Configuration Errors Research Articles

Artificial Intelligence of Internet of Things Based on Machine Learning and College Student Management

Under the background of the development of higher education, according to the characteristics of college student management, after analyzing its background and practical significance, this study constructs an intelligent college student management system of Internet of things based on machine learning. The data volume of the Internet of things is huge, so ensuring the normal and efficient operation of the system is the primary goal. In this study, the data management model of the system is constructed with the help of cyclic neural network in machine learning algorithm to predict the data and optimize the computer program. At the same time, the system data are filled and classified by the k-nearest neighbor model, and the data are trained and simulated by constructing a safe bilstm neural network system. Because the information related to students in the university database involves personal privacy, in order to ensure the security of the system and avoid relevant data leakage, in the judgment standard of configuration error data flow, this study calculates the monitoring abnormal data and loss function through the dark network flow and ip2vec algorithm, so as to establish the system abnormal monitoring model and identify the system error data flow. Finally, it constructs the college student management system and expounds on the basic requirements of the system use cases. After a series of tests of system performance, capacity, and stability, the results meet the basic requirements of system operation, which provides a certain reference for the application of college student management in the future.

A Tb/s Indoor MIMO Optical Wireless Backhaul System Using VCSEL Arrays

In this paper, the design of a multiple-input multiple-output (MIMO) optical wireless communication (OWC) link based on vertical cavity surface emitting laser (VCSEL) arrays is systematically carried out with the aim to support data rates in excess of 1 Tb/s for the backhaul of sixth generation (6G) indoor wireless networks. The proposed design combines direct current optical orthogonal frequency division multiplexing (DCO-OFDM) and a spatial multiplexing MIMO architecture. For such an ultra-high-speed line-of-sight (LOS) OWC link with low divergence laser beams, maintaining alignment is of high importance. In this paper, two types of misalignment error between the transmitter and receiver are distinguished, namely, radial displacement error and orientation angle error, and they are thoroughly modeled in a unified analytical framework assuming Gaussian laser beams, resulting in a generalized misalignment model (GMM). The derived GMM is then extended to MIMO arrays and the performance of the MIMO-OFDM OWC system is analyzed in terms of the aggregate data rate. Novel insights are provided into the system performance based on computer simulations by studying various influential factors such as beam waist, array configuration and different misalignment errors, which can be used as guidelines for designing short range Tb/s MIMO OWC systems.

Internet of Things (IoT) Device Management in Rural Areas to Support Precision Agriculture

Internet of Things (IoT) devices have an important role in supporting precision agriculture systems, namely acting as the first layer consisting of several nodes in a limited network group as a producer of land, environmental and plant parameter data that will be processed at the next level. This node is an embedded system consisting of sensors, microcontrollers, power supplies and wireless communication media. The challenge that arises is when the number of new nodes that will be connected to the concentrator in a limited network group is quite large and is still done manually by the user, so that it has the potential to cause complexity and errors in configuration. The purpose of this research is to design a self-registration scheme from each new node to the concentrator in a restricted network group automatically. The method used by new nodes for self-registration is to read the RSSI (Received Signal Strength Indicator) value from the concentrator, then exchange metadata according to the OCF (Open Connectivity Foundation) standard. The concentrator will refer to the maximum number of nodes allowed to join the group and check the metadata of the new node. If the registration process is successful, the new node will gain access rights to communicate with the concentrator and its closest neighbours in the network group. This mechanism will help farmers in rural areas to be able to configure Internet of Things (IoT) devices easily and provide benefits in supporting precision agriculture.

