Decision Paths Research Articles

GeoGraphVis: A Knowledge Graph and Geovisualization Empowered Cyberinfrastructure to Support Disaster Response and Humanitarian Aid

The past decade has witnessed an increasing frequency and intensity of disasters, from extreme weather, drought, and wildfires to hurricanes, floods, and wars. Providing timely disaster response and humanitarian aid to these events is a critical topic for decision makers and relief experts in order to mitigate impacts and save lives. When a disaster occurs, it is important to acquire first-hand, real-time information about the potentially affected area, its infrastructure, and its people in order to develop situational awareness and plan a response to address the health needs of the affected population. This requires rapid assembly of multi-source geospatial data that need to be organized and visualized in a way to support disaster-relief efforts. In this paper, we introduce a new cyberinfrastructure solution—GeoGraphVis—that is empowered by knowledge graph technology and advanced visualization to enable intelligent decision making and problem solving. There are three innovative features of this solution. First, a location-aware knowledge graph is created to link and integrate cross-domain data to make the graph analytics-ready. Second, expert-driven disaster response workflows are analyzed and modeled as machine-understandable decision paths to guide knowledge exploration via the graph. Third, a scene-based visualization strategy is developed to enable interactive and heuristic visual analytics to better comprehend disaster impact situations and develop action plans for humanitarian aid.

Evaluation of the decision-making process within the table-top exercise of the Terror and Disaster Surgical Care (TDSC®) course

PurposeThe threat of terror is omnipresent in Europe and the number of attacks worldwide is increasing. The target of attacks in Europe is usually the civilian population. Incalculable dangerous situations at the scene of the event and severe injury patterns such as complex gunshot and explosion injuries with a high number of highly life-threatening people present rescue forces, emergency physicians and subsequently hospitals with medical, organizational as well as tactical and strategic challenges. The Terror and Disaster Surgical Care (TDSC®) course trains clinical decision-makers to meet these challenges of a TerrorMASCAL in the first 24–48 h.MethodsA table-top exercise was developed for the TDSC® course as a decision training tool, which was prospectively evaluated in six courses. The evaluation took place in 3 courses of the version 1.0, in 3 courses in the further developed version 2.0 to different target values like, e.g., the accuracy of the in-hospital triage. Furthermore, 16 TDSC® course instructors were evaluated.ResultsFor the evaluation, n = 360 patient charts for version 1.0 and n = 369 for version 2.0 could be evaluated. Overall, the table-top exercise was found to be suitable for training of internal clinical decision makers. Version 2.0 was also able to depict the action and decision-making paths in a stable and valid manner compared to the previous version 1.0. The evaluation of the instructors also confirmed the further value and improvement of version 2.0.ConclusionWith this prospective study, the table-top exercise of the TDSC® course was tested for decision stability and consistency of the participants’ decision paths. This could be proven for the selected target variables, it further showed an improvement of the training situation. A further development of the table-top exercise, in particular also using digital modules, will allow a further optimization.http://www.bundeswehrkrankenhaus-ulm.de

Path Planning of Unmanned Aerial Systems for Visual Inspection of Power Transmission Lines and Towers

The inspection of power transmission and distribution systems is performed visually by foot patrolling and helicopter methods. These methods have various disadvantages such as time-consuming, high operating cost, safety issues, and improper results. To overcome this issue, power utility companies are searching for the alternatives like Unmanned Aerial Systems (UAS) or drones. UAS are safe, cost effective, and requires less time for power transmission line inspection compared to the regular methods. In this manuscript, a decisive flight path planning for UAS to visually inspect a power transmission line and towers is explained. The objective of this paper is to maximize the performance of three functions such as coverage of transmission tower, quality of captured image, and flight time. Second, proposing an automated inspection strategy for UAS to follow the overhead power transmission lines. These objectives are achieved by formulating a cost function, to convert the path planning into a safe operation for UAS. The results of Particle Swarm Optimization (PSO) and Simulated Annealing (SA) are compared to find the best path for UAS. The experimental finding shows that PSO algorithm has higher efficiency and effectiveness compared to the SA algorithm and benefits the UAS with a safe flight path for the inspection of power transmission towers.

