Reviewerly/exHarmony huggingface dataset:
Automatic facial expression recognition in an image sequence using conditional random field

Facial expression recognition is one of the fields that nowadays has attracted the attention of many researchers. It is possible to automate facial expression recognition using artificial intelligence methods. This will be of great help to researchers, especially in areas such as psychology. Automatic facial recognition can be derived from a static image of facial expression, but a better and more efficient way to do this is through a sequence of images. In this paper, a new method is proposed to automatically detect facial expressions from a sequence of images. Each sequence of facial images begins with a face neutral state and ends with one of the six main emotions. Motion vectors are extracted from the sequence using optical flow algorithm. These vectors are then used to train the conditional random field and finally to automatically determine the emotion. In this paper, in addition to the basic conditional random field, the hidden dynamic conditional random field is also investigated. Additionally, the effect of changing some parameters of these algorithms such as different optimization methods has been investigated. Given that a facial expression is recognized during a sequence of images, random field-based methods can be used for efficient classification of facial expressions reaching accuracy (more than 90%) competitive with the best existing methods for facial expression recognition.
Grover on SPARKLE

Quantum computers that take advantage of quantum mechanics efficiently model and solve certain hard problems. In particular, quantum computers are considered a major threat to cryptography in the near future. In this current situation, analysis of quantum computer attacks on ciphers is a major way to evaluate the security of ciphers. Several studies of quantum circuits for block ciphers have been presented. However, quantum implementations for Authenticated Encryption with Associated Data (AEAD) are not actively studied. In this paper, we present a quantum implementation for authenticated ciphers of SPARKLE, a finalist candidate of the National Institute of Standards and Technology (NIST) Lightweight Cryptography (LWC) project. We apply various techniques for optimization by considering trade-off between qubits and gates/depth in quantum computers. Based on proposed quantum circuit, we estimate the cost of applying key search using Grover's algorithm, which degrades the security of symmetric key ciphers. Afterward, we further explore the expected level of post-quantum security for SPARKLE on the basis of post-quantum security requirements of NIST.
Exemplifying the Opportunities and Limitations of Blockchain Technology Through Corporate Tax Losses

There is an increasing need to recognize the opportunity of blockchain technology beyond cryptocurrencies, such as bitcoin, and instead consider its potential as a technology forming part of the tax regulatory framework. We consider the potential for blockchain technology to play a digital infrastructure role for corporate tax loss compliance in Australia: a "RegTech" solution. In doing so, we identify the key features of blockchain and key use cases across numerous business sectors, then examine the role and function of blockchain in the context of corporate tax losses, where complex carry forward rules (e.g. continuity of ownership, business continuity tests) apply to avoid the erosion of government revenues. We find that in theory, blockchain could enable key efficiencies in tax compliance of corporate tax losses; however, the complexity and discretion within tax law creates real barriers for an effective blockchain solution. We conclude that blockchain offers an ability to track and flag resource allocation to broad, high level elements of the corporate tax compliance. It therefore offers potential for the greater digital ecosystem; however, it does not offer a solution in isolation. As the world progresses towards increased digitalization, this use case highlights the need for continual reflection of complex regulations and high levels of discretion, whilst balancing taxpayer rights, equity and fairness. Through digitalization, we may see increasing simplification for innovations to thrive and digitalization to meet the needs of a digitalized economy.
Using video calls to study children's conversational development: The case of backchannel signaling

Understanding children's conversational skills is crucial for understanding their social, cognitive, and linguistic development, with important applications in health and education. To develop theories based on quantitative studies of conversational development, we need (i) data recorded in naturalistic contexts (e.g., child-caregiver dyads talking in their daily environment) where children are more likely to show much of their conversational competencies, as opposed to controlled laboratory contexts which typically involve talking to a stranger (e.g., the experimenter); (ii) data that allows for clear access to children's multimodal behavior in face-to-face conversations; and (iii) data whose acquisition method is cost-effective with the potential of being deployed at a large scale to capture individual and cultural variability. The current work is a first step to achieving this goal. We built a corpus of video chats involving children in middle childhood (6–12 years old) and their caregivers using a weakly structured word-guessing game to prompt spontaneous conversation. The manual annotations of these recordings have shown a similarity in the frequency distribution of multimodal communicative signals from both children and caregivers. As a case study, we capitalize on this rich behavioral data to study how verbal and non-verbal cues contribute to the children's conversational coordination. In particular, we looked at how children learn to engage in coordinated conversations, not only as speakers but also as listeners, by analyzing children's use of backchannel signaling (e.g., verbal “mh” or head nods) during these conversations. Contrary to results from previous in-lab studies, our use of a more spontaneous conversational setting (as well as more adequate controls) revealed that school-age children are strikingly close to adult-level mastery in many measures of backchanneling. Our work demonstrates the usefulness of recent technology in video calling for acquiring quality data that can be used for research on children's conversational development in the wild.
Probabilistic instance shape reconstruction with sparse LiDAR for monocular 3D object detection

