Judgment Strategies Research Articles

Representation and value relevance of net income versus comprehensive income on millennials’ financial decision-making

PurposeFinancial representation research has gained considerable attention among researchers. The argument is on representation preferences and the effect of graph versus text representation of financial data. The display format for net income (NI) and comprehensive income (CI) has been proven to influence users’ financial decision-making process, depending upon users’ characteristics. It is worth noting that millennials are users whose characteristics and cognitive skills differ from those of preceding generations. This study aims to unravel millennials’ preferences for the earnings information and representation when making financial analysis and judgement, thus providing insight on their decision-making strategy, either perceptual or analytical.Design/methodology/approachThis study used a 2 × 2 full factorial of experimental design, in which the financial representation in the following two ways: the textual disclosure content (NI versus CI) and the graphical display content (NI versus CI) was manipulated. This study conducted an online experiment with a total of 60 final participants.FindingsThe results reveal that textual disclosure of CI influences millennials’ financial decisions. This study also discovered that millennials’ financial decisions are unaffected by graphical displays of financial data as they place greater importance on textual financial data, particularly on CI representations, when making financial decisions.Research limitations/implicationsMillennials are financial users who apply different financial analysis and judgement strategy from their predecessor. They value textual disclosure and CI when analysing firms’ performance.Originality/valueThis study contends that millennials are the financial users who will use analytical strategies while making financial decisions.

MMOEA-SP: A multistage many-objective evolutionary algorithm based on sampling points

For the optimization algorithm based on Pareto dominance, how to obtain excellent performance in the high-dimensional objective space is a crucial issue. If the extreme solutions are correctly identified, the algorithm process can be accelerated. However, the existence of the dominance resistant solutions makes it suffer a devastating blow, especially when solving many-objective optimization problems. To address this issue, a multistage many-objective evolutionary algorithm based on sampling points (MMOEA-SP) is proposed. First of all, a state judgment strategy based on sampling points is developed to guide the population to explore or exploit, which divides the population into two stages and assigns different matching pools. The purpose of this strategy is to balance the convergence and diversity of the population. In the environment selection mechanism, two strategies are considered. On the one hand, a cluster-based dominance resistant solutions elimination strategy is introduced to accelerate population convergence and narrow the search area. On the other hand, solutions with larger differences are given priority as candidate solutions in the environmental selection mechanism to increase the diversity. The proposed algorithm is compared with plentiful state-of-the-art algorithms on a number of well-known benchmarks with 5–20 objectives. Finally, the experimental results show that the proposed algorithm can perform well on most of the test functions and generally outperforms its competitors.

Deep Regression Neural Networks for Proportion Judgment

Deep regression models are widely employed to solve computer vision tasks, such as human age or pose estimation, crowd counting, object detection, etc. Another possible area of application, which to our knowledge has not been systematically explored so far, is proportion judgment. As a prerequisite for successful decision making, individuals often have to use proportion judgment strategies, with which they estimate the magnitude of one stimulus relative to another (larger) stimulus. This makes this estimation problem interesting for the application of machine learning techniques. In regard to this, we proposed various deep regression architectures, which we tested on three original datasets of very different origin and composition. This is a novel approach, as the assumption is that the model can learn the concept of proportion without explicitly counting individual objects. With comprehensive experiments, we have demonstrated the effectiveness of the proposed models which can predict proportions on real-life datasets more reliably than human experts, considering the coefficient of determination (>0.95) and the amount of errors (MAE < 2, RMSE < 3). If there is no significant number of errors in determining the ground truth, with an appropriate size of the learning dataset, an additional reduction of MAE to 0.14 can be achieved. The used datasets will be publicly available to serve as reference data sources in similar projects.

Research on Anti-Occlusion Correlation Filtering Tracking Algorithm Based on Adaptive Scale

Target tracking has always been an important research direction in the field of computer vision. The target tracking method based on correlation filtering has become a research hotspot in the field of target tracking due to its efficiency and robustness. In recent years, a series of new developments have been made in this research. However, traditional correlation filtering algorithms cannot achieve real-time tracking in complex scenes such as illumination changes, target occlusion, motion deformation, and motion blur due to their single characteristics and insufficient background information. Therefore, a scale-adaptive anti-occlusion correlation filtering tracking algorithm is proposed. First, solve the single feature problem of traditional correlation filters through feature fusion. Secondly, the scale pyramid is introduced to solve the problem of tracking failure caused by scale changes. In this paper, two independent filters are trained, namely the position filter and the scale filter, to locate and scale the target, respectively. Finally, an occlusion judgment strategy is proposed to improve the robustness of the algorithm in view of the tracking drift problem caused by the occlusion of the target. In addition, the problem of insufficient background information in traditional correlation filtering algorithms is improved by adding context-aware background information. The experimental results show that the improved algorithm has a significant improvement in success rate and accuracy compared when with the traditional kernel correlation filter tracking algorithm. When the target has large scale changes or there is occlusion, the improved algorithm can still keep stable tracking.