Analysis of acupoints selection rules and efficacy evaluation of acupuncture and moxibustion for allergic rhinitis

To explore the rules of acupoints selection of acupuncture and moxibustion in the treatment of allergic rhinitis (AR) by using data mining technology, as well as to compare the efficacy of different acupoints selection methods. Papers about acupuncture and moxibustion for treating AR published from January 2002 to August 2022 was retrieved from Chinese and English databases including CNKI, Wanfang, VIP, SinoMed and PubMed by using keywords of "acupuncture", "moxibustion" and "allergic rhinitis". According to the inclusion and exclusion criteria, the collected literature was screened to establish the AR database. Frequency statistic analysis was conducted for detecting high-frequency acupoints and specific acupoints frequency, and the curative effects of different acupoints selection methods were compared. SPSS26.0 software was used for factor analysis, cluster analysis and QUEST decision tree model identification. A total of 289 papers were included, with 384 acupuncture prescriptions extracted. A total of 99 acupoints were involved with the application frequency of 2 430 times. Among them, the application frequency of Yingxiang (LI20) is the highest (296 times, 12.18%), followed by Yintang (GV24+) and Hegu (LI4), etc. The main invloved meridians are the Bladder Meridian of Foot-Taiyang, the Large Intestine Meridian of Hand-Yangming and the Governor Vessel. The involved specific acupoints with the highest frequency of application is the crossing acupoints. Nine common factors of acupoints combinations units were extracted by factor analysis, and two cluster prescriptions of acupoints combinations correlation were obtained by cluster analysis. Three decision paths of simplified acupoints selection were simulated by the decision tree, with LI20 as the dependent variable. In the treatment of AR with acupuncture and moxibustion, the regularities and characteristics of acupoints selection are as follows: 1) often selecting local acupoints and acupoint combinations along meridians, 2) focusing on combination of dispelling pathogenic factors with strengthening vital energy, 3) advocating diversification of acupoint matching methods. The combination of factor analysis, cluster analysis and QUEST decision tree application provides three directions for clinical acupoints selection of AR.

VisRuler: Visual analytics for extracting decision rules from bagged and boosted decision trees

Bagging and boosting are two popular ensemble methods in machine learning (ML) that produce many individual decision trees. Due to the inherent ensemble characteristic of these methods, they typically outperform single decision trees or other ML models in predictive performance. However, numerous decision paths are generated for each decision tree, increasing the overall complexity of the model and hindering its use in domains that require trustworthy and explainable decisions, such as finance, social care, and health care. Thus, the interpretability of bagging and boosting algorithms—such as random forest and adaptive boosting—reduces as the number of decisions rises. In this paper, we propose a visual analytics tool that aims to assist users in extracting decisions from such ML models via a thorough visual inspection workflow that includes selecting a set of robust and diverse models (originating from different ensemble learning algorithms), choosing important features according to their global contribution, and deciding which decisions are essential for global explanation (or locally, for specific cases). The outcome is a final decision based on the class agreement of several models and the explored manual decisions exported by users. We evaluated the applicability and effectiveness of VisRuler via a use case, a usage scenario, and a user study. The evaluation revealed that most users managed to successfully use our system to explore decision rules visually, performing the proposed tasks and answering the given questions in a satisfying way.

Boosting Broader Receptive Fields for Salient Object Detection.

Salient Object Detection has boomed in recent years and achieved impressive performance on regular-scale targets. However, existing methods encounter performance bottlenecks in processing objects with scale variation, especially extremely large- or small-scale objects with asymmetric segmentation requirements, since they are inefficient in obtaining more comprehensive receptive fields. With this issue in mind, this paper proposes a framework named BBRF for Boosting Broader Receptive Fields, which includes a Bilateral Extreme Stripping (BES) encoder, a Dynamic Complementary Attention Module (DCAM) and a Switch-Path Decoder (SPD) with a new boosting loss under the guidance of Loop Compensation Strategy (LCS). Specifically, we rethink the characteristics of the bilateral networks, and construct a BES encoder that separates semantics and details in an extreme way so as to get the broader receptive fields and obtain the ability to perceive extreme large- or small-scale objects. Then, the bilateral features generated by the proposed BES encoder can be dynamically filtered by the newly proposed DCAM. This module interactively provides spacial-wise and channel-wise dynamic attention weights for the semantic and detail branches of our BES encoder. Furthermore, we subsequently propose a Loop Compensation Strategy to boost the scale-specific features of multiple decision paths in SPD. These decision paths form a feature loop chain, which creates mutually compensating features under the supervision of boosting loss. Experiments on five benchmark datasets demonstrate that the proposed BBRF has a great advantage to cope with scale variation and can reduce the Mean Absolute Error over 20% compared with the state-of-the-art methods.

