Lie detection and children: Impact of the mode of presentation

Although a great deal of research has examined lie-detection among adults, little research has examined the differences between audio and visual mediums for deception among children. In the current study participants were presented (n = 42) with recordings of four children, each describing his/her experience of getting glasses. Two of the accounts were truthful, two were fabricated. Half of the participants were presented with videos, half were presented with audio-recordings. Following the presentation of each recording, participants responded to questions regarding the truthfulness of each child’s account. Results showed that when evaluating truth-tellers, participants’ lie-detection accuracy was significantly greater than chance. Within the video condition, non-parents were shown to report significantly more lie-related cues than parents. Several deception cues were shown to be related to lie-detection accuracy.

说谎行为识别线索的比较研究

Studies on lie-detection by western psychologists indicate that lying cues people usually hold are not in accordance with the real verbal and non-verbal behaviors that liars usually show. A cross-culture study carried out by C.F.Bond and its global research team finds that the commonest view held by people from 75 nations about lying behavior is that liars usually avert gaze, while study shows that gaze-aversion has no relation with lying. In Bond’s view, stereotype of the liar reflect more about common cross-culture values than an objective description of how liars behave. Different culture has its norms based upon which people judge whether a person is credible or not. As a nation of long Confucianism tradition, how Chinese view liars differently from people of other culture is the interest of this study. By a comparative study with that of Bond’s research, it is found that, in line with Bond’s finding, Chinese generally hold the same stereotype about liars with that of the westerners; but it seems that Chinese rely significantly less on gaze-aversion as a cue to lying, and they concern more about senders’ motivation and emotion. It is also found that confidence about their detection ability among Chinese is lower than westerners. A further study on different professions and their view about lying behaviors shows that people in law-enforcement and related professions generally hold a more accurate view toward how liars behave. Possible explanations to the above mentioned findings in view of culture differences, aspects to be improved in this study and direction of future research are discussed in the later part of the thesis.

Nachrichtendienstpsychologie 1

Herausgeberwerk (Litzcke): 1. Die Intelligence-Acht. Überlegungen zu einer wissenschaftlichen Annäherung an nachrichtendienstliches Tun. 2. Korruption in Auslandsnachrichtendiensten 3. Interkulturelle Kommunikation Deutschland - China 4. Aspekte eines nachrichtendienstlichen Gesprächs 5. Nonverbale Lügen- und Machtmerkmale 6. Illegale Migration aus psychologischer und aus nachrichtendienstlicher Sicht 7. Stessbelastung operativ arbeitender Mitarbeiter 8. Psychologie verdeckter Ermittler 9. Psychologische Aspekte des Einsatzes kriminalpolizeilicher Verbindungsbeamter im Ausland 10. Agentinnen aus Liebe - psychologische Betrachtung der Romeomethode

Opening up a can of worms: how do decision-makers decide when witnesses are telling the truth?

Correctly determining witness credibility is integral to a fair trial. Assessments of credibility made by the triers of fact are made, amongst other things, by reference to behavioural stereotypes that are commonly thought to be associated with lying and truth-telling. These stereotypes are worthless but pervasive. In this study, potential jurors were given information such as would be given by way of judicial direction and/or expert testimony on those behavioural indicia that are useful in detecting deception. Major changes in perceptions of what does and does not work were found. This has significant implications for the conduct of criminal trials. Recommendations are presented which, it is argued, can be of real, practical, assistance in enabling decision-makers to assess the credibility of witnesses. © 2013 The Australian and New Zealand Association of Psychiatry, Psychology and Law.

Accuracy of social perception: An integration and review of meta-analyses

This review examines the overall accuracy of social perception across several research topics and identifies factors that inf luence the accuracy of social perception. Findings from 14 meta-analyses examining topics such as social/personality judgments, health judgments, legal judgments, and academic/vocational judg-ments were obtained. Social perception accuracy was generally moderate, yielding an average effect size (r) of .32. However, individual meta-analytic effects varied widely, with some topics yielding small effects (e.g., lie detection, eyewitness identification) and other topics yielding large effects (e.g., educational judgments, health judgments). Several moderators of social perception accuracy were identified, includ-ing the nature of the information source, familiarity of the target, type of personality trait, and severity of the outcome being judged. These findings provide a comprehensive summary and novel integration of disparate findings on the accuracy of social perception. Concluding remarks highlight avenues for future research and call for cross-disciplinary collaborations that would enhance our understanding of social perception.

