Manual Assignment Research Articles

NaViA: a program for the visual analysis of complex mass spectra.

MotivationNative mass spectrometry is now a well-established method for the investigation of protein complexes, specifically their subunit stoichiometry and ligand binding properties. Recent advances allowing the analysis of complex mixtures lead to an increasing diversity and complexity in the spectra obtained. These spectra can be time-consuming to tackle through manual assignment and challenging for automated approaches.ResultsNative Mass Spectrometry Visual Analyser is a web-based tool to augment the manual process of peak assignment. In addition to matching masses to the stoichiometry of its component subunits, it allows raw data processing, assignment and annotation and permits mass spectra to be shared with their respective interpretation.Availability and implementationNaViA is open-source and can be accessed online under https://navia.ms. The source code and documentation can be accessed at https://github.com/d-que/navia, under the BSD 2-Clause licence.Supplementary informationSupplementary data are available at Bioinformatics online.

Spatiotemporal registration and fusion of transthoracic echocardiography and volumetric coronary artery tree.

Cardiac multimodal image fusion can offer an image with various types of information in a single image. Many coronary stenosis, which are anatomically clear, are not functionally significant. The treatment of such kind of stenosis can cause irreversible effects on the patient. Thus, choosing the best treatment planning depend on anatomical and functional information is very beneficial. An algorithm for the fusion of coronary computed tomography angiography (CCTA) as an anatomical and transthoracic echocardiography (TTE) as a functional modality is presented. CCTA and TTE are temporally registered using manifold learning. A pattern search optimization algorithm, using normalized mutual information, is used to find the best match slice to TTE frame from CCTA volume. By employing a free-form deformation, the heart's non-rigid deformations are modeled. The spatiotemporal registered TTE frame is embedded to achieve the fusion result. The accuracy is evaluated on CCTA and TTE data obtained from 10 patients. In temporal registration, mean absolute error of 1.97 [Formula: see text] 1.23 is resulted from comparing the output frame numbers from the algorithm and from manual assignment by an expert. In spatial registration, the accuracy of the similarity between the best match slice from CCTA volume and TTE frame is resulted in 1.82 [Formula: see text] 0.024mm, 6.74 [Formula: see text] 0.013mm, and 0.901 [Formula: see text] 0.0548 due to mean absolute distance, Hausdorff distance, and Dice similarity coefficient, respectively. Without the use of ECG and Optical tracking systems, a semiautomatic framework of spatiotemporal registration and fusion of CCTA volume and TTE frame is presented. The experimental results showed the effectiveness of our proposed method to create complementary information from TTE and CCTA, which may help in the early diagnosis and effective treatment of cardiovascular diseases (CVDs).

An efficient mechanism to detect and mitigate an ARP spoofing attack in software-defined networks

The work focuses on software-defined network security, as it was always one of these foremost critical concerns due to the centralized nature in SDN architecture where many serious attacks in traditional networks still appear in SDN networks such as ARP spoofing attack despite many existing security algorithms, methods and systems. In this work, we proposed a new approach to secure SDN from an ARP poisoning attack. The new solution extends the controller with a new module that uses a new algorithm to detect and mitigate the ARP spoofing attacks according to three states of each host in the network. The new mechanism involves the DHCP and manual assignment of IP addresses using three classes to classify the hosts according to their situations in the network. The CHT helps to set the host in an intermediate state between verifying and banning and detect the attack according to the next step of the host. The proposed mechanism was tested successfully in a simulated environment using Mininet and POX controller. The solution was effectively able to accomplish the objective for which it was built, with a limited overhead on the network. This proposed solution neither has an extra overload in the network, nor requires any changes in the infrastructure or additional hardware to install. According to the experiment results of this solution, the average time to detect the ARP spoofing attack is about 11 ms, with minor overhead on the controller CPU.

Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis.