Research on the Trading Strategies for Maximizing the Value of Assets in Quantitative Trading

This study focuses on trading strategies for maximizing the value of assets, when traders buy, sell and hold bitcoin and gold daily. We have built a price forecasting model using Multiple Linear Regression (MLR) theory, and an executable trading model to provide daily trading orders. First, a 5-year data of daily prices were checked and few of missing values in gold price were filled with the value of the day before. Second, the MLR model was adopted using today's price, today's 5-day moving average (MA5) and today's 20-day moving average (MA20) to forecast prices in future days. The first 8% of the data was used as training set, and the goodness of fit is up to 99.64% on the remaining data. Third, we determined the overall trading strategy, the buying/selling point judgment strategy and the combination trading strategy for bitcoin and gold, based on the forecasting model. Finally, a trading model was built to solve daily trading orders (sell, buy or hold on) and the corresponding values for the three currencies (cash, bitcoin and gold), for a 5-year trading period. We have calculated that the value of assets increased from the initial $1,000 to $38.34 million, after 5 years of investment.

Is There a Solution to Domestic Violence? Digging Deeper Into the Problem of Domestic Violence in Uganda a Case of Ibanda District

This paper is a deeper analysis to whether there is an end to domestic violence. The fact that there are a numbers of increased cases reported especially during corona virus (covid-19) period, it was inevitable to carry out this study to understand more about domestic violence. The study mainly focused on clear features and causes of domestic violence. It also aimed at coming out with practical approaches to curbing the problem. A mixed design approach was used in both collection and analysis of data. The study used judgmental sampling strategy in selecting the participants because the data that was required more tailored to knowledge based participants. A total of 57 participants were involved from whom interviews and questionnaires were administered. Results reveal a high increase in domestic violence evidenced mainly by assaults of physical, mental emotional and verbal. Other cases are through harassment behaviuors, disrespect and confrontation. The leading caused to domestic violence are attributed to poverty, attitude of male dominance and alcoholism as well as having outside relationships. Creation of extensive public awareness through training and public campaign is recommended as the practical intervention to curb domestic violence.

A Woodbury solution method for efficient seismic collapse analysis of space truss structures based on hybrid nonlinearity separation

AbstractThis study presents a fast algorithm for collapse behavior simulation of space truss structures under extreme earthquake excitation by introducing the Woodbury formula to efficiently solve the structural response caused by material and geometric nonlinearity (hybrid nonlinearity). The Woodbury formula, which is an efficient tool in mathematics for solving low‐rank perturbation problems, has successfully been used to improve the efficiency of local material nonlinear analysis but still has difficulties with seismic collapse analysis in which geometric nonlinearity should be considered. In this study, by implementing stiffness matrix decomposition according to the unchanged reference configuration, the effects of hybrid nonlinearity on the change in tangent stiffness of truss structures are uniformly formulated in the form of hybrid nonlinear perturbation to the reference elastic stiffness. Thus, a hybrid nonlinearity separated governing equation can be established, in which the hybrid nonlinear behaviors are depicted by the additional nonlinear degrees of freedom (NLDOFs) separated from the reference system. This allows for employing the Woodbury formula to perform seismic collapse analysis of space truss structures for avoiding the repeated updating of the global stiffness. To overcome the adverse effect of the large NLDOF number caused by the global characteristics of geometric nonlinearity on the efficiency advantages of the Woodbury formula during seismic collapse analysis, an element state judgment strategy and an adaptive restart mechanism are presented to activate only a small number of NLDOFs within critical local regions. The accuracy and efficiency of the proposed method are verified by two numerical examples.

Control and Real-Time Data Acquisition of an Experimental Platform for Stored Grain Aeration Study.