Expert consensus on clinical application of multi-chamber bag for parenteral nutrition (2022)

Parenteral nutrition has become a routine clinical nutritional treatment. The guidelines at home and abroad unanimously recommend and standardize the application of "all in one" parenteral nutrition."Multi-chamber bag" parenteral nutrition encapsulates nutrients such as fat emulsion, amino acid and glucose in a non polyvinyl chloride soft bag composed of two or three cavities. When in use, the inner cavity separator is opened. The multi-chamber bag preparation has a variety of specifications and has the characteristics of reasonable prescription, strict quality standards, ready-to-use, and other characteristics, which can reduce prescription and configuration errors, reduce bloodstream infections, and meet the clinical nutritional needs of most patients. In view of the non-standard application of multi-chamber bag, the Chinese Society for Parenteral and Enteral Nutrition organized domestic experts in general surgery, critical care medicine, gastroenterology, geriatrics, nutrition, clinical pharmacy, and evidence-based medicine to write this expert consensus in accordance with the latest guidelines, expert consensus, and the latest clinical evidence. According to the improved Delphi method, 43 experts discussed and modified 23 recommendations one by one in the first round, and 219 experts voted and put forward modification suggestions in the second round. Each recommendation shall be established only when the approval rate of ≥ 90%. It is hoped that this consensus can promote the rational application of multi-chamber bag for parenteral nutrition.

Sensor configuration optimization based on the entropy of adjoint concentration distribution for stochastic source term estimation in urban environment

Sensor monitoring is foundational to source term estimation (STE). Recently, stochastic STE methods based mainly on Bayesian inference have been attracting attention. However, few studies have developed sensor configuration optimization (SCO) methods aiming to ensure good STE performance in the monitoring area, such that the posterior probability could aggregate around the truths while addressing most sources. This research proposes a deployment method by designing an objective function and applying a simulated annealing (SA) algorithm. The objective function is set as the information joint entropy of the adjoint concentration. The performance of the proposed method was assessed by Bayesian inference STE for 25 unknown sources based on the obtained optimal configuration in a regular block-arrayed building group model. The STE results were compared with those of uniform and random configurations. According to the results, because the optimal configuration can provide the most informative measurements, it yields the best estimations. If we quantize the STE errors and take the average for all unknown sources, the location and strength estimation errors of the optimal configuration significantly decrease by approximately 45% and 99%, respectively, when compared with the random configuration and 39% and 96%, respectively, when compared with the uniform configuration.

UBUMonitor: An Open-Source Desktop Application for Visual E-Learning Analysis with Moodle

An inherent requirement of teaching using online learning platforms is that the teacher must analyze student activity and performance in relation to course learning objectives. Therefore, all e-learning environments implement a module to collect such information. Nevertheless, these raw data must be processed to perform e-learning analysis and to help teachers arrive at relevant decisions for the teaching process. In this paper, UBUMonitor is presented, an open-source desktop application that downloads Moodle (Modular Object-Oriented Dynamic Learning Environment) platform data, so that student activity and performance can be monitored. The application organizes and summarizes these data in various customizable charts for visual analysis. The general features and uses of UBUMonitor are described, as are some approaches to e-teaching improvements, through real case studies. These include the analysis of accesses per e-learning object, statistical analysis of grading e-activities, detection of e-learning object configuration errors, checking of teacher activity, and comparisons between online and blended learning profiles. As an open-source application, UBUMonitor was institutionally adopted as an official tool and validated with several groups of teachers at the Teacher Training Institute of the University of Burgos.

Self-stabilisation of Cellular Automata on Tilings

Given a finite set of local constraints, we seek a cellular automaton (i.e., a local and uniform algorithm) that self-stabilises on the configurations that satisfy these constraints. More precisely, starting from a finite perturbation of a valid configuration, the cellular automaton must eventually fall back into the space of valid configurations where it remains still. We allow the cellular automaton to use extra symbols, but in that case, the extra symbols can also appear in the initial finite perturbation. For several classes of local constraints (e.g., k-colourings with k ≠ 3, and North-East deterministic constraints), we provide efficient self-stabilising cellular automata with or without additional symbols that wash out finite perturbations in linear or quadratic time, but also show that there are examples of local constraints for which the self-stabilisation problem is inherently hard. We note that the optimal self-stabilisation speed is the same for all local constraints that are isomorphic to one another. We also consider probabilistic cellular automata rules and show that in some cases, the use of randomness simplifies the problem. In the deterministic case, we show that if finite perturbations are corrected in linear time, then the cellular automaton self-stabilises even starting from a random perturbation of a valid configuration, that is, when errors in the initial configuration occur independently with a sufficiently low density.