A specific model for drug absorption is necessarily assumed in pharmacokinetic (PK) analyses following extravascular dosing. Unfortunately, an inappropriate absorption model may force other model parameters to be poorly estimated. An added complexity arises in population PK analyses when different individuals appear to have different absorption patterns. The aim of this study is to demonstrate that a deep neural network (DNN) can be used to prescreen data and assign an individualized absorption model consistent with either a first-order, Erlang, or split-peak process. Ten thousand profiles were simulated for each of the three aforementioned shapes and used for training the DNN algorithm with a 30% hold-out validation set. During the training phase, a 99.7% accuracy was attained, with 99.4% accuracy during in the validation process. In testing the algorithm classification performance with external patient data, a 93.7% accuracy was reached. This algorithm was developed to prescreen individual data and assign a particular absorption model prior to a population PK analysis. We envision it being used as an efficient prescreening tool in other situations that involve a model component that appears to be variable across subjects. It has the potential to reduce the time needed to perform a manual visual assignment and eliminate inter-assessor variability and bias in assigning a sub-model.

Record Linkage in Clinical Cancer Registration: Experiences and Findings from Lower Saxony.

In cancer registries, record linkage procedures are used to link records of the same patient from different health care providers. In the Clinical Cancer Registry of Lower Saxony, a multi-level combination of exact assignment using the statutory health insurance number and a probabilistic procedure with control numbers and address data is applied. The procedure implemented in the register application assigns the incoming messages in this way as far as possible automatically. The aim of the observation carried out was to check the efficiency of the match variables and threshold values used, above which manual assignment is required. Weak points were identified and approaches to solutions were developed.

Effective Prediction of Bug-Fixing Priority via Weighted Graph Convolutional Networks

With the increasing number of software bugs, bug fixing plays an important role in software development and maintenance. To improve the efficiency of bug resolution, developers utilize bug reports to resolve given bugs. Especially, bug triagers usually depend on bugs' descriptions to suggest priority levels for reported bugs. However, manual priority assignment is a time-consuming and cumbersome task. To resolve this problem, recent studies have proposed many approaches to automatically predict the priority levels for the reported bugs. Unfortunately, these approaches still face two challenges that include words' nonconsecutive semantics in bug reports and the imbalanced data. In this article, we propose a novel approach that graph convolutional networks (GCN) based on weighted loss function to perform the priority prediction for bug reports. For the first challenge, we build a heterogeneous text graph for bug reports and apply GCN to extract words' semantics in bug reports. For the second challenge, we construct a weighted loss function in the training phase. We conduct the priority prediction on four open-source projects, including Mozilla, Eclipse, Netbeans, and GNU compiler collection. Experimental results show that our method outperforms two baseline approaches in terms of the F-measure by weighted average of 13.22%.

Fluorescence-Based Quantification of Gastrointestinal Perfusion: A Step Towards an Automated Approach.

Background: Qualitative fluorescence angiography (FA) provides insights into intestinal tissue perfusion, but today it is not yet accurate in predicting anastomotic leakage. To improve peroperative detection of impaired perfusion, quantified parameters should be investigated using a standardized method. The aim of this study was to develop a (semi)automated algorithm for comprehensive and convenient analysis of FA parameters. Materials and Methods: An analysis tool was developed for the extraction of quantified FA parameters. The start- and endpoint of intensity increase (T0 and ) were automatically detected in the intensity-time curves. Algorithm performance was measured against manual assignment of T0 and by 9 independent observers in 18 in vivo generated test signals, using the intraclass correlation coefficient (ICC). Characteristics of parameter (time to 50% of maximal intensity) were analyzed in normally perfused small intestine of 32 subjects who underwent robotic laparoscopic surgery. Since ethical approval was not required under the Dutch law, the need for informed consent was waived. Results: Automated detection of T0 and was successful in all subjects. Output of the algorithm had an excellent agreement with the median of the human observations: ICC = 0.95 (95% confidence interval: 0.86-0.96). Overall, had a median value of 5.1 (interquartile range = 2.4) seconds and a minimal and maximal value of 1.3 and 9.9 seconds, respectively. Conclusions: The presented method provided convenient data analysis in the search for effective FA quantification. Future research should expand the data to find adequate threshold values for peroperatively identifying insufficient perfusion and investigate the influence of physiological conditions.

