scholarly journals The Impact of Presentation Modality on Perceptions of Truthful and Deceptive Confessions

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Deborah Bradford ◽  
Jane Goodman-Delahunty ◽  
Kevin R. Brooks
Keyword(s):  
Presentation Modality ◽  
Detection Rates ◽  
Wrongful Convictions ◽  
Written Text ◽  
Video Recordings ◽  
Indirect Measures ◽  
Lower Accuracy ◽  
The Impact ◽  
Accuracy Rates ◽  
Level Performance

This study examined the impact of presentation modality and the effectiveness of direct and indirect measures of deception to distinguish truthful from deceptive confessions. Confession statements were presented in one of three formats: audiovisual, audio-only, or written text. Forty-six observers classified each statement as true or false and provided ratings of confidence, information sufficiency, perceived cognitive load, and suspiciousness. Compared to audio and written confessions, exposure to audiovisual recordings yielded significantly lower accuracy rates for direct veracity judgements, with below chance level performance. There was no evidence that indirect measures assisted observers in discriminating truthful from deceptive confessions. Overall, observers showed a strong bias to believe confessions with poor detection rates for false statements. Reliance on video recordings to assess the veracity of confession evidence is unlikely to reduce wrongful convictions arising from false confessions.

Impact of Determinants of Industrial Development on Employment and Wages in Indian Manufacturing

2020 ◽  
pp. 18-30
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Technical Change ◽  
Positive Impact ◽  
Manufacturing Sector ◽  
Factor Productivity ◽  
Firm Level ◽  
Employment Level ◽  
The Impact ◽  
Level Performance

ABSTRACT The present study was undertaken to explore the evolution of the impact of firm-level performance on employment level and wages in the Indian organized manufacturing sector over the period 1989-90 to 2013-14. One of the major components of the economic reform package was the deregulation and de-licensing in the Indian organized manufacturing sector. The impact of firm-level performance on employment and wages were estimated for Indian organized manufacturing sector in major sub-sectors in India during the period from 1989-90 to 2013-14 of the various variables namely profitability ratio, total factor productivity change, technical change, technical efficiency, openness (export-import), investment intensity, raw material intensity and FECI in total factor productivity index, technical efficiency, and technical change. The study exhibited that all explanatory variables except profitability ratio and technical change cost had a positive impact on the employment level. Out of eight variables, four variables such as net of foreign equity capital, investment intensity, TFPCH, and technical efficiency change showed a positive impact on wages and salary ratio and rest of the four variables such as openness intensity, technology acquisition index, profitability ratio, and technical change had negative impact on wages and salary ratio. In this context, the profit ratio should be distributed as per the marginal rule of economics such as the marginal productivity of labour and capital.

scholarly journals Effect of temperature, CO2 and O2 on motility and mobility of Anisakidae larvae

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aiyan Guan ◽  
Inge Van Damme ◽  
Frank Devlieghere ◽  
Sarah Gabriël
Keyword(s):  
Enzymatic Digestion ◽  
Infected Fish ◽  
Effect Of Temperature ◽  
Video Recordings ◽  
Different Temperatures ◽  
Semi Solid ◽  
Zoonotic Parasites ◽  
The Impact ◽  
Phosphate Buffered Saline

AbstractAnisakidae, marine nematodes, are underrecognized fish-borne zoonotic parasites. Studies on factors that could trigger parasites to actively migrate out of the fish are very limited. The objective of this study was to assess the impact of different environmental conditions (temperature, CO2 and O2) on larval motility (in situ movement) and mobility (migration) in vitro. Larvae were collected by candling or enzymatic digestion from infected fish, identified morphologically and confirmed molecularly. Individual larvae were transferred to a semi-solid Phosphate Buffered Saline agar, and subjected to different temperatures (6 ℃, 12 ℃, 22 ℃, 37 ℃) at air conditions. Moreover, different combinations of CO2 and O2 with N2 as filler were tested, at both 6 °C and 12 °C. Video recordings of larvae were translated into scores for larval motility and mobility. Results showed that temperature had significant influence on larval movements, with the highest motility and mobility observed at 22 ℃ for Anisakis spp. larvae and 37 ℃ for Pseudoterranova spp. larvae. During the first 10 min, the median migration of Anisakis spp. larvae was 10 cm at 22 ℃, and the median migration of Pseudoterranova spp. larvae was 3 cm at 37 ℃. Larval mobility was not significantly different under the different CO2 or O2 conditions at 6 °C and 12 ℃. It was concluded that temperature significantly facilitated larval movement with the optimum temperature being different for Anisakis spp. and Pseudoterranova spp., while CO2 and O2 did not on the short term. This should be further validated in parasite-infected/spiked fish fillets.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals Powerful Effects of Diagnostic Information on Automatic and Self-Reported Evaluation: The Moderating Role of Memory Recall