Management between crises and emergencies. Toward an "absential" approach

The aim of this study is to propose a new approach to decision making based on information variety endowment and configuration. The research adopts an exploratory methodology and a qualitative approach, deepening and analyzing both academic and non-academic studies. The study found the existence of general characteristics in the dynamics of human behavior and in that of social phenomena, characters that lead to specific paths of decision and action based on information variety endowment of decision makers. The developed conceptualization needs verification through appropriate tools and tested in empirical settings. The study can support the decision-making process of individuals, organizations, and whole societies - understood as viable systems - in the current dynamic and unpredictable context. This is the first work to propose an absential approach for the framing of managerial problems, recognizing that current methodologies are not capable of effectively responding to uncertainty and unpredictability of the melted result of economic, political, and social environments interaction. Eventually, this is the first work to engage in a mathematical demonstration of decision-making essentiality.

A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision Trees

Phishing performs by trying to trick the victim into accessing any computing information which looks original, then instructing them to send important data to unrestricted/unwanted privacy resource. For prevention, it is essential to develop a phishing detection system. Recent phishing detection systems are based on data mining and machine learning techniques. Most of the related work literature require collection of previous phishing attack logs, analyze them and create a list of such activities and block traffic from such sources. But this is a cumbersome task because the data size is very large, continue changing and dynamic nature. [1]. Instead of using single algorithm approach it would be better to use a hybrid approach. A hybrid approach would be better at mitigating phishing attacks because classification of different format of data is handled; whether the intruder want to use images or textural input to gain into another user system for phishing. Hybrid recommendation decision tress enhances any of machine learning and deep learning algorithms performance. The decision path of the model followed a series of if/else/then statements that connect the predicted class from the root of the tree through the branches of the tree to detect true positive and false negatives of phishing attempts. 10 decision trees were considered and used the features to train the recommendation decision regression model. The developed hybrid recommendation decision tree approach provided an overall true positive rate of the model of 92.28 % and false negative rate is 7.4%.

CED: A case-level explainable paramedical diagnosis via AdaGBDT

Objective:The rapid growth of medical data has greatly promoted the wide exploitation of machine learning for paramedical diagnosis. Inversely proportional to their performance, most machine learning models generally suffer from the lack of explainability, especially the local explainability of the model, that is, the case-specific explainability. Materials and Methods:In this paper, we proposed a GBDT (Gradient Boosting Decision Tree)-based explainable model for case-specific paramedical diagnostics, and mainly make the following contributions: (1) an adaptive gradient boosting decision tree (AdaGBDT) model is proposed to boost the path-mining for decision effectively; (2) to learn a case-specific feature importance embedding for a specific patient, the bi-side mutual information is applied to characterize the backtracking on the decision path; (3) through the collaborative decision-making by globally explainable AdaGBDT with case-based reasoning (CBR) in the case-specific metric space, some hard cases can be identified by the means of visualized interpretation. The performance of our model is evaluated on the Wisconsin diagnostic breast cancer dataset and the UCI heart disease dataset. Results:Experiments conducted on two datasets show that our AdaGBDT achieves the best performance, with the F1-value of 0.9647 and 0.8405 respectively. Moreover, a series of experimental analyses and case studies further illustrate the excellent performance of feature importance embedding. Conclusion:The proposed case-specific explainable paramedical diagnosis via AdaGBDT has excellent predictive performance, with both promising case-level and consistent global explainability.