Can a dam type of an alpine lake be derived from lake geometry? A negative result

Glacial lake outburst floods (GLOFs) represent one of the most serious hazard and risk in deglaciating high mountain regions worldwide and the need for GLOF hazard and risk assessment is apparent. As a consequence, numerous region- and nation-wide GLOF assessment studies have been published recently. These studies cover large areas and consider hundreds to thousands of lakes, prioritizing the hazard posed by them. Clearly, certain simplification is required for executing such studies, often resulting in neglecting qualitative characteristics which would need manual assignment. Different lake dam types (e.g., bedrock-dammed, moraine-dammed) are often not distinguished, despite they control GLOF mechanism (dam overtopping/dam breach) and thus GLOF magnitude. In this study, we explore the potential of easily measurable quantitative characteristics and four ratios to approximate the lake dam type. Our dataset of 851 lakes of the Cordillera Blanca suggests that while variances and means of these characteristics of individual lake types differ significantly (F-test, t-test), value distribution of different geometrical properties can’t be used for the originally proposed purpose along the spectra. The only promising results are obtained for extreme values (selected bins) of the ratios. For instance, the low width to length ratio indicates likely moraine-dammed lake while the high value of ratio indicating round-shape of the lake indicates increased likelihood of bedrock-dammed lake. Overall, we report a negative result of our experiment since there are negligible differences of relative frequencies in most of the bins along the spectra.

Railway wheel profile fine-tuning system for profile recommendation

This paper develops a wheel profile fine-tuning system (WPFTS) that comprehensively considers the influence of wheel profile on wheel damage, vehicle stability, vehicle safety, and passenger comfort. WPFTS can recommend one or more optimized wheel profiles according to train operators’ needs, e.g., reducing wheel wear, mitigating the development of wheel out-of-roundness (OOR), improving the shape stability of the wheel profile. Specifically, WPFTS includes four modules: (I) a wheel profile generation module based on the rotary-scaling fine-tuning (RSFT) method; (II) a multi-objective generation module consisting of a rigid multi-body dynamics simulation (MBS) model, an analytical model, and a rigid–flexible MBS model, for generating 11 objectives related to wheel damage, vehicle stability, vehicle safety, and passenger comfort; (III) a weight assignment module consisting of an adaptive weight assignment strategy and a manual weight assignment strategy; and (IV) an optimization module based on radial basis function (RBF) and particle swarm optimization (PSO). Finally, three cases are introduced to show how WPTFS recommends a wheel profile according to train operators’ needs. Among them, a wheel profile with high shape stability, a wheel profile for mitigating the development of wheel OOR, and a wheel profile considering hunting stability and derailment safety are developed, respectively.

A beginner’s guide and best practices for using crowdsourcing platforms for survey research: The Case of Amazon Mechanical Turk (MTurk)

A beginner’s guide and best practices for using crowdsourcing platforms for survey research: The Case of Amazon Mechanical Turk (MTurk)

ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System

Aspect based sentient analysis (ABSA) is identified as one of the current research problems in Natural Language Processing (NLP). Traditional ABSA requires manual aspect assignment for aspect extraction and sentiment analysis. In this paper, to automate the process, a domain-independent dynamic ABSA model by the fusion of Efficient Named Entity Recognition (E-NER) guided dependency parsing technique with Neural Networks (NN) is proposed. The extracted aspects and sentiment terms by E-NER are trained to a Convolutional Neural Network (CNN) using Word embedding’s technique. Aspect categorybased polarity prediction is evaluated using NLTK Vader Sentiment package. The proposed model was compared to traditional rule-based approach, and the proposed dynamic model proved to yield better results by 17% when validated in terms of correctly classified instances, accuracy, precision, recall and F-Score using machine learning algorithms.

A general algorithm for distributed treatments of multiple brain metastases.