2021 ◽  
pp. 014616722110072
Author(s):  
Pieter Van Dessel ◽  
Jeremy Cone ◽  
Anne Gast
Keyword(s):  
Diagnostic Information ◽  
Memory Recall ◽  
Negative Behavior ◽  
Indirect Measures ◽  
Affect Misattribution Procedure ◽  
Elaborative Rehearsal ◽  
The Impact ◽  
Moderating Role

We sometimes learn about certain behaviors of others that we consider diagnostic of their character (e.g., that they did immoral things). Recent research has shown that such information trumps the impact of other (less diagnostic) information both on self-reported evaluations and on more automatic evaluations as probed with indirect measures such as the Affect Misattribution Procedure (AMP). We examined whether facilitating memory recall of alternative information moderates the impact of diagnostic information on evaluation. In Experiments 1 and 2, participants learned one diagnostic positive and one diagnostic negative behavior of two unfamiliar people. Presenting a cue semantically related to this information during evaluation influenced AMP scores but not self-reported liking scores. Experiments 3 and 4 showed that elaborative rehearsal of low diagnostic information eliminated diagnosticity effects on AMP scores and reduced them on self-reported liking scores. These findings help elucidate the role of memory recall and diagnosticity in evaluation.

scholarly journals The Effects of Social Distancing Policies on non-SARS-CoV-2 Respiratory Pathogens

2021 ◽  
Author(s):  
Jeff Nawrocki ◽  
Katherine Olin ◽  
Martin C Holdrege ◽  
Joel Hartsell ◽  
Lindsay Meyers ◽  
...  
Keyword(s):  
Public Health ◽  
Influenza A ◽  
Detection Rate ◽  
Respiratory Illness ◽  
Respiratory Pathogens ◽  
Public Health Response ◽  
Mixed Effect ◽  
Detection Rates ◽  
Social Distancing ◽  
The Impact

Abstract Background The initial focus of the US public health response to COVID-19 was the implementation of numerous social distancing policies. While COVID-19 was the impetus for imposing these policies, it is not the only respiratory disease affected by their implementation. This study aimed to assess the impact of social distancing policies on non-SARS-CoV-2 respiratory pathogens typically circulating across multiple US states. Methods Linear mixed-effect models were implemented to explore the effects of five social distancing policies on non-SARS-CoV-2 respiratory pathogens across nine states from January 1 through May 1, 2020. The observed 2020 pathogen detection rates were compared week-by-week to historical rates to determine when the detection rates were different. Results Model results indicate that several social distancing policies were associated with a reduction in total detection rate, by nearly 15%. Policies were associated with decreases in pathogen circulation of human rhinovirus/enterovirus and human metapneumovirus, as well as influenza A, which typically decrease after winter. Parainfluenza viruses failed to circulate at historical levels during the spring. Total detection rate in April 2020 was 35% less than historical average. Many of the pathogens driving this difference fell below historical detection rate ranges within two weeks of initial policy implementation. Conclusion This analysis investigated the effect of multiple social distancing policies implemented to reduce transmission of SARS-CoV-2 on non-SARS-CoV-2 respiratory pathogens. These findings suggest that social distancing policies may be used as an impactful public health tool to reduce communicable respiratory illness.

scholarly journals Sustainable Investments in Responsible SMEs: That’s What’s Distinguish Government VCs from Private VCs

2021 ◽  
Vol 14 (1) ◽  
pp. 25
Author(s):  
Jeaneth Johansson ◽  
Malin Malmström ◽  
Joakim Wincent
Keyword(s):  
Decision Making ◽  
Investment Decisions ◽  
Societal Impact ◽  
Venture Capitalists ◽  
Decision Making Processes ◽  
Micro Level ◽  
Sustainable Investments ◽  
Private Venture ◽  
The Impact ◽  
Level Performance

Researchers question the impact of governmental venture capitalists (GVC) compared to private venture capitalists (PVC), but we know little about why this difference occurs and if this criticism is justified. We observed a group of GVCs and developed a new model that describes the way that GVCs process signals pre- and post-decisions. Certain macro level factors severely undermine micro level performance, causing GVCs to financially underperform with respect to PVCs. This helped us to understand that GVCs do not make investment decisions in the same way as PVCs, and what undermines the performance of GVCs’ decision-making processes. The main goals of GVCs are to promote investments in responsible SMEs, mobilizing societal impact. We discuss that the criticism of GVC needs to be more nuanced, as they have a different role than PVC in the financial system as providers of sustainable investments in responsible SMEs.

scholarly journals QiOi: Performance Isolation for Hyperledger Fabric

2021 ◽  
Vol 11 (9) ◽  
pp. 3870
Author(s):  
Jeongsu Kim ◽  
Kyungwoon Lee ◽  
Gyeongsik Yang ◽  
Kwanhoon Lee ◽  
Jaemin Im ◽  
...  
Keyword(s):  
Data Centers ◽  
Component Level ◽  
Isolation Technique ◽  
Cloud Data ◽  
Cpu Scheduling ◽  
Performance Isolation ◽  
Cloud Data Centers ◽  
A New Technique ◽  
The Impact ◽  
Level Performance