Modern constitutionalism of Ukraine in the conditions of europeization of native law

The article states that Ukraine was and remains a component of the single European democratic space. Taking into account the sad lessons of the past, we will follow the path of broad public dialogue and legitimate decisions. The further constitutional transit of Ukraine will be connected only with strengthening of representative democracy and strengthening of local selfgovernment, guarantees of protection of human rights and freedoms. Thus, today, Ukraine is experiencing an era of global and rapid changes that are transforming the foundations of social life and the usual paradigms of constitutional thinking (we are not just talking about one or another change, but about such transformational processes that relate precisely to the level of the essential characteristics of the society that is passing through the stage constitutional transit).It is indicated that in the domestic science of constitutional law, the scientific-theoretical analysis of the paradigm of constitutionalism in the conditions of permanent constitutional transit, as a rule, extends only to elements of the contentof constitutionalism, individual institutional components of its system.Particular features of the constitutional transit in Ukraine and, as a consequence, the implementation of a new paradigm of constitutionalism in the conditions of the Europeanization of national law are highlighted. The author examines certain aspects of the terminological issues of constitutional transit as a condition for the Europeanization of domestic law. As a result of the research, the category «constitutional transit» as a condition of the Europeanization of modern Ukrainian law should be considered as a state of transition of the constitutionalism of the Ukrainian state, from one state to another, caused by theneed for convergence of national constitutional legislation with the law of the European Union. It is noted that such a state of transition, its passage is not a linear process, but can have a multi-vector nature of development. That is, constitutional transit can cover all types of transitional states of constitutionalism from the point of view of a significant change in their character: not only from authoritarianism to democracy, but, on the contrary, in accordance with national sociocultural and political features, values, of course, taking into account advanced scientific world achievements.

Interpretable prognostic modeling of endometrial cancer

Endometrial carcinoma (EC) is one of the most common gynecological cancers in the world. In this work we apply Cox proportional hazards (CPH) and optimal survival tree (OST) algorithms to the retrospective prognostic modeling of disease-specific survival in 842 EC patients. We demonstrate that linear CPH models are preferred for the EC risk assessment based on clinical features alone, while interpretable, non-linear OST models are favored when patient profiles can be supplemented with additional biomarker data. We show how visually interpretable tree models can help generate and explore novel research hypotheses by studying the OST decision path structure, in which L1 cell adhesion molecule expression and estrogen receptor status are correctly indicated as important risk factors in the p53 abnormal EC subgroup. To aid further clinical adoption of advanced machine learning techniques, we stress the importance of quantifying model discrimination and calibration performance in the development of explainable clinical prediction models.

Modeling Pedestrian Detour Behavior By-Passing Conflict Areas

In the process of walking, most pedestrians prefer to choose the shortest path, which requires passing through the conflict area. However, in the case of high crowd density, 5–20% of the total population will choose to follow the pre-planned route before walking or during the initial period of the trip to bypass the conflict area. Aiming at reproducing this detour behavior phenomenon, an extended social force model (SFM) is proposed according to a three-layer pedestrian simulation framework. This model not only fully considers the choice of detour mode, but also contains the avoidance and game behavior at the conflict point. At the strategic layer, a detour mode selection model based on the Logit model is established considering the pedestrian starting time and detour angle, to distinguish between the two groups of pedestrians who follow the pre-planned route and those who repeatedly adjust the route during the trip. Then, the path decision based on visual perception density at the tactical layer and the Voronoi-based SFM at the operational layer are combined to guide the specific movement of the two types of pedestrian groups. A series of evaluation indexes such as the central density, the mean local density, and detour level is selected, and Kolmogorov–Smirnov (K-S) test and dynamic time warping (DTW) method are used to evaluate and compare the scores of each index of different models. The results show that the model can improve the existing pedestrian detour simulation model to a certain extent. In sum, the travel time score, the detour level, and the mean local density score respectively increase from 0.71 to 0.81, 0.46 to 0.81, and 0.39 to 0.48, which indicates a significant improvement in walking performance.

Construction of cultural heritage evaluation system and personalized cultural tourism path decision model: An international historical and cultural city

World cultural heritage (WHS) is of great significance to promoting the sustainable development of cities, and history culture is very important to achieve the goal of sustainable development. The purpose of this paper is to construct a cultural heritage evaluation system and to explore and optimize tourist routes. We take cultural heritage sites as the research object, and comprehensively use Analytic Hierarchy Process (AHP), Delphi method and fuzzy mathematics to establish a cultural heritage tourism resource evaluation system. According to the evaluation results and questionnaires, a decision-making system for cultural tourism route planning is established, and additional sites of Qin culture are found, and a complete Qin culture route is established to provide data and theoretical basis for Xi'an's perfect cultural tourism route. The traveling salesman problem is the most basic path problem. In this study, the ant colony algorithm is used to improve the model combined with the actual problem of Xi'an tourist routes, and the optimal solution of the cultural parade route is obtained. Xi'an authorities and managers can strategically use these findings to promote Xi'an as a green destination WHS.

