In our day-to-day life, Lie detection has a significant concern. We human beings are very much inaccurate while detecting the liars and We believe in what we are told. Lie detection is important in today’s life, because Concealing the information or faking it can sometimes take you to huge problems. In any areas like airport management[2], criminal investigations, counterterrorism, etc this concept has great importance. It is an evergreen challenging and changing topic. This paper presents the common technique which was followed up till now and why it was not considered effective and a review of Robust solutions to detection of deception. People generally do not[3] always believe on what someone says but also try to visualize their facial expressions. While in Robust solution these facial micro-expressions are identified, which are tiny, natural expressions seen on the individual’s face, when they try to conceal or suppress emotions. In addition, the article also provides the year-wise assessment and analysis of research articles published in the area of Lie detection from 2011 to 2022. In the end, our proposed framework for lie detection system is also presented. This paper cover up current issues as well as challenges that could be helpful to resolve in future research works. The review paper closes up by supporting future directions.

There is a growing consensus that culture influences the perception of facial expressions of emotion. However, little is known about whether and how culture shapes the production of emotional facial expressions, and even less so about whether culture differentially shapes the production of posed versus spontaneous expressions. Drawing on prior work on cultural differences in emotional communication, we tested the prediction that people from the Netherlands (a historically heterogeneous culture where people are prone to low-context communication) produce facial expressions that are more distinct across emotions compared to people from China (a historically homogeneous culture where people are prone to high-context communication). Furthermore, we examined whether the degree of distinctiveness varies across posed and spontaneous expressions. Dutch and Chinese participants were instructed to either pose facial expressions of anger and disgust, or to share autobiographical events that elicited spontaneous expressions of anger or disgust. Using the complementary approaches of supervised machine learning and information-theoretic analysis of facial muscle movements, we show that posed and spontaneous facial expressions of anger and disgust were more distinct when produced by Dutch compared to Chinese participants. These findings shed new light on the role of culture in emotional communication by demonstrating, for the first time, effects on the distinctiveness of production of facial expressions.

Previous studies have indicated that facial expressions can serve as an objective evaluation method for hedonics (overall pleasure) of food and beverages. In this study, we aimed to validate the findings of our previous research, which demonstrated that facial expressions induced by tastants can predict the perceived hedonic ratings of these tastants. Facial expressions of 29 female participants (aged 18-55 years) were recorded using a digital camera while they consumed 12 different concentrations of solutions representing five basic tastes. The facial expressions were then analyzed using the widely-used facial expression analysis application, FaceReader, to identify seven emotions (surprise, happiness, scare, neutral, disgust, sadness, and anger) with scores ranging from 0 to 1. Participants also rated the hedonics of each solution on a scale from -5 (extremely unpleasant) to +5 (extremely pleasant). A multiple linear regression analysis was conducted to develop a formula to predict perceived hedonic ratings. The formula's applicability was tested by analyzing emotion scores for 11 additional taste solutions consumed by 20 other participants. The predicted hedonic ratings demonstrated good correlation and concordance with the perceived ratings, supporting the validity of our previous findings using different software and taste stimuli among diverse participants.

This paper investigates nonverbal communication in human interactions, with a specific focus on facial expressions. Employing a Long Short-Term Memory (LSTM) architecture and a custom-ized facial expression framework, our approach aims to improve virtual agent interactions by incorporating subtle nonverbal cues. The paper contributes to the emerging field of facial expres-sion generation, addressing gaps in current research and presenting a novel framework within Unreal Engine 5. The model's architecture, trained on the CANDOR corpus, captures temporal dynamics, and refines hyperparameters for optimal performance. During testing, the trained model showed a cosine similarity of -0.95. This enables the algorithm to accurately respond to non-verbal cues and interact with humans in a way that is comparable to human-human interac-tion. Unlike other approaches in the field of facial expression generation, the presented method is more comprehensive and enables the integration of a multi-modal approach for generating facial expressions. Future work involves integrating blendshape generation, real-world testing, and the inclusion of additional modalities to create a comprehensive framework for seamless hu-man-agent interactions beyond facial expressions.

Animals’ facial expressions have been widely used as a readout for emotion. Scientific interest in the facial expressions of laboratory animals has centered primarily on negative experiences, such as pain, experienced as a result of scientific research procedures. Recent attempts to standardize evaluation of facial expressions associated with pain in laboratory animals has culminated in the development of “grimace scales”. In the context of laboratory animals, these have been developed and evaluated for mice, rats, rabbits, sheep, and ferrets. The prevention or relief of pain in laboratory animals is a fundamental requirement for in vivo research to satisfy community expectations. However, to date it appears that the grimace scales have not seen widespread implementation as clinical pain assessment techniques in biomedical research. In this review, we discuss some of the barriers to implementation of the scales in clinical laboratory animal medicine, progress made in automation of collection, and suggest avenues for future research.

Emotional dysfunction, including flat affect and emotional perception deficits, is a specific symptom of schizophrenia disorder. We used a modified multimodal odd-ball paradigm with fearful facial expressions accompanied by congruent and non-congruent sounds to investigate the impairment of emotional perception and reaction to other people's emotions. We analyzed subjective assessments and ERP data for emotionally charging congruent and non-congruent stimuli in patients with schizophrenia and healthy peers. The results showed the deficit of multimodal perception of fearful stimuli in patients with schizophrenia compared to healthy controls. The amplitude of N50 was significantly higher in subjects of the control group for non-congruent stimuli than congruent and did not differ in patients with schizophrenia. The dynamics of P100 and N200 components confirmed the impaired sensory gating in patients with schizophrenia. The lower amplitude of P3a could be associated with deficits in verbal memory and attention, less emotional arousal, or incorrect interpretation of emotional valence as specific features of patients. The difficulties in identifying the incoherence of facial and audial components of emotional expression could be significant in understanding the psychopathology of schizophrenia.

