Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset

Lie detection is considered a concern for everyone in their day-to-day life, given its impact on human interactions. Thus, people normally pay attention to both what their interlocutors are saying and to their visual appearance, including the face, to find any signs that indicate whether or not the person is telling the truth. While automatic lie detection may help us to understand these lying characteristics, current systems are still fairly limited, partly due to lack of adequate datasets to evaluate their performance in realistic scenarios. In this work, we collect an annotated dataset of facial images, comprising both 2D and 3D information of several participants during a card game that encourages players to lie. Using our collected dataset, we evaluate several types of machine learning-based lie detectors in terms of their generalization, in person-specific and cross-application experiments. We first extract both handcrafted and deep learning-based features as relevant visual inputs, then pass them into multiple types of classifier to predict respective lie/non-lie labels. Subsequently, we use several metrics to judge the models’ accuracy based on the models predictions and ground truth. In our experiment, we show that models based on deep learning achieve the highest accuracy, reaching up to 57% for the generalization task and 63% when applied to detect the lie to a single participant. We further highlight the limitation of the deep learning-based lie detector when dealing with cross-application lie detection tasks. Finally, this analysis along the proposed datasets would potentially be useful not only from the perspective of computational systems perspective (e.g., improving current automatic lie prediction accuracy), but also for other relevant application fields, such as health practitioners in general medical counselings, education in academic settings or finance in the banking sector, where close inspections and understandings of the actual intentions of individuals can be very important.

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Metrics for Automatic Evaluation of Text from NLP Models for Text to Scene Generation

European Journal of Electrical Engineering and Computer Science ◽

10.24018/ejece.2021.5.4.341 ◽

2021 ◽

Vol 5 (4) ◽

pp. 20-25

Author(s):

S. Yashaswini ◽

S. S. Shylaja

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Cost Function ◽

Performance Metrics ◽

Evaluation Criteria ◽

Ground Truth ◽

Automatic Evaluation ◽

Mean Square ◽

Learning Models ◽

Classification Tasks

Performance metrics give us an indication of which model is better for which task. Researchers attempt to apply machine learning and deep learning models to measure the performance of models through cost function or evaluation criteria like Mean square error (MSE) for regression, accuracy, and f1-score for classification tasks Whereas in NLP performance measurement is a complex due variation of ground truth and results obta.

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Non–human primate epidural ECoG analysis using explainable deep learning technology

Journal of Neural Engineering ◽

10.1088/1741-2552/ac3314 ◽

2021 ◽

Author(s):

Hoseok Choi ◽

Seokbeen Lim ◽

Kyeongran Min ◽

Kyoung-ha Ahn ◽

Kyoung-Min Lee ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Hand Movement ◽

Point Of View ◽

Machine Learning Techniques ◽

High Dimensional ◽

Learning Technology ◽

3D Information

Abstract Objective: With the development in the field of neural networks, Explainable AI (XAI), is being studied to ensure that artificial intelligence models can be explained. There are some attempts to apply neural networks to neuroscientific studies to explain neurophysiological information with high machine learning performances. However, most of those studies have simply visualized features extracted from XAI and seem to lack an active neuroscientific interpretation of those features. In this study, we have tried to actively explain the high-dimensional learning features contained in the neurophysiological information extracted from XAI, compared with the previously reported neuroscientific results. Approach: We designed a deep neural network classifier using 3D information (3D DNN) and a 3D class activation map (3D CAM) to visualize high-dimensional classification features. We used those tools to classify monkey electrocorticogram (ECoG) data obtained from the unimanual and bimanual movement experiment. Main results: The 3D DNN showed better classification accuracy than other machine learning techniques, such as 2D DNN. Unexpectedly, the activation weight in the 3D CAM analysis was high in the ipsilateral motor and somatosensory cortex regions, whereas the gamma-band power was activated in the contralateral areas during unimanual movement, which suggests that the brain signal acquired from the motor cortex contains information about both contralateral movement and ipsilateral movement. Moreover, the hand-movement classification system used critical temporal information at movement onset and offset when classifying bimanual movements. Significance: As far as we know, this is the first study to use high-dimensional neurophysiological information (spatial, spectral, and temporal) with the deep learning method, reconstruct those features, and explain how the neural network works. We expect that our methods can be widely applied and used in neuroscience and electrophysiology research from the point of view of the explainability of XAI as well as its performance.

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A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

Sensors ◽

10.3390/s21237786 ◽

2021 ◽

Vol 21 (23) ◽

pp. 7786

Author(s):

Sharnil Pandya ◽

Aanchal Thakur ◽

Santosh Saxena ◽

Nandita Jassal ◽

Chirag Patel ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Immune System ◽

Future Research ◽

Human Immune System ◽

Specialized Knowledge ◽

Recent Developments ◽

The Face ◽

Future Research Directions ◽

Recent Trends

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

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Development of the "smart library" application using the Intel Distribution of OpenVINO toolkit

10.30987/graphicon-2019-1-122-125 ◽

2019 ◽

Author(s):

Евгений Васильев ◽

Evgeniy Vasil'ev ◽

Валентина Кустикова ◽

Valentina Kustikova ◽

Иван Вихрев ◽

...

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Deep Learning ◽

Face Recognition ◽

Source Code ◽

Face Features ◽

Machine Learning Model ◽

Library Users ◽

The Face

We represent a case study of using deep learning and computer vision library - the Intel Distribution of OpenVINO toolkit. We develop the automated “smart library” using DL and computer vision methods implemented in OpenVINO toolkit. The application involves the registration of the reader (adding information and photos of the new user); updating the machine learning model that describes the face features of the library users; authorization of the reader through face recognition; receiving and returning books by comparing the cover image with the database of flat images available in the library of books. The source code of the application is free available on GitHub: https://github.com/itlab-vision/openvino-smart-library. The developed application is planned to be published as a sample of the OpenVINO toolkit.

