repair and replacement

Integrated high precision time-of-flight depth sensing and optical communication based on Hamiltonian coding

Convergence of sensing and communication in the same architecture is being considered for implementation in the future sixth-generation (6G) networks due to spectrum congestion and cost efficiency. This paper presents an integrated architecture for time-of-flight (ToF) depth sensing and optical communication simultaneously based on Hamiltonian coding function. The feasibility and performance of this framework are verified by theoretical simulation and experiments. Compared with sinusoid coding scheme, the proposed system based on single-frequency Hamiltonian coding modulation achieves a depth measurement accuracy of tens of millimeters and zero error code in communication, and the data rate 20 Mbps. By developing a multi-frequency Hamiltonian coding fusion algorithm, not only the maximum unambiguous range is increased from 15 m to 60 m, but the average ranging precision is improved to 4.46 mm as well. Experimentally, information of 1000 bits carried by single-frequency is demonstrated. Multi-frequency fusion system can accommodate three times that of single frequency. Within the maximum measurable experimental scope, the value of communication bit error rate (BER) always tends to be zero, under 7% pre-forward error correction (pre-FEC) limit of 3.8×10−3.

The NASA Ames legacy Mars global climate model: Radiation code error correction and new baseline water cycle simulation

Haberle et al. (2019) documented the status of the NASA Ames Legacy Mars Global Climate Model (GCM) and presented a baseline water cycle simulation. Since publishing that manuscript, we have found an error in the Legacy Mars GCM radiation code related to how the infrared cloud opacities were computed. Here we document this code error, its correction, how the corrected radiation code affects the simulated water cycle from Haberle et al. (2019), and what is required to produce a new realistic baseline water cycle simulation. We show that we can recover many, but not all, of the key aspects of the Haberle et al. (2019) baseline simulation by implementing a small change to the contact parameter, which is a microphysical parameter that affects the ease at which nucleation occurs. The fact that such a small change to the contact parameter can yield very different water cycles illustrates how sensitive the climate system is to cloud microphysical processes and highlights the need for future numerical and experimental study.