Robust Terminal Sliding Mode Control on SE(3) for Gough–Stewart Flight Simulator Motion Platform with Payload Uncertainty

This work proposes a robust terminal sliding mode control scheme on Lie group space SE(3) for Gough–Stewart flight simulator motion systems with payload uncertainty. A complete dynamic model with geometric mechanical structures and a computer dynamic model built in the MATLAB/Simulink package are briefly presented. The robust control strategy on the Lie group SE(3) is applied at the workspace level to counteract the effects of imperfect compensation due to model simplification and payload uncertainty in flight simulator application. With exponential coordinates for configuration error and adjoint operator on Lie algebra se(3), the robust control strategy is designed to guarantee almost global finite-time convergence over state space through the Lyapunov stability theory. Finally, a describing function and a step acceleration response to characterize the performance of a flight simulator motion base are employed to compare the robustness performance of the proposed controller on SE(3) with the conventional terminal sliding mode controller on Cartesian space. The comparison experimental results verify that the proposed controller on SE(3) provides better robustness than the conventional controller on Cartesian space, which means higher bandwidth in two degrees of freedom and faster response with smaller tracking error in six degrees of freedom.

A Real-Time Detection Method of Software Configuration Errors Based on Fine-Grained Configuration Item Types

With the continuous expansion of software scale and the continuous complexity of software functions, abnormal parameter configuration often brings adverse effects to the software system and even leads to system failure. Therefore, a method is needed to detect whether the parameter configuration is correct. Most of the current configuration errors detection methods focus on the passive diagnosis after the system failure, which cannot solve the potential delay problem of configuration error. This paper proposes an automatic real-time detection method of software configuration errors, in which the configuration items are classified based on the fine-grained configuration item types and related syntax patterns, and the configuration constraint rule base is generated. Then, the real-time exception detection of configuration update operation is realized by using the file operation monitoring function. The experimental results show that this method can accurately classify the types of configuration items and verify the effectiveness of detecting software configuration update exceptions in real time through the constraint rule base. The classification accuracy reaches up to 90.4% on MySQL and 87.4% on Apache.

Intelligent Software Service Configuration Technology Based on Association Mining

Association relationship and types between software service configuration parameters determine that the configuration items must meet the corresponding rules and constraints in parameter value and setting order. However, the distribution of software services and the architecture based on the loose coupling of heterogeneous components make the association of configuration parameters more concealed. The correlation and constraints of configuration parameters are crucial to ensure system configuration. When deploying, migrating, and updating systems, violating constraints will lead to configuration errors, resulting in system failures. To minimize the loss caused by configuration errors, association rules between configuration items can be mined to avoid potential incorrect settings. This paper proposes an intelligent software service configuration technology based on association mining by analyzing configuration items. The sample data of the configuration file is extracted from the open source code base, and the configuration file is parsed to obtain the configuration item name and value. Then mine the association rule of each configuration item pair based on the configuration item name, value, and type. According to the mined configuration constraints, configuration item error detection, cascade update and value recommendation are performed. Finally, the proposed method is tested and evaluated on six typical open source software, and the experimental results prove the accuracy and effectiveness of the method in this paper.