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Application of Deep Learning to Predict RF Heating of Cardiac Leads During Magnetic Resonance Imaging at 1.5 T and 3 T

10.21203/rs.3.rs-701811/v1 ◽

2021 ◽

Author(s):

Xinlu Chen ◽

Can Zheng ◽

Bach Thanh Nguyen ◽

Pia Sanpitak ◽

Kelvin Chow ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Machine Learning ◽

Deep Learning ◽

Magnetic Resonance ◽

Ground Truth ◽

Orthopedic Implants ◽

Rf Heating ◽

Rf Coil ◽

Resonance Imaging ◽

Electromagnetic Simulations

Abstract Purpose: Predicting magnetic resonance imaging (MRI)-induced heating of elongated conductive implants such as leads in cardiovascular implantable electronic devices (CIEDs) is essential to assessing patient safety. Phantom experiments and electromagnetic simulations have been traditionally used to estimate radiofrequency (RF) heating of implants, but they are notably time-consuming. Recently, machine learning has shown promise for fast prediction of RF heating of orthopedic implants, when the position of the implant within the MRI RF coil was predetermined. Here we explored whether deep learning could be applied to predict RF heating of conductive leads with variable positions/orientations during MRI at 1.5 T and 3 T.Methods: Models of 600 cardiac leads with clinically relevant trajectories were generated and electromagnetic simulations were performed to calculate the maximum of 1g-averaged SAR at the tips of lead models during MRI at 1.5 T and 3 T. Deep learning algorithms were trained to predict the maximum SAR at the lead’s tip from the knowledge of coordinates of points along the lead’s trajectory.Results: Despite the large range of SAR values (~200 W/kg-~3300 W/kg), the RMSE of predicted vs ground truth SAR remained at 223W/kg and 206 W/kg, with the R2 scores of 0.89 and 0.85 on the testing set for 1.5 T and 3 T models, respectively.Conclusion: Machine learning shows promise for fast assessment of RF heating of lead-like implants with only the knowledge of the lead’s geometry and MRI RF coil features.

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“Garbage In, Garbage Out” Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?

Quantitative Science Studies ◽

10.1162/qss_a_00144 ◽

2021 ◽

pp. 1-32

Author(s):

R. Stuart Geiger ◽

Dominique Cope ◽

Jamie Ip ◽

Marsha Lotosh ◽

Aayush Shah ◽

...

Keyword(s):

Machine Learning ◽

Best Practices ◽

Ground Truth ◽

Training Data ◽

Supervised Machine Learning ◽

Social Media Platforms ◽

Learning Research ◽

Research And Education ◽

Application Fields

Abstract Supervised machine learning, in which models are automatically derived from labeled training data, is only as good as the quality of that data. This study builds on prior work that investigated to what extent ‘best practices’ around labeling training data were followed in applied ML publications within a single domain (social media platforms). In this paper, we expand by studying publications that apply supervised ML in a far broader spectrum of disciplines, focusing on human-labeled data. We report to what extent a random sample of ML application papers across disciplines give specific details about whether best practices were followed, while acknowledging that a greater range of application fields necessarily produces greater diversity of labeling and annotation methods. Because much of machine learning research and education only focuses on what is done once a “ground truth” or “gold standard” of training data is available, it is especially relevant to discuss issues around the equally-important aspect of whether such data is reliable in the first place. This determination becomes increasingly complex when applied to a variety of specialized fields, as labeling can range from a task requiring little-to-no background knowledge to one that must be performed by someone with career expertise. Peer Review https://publons.com/publon/10.1162/qss_a_00144

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Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽

10.32964/tj18.11.679 ◽

2019 ◽

Vol 18 (11) ◽

pp. 679-689

Author(s):

CYDNEY RECHTIN ◽

CHITTA RANJAN ◽

ANTHONY LEWIS ◽

BETH ANN ZARKO

Keyword(s):

Machine Learning ◽

Real Time ◽

Predictive Analytics ◽

Paper Industry ◽

Quality Parameters ◽

Fast Change ◽

The Face ◽

Model Tuning ◽

Reducing Costs ◽

Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

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Deep Learning Based High-Resolution Remote Sensing Image classification

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i10.384 ◽

2017 ◽

Vol 7 (10) ◽

pp. 22

Author(s):

Sumit Kaur

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Image Classification ◽

Language Processing ◽

Object Perception ◽

Remote Sensing Image ◽

Research Area ◽

Remote Sensing Image Classification ◽

Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification.

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Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

10.26434/chemrxiv.13047863 ◽

2020 ◽

Author(s):

Jingbai Li ◽

Patrick Reiser ◽

André Eberhard ◽

Pascal Friederich ◽

Steven Lopez

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Excited State ◽

Adaptive Sampling ◽

Computational Cost ◽

Ground Truth ◽

Absolute Error ◽

Photochemical Reactions ◽

Computational Techniques ◽

Full Potential

Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr et al. demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH2NH2+).We have developed a Python-based code, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD), to accomplish the unprecedented 10 ns cis-trans photodynamics of trans-hexafluoro-2-butene (CF3–CH=CH–CF3) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S1 half-life (60.5 fs), the photochemical product ratio (trans: cis = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (trans → cis but not cis → trans pathways) that may offer future automated photochemical reaction discoveries.

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Data science in economics: comprehensive review of advanced machine learning and deep learning methods

10.31232/osf.io/4pxq2 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Ferdinand Filip ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Science ◽

State Of The Art ◽

Science Methods ◽

Learning Models ◽

Diverse Range ◽

Hybrid Machine ◽

Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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