Low-Latency Service Chaining With Predefined NSH-Based Multipath Across Multiple Datacenters

Service Function Chaining (SFC) provides a method of forwarding traffic flows through one or more service functions (SFs). For service providers, chaining SFs across multiple datacenters to deliver end-to-end services not only provides better utilization of computing resources of datacenters, but also achieves scalability and fault tolerance. However, most telecommunication applications are sensitive to latency, which tends to degrade due to both virtualization and the long distances among datacenters. In this paper, we extend the network service header (NSH) protocol and propose a multipath chaining with the partially-ordered NSH (MCPON) mechanism to achieve low-latency, partially-ordered service function chaining. MCPON adopts a proactive multipath installation for commonly-used service function paths (SFP, a sequence of requisite SFs) to eliminate reactive path decision delays and to reduce end-to-end service latency. To increase multipath diversity for better load balancing, we modify the original NSH encapsulation design so that the multiple paths selected for an SFP are not limited to having the same execution orders of some non-order-constrained SFs. MCPON also utilizes an entry-saving forwarding table design which enables forwarding entries to be shared among different SFC requests. Our evaluations show that proactive k-path computation for an SFC of length l at a scale of n SFFs saves time complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ O}}(kl^{3}n^{3})$ </tex-math></inline-formula> , and multipath service chaining reduces latency by 33–68% compared to single-path service chaining in our simulation scenarios.

Immuno-AML, a Novel Immunogenomic Classifier Predicting Chemotherapy Response in Pediatric Patients with AML

Introduction Acute myeloid leukemia (AML) represents 20% of pediatric leukemias. Despite therapeutic improvements, ∼30% of children die due to therapy-related toxicity and relapse. In solid tumors, the density and functional orientation of the tumor microenvironment (TME) influence prognosis and response to chemotherapy and immunotherapy. Defining immunologic features associated with unfavorable response to standard treatment in pediatric AML might improve stratification systems that can be used for experimental interventions and personalized approaches. In this study, we investigated the potential mechanisms and effect of immune response and evasion in bone marrow (BM) of AML pediatric patients through an integrative gene expression analysis. Methods Thirty-two BM aspirates were collected from pediatric patients with non-promyelocytic AML at diagnosis in 3 different hospitals in Qatar, Pakistan, and Italy. Patients were assigned to 2 prognosis-related groups: patients who maintained a complete remission (CR) after first line of chemotherapy (sustained CR) vs patients who progressed/relapsed and/or obtained CR after hematopoietic stem cells transplantation (HSCT) (non-sustained CR). BM mRNA samples were analyzed with RNA sequencing (RNA-Seq) and/or NanoString's PanCancer IO 360 Gene Expression research panel. In particular, mRNA of 25 samples (Cohort 1, discovery set) were analyzed by NanoString's PanCancer IO 360. Subsequently, a subset of samples from Cohort 1 (n=19), was analyzed by RNA-Seq for technical validation. Cohort 1 results were then used to build a predictive classifier that was tested on Cohort 2 samples (n=7, validation set), analyzed with RNA-Seq. Results Gene expression analysis of 25 samples (Cohort 1, sustained CR, n=6; non-sustained CR, n=19) using NanoStringassay revealed 67 significant differentially expressed genes (DEGs; p < 0.05, LIMMA) among sustained CR vs non-sustained CR groups. Patients with sustained CR displayed coherent overexpression of immune genes related to IFN activation (IFIT3, IFITM1, IFI27, STAT1, GBP1), cytotoxic function (PRF1, GNLY), antigen presentation and B cell functions (TAP1, CD19), and T cell infiltration (CD3D, CD8A), consistent with a favorable T helper 1 (Th1)-cytotoxic polarization of the TME. This pattern was not observed in non-sustained CR patients, which overexpressed genes consistent with macrophage-myeloid suppression (CD68, TREM1) and intrinsic oncogene signaling linked with T cell exclusion and immune suppression (WNT5A, WNT-β- catenin pathway, and EFGR) (Fig1 A,B). This 67-gene signature (67GenSig) was technically validated using an orthogonal platform (RNA-Seq) on a subset of 19 patients (sustained CR, n=5; non-sustained CR, n=14) for which sufficient RNA-Seq data was available (65 out of 67 transcripts were available for analysis, 2 transcripts were filtered out after QC). Unsupervised PCA (Fig1 C) based on 67GenSig clearly segregated samples according to outcome, confirming the previous NanoString results. Next, we sought to identify a predictive classifier to precisely discriminate sustained CR from non-sustained CR AML pediatric patients using a minimal number of genes. By applying a Random Forest-based feature selection on 67GenSig transcripts of RNA-Seq data from Cohort 1 samples, we developed a 13-gene classifier (Immuno-AML) that was tested in a new independent dataset (Fig1 D; Cohort 2, n=7: sustained CR, n=4; non-sustained CR, n=3). Overall, genes included in the Immuno-AML predictor substantiated a positive Th1/cytotoxic vs myeloid polarization. The Immuno-AML predictor was able to classify patients in the validation cohort (Cohort 2) in "sustained CR" and "non-sustained CR" prognostic groups with a very good performance achieving 85.71% accuracy, 100% specificity, and 75% sensitivity. Conclusion Our data suggest that response to conventional therapy in AML pediatric patients is conditional to the presence of a Th1 polarization of the TME while is prevented by myeloid-mediated immune suppression and intrinsic oncogenic signaling. The optimized predictive signature (Immuno-AML) might be tested and/or refined in larger cohort to develop a clinical-grade predictive biomarker. It might help to provide a rational guidance for risk stratification criteria and personalized treatment decision paths, implying the possibility of combining immunotherapy drugs. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