Monocular 3D object detection aims to localize objects in 3D space from a single image. This is a difficult problem due to the lack of accurate depth measurements. Many methods predict depths upfront using a pre-trained vision-based depth estimator to assist in 3D object detection. However, the methods show limited improved performances because of the depth inaccuracy and the neglect of depth confidence. In this paper, we propose a new end-to-end 3D object detection framework by combining a monocular camera with a cheap 4-beam LiDAR. The minimal LiDAR signal is leveraged as an additional input to predict high-quality and dense depth maps from monocular images. Meanwhile, several 3D proposals are generated by a keypoint-based detector. The key challenge is encoding the depth confidence to capture the depth estimation uncertainty. Therefore, we propose probabilistic instance shape reconstruction to exploit instance shape information for box refinement. Our method employs a fully differentiable end-to-end framework, making it simple and efficient. The experimental results on the KITTI dataset demonstrate that the proposed method achieves state-of-the-art performance, thus validating the effectiveness of the sparse LiDAR data and the probabilistic instance shape reconstruction. Code is available at https://github.com/xjtuwh/SparseLiDAR_fusion.
EvaGoNet: An integrated network of variational autoencoder and Wasserstein generative adversarial network with gradient penalty for binary classification tasks

Feature engineering is an effective method for solving classification problems. Many existing feature engineering studies have focused on image or video data and not on structured data. This study proposes EvaGoNet, which refines the decoder module of the Gaussian mixture variational autoencoder using the Wasserstein generative adversarial network with gradient penalty (WGANgp) and embeds the top-ranked original features to update the latent features based on their discriminative powers. Comprehensive experiments show that EvaGoNet-encoded features outperform existing classifiers on 12 benchmark datasets, particularly on the small, imbalanced datasets col (accuracy = 0.8581), spe (accuracy = 1.0000), and leu (accuracy = 0.8021). EvaGoNet-engineered features improve binary classification task outcomes on six high-dimensional, imbalanced bioOMIC datasets. EvaGoNet achieves a medium-ranked training speed among the compared algorithms and considerably fast prediction speeds in the predictions of the testing samples. Therefore, EvaGoNet can be a candidate feature engineering framework for many practical applications that require one training procedure and many prediction tasks of the testing samples. EvaGoNet is implemented in Python TensorFlow and is available at https://healthinformaticslab.org/supp/resources.php.
Can behavioral biometrics make everyone happy?

The market for behavioral biometrics—a technological evolution whereby patterns in human movement and activities can be identified, captured, and analyzed—is expected to exceed US $11 billion by 2031. We highlight the evolution from early physiological biometrics (e.g., fingerprints and iris scans used to verify the identity of individuals) to today's behavioral biometrics. Technological advancements now turn our retail stores, offices, and warehouses into live data streams that let us closely and automatically monitor employees' conduct at work. Although this development raises several legitimate surveillance and privacy concerns, behavioral biometrics can potentially benefit organizations and employees alike. Such mutual benefits compel managers to approach behavioral biometrics using our TRUST framework: transparency of intentions, respect for concerns, understanding the importance of choice, sharing the data benefits, and proactively timing the development. Managers who do so will gain a clear focus on organizational and employee well-being.
Industrial anomaly detection with multiscale autoencoder and deep feature extractor‐based neural network

Abstract With the maturity of deep learning image recognition technology and the popularity of automated production lines, deep learning industrial anomaly detection has become an important research topic in recent years. In this study, an anomaly detection model with a multi‐scale autoencoder and deep feature extractor is proposed. This model was confirmed to obtain the highest area under the curve (AUC) in 14 of the 17 industrial detection tasks. In addition, the receiver operating characteristic (ROC) curves show that an appropriate threshold of the proposed model exists, which can achieve a low false‐positive rate and maintain a high true‐positive rate. Furthermore, the influence of different feature extractors on the method is discussed. It was shown that the proposed method can maintain good detection ability with most of the feature extractor. Therefore, it is suitable for industrial optical inspection systems with different hardware conditions.
Recent progress in image denoising: A training strategy perspective