Survey data from 42 Australian eastern seaboard veterinary practices involving 506 cases are reported with regard to clinical signs, disease severity, mortality, use of pharmaceuticals, and recovery times. New measures of disease severity (visual analogue scales (VAS) and facial expressions) were tested alongside “gold standard” measures (neuromuscular junction (NMJ) scores). Univariable and multivariable logistic regression analyses were conducted to evaluate associations between variables. The VAS scores were progressive, prognostic (especially the respiratory scores) and correlated with the NMJ scores. The presence of inspiratory dyspnoea and crackles on the day of hospitalization, progressing to expiratory dyspnoea and an expiratory wheeze 24 hours later were highly predictive of mortality. Altered facial features on hospital admission were also highly predictive of mortality. The previously used respiratory score (using various clinical signs) was not predictive of mortality. Older animals had a higher mortality rate and no gender or breed susceptibility was found. The only pharmaceuticals that were positively associated with mortality were tick anti-serum and, in severe cases, antibiotics. The use of many pharmaceutical products (acepromazine, atropine, steroids, antihistamines, antiemetics, diuretics and S8 anti-anxiety and sedation drugs) had no effect on mortality. More drug classes were used with increasing clinical severity and specific factors (e.g. vomiting/retching, hydration) affected the period of hospitalisation. Geographic variation in respiratory signs and toxicity scores was evident, whereas mortality and disease severity were not different across regions.

Humans can recombine thousands of different facial expressions. This variability is due to the ability to voluntarily or involuntarily modulate emotional expressions, which, in turn, depends on the existence of two anatomically separate pathways. The Voluntary (VP) and Involuntary (IP) pathways mediate the production of posed and spontaneous facial expressions, respectively, and might also affect the left and right sides of the face differently. This is a neglected aspect in the emotion literature, where posed expressions instead of genuine expressions are often used as stimuli. Two experiments with different induction methods were specifically designed to inves-tigate the unfolding of spontaneous and posed facial expressions of happiness along the facial vertical axis (left, right) with a high-definition 3D optoelectronic system. The results showed that spontaneous expressions were distinguished from posed facial movements as revealed by relia-ble spatial and speed key kinematic patterns in both experiments. Moreover, VP activation pro-duced a lateralization effect: compared with the felt smile, the posed smile involved an initial acceleration of the left corner of the mouth, while an early deceleration of the right corner oc-curred in the second phase of the movement, after the velocity peak.

SORL1 gene encodes LR11/SorLA, a protein that binds -amyloid precursor protein (bAPP) and drives its intracellular trafficking. SORL1 mutations occurring frequently in a subset of familial cases of Alzheimer’s disease (AD) have been documented but their pathogenic potential is not yet clear and questions remain concerning their putative influence on the physiopathological processing of bAPP. We have assessed the influence of three SORL1 mutations that were described as likely disease-causing and that were associated with either benign (SorLA924) or severe (SorLA141 and SorLA511) AD phenotypes. We examined the influence of wild-type and mutants SorLA in transiently transfected HEK293 cells expressing either wild-type or Swedish mutated bAPP on bAPP expression, secreted Ab and sAPPa levels, intracellular Ab40 and Ab42 peptides, APP-CTFs (C99 and C83) expressions, a-, b- and g-secretases expressions and activities as well as Ab and CTFs-degrading enzymes. These paradigms were studied in control conditions or after pharmacological proteasomal modulation. We also established stably transfected CHO cells expressing wild-type SorLA and established the colocalization of wild-type SorLA and bAPP. Overall, although we mostly confirmed previous data concerning the influence of wild-type SorLA on bAPP processing, we were unable to evidence significant alterations triggered by our set of SorLA mutants, whatever the cells or pharmacological conditions examined. Our study however does not rule out the possibility that other AD-linked SORL1 mutations could indeed affect bAPP processing and that pathogenic mutations examined in the present study could interfere with other cellular pathways/triggers in AD.

In this paper, we critically examine the burgeoning role of advanced computational methodologies in deciphering the complex tapestry of farm animal behaviors and emotions. Leveraging digital imaging and artificial intelligence, we unearth nuanced behavioral patterns and micro-expressions, offering predictive insights into animal emotional states. Sound vocalization analysis, often overlooked, emerges as a pivotal tool, decoding intricate communicative nuances and emotional undertones. Cognitive tests, including mirror and bias assessments, challenge long-standing perceptions, revealing surprising depths of animal self-awareness and cognitive sophistication. However, the paper also underscores the imperative of integrating these tools with a profound understanding of animal psyche, ensuring technology serves as an enhancer, not a replacement, of traditional observational methods. This research not only highlights the transformative potential of cognitive computing in animal welfare but also calls for a judicious application, ensuring technology augments, not undermines, the intrinsic value of human-animal interactions and understanding.

Building electric energy is characterized by a significant increase of its uses (e.g. vehicle charging), a rapidly declining cost of all related data collection and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting related issues. First, on the forecasting explainability, i.e. the ability to understand and explain to the user what shapes the forecast. To this extent we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that a fast training would allow for faster deployment of forecasting in real life solutions. And third, we explore the concept of counterfactual analysis on actionable features, i.e. features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. As for GP models, we have found comparable and in some cases superior accuracy when compared to its neural-network and time-series counterparts but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found, and report here on an important potential especially for practical, decision support, solutions of counterfactuals built on actionable features and short training timeframes.