Aeration is one of the most important methods to keep stored grain safe and maintain its quality. Experimental platforms are used for stored grain aeration study in a laboratory-scale. The purpose of this paper was to provide the real-time data acquisition and control system design of a new experimental platform with multifunction for stored grain study. Requirements of the aeration experiments were analyzed, and multi running modes were designed. The aeration inlet air conditions were designed to be adjustable and multi variables need to be controlled simultaneously, which was a key problem to be solved for the platform. An ON/OFF-PID based multivariable cooperative control method was proposed, and two control loops were formed where inlet air temperature and humidity were considered separately while could be controlled simultaneously with a logic judgement strategy. Real-time data needed to be monitored was acquired with different sensors and displayed intuitively. Experiments were carried out to test the static and dynamic characteristics of the control method and three inlet air flow rates of 0.03, 0.08 and 0.13 m·s−1were used. Performance of the data acquisition system was also tested. The results showed that, the inlet air conditions control error was within ±1 °C and 10% for temperature and relative humidity, respectively. The real-time data acquisition of multi parameters during aeration process was realized. The experimental platform can be used for studies of different aeration objectives.

Dual strategies in human confidence judgments.

Although confidence is commonly believed to be an essential element in decision-making, it remains unclear what gives rise to one's sense of confidence. Recent Bayesian theories propose that confidence is computed, in part, from the degree of uncertainty in sensory evidence. Alternatively, observers can use physical properties of the stimulus as a heuristic to confidence. In the current study, we developed ideal observer models for either hypothesis and compared their predictions against human data obtained from psychophysical experiments. Participants reported the orientation of a stimulus, and their confidence in this estimate, under varying levels of internal and external noise. As predicted by the Bayesian model, we found a consistent link between confidence and behavioral variability for a given stimulus orientation. Confidence was higher when orientation estimates were more precise, for both internal and external sources of noise. However, we observed the inverse pattern when comparing between stimulus orientations: although observers gave more precise orientation estimates for cardinal orientations (a phenomenon known as the oblique effect), they were more confident about oblique orientations. We show that these results are well explained by a strategy to confidence that is based on the perceived amount of noise in the stimulus. Altogether, our results suggest that confidence is not always computed from the degree of uncertainty in one's perceptual evidence but can instead be based on visual cues that function as simple Heuristics to confidence.

A Strategy for judging real-time and active reporting outage based on the power consumption information acquisition system

To optimize the energy structure, smart grids are constructed rapidly. The optimization of the power consumption information acquisition system helps to eliminate outages caused by device quality failures, and makes the processing of outages more efficient. A reasonable outage reporting strategy can detect and handle outage faults on the low-voltage side in time, reducing the safety threats to people and devices caused by continuous faults. Therefore, given all the technical means mastered and the shortcomings of the original outage analysis strategy, an outage judgment strategy is established in this paper to make outage judgments for each level. First, the outage judgment strategy of the collection devices is used to analyze the information about outages reported by the collection devices (i.e., special transformer terminals, public transformer terminals, type I concentrators, type II concentrators, and smart meters), and create corresponding outage analysis units; Second, the outage judgment strategy of the outage analysis unit is used to determine the area where outages occurred. A display interface of the master station is designed in this paper based on the function of the strategy. Combined with the results of the actual application, the outage judgment strategy analyzes and judges each outage faults. The outage area in transformer terminals, station areas, and low-voltage branch lines are determined and the corresponding alarms are sent to the emergency maintenance platform to realize power recovery.

The Relationships between Web-Based Information Credibility Judgment, Critical Thinking, and Learning Styles of Iranian EFL University Students

The advent of Web 2 tools and their features have allowed Internet users not only to seek information but also to generate and edit on-line information. However, excessive reliance of students, particularly EFL students, on the Internet has raised the issue of information credibility or quality of the information found online. This study sought to investigate the relationship between Iranian EFL university students’ credibility judgment and their learning styles and critical thinking. It also intended to examine the extent to which the learners employ credibility judgment strategies to verify web-based information. To this end, 212 Iranian EFL students, 165 female(s) and 47 male(s) (or add) students, received three questionnaires: Honey’s (2004) Critical Thinking questionnaire, Kolb's (1984) Learning Style inventory, and the adapted version of Credibility Judgment questionnaire originally developed by Metzger, Flanagin, and Zwarun (2003). The results of descriptive as well as bivariate and multivariate correlation analysis revealed that, first, Iranian EFL students employ credibility judgment strategies at a moderate level, and second, critical thinking is a better predictor for credibility judgment behavior of EFL students than learning style. Moreover, three learning styles were significantly related to the currency sub-scale of credibility judgment. The findings can assist curriculum planners, policymakers, and instructors to develop plans for empowering EFL students with the skills they need to evaluate on-line information for accuracy, objectivity, authority, and currency.