Stereotactic radiosurgery (SRS) has become a primary treatment for multiple brain metastases (BM) but may require distribution of BMs over several sessions to make delivery time and radiation toxicity manageable. Contrasting to equal fraction dose in conventional fractionation, distributed SRS delivers full dose to a subset of BMs in each session while avoiding adjacent BMs in the same session to reduce toxicity from overlapping radiation. However, current clinical treatment planning for distributed SRS relies on manual BM assignment, which can be tedious and error prone. This work describes a novel approach to automate the distribution of BM in the Gamma Knife (GK) clinical workflow. We represent each BM as an electrostatic field of the same polarity that exerts repulsive forces on other BMs in the same session. This representation naturally leads to separation of close BMs into different sessions to lower the potential energy. Indeed, the BM distribution problem can be formulated as minimization of the total potential energy from all treatment sessions subject to delivery time constraints in mixed-integer quadratic programming (MIQP). We retrospectively studied eight clinical GK cases of multiple BM and compared the automated MIQP solution with clinically used BM distribution to demonstrate the efficacy of the proposed approach. With the problem size equal to the number of BMs times the number of sessions, this MIQP can be solved in a minute on a personal workstation. The MIQP solution effectively separated BMs for a given number of treatment sessions and evened out the delivery time distribution among sessions. Compared to the clinically used manual BM distributions in paired t-test for a similar range of delivery time variation, the automated BM distributions had lower energy objectives (range of decrease: [11% 89%]; median: 25%; ), more uniformly distributed treatment volumes (range of decrease for the normalized standard deviation of volume distribution: [0.02 0.95]; median: 0.16; ), more scattered BMs in each treatment session (range of increase for the mean minimum BM distance: [0 14]mm; median: 6mm; ), and lower overall (range of decrease: [0.0 1.6]cc; median: 0.2cc; ). Moreover, without distribution, that is, with all BMs treated in the same session, was substantially larger compared to both manual and automated BM distributions; the increase ranged from 0.1 to 16.6cc with a median of 1.3cc. The proposed approach models the clinical practice and provides an efficient solution for optimal selection of BM subsets for distributed SRS. Further evaluations are underway to establish this approach as a tool for improving clinical workflow and to facilitate systematic study on the benefits of distributed SRS treatments.

Efficient referee assignment in Argentinean professional basketball leagues using operations research methods

The top two divisions of the Argentinean professional basketball system have since 2014–2015 used a season schedule format similar to that used by the NBA in which games are played all through the week, replacing the previous setup where all games were scheduled on weekends. This change has confronted the Argentinean league organizers with new scheduling challenges, one of which is the assignment of referees to games. The present article addresses this assignment problem using a tool based on an integer linear programming model. The objective is to minimize the total cost of trips made by the referees while also satisfying a series of other conditions. The problem is broken down into a series of relatively small subproblems representing successive periods of the season, and the solution is obtained using a rolling horizon heuristic. The approach was tested by applying it to the First Division’s 2015–2016 season, the last one before introducing the approach presented in this article, when referees were still assigned using manual methods. The travel costs simulated by the model were 26% lower than the total travel costs actually incurred under the manual assignments, and all of the restrictions that had been requested by league officials were satisfied. The model was used by the First Division for the 2016–2017 and 2017–2018 seasons and in 2017–2018 also by the Second Division.

Optimization of robot-assisted thoracoscopic esophagectomy in the lateral decubitus position.

On the introduction of robot-assisted thoracoscopic esophagectomy (RATE), we refined the robotic system application to enhance our surgical experience obtained through thoracoscopic esophagectomy (TE) in the lateral decubitus position (LDP). Herein, we evaluate our methods introduced to optimize RATE in the LDP. We performed RATE in the LDP with camera rotation and manual hand control assignment to reproduce the surgical view and manipulation of open esophagectomy. Forty patients underwent RATE between July 2018 and August 2020. After the initial 30 cases (initial RATE group), we optimized the port arrangement and robot settings in the most recent ten cases (recent RATE group). The surgical results of RATE were compared with those of 30 patients underwent TE between April 2014 and May 2019 selected by propensity score-matched analysis based on cStage (TE group). Operative duration was significantly longer in the initial RATE group than the TE group and the recent RATE group. Thoracic blood loss was significantly less in the initial RATE group than the TE group. Console time was significantly shorter in the recent RATE group than the initial RATE group. There was no surgical mortality in RATE and the surgical morbidity rate was similar in the three groups. Camera rotation and manual hand control assignment during RATE in the LDP reproduced the surgical view and manipulation of open esophagectomy and TE in the LDP. The robotic platform enabled meticulous dissection and reduced blood loss, but was initially time-consuming. Optimization of the port arrangement minimized operative duration.