This paper investigates the performance interference of blockchain services that run on cloud data centers. As the data centers offer shared computing resources to multiple services, the blockchain services can experience performance interference due to the co-located services. We explore the impact of the interference on Fabric performance and develop a new technique to offer performance isolation for Hyperledger Fabric, the most popular blockchain platform. First, we analyze the characteristics of the different components in Hyperledger Fabric and show that Fabric components have different impacts on the performance of Fabric. Then, we present QiOi, component-level performance isolation technique for Hyperledger Fabric. The key idea of QiOi is to dynamically control the CPU scheduling of Fabric components to cope with the performance interference. We implement QiOi as a user-level daemon and evaluate how QiOi mitigates the performance interference of Fabric. The evaluation results demonstrate that QiOi mitigates performance degradation of Fabric by 22% and improves Fabric latency by 2.5 times without sacrificing the performance of co-located services. In addition, we show that QiOi can support different ordering services and chaincodes with negligible overhead to Fabric performance.

Speech Methodologies in Cleft Palate Assessment: A Pilot Project to Compare Two Phonetically Different Sentence Sets

2013 ◽  
Vol 20 (1) ◽  
pp. 49-72
Author(s):  
Jennie Smith ◽  
Tim Pring ◽  
Debbie Sell
Keyword(s):  
Cleft Palate ◽  
Pilot Project ◽  
Wilcoxon Test ◽  
Consecutive Series ◽  
Video Recordings ◽  
Significant Difference ◽  
The Difference ◽  
Audio Video ◽  
Age Range ◽  
The Impact

Objective: To investigate the impact of the phonetic content of two sentence sets on speech outcomes, specifically the effects of nasal phonemes. Method: Audio-video recordings of a consecutive series of 15 participants (age range 4–22 years), with cleft palate (syndromic or non-syndromic), with and without velopharyngeal dysfunction were taken. Participants repeated Sentence Set 1 (with nasals across sentences) and Sentence Set 2 (without nasals except the three nasal target sentences) during a routine speech recording. Two experienced Specialist Speech and Language Therapists, blinded to the study’s purpose, analyzed participants’ speech using the Cleft Audit Protocol for Speech-Augmented (CAPS-A). On day 1, recordings included Sentence Set 1. On day 2, 23 days later, recordings included Sentence Set 2. Main results: The difference between Sentence Set 1 and Sentence Set 2 ‘total scores’ (sum of scores on all CAPS-A parameters) was significant. The Pearson Product Moment showed high correlation. A Wilcoxon test revealed a significant difference between Sets 1 and 2 on the hypernasality parameter, and this alone accounted for the significant difference in total scores. Conclusion: The inclusion or exclusion of nasal consonants in the sentence set significantly affected perceptual ratings of hypernasality but none of the other CAPS-A parameters, highlighting the need for further investigation into perceptual nasality ratings.

scholarly journals Pallidal stimulation as treatment for camptocormia in Parkinson’s disease

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yijie Lai ◽  
Yunhai Song ◽  
Daoqing Su ◽  
Linbin Wang ◽  
Chencheng Zhang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Case Reports ◽  
Structural Connectivity ◽  
Video Recordings ◽  
Lead Placement ◽  
Pallidal Stimulation ◽  
The Impact ◽  
Deep Brain

AbstractCamptocormia is a common and often debilitating postural deformity in Parkinson’s disease (PD). Few treatments are currently effective. Deep brain stimulation (DBS) of the globus pallidus internus (GPi) shows potential in treating camptocormia, but evidence remains limited to case reports. We herein investigate the effect of GPi-DBS for treating camptocormia in a retrospective PD cohort. Thirty-six consecutive PD patients who underwent GPi-DBS were reviewed. The total and upper camptocormia angles (TCC and UCC angles) derived from video recordings of patients who received GPi-DBS were used to compare camptocormia alterations. Correlation analysis was performed to identify factors associated with the postoperative improvements. DBS lead placement and the impact of stimulation were analyzed using Lead-DBS software. Eleven patients manifested pre-surgical camptocormia: seven had lower camptocormia (TCC angles ≥ 30°; TCC-camptocormia), three had upper camptocormia (UCC angles ≥ 45°; UCC-camptocormia), and one had both. Mean follow-up time was 7.3 ± 3.3 months. GPi-DBS improved TCC-camptocormia by 40.4% (angles from 39.1° ± 10.1° to 23.3° ± 8.1°, p = 0.017) and UCC-camptocormia by 22.8% (angles from 50.5° ± 2.6° to 39.0° ± 6.7°, p = 0.012). Improvement in TCC angle was positively associated with pre-surgical TCC angles, levodopa responsiveness of the TCC angle, and structural connectivity from volume of tissue activated to somatosensory cortex. Greater improvement in UCC angles was seen in patients with larger pre-surgical UCC angles. Our study demonstrates potential effectiveness of GPi-DBS for treating camptocormia in PD patients. Future controlled studies with larger numbers of patients with PD-related camptocormia should extend our findings.

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

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