IoT-Cloud Empowered Aerial Scene Classification for Unmanned Aerial Vehicles

Recent trends in communication technologies and unmanned aerial vehicles (UAVs) find its application in several areas such as healthcare, surveillance, transportation, etc. Besides, the integration of Internet of things (IoT) with cloud computing environment offers several benefits for the UAV communication. At the same time, aerial scene classification is one of the major research areas in UAV-enabled MEC systems. In UAV aerial imagery, efficient image representation is crucial for the purpose of scene classification. The existing scene classification techniques generate mid-level image features with limited representation capabilities that often end up in producing average results. Therefore, the current research work introduces a new DL-enabled aerial scene classification model for UAV-enabled MEC systems. The presented model enables the UAVs to capture aerial images which are then transmitted to MEC for further processing. Next, Capsule Network (CapsNet)-based feature extraction technique is applied to derive a set of useful feature vectors from the aerial image. It is important to have an appropriate hyperparameter tuning strategy, since manual parameter tuning of DL model tend to produce several configuration errors. In order to achieve this and to determine the hyperparameters of CapsNet model, Shuffled Shepherd Optimization (SSO) algorithm is implemented. Finally, Backpropagation Neural Network (BPNN) classification model is applied to determine the appropriate class labels of aerial images. The performance of SSO-CapsNet model was validated against two openly-accessible datasets namely, UC Merced (UCM) Land Use dataset and WHU-RS dataset. The proposed SSO-CapsNet model outperformed the existing state-of-the-art methods and achieved maximum accuracy of 0.983, precision of 0.985, recall of 0.982, and F-score of 0.983.

A Real-Time FPGA Implementation of the CCSDS 123.0-B-2 Standard

Hyperspectral images are a useful remote sensing tool that often reaches hundreds of megabytes in size. The CCSDS 123.0-B-2 is a recent algorithm that achieves lossless and near-lossless compression of hyperspectral images by introducing a configurable maximum error over its predecessor CCSDS 123.0-B-1. In this article, a field-programmable gate array (FPGA) implementation of the revised standard that works in real-time is presented. We have developed an extremely pipelined and fast core in VHDL, that is able to process a sample per cycle at over 250 MHz, working eight times faster than in real time for the Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor. New dependencies in the revised standard are avoided by using a novel sample ordering called frame interleaved by diagonal. The predictor stage has been designed to work in this order, and two reorder buffers encapsulate it to be band interleaved by pixel compliant. Predictor data are encoded using a novel FPGA implementation of the CCSDS 123.0-B-2 hybrid coder. The modules are tested and verified on a Virtex-7 VC709 board. For medium (256 bands <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times4096$ </tex-math></inline-formula> frames <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times512$ </tex-math></inline-formula> samples) and large ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$512\times 4096\times 1024$ </tex-math></inline-formula> ) images, the core occupies, respectively, 14% and 50% of an XQRKU060 FPGA.

A data-driven approach to increasing the lifetime of IoT sensor nodes

Data transmission accounts for significant energy consumption in wireless sensor networks where streaming data is generated by the sensors. This impedes their use in many settings, including livestock monitoring over large pastures (which forms our target application). We present Ambrosia, a lightweight protocol that utilizes a window-based timeseries forecasting mechanism for data reduction. Ambrosia employs a configurable error threshold to ensure that the accuracy of end applications is unaffected by the data transfer reduction. Experimental evaluations using LoRa and BLE on a real livestock monitoring deployment demonstrate 60% reduction in data transmission and a 2 times increase in battery lifetime.

A new methodological framework for hazard detection models in health information technology systems

The adoption of health information technology (HIT) has facilitated efforts to increase the quality and efficiency of health care services and decrease health care overhead while simultaneously generating massive amounts of digital information stored in electronic health records (EHRs). However, due to patient safety issues resulting from the use of HIT systems, there is an emerging need to develop and implement hazard detection tools to identify and mitigate risks to patients. This paper presents a new methodological framework to develop hazard detection models and to demonstrate its capability by using the US Department of Veterans Affairs’ (VA) Corporate Data Warehouse, the data repository for the VA’s EHR. The overall purpose of the framework is to provide structure for research and communication about research results. One objective is to decrease the communication barriers between interdisciplinary research stakeholders and to provide structure for detecting hazards and risks to patient safety introduced by HIT systems through errors in the collection, transmission, use, and processing of data in the EHR, as well as potential programming or configuration errors in these HIT systems. A nine-stage framework was created, which comprises programs about feature extraction, detector development, and detector optimization, as well as a support environment for evaluating detector models. The framework forms the foundation for developing hazard detection tools and the foundation for adapting methods to particular HIT systems.