Detecting deception: Identifying differences in liars' and truth tellers' verbal strategies

Deception research has traditionally focused on three methods of identifying liars and truth tellers: observing non-verbal or behavioral cues, analyzing verbal cues, and monitoring changes in physiological arousal during polygraph tests. Research shows that observers are often incapable of discriminating between liars and truth tellers with better than chance accuracy when they use these methods. One possible explanation for observers' poor performance is that they are not properly applying existing lie detection methods. An alternative explanation is that the cues on which these methods — and observers' judgments — are based do not reliably discriminate between liars and truth tellers. It may be possible to identify more reliable cues, and potentially improve observers' ability to discriminate, by developing a better understanding of how liars and truth tellers try to tell a convincing story. ^ This research examined (a) the verbal strategies used by truthful and deceptive individuals during interviews concerning an assigned activity, and (b) observers' ability to discriminate between them based on their verbal strategies. In Experiment I, pre-interview instructions manipulated participants' expectations regarding verifiability; each participant was led to believe that the interviewer could check some types of details, but not others, before deciding whether the participant was being truthful or deceptive. Interviews were then transcribed and scored for quantity and type of information provided. In Experiment II, observers listened to a random sample of the Experiment I interviews and rendered veracity judgments; half of the observers were instructed to judge the interviews according to the verbal strategies used by liars and truth tellers and the other half were uninstructed. ^ Results of Experiment I indicate that liars and truth tellers use different verbal strategies, characterized by a differential amount of detail. Overall, truthful participants provided more information than deceptive participants. This effect was moderated by participants' expectations regarding verifiability such that truthful participants provided more information only with regard to verifiable details. Results of Experiment II indicate that observers instructed about liars' and truth tellers' verbal strategies identify them with greater accuracy than uninstructed observers. ^

The Effect of Cognitive Load on Deception

The current study applied classic cognitive capacity models to examine the effect of cognitive load on deception. The study also examined whether the manipulation of cognitive load would result in the magnification of differences between liars and truth-tellers. In the first study, 87 participants engaged in videotaped interviews while being either deceptive or truthful about a target event. Some participants engaged in a concurrent secondary task while being interviewed. Performance on the secondary task was measured. As expected, truth tellers performed better on secondary task items than liars as evidenced by higher accuracy rates. These results confirm the long held assumption that being deceptive is more cognitively demanding than being truthful. In the second part of the study, the videotaped interviews of both liars and truth-tellers were shown to 69 observers. After watching the interviews, observers were asked to make a veracity judgment for each participant. Observers made more accurate veracity judgments when viewing participants who engaged in a concurrent secondary task than when viewing those who did not. Observers also indicated that participants who engaged in a concurrent secondary task appeared to think harder than participants who did not. This study provides evidence that engaging in deception is more cognitively demanding than telling the truth. As hypothesized, having participants engage in a concurrent secondary task led to the magnification of differences between liars and truth tellers. This magnification of differences led to more accurate veracity rates in a second group of observers. The implications for deception detection are discussed.

Robust numeric set watermarking : numbers don’t lie

Ever since Cox et. al published their paper, “A Secure, Robust Watermark for Multimedia” in 1996 [6], there has been tremendous progress in multimedia watermarking. The same pattern re-emerged with Agrawal and Kiernan publishing their work “Watermarking Relational Databases” in 2001 [1]. However, little attention has been given to primitive data collections with only a handful works of research known to the authors [11, 10]. This is primarily due to the absence of an attribute that differentiates marked items from unmarked item during insertion and detection process. This paper presents a distribution-independent, watermarking model that is secure against secondary-watermarking in addition to conventional attacks such as data addition, deletion and distortion. The low false positives and high capacity provide additional strength to the scheme. These claims are backed by experimental results provided in the paper.