Abstract Image denoising is one of the hottest topics in image restoration area, it has achieved great progress both in terms of quantity and quality in recent years, especially after the wide and intensive application of deep neural networks. In many deep learning based image denoising models, the performance can greatly benefit from the prepared clean/noisy image pairs used for model training, however, it also limits the application of these models in real denoising scenes. Therefore, more and more researchers tend to develop models that can be learned without image pairs, namely the denoising models that can be well generalised in real‐world denoising tasks. This motivates to make a survey on the recent development of image denoising methods. In this paper, the typical denoising methods from the perspective of model training are reviewed, the reviewed methods are categorised into four classes: the models need clean/noisy image pairs to train, the models trained on multiple noisy images, the models can be learned from a single noisy image, and the visual transformer based models. The denoising results of different denoisers were compared on some public datasets to discover the performance and advantages. The challenges and future directions in image denoising area are also discussed.
Recognition of printed Urdu script in Nastaleeq font by using CNN-BiGRU-GRU Based Encoder-Decoder Framework

RNN based Deep learning models has shown tremendous success in sequential and temporal data where the order is critical to achieve higher accuracy in context understanding. RNN family like LSTM, BLSTM, GRU, BiGRU etc. are the mainly used models in these kind of sequential tasks. RNN family based Encoder-decoder frameworks are widely used for the recognition of various languages scripts. However, in Urdu, very less research has been done especially with the deep learning models. The existing research work for printed Urdu recognition have shown that the current models only work for very basic sentences of Urdu but in case of complex words and sentences, these algorithms totally fail in terms of accuracy and the time complexity in identification of the Nastaleeq font writing. To identify printed Urdu text in images, we have proposed an encoder-decoder based hybrid deep learning approach with Convolutional Neural Network (CNN) for feature extraction part, bi-directional Gated Recurrent Unit network (BiGRU) as encoder and Gated Recurrent Unit network (GRU) as decoder. The CNN layer of the algorithm is used to obtain ligature features in Urdu, which are subsequently utilized by encoder (BiGRU) and decoder (GRU) to recognize the sentences by accurately distinguishing the characters and joiners. Experimental results have shown that our proposed CNN-BiGRU-GRU hybrid technique with specific hyper-parameter tuning performs well as compared to other state-of-the-art algorithms in terms of epochs (70 epochs as compared to 100 with BLSTM-LSTM based encoder decoder), 6 percent increase of Character Recognition Accuracy (86.95 percent as compared to 81.08 percent of BLSTM-LSTM), 10 percent increase of Word Recognition Accuracy (WRA) (89.48% as compared to 79.06 percent of BLSTM-LSTM) and less time complexity (18 seconds less than BLSTM-LSTM with same system configuration).
ChainSure: Agent free insurance system using blockchain for healthcare 4.0

The health insurance industry requires a secure data management architecture characterized by reliable information processing and fast response. Classical health insurance management system runs on a manually controlled centralized architecture which leverages a vulnerable single point failure and requires frequent human intervention. This makes the model prone to human errors and security attacks. The existing system also lacks the flexibility to choose appropriate health insurance policies for different consumers. To this end, we propose an Ethereum blockchain-based framework, ChainSure, to address this problem. With the power of TOPSIS and smart contracts, ChainSure provides an automated, tamperproof, transparent, scalable system that takes care of all the major functional blocks in a medical insurance environment. We have used the TOPSIS method in this proposed model to help the users to find an insurance policy that best suits their needs. ChainSure works in a decentralized environment and provides an interactive interface for the user. The proposed model is implemented on the Ethereum test network and its performance has been compared empirically with other state-of-art models. ChainSure is found to outperform others in terms of service integrity, latency and cost.
Evaluating Student Behaviour on the MathE Platform - Clustering Algorithms Approaches

The MathE platform is an online educational platform that aims to help students who struggle to learn college mathematics as well as students who wish to deepen their knowledge on subjects that rely on a strong mathematical background, at their own pace. The MathE platform is currently being used by a significant number of users, from all over the world, as a tool to support and engage students, ensuring new and creative ways to encourage them to improve their mathematical skills. This paper is addressed to evaluate the students’ performance on the Linear Algebra topic, which is a specific topic of the MathE platform. In order to achieve this goal, four clustering algorithms were considered; three of them based on different bio-inspired techniques and the k-means algorithm. The results showed that most students choose to answer only basic level questions, and even within that subset, they make a lot of mistakes. When students take the risk of answering advanced questions, they make even more mistakes, which causes them to return to the basic level questions. Considering these results, it is now necessary to carry out an in-depth study to reorganize the available questions according to other levels of difficulty, and not just between basic and advanced levels as it is.
Identification of flight accidents causative factors base on SHELLO and improved entropy gray correlation method

