Belief Decay or Persistence? A Mixed-method Study on Belief Movement Over Time

When individuals encounter new information (data), that information is incorporated with their existing beliefs (prior) to form a new belief (posterior) in a process referred to as belief updating. While most studies on rational belief updating in visual data analysis elicit beliefs immediately after data is shown, we posit that there may be critical movement in an individual's beliefs when elicited immediately after data is shown v. after a temporal delay (e.g., due to forgetfulness or weak incorporation of the data). Our paper investigates the hypothesis that posterior beliefs elicited after a time interval will “decay” back towards the prior beliefs compared to the posterior beliefs elicited immediately after new data is presented. In this study, we recruit 101 participants to complete three tasks where beliefs are elicited immediately after seeing new data and again after a brief distractor task. We conduct (1) a quantitative analysis of the results to understand if there are any systematic differences in beliefs elicited immediately after seeing new data or after a distractor task and (2) a qualitative analysis of participants' reflections on the reasons for their belief update. While we find no statistically significant global trends across the participants beliefs elicited immediately v. after the delay, the qualitative analysis provides rich insight into the reasons for an individual's belief movement across 9 prototypical scenarios, which includes (i) decay of beliefs as a result of either forgetting the information shown or strongly held prior beliefs, (ii) strengthening of confidence in updated beliefs by positively integrating the new data and (iii) maintaining a consistently updated belief over time, among others. These results can guide subsequent experiments to disambiguate when and by what mechanism new data is truly incorporated into one's belief system.     

Knowledge Diffusion of the Internet of Things (IoT): A Main Path Analysis

Wireless Personal Communications - The Internet of Things (IoT) is a concept that has attracted significant attention since the emergence of wireless technology. The knowledge diffusion of IoT...     

Exploiting probability density function of deep convolutional autoencoders’ latent space for reliable COVID-19 detection on CT scans

The Journal of Supercomputing - We present a probabilistic method for classifying chest computed tomography (CT) scans into COVID-19 and non-COVID-19. To this end, we design and train, in an...     

Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection

The Journal of Supercomputing - The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare...     

Corona virus optimization (CVO): a novel optimization algorithm inspired from the Corona virus pandemic

This research introduces a new probabilistic and meta-heuristic optimization approach inspired by the Corona virus pandemic. Corona is an infection that originates from an unknown animal virus, which is of three known types and COVID-19 has been rapidly spreading since late 2019. Based on the SIR model, the virus can easily transmit from one person to several, causing an epidemic over time. Considering the characteristics and behavior of this virus, the current paper presents an optimization algorithm called Corona virus optimization (CVO) which is feasible, effective, and applicable. A set of benchmark functions evaluates the performance of this algorithm for discrete and continuous problems by comparing the results with those of other well-known optimization algorithms. The CVO algorithm aims to find suitable solutions to application problems by solving several continuous mathematical functions as well as three continuous and discrete applications. Experimental results denote that the proposed optimization method has a credible, reasonable, and acceptable performance.

     

ConsilientSFL: using preferential voting system to generate combinatorial ranking metrics for spectrum-based fault localization

Spectrum-based fault localization (SFL) techniques have shown considerable effectiveness in localizing software faults. They leverage a ranking metric to automatically assign suspiciousness scores to certain entities in a given faulty program. However, for some programs, the current SFL ranking metrics lose effectiveness. In this paper, we introduce ConsilientSFL that is served to synthesize a new ranking metric for a given program, based on a customized combination of a set of given ranking metrics. ConsilientSFL can be significant since it demonstrates the usage of voting systems into a software engineering task. First, several mutated, faulty versions are generated for a program. Then, the mutated versions are executed with the test data. Next, the effectiveness of each existing ranking metric is computed for each mutated version. After that, for each mutated version, the computed existing metrics are ranked using a preferential voting system. Consequently, several top metrics are chosen based on their ranks across all mutated versions. Finally, the chosen ranking metrics are normalized and synthesized, yielding a new ranking metric. To evaluate ConsilientSFL, we have conducted experiments on 27 subject programs from Code4Bench and Siemens benchmarks. In the experiments, we found that ConsilientSFL outperformed every single ranking metric. In particular, for all programs on average, we have found performance measures recall, precision, f-measure, and percentage of code inspection, to be nearly 7, 9, 12, and 5 percentages larger than using single metrics, respectively. The impact of this work is twofold. First, it can mitigate the issue with the choice and usage of a proper ranking metric for the faulty program at hand. Second, it can help debuggers find more faults with less time and effort, yielding higher quality software.