Gesture errors in left and right hemisphere damaged patients: A behavioural and anatomical study

ObjectiveErroneous gesture execution is at the core of motor cognition difficulties in apraxia. While a taxonomy of errors may provide important information about the nature of the disorder, classifications are currently often inconsistent. This study aims to identify the error categories which distinguish apraxic from non-apraxic patients. Method. Two groups of mixed (bucco-facial and limb) and bucco-facial apraxic patients suffering from stroke were compared to non-apraxic, left and right hemisphere damaged patients in tasks tapping the ability to perform limb and bucco-facial actions. The errors were analysed and classified into 6 categories relating to content, configuration or movement, spatial or temporal parameters and unrecognisable actions. Furthermore, an anatomical investigation (VLMS) was conducted in the whole group of left hemisphere damaged patients to investigate potential correlates of the various error categories. Results. Although all the above error typologies may be observed, the most indicative of mixed apraxia is the content-related one in all the typologies of actions (transitive and intransitive), and configuration errors in transitive ones. Configuration and content errors in mouth actions seem to be typical of bucco-facial apraxia. Spatial errors are similar in both apraxic and right brain damaged, non-apraxic patients. A lesion mapping analysis of left-brain damaged patients demonstrates that all but the spatial error category are associated with the fronto-parietal network. Moreover, content errors are also associated with fronto-insular lesions and movement errors with damage to the paracentral territory (precentral and postcentral gyri). Spatial errors are often associated to ventral frontal lesions. Conclusions. Bucco-facial and mixed apraxic patients make different types of errors in different types of actions. Not all errors are equally indicative of apraxia. In addition, the various error categories are associated with at least partially different neural correlates.

Mspack: efficient lossless and lossy mass spectrometry data compression.

Mass spectrometry (MS) data, used for proteomics and metabolomics analyses, have seen considerable growth in the last years. Aiming at reducing the associated storage costs, dedicated compression algorithms for MS data have been proposed, such as MassComp and MSNumpress. However, these algorithms focus on either lossless or lossy compression, respectively, and do not exploit the additional redundancy existing across scans contained in a single file. We introduce mspack, a compression algorithm for MS data that exploits this additional redundancy and that supports both lossless and lossy compression, as well as the mzML and the legacy mzXML formats. mspack applies several preprocessing lossless transforms and optional lossy transforms with a configurable error, followed by the general purpose compressors gzip or bsc to achieve a higher compression ratio. We tested mspack on several datasets generated by commonly used MS instruments. When used with the bsc compression backend, mspack achieves on average 76% smaller file sizes for lossless compression and 94% smaller file sizes for lossy compression, as compared with the original files. Lossless mspack achieves 10-60% lower file sizes than MassComp, and lossy mspack compresses 36-60% better than the lossy MSNumpress, for the same error, while exhibiting comparable accuracy and running time. mspack is implemented in C++ and freely available at https://github.com/fhanau/mspack under the Apache license. Supplementary data are available at Bioinformatics online.

A comprehensive study on security bug characteristics

AbstractSecurity bugs can catastrophically impact our increasingly digital lives. Designing effective tools for detecting and fixing software security bugs requires a deep understanding of security bug characteristics. In this paper, we conducted a comprehensive study on security bugs and proposed the classification criteria for security bug category, that is, root cause, consequence, and location. In addition, we selected 1076 bug reports from five projects (i.e., Apache Tomcat, Apache HTTP Server, Mozilla Firefox, Linux Kernel, and Eclipse) in the NVD for investigation. Finally, we investigated the correlation between the classification results and obtained some findings: (1) memory operation is the most common security bug; (2) the primary root causes of security bugs are CON (Configuration Error), INP (Input Validation Error), and MEM (Memory Error); (3) the severity of more than 40% of security bugs is high; (4) security bugs caused by INP mainly occur on web; and (5) security bugs caused by LOG (Logic Resource Error) usually lead to DoS (Denial of Service). We discussed these findings through data analysis, which can also help developers better understand the characteristics of security bugs.