DeepSym: Deep Symbol Generation and Rule Learning for Planning from Unsupervised Robot Interaction

Symbolic planning and reasoning are powerful tools for robots tackling complex tasks. However, the need to manually design the symbols restrict their applicability, especially for robots that are expected to act in open-ended environments. Therefore symbol formation and rule extraction should be considered part of robot learning, which, when done properly, will offer scalability, flexibility, and robustness. Towards this goal, we propose a novel general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them for non-trivial action planning. Our robot interacts with objects using an initial action repertoire that is assumed to be acquired earlier and observes the effects it can create in the environment. To form action-grounded object, effect, and relational categories, we employ a binary bottleneck layer in a predictive, deep encoderdecoder network that takes the image of the scene and the action applied as input, and generates the resulting effects in the scene in pixel coordinates. After learning, the binary latent vector represents action-driven object categories based on the interaction experience of the robot. To distill the knowledge represented by the neural network into rules useful for symbolic reasoning, a decision tree is trained to reproduce its decoder function. Probabilistic rules are extracted from the decision paths of the tree and are represented in the Probabilistic Planning Domain Definition Language (PPDDL), allowing off-the-shelf planners to operate on the knowledge extracted from the sensorimotor experience of the robot. The deployment of the proposed approach for a simulated robotic manipulator enabled the discovery of discrete representations of object properties such as ‘rollable’ and ‘insertable’. In turn, the use of these representations as symbols allowed the generation of effective plans for achieving goals, such as building towers of the desired height, demonstrating the effectiveness of the approach for multi-step object manipulation. Finally, we demonstrate that the system is not only restricted to the robotics domain by assessing its applicability to the MNIST 8-puzzle domain in which learned symbols allow for the generation of plans that move the empty tile into any given position.

Understanding and Explaining Diagnostic Paths: Toward Augmented Decision Making.

The process of finding a diagnosis in the medical domain relies on implicit knowledge and the experience of a human expert. In this article, we report on the observation of human decision making, shown by the example of pathology. By tracking the diagnostic steps, individual building blocks are identified, which not only contribute to a diagnostic finding, but can also be used in the future to train and develop artificial intelligence (AI) algorithms. This work also provides insights into the interaction of human experts regarding the observation time of so-called "hot spots," the magnification used for specific findings, and the overall observation and decision path followed. The documentation scheme yields a standardized examination procedure that shows the concept the pathologist is actually looking for as well as the possible features of findings that can be identified. This contribution indicates how important visualization is for human-centered AI, and specifically for enabling human oversight with respect to AI implementation in high-stake areas, such as medicine.