EHooke: A tool for automated image analysis of spherical bacteria based on cell cycle progression.

Fluorescence microscopy is a critical tool for cell biology studies on bacterial cell division and morphogenesis. Because the analysis of fluorescence microscopy images evolved beyond initial qualitative studies, numerous images analysis tools were developed to extract quantitative parameters on cell morphology and organization. To understand cellular processes required for bacterial growth and division, it is particularly important to perform such analysis in the context of cell cycle progression. However, manual assignment of cell cycle stages is laborious and prone to user bias. Although cell elongation can be used as a proxy for cell cycle progression in rod-shaped or ovoid bacteria, that is not the case for cocci, such as Staphylococcus aureus. Here, we describe eHooke, an image analysis framework developed specifically for automated analysis of microscopy images of spherical bacterial cells. eHooke contains a trained artificial neural network to automatically classify the cell cycle phase of individual S. aureus cells. Users can then apply various functions to obtain biologically relevant information on morphological features of individual cells and cellular localization of proteins, in the context of the cell cycle.

SENTIMENT ANALYSIS OF ELECTRIC CARS USING RECURRENT NEURAL NETWORK METHOD IN INDONESIAN TWEETS

Sentiment analysis is computational research of the opinions of many people who are textually expressed against a particular topic. Twitter is the most popular communication tool among Internet users today to express their opinions. Deep Learning is a solution to allow computers to learn from experience and understand the world in terms of the hierarchy concept. Deep Learning objectives replace manual assignments with learning. The development of deep learning has a set of algorithms that focus on learning data representation. The recurrent Neural Network is one of the machine learning methods included in Deep learning because the data is processed through multi-players. RNN is also an algorithm that can recall the input with internal memory, therefore it is suitable for machine learning problems involving sequential data. The study aims to test models that have been created from tweets that are positive, negative, and neutral sentiment to determine the accuracy of the models. The models have been created using the Recurrent Neural Network when applied to tweet classifications to mark the individual classes of Indonesian-language tweet data sentiment. From the experiments conducted, results on the built system showed that the best test results in the tweet data with the RNN method using Confusion Matrix are with Precision 0.618, Recall 0.507 and Accuracy 0.722 on the data amounted to 3000 data and comparative data training and data testing of ratio data 80:20

Method for Ranking the Helpfulness of Online Reviews Based on SO-ILES TODIM

Online shopping has become a habit for consumers, who often make purchase decisions based on online reviews. However, the gradual accumulation of reviews has caused an issue associated with information redundancy. Therefore, recommending helpful reviews for consumers has become an urgent problem. Current research on the helpfulness of reviews is mostly at the level of analyzing influencing factors, and few studies have focused on the problem of ranking the helpfulness of reviews. Taking film reviews as the research object, we proposed a SO-ILES TODIM method (a TODIM method based on the intuitive language evaluation set of emotional and ontological features). This method takes into account both semantic indicators (emotional factors and ontological features) and statistical indicators (review length), considers comprehensive information in the review text and has better domain adaptability. First, an intuitive language evaluation set that considers emotional and ontological features was constructed based on statistical rules. Second, a quantitative calculation method that includes an index weight value based on the logit regression model was designed, and it can effectively avoid the subjectivity of the manual assignment method. Finally, based on the degree of membership deviation, the score function and the exact function were designed to realize a ranking of the helpfulness of reviews. Through a case simulation, we show that this method can prioritize reviews that directly evaluate the product. Through a comparative analysis and parameter sensitivity analysis, the stability and scientificity of the SO-ILES TODIM method was demonstrated. This paper broadens the research scope of reviews, enriches the research method of review helpfulness ranking and provides insights for merchants or third-party platforms to manage online reviews.