Developmental Process of Children's Comparative Judgment Strategies Using a Self-Generation Task

Developmental Process of Children's Comparative Judgment Strategies Using a Self-Generation Task

Video Super-Resolution Reconstruction Based on Deep Learning and Spatio-Temporal Feature Self-Similarity

To address the problems in the existing video super-resolution methods, such as noise, over smooth and visual artifacts, which are caused by the reliance on limited external training or mismatch of internal similarity patch instances, this study proposes a novel video super-resolution reconstruction algorithm based on deep learning and spatio-temporal feature similarity (DLSS-VSR). The video super-resolution reconstruction mechanism with the joint internal and external constraints is established utilizing the complementary advantages of both external deep correlation mapping learning and internal spatio-temporal nonlocal self-similarity prior constraint. A deep learning model based on deep convolutional neural network is constructed to learn the nonlinear correlation mapping between low-resolution and high-resolution video frame patches. A novel spatio-temporal feature similarity calculation method is proposed, which considers both internal video spatio-temporal self-similarity and external clean nonlocal similarity. For the internal spatio-temporal feature self-similarity, we improve the accuracy and robustness of similarity matching by proposing a similarity measure strategy based on spatio-temporal moment feature similarity and structural similarity. The external nonlocal similarity prior constraint is learned by the patch group-based Gaussian mixture model. The time efficiency for spatio-temporal similarity matching is further improved based on saliency detection and region correlation judgment strategy, which achieves a better tradeoff between super-resolution accuracy and speed. Experimental results demonstrate that the DLSS-VSR algorithm achieves competitive super-resolution quality compared to other state-of-the-art algorithms in both subjective and objective evaluations.

Improved Weighted k-Nearest Neighbor Based on PSO for Wind Power System State Recognition

In this paper, we propose using particle swarm optimization (PSO) which can improve weighted k-nearest neighbors (PWKNN) to diagnose the failure of a wind power system. PWKNN adjusts weight to correctly reflect the importance of features and uses the distance judgment strategy to figure out the identical probability of multi-label classification. The PSO optimizes the weight and parameter k of PWKNN. This testing is based on four classified conditions of the 300 W wind generator which include healthy, loss of lubrication in the gearbox, angular misaligned rotor, and bearing fault. Current signals are used to measure the conditions. This testing tends to establish a feature database that makes up or trains classifiers through feature extraction. Not lowering the classification accuracy, the correlation coefficient of feature selection is applied to eliminate irrelevant features and to diminish the runtime of classifiers. A comparison with other traditional classifiers, i.e., backpropagation neural network (BPNN), k-nearest neighbor (k-NN), and radial basis function network (RBFN) shows that PWKNN has a higher classification accuracy. The feature selection can diminish the average features from 16 to 2.8 and can reduce the runtime by 61%. This testing can classify these four conditions accurately without being affected by noise and it can reach an accuracy of 83% in the condition of signal-to-noise ratio (SNR) is 20dB. The results show that the PWKNN approach is capable of diagnosing the failure of a wind power system.

Visual Reasoning Strategies for Effect Size Judgments and Decisions.

Uncertainty visualizations often emphasize point estimates to support magnitude estimates or decisions through visual comparison. However, when design choices emphasize means, users may overlook uncertainty information and misinterpret visual distance as a proxy for effect size. We present findings from a mixed design experiment on Mechanical Turk which tests eight uncertainty visualization designs: 95% containment intervals, hypothetical outcome plots, densities, and quantile dotplots, each with and without means added. We find that adding means to uncertainty visualizations has small biasing effects on both magnitude estimation and decision-making, consistent with discounting uncertainty. We also see that visualization designs that support the least biased effect size estimation do not support the best decision-making, suggesting that a chart user's sense of effect size may not necessarily be identical when they use the same information for different tasks. In a qualitative analysis of users' strategy descriptions, we find that many users switch strategies and do not employ an optimal strategy when one exists. Uncertainty visualizations which are optimally designed in theory may not be the most effective in practice because of the ways that users satisfice with heuristics, suggesting opportunities to better understand visualization effectiveness by modeling sets of potential strategies.

Influence of physicians' seniority on head computed tomography use for patients with isolated vertigo/dizziness.