Logistics Path Decision Optimization Method of Fresh Product Export Cold Chain Considering Transportation Risk.

The cold chain logistics route of fresh product export is characterized by large quantity and complexity, which is prone to cause transportation risks of different degrees in the process of fresh product export transportation and affects the decision-making effect of the cold chain logistics route. Therefore, in order to improve the ability of cold chain logistics route planning and shorten the transportation time, an optimization method of fresh product export cold chain logistics route decision considering transportation risk was proposed. This paper analyzes the basic characteristics and classification of cold chain logistics by means of risk quantification and uses the K-nearest neighbor algorithm to predict the risk of traffic congestion, so as to shorten the transportation time. Ahp process is used to construct a risk factor judgment matrix and determine the index weight of risk factors, so as to reduce the error of path planning. A genetic algorithm is introduced to construct the optimal decision function of the cold chain logistics route of new product export and realize the optimization of cold chain logistics route decision of fresh product export. Experimental results show that the method presented in this paper can effectively improve the decision-making effect of cold chain logistics route and select the shortest and most smooth transportation path to complete logistics distribution. The decision-making accuracy of the route decision effect is 90%, and the transportation time is 31.45 min, which has certain feasibility and applicability.

OLP++: An online local classifier for high dimensional data

Ensemble diversity is an important characteristic of Multiple Classifier Systems (MCS), which aim at improving the overall performance of a classification system by combining the response of several models. While diversity may be introduced through various manipulations at the data level and the model level, some MCSs incorporate local information in order to increase it and/or take advantage of it, based on the idea that the different classifiers in the ensemble may have expertise in distinct areas of the feature space.11Code available at: https://github.com/marianaasouza/olp_plusplus.Following a similar reasoning, we introduced in a previous work an ensemble method which produces in test time a few experts in the local region where each given query sample is located. These local experts, which are generated with slightly differing views of the target area, are then used to label the corresponding unknown instance. While the framework was shown to perform well especially over imbalanced problems, the locality definition in the method is based on the nearest neighbors rule and Euclidian distance, as is the case of various local-based ensembles, which may suffer from the effects of the curse of dimensionality over high dimensional problems. Thus, in this work, we propose a local ensemble method in which we leverage the data partitions given by decision trees for locality definition. More specifically, the partitions defined at different levels of the decision path that a given query instance traverses in the tree(s) are used as the regions over which the local experts are produced. By using different node levels from the path, each classifier in the local pool has a moderately distinct view of the target region without resorting to a dissimilarity metric, which might be susceptible to high dimensional spaces. Experimental results over 39 high dimensional problems showed that the proposed approach was significantly superior to our previous, distance-based framework in balanced accuracy rate. Compared to other six local-based ensemble methods, including dynamic selection and weighting schemes, the proposed method achieved competitive results, outperforming the random forest baseline and two state-of-the-art dynamic ensemble selection techniques.

Evolutionary game analysis of WEEE recycling tripartite stakeholders under variable subsidies and processing fees.

The standardization of formal recycling and rational subsidy plays an important role in waste electrical and electronic equipment recycling. In order to explore the tripartite decision and evolution path of waste electrical and electronic equipment recycling in different time periods, a tripartite evolutionary game model consisting of recyclers, manufacturers, and government are presented. Moreover, the evolution stability strategies and conditions in each period are calculated by replicating the dynamic equation and Jacobian matrix. Numerical simulations on tripartite evolution stability strategies corresponding to different stages of industry development are used to verify the rationality of the model. The results indicate that there is existed an indirect effect between tripartite decisions, and the indirect effect can expand the slack of tripartite decisions' thresholds of waste electrical and electronic equipment recycling. The variable subsidy in waste electrical and electronic equipment recycling proposed in this paper is useful to incentive recyclers to choose a formal recycling strategy, and manufacturers also choose production with recycled materials as subsidy varies. Besides, the appropriate waste electrical and electronic equipment processing fee is a conducive indirect effect for the tripartite decision to the optimal evolutionary stability strategy in waste electrical and electronic equipment recycling and can promote manufacturers to produce with the recycled materials. The research can assist in benefit coordination and behavior adjustment of waste electrical and electronic equipment recycling members and provide a theoretical basis for the government to formulate appropriate recycling subsidies to promote the formal recycling of electronic waste recycling.