     

Analysis of Reflectance Properties in 1D Photonic Crystal Containing Metamaterial and High-Temperature Superconductor

In the present work, reflectance properties of one-dimensional photonic crystal (1D PC) containing a metamaterial and high-temperature superconductor have been investigated theoretically and analyzed. The reflectance/transmittance spectrum of the proposed structure is obtained by using the characteristic or transfer-matrix method (TMM). The results show that by increasing the thickness of the metamaterial layer, the width of the second reflection band decreases while the width of the first reflection band remains almost the same though it shifts towards the higher frequency side. In addition to this, a new band gap arises in the lower side of frequency. But, when the thickness of the superconductor layer is increased, the width of both the bands increases and no additional band arises in this case. Moreover, the reflection band is also affected by varying the operating temperature of the superconducting layer and the results show that bands get narrower by increasing the operating temperature. Finally, the effect of incident angle on the reflection band has been discussed for both transverse electric (TE) and transverse magnetic (TM) polarizations.     

GaP/GaNP Heterojunctions for Efficient Solar‐Driven Water Oxidation

The growth and characterization of an n‐GaP/i‐GaNP/p⁺‐GaP thin film heterojunction synthesized using a gas‐source molecular beam epitaxy (MBE) method, and its application for efficient solar‐driven water oxidation is reported. The TiO₂/Ni passivated n‐GaP/i‐GaNP/p⁺‐GaP thin film heterojunction provides much higher photoanodic performance in 1 m KOH solution than the TiO₂/Ni‐coated n‐GaP substrate, leading to much lower onset potential and much higher photocurrent. There is a significant photoanodic potential shift of 764 mV at a photocurrent of 0.34 mA cm^?2, leading to an onset potential of ≈0.4 V versus reversible hydrogen electrode (RHE) at 0.34 mA cm^?2 for the heterojunction. The photocurrent at the water oxidation potential (1.23 V vs RHE) is 1.46 and 7.26 mA cm^?2 for the coated n‐GaP and n‐GaP/i‐GaNP/p⁺‐GaP photoanodes, respectively. The passivated heterojunction offers a maximum applied bias photon‐to‐current efficiency (ABPE) of 1.9% while the ABPE of the coated n‐GaP sample is almost zero. Furthermore, the coated n‐GaP/i‐GaNP/p⁺‐GaP heterojunction photoanode provides a broad absorption spectrum up to ≈620 nm with incident photon‐to‐current efficiencies (IPCEs) of over 40% from ≈400 to ≈560 nm. The high low‐bias performance and broad absorption of the wide‐bandgap GaP/GaNP heterojunctions render them as a promising photoanode material for tandem photoelectrochemical (PEC) cells to carry out overall solar water splitting.     

Dynamic estimation of the modeling error statistics in Diffuse Optical Tomography

Diffuse Optical Tomography (DOT) is a non-invasive imaging technique that suffers from a typical large-scale and ill-posed inverse problem with low spatial resolution. In DOT, the inverse problem is computationally intensive and decreasing the computation complexity and making it well-posed is the one of the most challenging research areas. More precisely, one of the well-known complexity reduction techniques is defined as applying modelling error originated from discretization of forward problem. Applying the discretization error in Bayesian inference has already been discussed; the method in which the likelihood is modified by an off-line prior density estimation. This paper implements a new method to enhance the modelling error approach using an iterative scheme to update statistical parameters of modelling discrepancy in DOT. The algorithm is very similar to Ensemble Kalman Filter. Moreover, the reconstruction process in the applied method is conducted by a small sample size rather than off-line method. Hence, the computation complexity is decreased and the algorithm converges in few iterations. The efficiency of the proposed method is illustrated by simulations.