Provisioning, Authentication and Secure Communications for IoT Devices on FIWARE.

The increasing pervasiveness of the Internet of Things is resulting in a steady increase of cyberattacks in all of its facets. One of the most predominant attack vectors is related to its identity management, as it grants the ability to impersonate and circumvent current trust mechanisms. Given that identity is paramount to every security mechanism, such as authentication and access control, any vulnerable identity management mechanism undermines any attempt to build secure systems. While digital certificates are one of the most prevalent ways to establish identity and perform authentication, their provision at scale remains open. This provisioning process is usually an arduous task that encompasses device configuration, including identity and key provisioning. Human configuration errors are often the source of many security and privacy issues, so this task should be semi-autonomous to minimize erroneous configurations during this process. In this paper, we propose an identity management (IdM) and authentication method called YubiAuthIoT. The overall provisioning has an average runtime of 1137.8 ms . We integrate this method with the FIWARE platform, as a way to provision and authenticate IoT devices.

Influence of Configuration Error in Bolted Joints on Detection Error of Clamp Force Detection Method

Clamp force errors in bolted joints often cause accidents in various mechanical structures. Therefore, the clamp force must be controlled accurately and maintained for securing the reliability of mechanical structures such as vehicles. However, the clamp force cannot be controlled easily during tightening. Moreover, it is difficult to detect the clamp force after tightening. We previously proposed a method to easily detect the clamp force of a bolted joint that has been tightened. In that method, the bolt thread protruding from the nut is pulled while the nut’s upper surface is supported. The relationship between tensile force and displacement at the pulling point where the tensile force is applied differs before and after the tensile force reaches the clamp force. The method detects the tensile force at the point, where the relationship changes, as the clamp force. In this study, we investigate the influence of squareness error on the bearing surface of the clamped part in a bolted joint on the detection error of the method using experiments and finite element (FE) analysis. The experimental results show that the squareness error has an influence on the detection accuracy. The average detection error in the experiments increases by approximately 10% with an increase in the squareness error. To understand the cause of this phenomenon, we investigate the effects of backlash between mating thread surfaces of bolts and nuts on the detection error. The results show that the error decreased because of the backlash. Consequently, it is assumed that the error is caused by the non-separation of the mating thread surfaces when the tensile force reached the clamp force. Furthermore, the FE analysis results show that the squareness error on the bearing surface of the clamped part has an influence of the squareness error on the detection accuracy. The results indicate that we should control the tolerance of squareness errors on the bearing surface of the clamped part when the clamp force detection method is applied to bolted joints.

Detection of SSL/TLS Implementation Errors in Android Applications

Security Socket Layer (SSL) / Transport Layer Security (TLS) protocols are utilized to secure network communication (e.g., transmitting user data). Failing to properly implement SSL/TLS configuration during the app development results in security risks. The weak implementations include trusting all host names, trusting all certificates, ignoring certificate verification errors, even lack of SSL public key pinning usage. These unsecured implementations may cause Man-In-The-Middle (MITM) attacks. The major aim of this research is to detect configuration errors of SSL/TLS implementation in Android apps. We combine existing open-source tools and streamline the analysis process with the combination of automated static analysis and dynamic analysis with manual assistance. We scan for four types of vulnerabilities in the static analysis phase and verify misuse of SSL/TLS in the dynamic analysis phase. The dynamic analysis is essential for eliminating false positives generated at the static analysis stage. We analyze 109 apps from Google Play Store and the experimental results show that 45 (41.28%) apps contain potential security errors in the application of SSL/TLS. We verify that 19 (17.43%) out of 109 apps are vulnerable to MITM attacks.