On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow

Detecting and quantifying the presence of human-induced climate change in regional hydrology is important for studying the impacts of such changes on the water resources systems as well as for reliable future projections and policy making for adaptation. In this article a formal fingerprint-based detection and attribution analysis has been attempted to study the changes in the observed monsoon precipitation and streamflow in the rain-fed Mahanadi River Basin in India, considering the variability across different climate models. This is achieved through the use of observations, several climate model runs, a principal component analysis and regression based statistical downscaling technique, and a Genetic Programming based rainfall-runoff model. It is found that the decreases in observed hydrological variables across the second half of the 20th century lie outside the range that is expected from natural internal variability of climate alone at 95% statistical confidence level, for most of the climate models considered. For several climate models, such changes are consistent with those expected from anthropogenic emissions of greenhouse gases. However, unequivocal attribution to human-induced climate change cannot be claimed across all the climate models and uncertainties in our detection procedure, arising out of various sources including the use of models, cannot be ruled out. Changes in solar irradiance and volcanic activities are considered as other plausible natural external causes of climate change. Time evolution of the anthropogenic climate change ``signal'' in the hydrological observations, above the natural internal climate variability ``noise'' shows that the detection of the signal is achieved earlier in streamflow as compared to precipitation for most of the climate models, suggesting larger impacts of human-induced climate change on streamflow than precipitation at the river basin scale.

SOI CMOS integrated zinc oxide nanowire for toluene detection

The paper reports on the in-situ growth of zinc oxide nanowires (ZnONWs) on a complementary metal oxide semiconductor (CMOS) substrate, and their performance as a sensing element for ppm (parts per million) levels of toluene vapour in 3000 ppm humid air. Zinc oxide NWs were grown using a low temperature (only 90°C) hydrothermal method. The ZnONWs were first characterised both electrically and through scanning electron microscopy. Then the response of the on-chip ZnONWs to different concentrations of toluene (400-2600ppm) was observed in air at 300°C. Finally, their gas sensitivity was determined and found to lie between 0.1% and 0.3% per ppm. © 2013 IEEE.

Sequential anomaly detection in the presence of noise and limited feedback

This paper describes a methodology for detecting anomalies from sequentially observed and potentially noisy data. The proposed approach consists of two main elements: 1) filtering, or assigning a belief or likelihood to each successive measurement based upon our ability to predict it from previous noisy observations and 2) hedging, or flagging potential anomalies by comparing the current belief against a time-varying and data-adaptive threshold. The threshold is adjusted based on the available feedback from an end user. Our algorithms, which combine universal prediction with recent work on online convex programming, do not require computing posterior distributions given all current observations and involve simple primal-dual parameter updates. At the heart of the proposed approach lie exponential-family models which can be used in a wide variety of contexts and applications, and which yield methods that achieve sublinear per-round regret against both static and slowly varying product distributions with marginals drawn from the same exponential family. Moreover, the regret against static distributions coincides with the minimax value of the corresponding online strongly convex game. We also prove bounds on the number of mistakes made during the hedging step relative to the best offline choice of the threshold with access to all estimated beliefs and feedback signals. We validate the theory on synthetic data drawn from a time-varying distribution over binary vectors of high dimensionality, as well as on the Enron email dataset. © 1963-2012 IEEE.

An ESPRIT-SAA-Based Detection Method for Broken Rotor Bar Fault in Induction Motors

This paper presents a novel detection method for broken rotor bar fault (BRB) in induction motors based on Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) and Simulated Annealing Algorithm (SAA). The performance of ESPRIT is tested with simulated stator current signal of an induction motor with BRB. It shows that even with a short-time measurement data, the technique is capable of correctly identifying the frequencies of the BRB characteristic components but with a low accuracy on the amplitudes and initial phases of those components. SAA is then used to determine their amplitudes and initial phases and shows satisfactory results. Finally, experiments on a 3kW, 380V, 50Hz induction motor are conducted to demonstrate the effectiveness of the ESPRIT-SAA-based method in detecting BRB with short-time measurement data. It proves that the proposed method is a promising choice for BRB detection in induction motors operating with small slip and fluctuant load.

The Illinois Detection of Deception Examiners Act.

Description based on: 1985; title from cover.

Detection and Classification of Whale Acoustic Signals

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.