In order to effectively identify the key causative factors of civil aviation flight accidents, and establish a forward-looking effective prevention mechanism for flight accidents. Firstly, Corrected SHELLO model is established to classify the causes of civil aviation accidents in China (2015-2019) based on the integration of SHELL analysis model and Reason organization system concept. Secondly, in view of the randomness and uncertainty gray characteristics of the flight accidents inducing factors, the improved entropy gray correlation algorithm is established for the purpose of importance recognition, which combined with the characteristics of the data sample of inducement classification. Finally, the improved entropy gray correlation algorithm is used to identify and rank the key causative factors of flight accidents. The results showed that the flight accidents crucial causative factor is the human factors which we should pay more attention including the pilot perceptual errors, skill-based errors, decision errors and violation main factors, environmental and organizational factors also play an important role in inducing flight accidents, including complex terrain for approach landing and poor safety management mechanism factors. The method has great practical significance for identifying critical causative factors of flight accidents and improving flight safety.
Analysing the impact of contextual segments on the overall rating in multi-criteria recommender systems

Depending on the RMSE and sites sharing travel details, enormous reviews have been posted day by day. In order to recognize potential target customers in a quick and effective manner, hotels are necessary to establish a customer recommender system. The data adopted in this study was rendered by the Trip Advisor which permits the customers to rate the hotel on the basis of six criteria such as, Service, Sleep Quality, Value, Location, Cleanliness and Room. This study suggest the multi-criteria recommender system to analyse the impact of contextual segments on the overall rating based on trip type and hotel classes. In this research we have introduced item-item collaborative filtering approach. Here, the adjusted cosine similarity measure is applied to identify the missing value for context in the dataset. For the selection of significant contexts the backward elimination with multi regression algorithm is introduced. The multi-collinearity among predictors is examined on the basis of Variance Inflation Factor (V.I.F). In the experimental scenario, the results are rendered based on hotel class and trip type. The performance of the multiregression model is evaluated by the statistical measures such as R-square, MAE, MSE and RMSE. Along with this, the ANOVA study is conducted for different hotel classes and trip types under 2, 3, 4 and 5 star hotel classes.
Aerial image recognition in discriminative bi-transformer

Aerial image recognition (AIR) has received increasing interest in the remote sensing community, and complex backgrounds with various types of objects bring huge challenges to recognition. Vision transformer (ViT) can capture long-range contextual relations, but it tends to fall into over-fitting for training samples. This problem is particularly acute in AIR where trainable data is insufficient. While training ViT with label smoothing can alleviate this problem, it is easy to pose the risk of underfitting and relies heavily on data quality. Furthermore, the application of ViT in AIR has always been limited because aerial images generally have greater intra-class variation and inter-class similarity than natural images. Thus, we propose a ViT-based framework called D-BiT to extend ViT to learn discriminative features in aerial images by integrating two heads on the ViT backbone. One is a classifier head with the Discriminative Label Smoothing Module (DLSM) to fully learn effective features without overfitting the distribution of noisy data, even in the condition of insufficient data volume. Another is a projector head with a newly-designed supervised contrastive loss to effectively combine label information and achieve a more compact and reasonable intra-class structure. Experimental results on three popular AIR benchmarks demonstrate the effectiveness of D-BiT: Aerial Image Dataset (AID), UC Merced and Event Recognition in Aerial videos (ERA), where we achieve state-of-the-art (SOTA) performance. In particular, D-BiT can significantly improve the accuracy of ViT for recognizing aerial images.
Biobjective route planning of an unmanned air vehicle in continuous space

We consider the route planning problem of an unmanned air vehicle (UAV) in a continuous space that is monitored by radars. The UAV visits multiple targets and returns to the base. The routes are constructed considering the total distance traveled and the total radar detection threat objectives. The UAV is capable of moving to any point in the terrain. This leads to infinitely many efficient trajectories between target pairs and infinitely many efficient routes to visit all targets. We use a two stage approach in solving the complex problem of finding all efficient routes. In the first stage, we structure the nondominated frontiers of the efficient trajectories between all target pairs. For this, we first identify properties shared by efficient trajectories between target pairs that are protected by a radar. This helps to structure the nondominated frontier between any target pair by identifying at most four specific efficient trajectories. We develop a search-based algorithm that finds these efficient trajectories effectively. For the second stage, we develop a mixed integer nonlinear program that exploits the structured nondominated frontiers between target pairs to construct the efficient routes. We compare the nondominated front we generate in the continuous space with its counterpart in a terrain discretized with three different grid fidelities. The continuous space representation outperforms all discrete representations in terms of solution quality and computational times.