Route and Bus Stop Plan of Agglomeration Transportation on Educational Facility in Yogyakarta Urban Agglomeration Area

Urban Public Transportation has important existence to make good urban transportation. To realize it, the objective of this research is to improve Trans Jogja service as Yogyakarta Urban Agglomeration Transportation that appropriate for student needs. The plan that will be formulated in this research is focused on route and bus stop distribution in educational facility that covering high schools and universities located in Yogyakarta Agglomeration Area. Route plan formulation is done with manual assignment method through total road network trip assignment based on origin destination matrix of school trip. Then, to formulate bus stop plan is done with spatial analytical method that covering bus stop accessibility to schools and universities through 2 scenarios, the first scenario is about 200-meters radius of accessibility and second scenario is 500-meters radius of accessibility. The result is there will be 31 new routes of Yogyakarta Urban Agglomeration Transportation that will reach the whole sub-district in Yogyakarta Agglomeration Area include Godean and Ngemplak Sub-District that previously are not served by Trans Jogja. That new routes are consist of 3 outer city routes (OCR), 23 inner city routes (ICR) and 5 connecting routes that connecting sub-urban and urban area. Then, the bus stop is planned along new route plan and focused on residential land use in sub-urban area, high schools, and universities. On the first scenario with radius of 200-meter, there is 99 units of additional bus stop plan, while on the second scenario with radius of 500 meters is 66 units. Thus, second scenario is assumed better than the first scenario because of more schools and universities are served by Trans Jogja.

A semi-automated brain atlas-based analysis pipeline for c-Fos immunohistochemical data

BackgroundThe use of immunohistochemistry to quantify neural markers in various brain regions is a staple of neuroscience research. Numerous programs exist to automate quantification, but manual assignment of regions of interest (ROIs) within individual brain sections remains time consuming and can introduce interobserver variability. New methodWe have developed a novel open source FIJI-based immunohistochemical data analysis pipeline, Atlas-Based Analysis (ABA). ABA uses landmark-based image warping to adjust the experimental image to closely align with a published rat brain atlas. c-Fos positive cells are then quantified within predetermined ROI coordinates derived from the brain atlas. Image warping adjusts for natural variation in brain sections to ensure reliable alignment of ROIs for data analysis. This pipeline can be adapted for new atlases, landmarks, ROIs, and quantification measurements. ResultsABA permits rapid quantification of immunoreactivity in multiple ROIs and produces results with high levels of interobserver consistency. Comparison with existing methodsCompared to manual ROI designation, ABA reduces total analysis time by ∼70%. With correct use of landmarks for image warping, ABA produces similar results to manually drawn ROIs, results in no interobserver variability, and maintains c-Fos+ pixel dimensions. ConclusionsABA reduces time to obtain reliable results when performing automated immunoreactivity quantification and allows multiple users to analyze data without compromising the reliability of data obtained.

Multilabel graph-based classification for missing labels

Assigning several labels to digital data is becoming easier as this can be achieved in a collaborative manner with Internet users. However, this process is still a challenge, especially in cases where several labels are assigned to each datum, as some suitable labels may be missed. The missing labels lead to inaccuracies in classification. In this study, we propose a novel graph-based multi-label classifier that exhibits stability for obtaining high-accuracy results; this is achieved even where there are missing labels in training data. The core process of our algorithm is to smoothen the label values of the training data from their top-k similar data by propagating their values and averaging them to generate values for the missing labels in the training data. In experimental evaluations, we used multi-labeled document and image datasets to evaluate classifiers, and then measured micro-averaged F-scores for eight classifiers. Even though we incrementally removed correct labels from the two datasets, the proposed algorithm tended to maintain the F-scores, whereas other classifiers decreased the scores. In addition, we evaluated the algorithm using Wikipedia, which comprises a real dataset that includes missing labels, in order to determine how well the algorithm predicted the correct labels and how useful it was for manual annotations, as initial decisions. We have confirmed that LPAC is useful for not only automatic annotation, but also the facilitation of decision making in the initial manual category assignment.