ObjectiveVertigo/dizziness is a common reason for emergency department (ED) visits. Emergency physicians (EPs) must distinguish patients with dizziness/vertigo owing to serious central nervous system (CNS) disorders. We aimed to evaluate the association between physician seniority and use of head computed tomography (CT) and ED length of stay (LOS) in patients presenting to the ED with isolated dizziness/vertigo.MethodsThis retrospective cohort study included adult patients with non-traumatic dizziness/vertigo in the ED. EPs were categorized according to seniority: junior (less than 6 years’ clinical experience), intermediate (7–12 years), and senior (≥12 years).ResultsAmong 2589 patients with isolated dizziness/vertigo, 460 (17.8%) received brain CT; 46 (1.78%) had CNS disorder as a final diagnosis. Junior and intermediate EPs ordered more CT examinations than senior EPs: (odds ratio [OR] = 1.329, 95% confidence interval [CI]: 1.002–1.769 and OR = 1.531, 95% CI: 1.178–2.001, respectively). Patients treated by junior and intermediate EPs had lower patient ED LOS (OR = −0.432, 95% CI: −0.887 to 0.024 and OR = −0.436, 95% CI: −0.862 to −0.011).ConclusionsWe revealed different judgment strategies among senior, intermediate, and junior EPs. Senior EPs ordered fewer CT examinations for patients with isolated vertigo/dizziness but had longer patient LOS.

Reasoning about privacy in mobile application install decisions: Risk perception and framing

• Users’ risk perception towards mobile privacy can be influenced by the subjective experience of ease or difficulty of bringing privacy concerns or potential risks to mind. • Users are more likely to install a low privacy rated app when generation fluency for privacy concerns is lower due to downgraded risk perception caused by difficult generation experience. • Users consider privacy as part of the app-install decision-making process if privacy information is presented in a simple and salient way. Data sharing has become prevalent with the rapid growth of mobile technologies. A lack of awareness and understanding of privacy practices often results in the installation of privacy-invasive applications (apps) which could potentially put users' personal data at risk. This study aimed to explore how users’ risk perception could be shifted towards more privacy-aware decisions through generation fluency and framing manipulations. It is an online study composed of three components, an experiment and two questionnaires. We manipulated the availability of privacy worries, by asking participants to generate either 2 or 10 privacy worries. Generating 10 worries was experienced as difficult, whereas generating 2 was easy. The difficult experience led to downgraded perception of risk, and consequently increased likelihood of installing a low privacy rated fictional app. Therefore, we suggest that improving generation fluency of privacy concerns could encourage users’ adoption of a more conservative judgment strategy when installing an app, safeguarding them against privacy-invasive apps.

Implementation of Intelligent Distributed Feeder Automation for Distribution Network

With the increasing demand for power supply reliability in the society, the demand for the degree of feeder automation in the distribution network is also increasing, and the feeder automation function for the purpose of improving power supply reliability is gradually improving. The research and judgment strategy of fault section location, isolation and non-fault section power supply recovery based on the feeder automation function has become a research hotspot. This paper introduces the research and judgment strategy of fault information interaction based on communication network for fast location and isolation of fault section, and expounds the research and judgment strategy of intelligent distributed feeder automation of boundary switch and contact switch in main line and branch line of distribution network in detail.

Consensus of nonlinear multi-agent systems with fuzzy modelling uncertainties via state-constraint hybrid impulsive protocols

In this paper, the nonlinear multi-agent system which contains uncertainty and is controlled by state-constraint impulsive protocol is taken into consideration. For the uncertainty of the multi-agent system, it is replaced by fuzzy logic system approximately and a judgement strategy which only contains the relative information with neighbors is proposed in this paper. In order to do research in state-constraint impulsive protocol, three kinds of impulsive control protocols which conclude partial input saturation, double actuator saturation and single actuator saturation are discussed. Then, some sufficient conditions of the system are obtained to reach consensus. Finally, some numerical simulation examples are provided to prove the effectiveness of the theoretical analysis.

A Learning Frequency-Aware Feature Siamese Network for Real-Time Visual Tracking

Visual object tracking by Siamese networks has achieved favorable performance in accuracy and speed. However, the features used in Siamese networks have spatially redundant information, which increases computation and limits the discriminative ability of Siamese networks. Addressing this issue, we present a novel frequency-aware feature (FAF) method for robust visual object tracking in complex scenes. Unlike previous works, which select features from different channels or layers, the proposed method factorizes the feature map into multi-frequency and reduces the low-frequency information that is spatially redundant. By reducing the low-frequency map’s resolution, the computation is saved and the receptive field of the layer is also increased to obtain more discriminative information. To further improve the performance of the FAF, we design an innovative data-independent augmentation for object tracking to improve the discriminative ability of tracker, which enhanced linear representation among training samples by convex combinations of the images and tags. Finally, a joint judgment strategy is proposed to adjust the bounding box result that combines intersection-over-union (IoU) and classification scores to improve tracking accuracy. Extensive experiments on 5 challenging benchmarks demonstrate that our FAF method performs favorably against SOTA tracking methods while running around 45 frames per second.