Reviewerly/exHarmony huggingface dataset:
AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning

Visual events are a composition of temporal actions involving actors spatially interacting with objects. When developing computer vision models that can reason about compositional spatio-temporal events, we need benchmarks that can analyze progress and uncover shortcomings. Existing video question answering benchmarks are useful, but they often conflate multiple sources of error into one accuracy metric and have strong biases that models can exploit, making it difficult to pinpoint model weaknesses. We present Action Genome Question Answering (AGQA), a new benchmark for compositional spatio-temporal reasoning. AGQA contains 192M unbalanced question answer pairs for 9.6K videos. We also provide a balanced subset of 3.9M question answer pairs, 3 orders of magnitude larger than existing benchmarks, that minimizes bias by balancing the answer distributions and types of question structures. Although human evaluators marked 86.02% of our question-answer pairs as correct, the best model achieves only 47.74% accuracy. In addition, AGQA introduces multiple training/test splits to test for various reasoning abilities, including generalization to novel compositions, to indirect references, and to more compositional steps. Using AGQA, we evaluate modern visual reasoning systems, demonstrating that the best models barely perform better than non-visual baselines exploiting linguistic biases and that none of the existing models generalize to novel compositions unseen during training.
MedBlock: An AI-enabled and Blockchain-driven Medical Healthcare System for COVID-19

An Artificial Intelligence (AI)-enabled and blockchain-driven Electronic Health Record (EHR) maintenance system has a tremendous potential to facilitate reliable, secure, and robust storage systems for EHRs. Such an EHR system would also facilitate researchers, doctors, and government authorities to access data for research, perform analytics, and help in making well-informed decisions. The Artificial Neural Network (ANN) is employed to classify the patients as potentially COVID-19 positive and potentially COVID-19 negative based on the clinical reports and reports of CT-scan. The data of potentially COVID-19 positive patients is stored on blockchain employing InterPlanetary File System (IPFS) protocol. The accessibility of EHR can be done by authorized entities post verification and validation of entities. We analyze the performance of various AI-based algorithms employing metrics such as loss curve, accuracy, etc. for the task of predicting the patient’s potential COVID-19 infection. The 6G network significantly mitigates the network latency and reliability issues and also facilitates the real-time transmission of information. The amount of data generated is pretty high amidst this pandemic and so we employed IPFS protocol which suffices to be a cost-effective solution, moreover satisfying all are stringent requirements. At last, we evaluate the network, security, and storage performance of our architecture MedBlock, which outperformed other state-of-the-art systems.
Adapting or Changing: The COVID-19 Pandemic and Teacher Education in Russia

Adaptation of the Russian education system to the changes forced by the COVID-19 pandemic was complicated by the sheer size of the country, which entails the differentiation of the regions in terms of the quality of education, access to knowledge networks, communication resources, and top universities. Amid the pandemic, the country’s education system experienced an initial shock after the introduction of distance learning in March 2020, attempted to return to classroom learning in September 2020, and introduced blended learning in October 2020. Each stage brought about changes in organization and management of teaching and learning processes, development of online learning platforms and courses, and technological improvements. A diversified teacher education system impeded these changes. In effect, only the largest among 300 education institutions that offer teacher education programs had the necessary resources to provide high-quality distance and blended learning. Their experience could form the basis for creating a standardized model of teacher training for the purposes of blended learning. This is most probable in the context of the tight control and the top–down approach typical of the Russian education system. The article, therefore, analyzes some examples regarding teacher training during the pandemic implemented at Russian universities. These cases have the potential to become major trends that would ensure consistency of the country’s education system in extreme situations that might recur in the future.
SportsCap: Monocular 3D Human Motion Capture and Fine-Grained Understanding in Challenging Sports Videos

Markerless motion capture and understanding of professional non-daily human movements is an important yet unsolved task, which suffers from complex motion patterns and severe self-occlusion, especially for the monocular setting. In this paper, we propose SportsCap—the first approach for simultaneously capturing 3D human motions and understanding fine-grained actions from monocular challenging sports video input. Our approach utilizes the semantic and temporally structured sub-motion prior in the embedding space for motion capture and understanding in a data-driven multi-task manner. To enable robust capture under complex motion patterns, we propose an effective motion embedding module to recover both the implicit motion embedding and explicit 3D motion details via a corresponding mapping function as well as a sub-motion classifier. Based on such hybrid motion information, we introduce a multi-stream spatial-temporal graph convolutional network to predict the fine-grained semantic action attributes, and adopt a semantic attribute mapping block to assemble various correlated action attributes into a high-level action label for the overall detailed understanding of the whole sequence, so as to enable various applications like action assessment or motion scoring. Comprehensive experiments on both public and our proposed datasets show that with a challenging monocular sports video input, our novel approach not only significantly improves the accuracy of 3D human motion capture, but also recovers accurate fine-grained semantic action attribute.
NL-SHADE-RSP Algorithm with Adaptive Archive and Selective Pressure for CEC 2021 Numerical Optimization

This paper proposes a variant of the adaptive differential evolution algorithm, which combines several important concepts, including non-linear population size reduction, rank-based selective pressure in the mutation strategy, adaptive archive set usage, as well as a set of rules to control crossover rate. The developed approach is applied to solve the CEC 2021 Bound Constrained Single Objective Optimization Parametrized Benchmark problems. The performed computational experiments and their statistical analysis show that the proposed NL-SHADE-RSP algorithm is capable of demonstrating high efficiency of fining solutions to biased, shifted and rotated functions compared to other state-of-the-art algorithms, including the winners of the previous competitions.
Age Bias in Emotion Detection: An Analysis of Facial Emotion Recognition Performance on Young, Middle-Aged, and Older Adults

The growing potential for facial emotion recognition (FER) technology has encouraged expedited development at the cost of rigorous validation. Many of its use-cases may also impact the diverse global community as FER becomes embedded into domains ranging from education to security to healthcare. Yet, prior work has highlighted that FER can exhibit both gender and racial biases like other facial analysis techniques. As a result, bias-mitigation research efforts have mainly focused on tackling gender and racial disparities, while other demographic related biases, such as age, have seen less progress. This work seeks to examine the performance of state of the art commercial FER technology on expressive images of men and women from three distinct age groups. We utilize four different commercial FER systems in a black box methodology to evaluate how six emotions - anger, disgust, fear, happiness, neutrality, and sadness - are correctly detected by age group. We further investigate how algorithmic changes over the last year have affected system performance. Our results found that all four commercial FER systems most accurately perceived emotion in images of young adults and least accurately in images of older adults. This trend was observed for analyses conducted in 2019 and 2020. However, little to no gender disparities were observed in either year. While older adults may not have been the initial target consumer of FER technology, statistics show the demographic is quickly growing more keen to applications that use such systems. Our results demonstrate the importance of considering various demographic subgroups during FER system validation and the need for inclusive, intersectional algorithmic developmental practices.
AgriOnBlock: Secured data harvesting for agriculture sector using blockchain technology

The existing agriculture system is having several components such as supply chain management, crop insurance, the shipment of goods, and it involves numerous untrusted stakeholders such as users (farmers, retailers, customers, wholesalers, etc.), agencies (bank, insurance company) and regulators (assessor, surveyors). Due to the active engrossment of the middle man (mostly human beings or agencies operated by human beings) some key issues are raised such as transparency, timeliness, traceability, security, and immutability resulting in financial loss, crop contamination, and spoilage. Secure distributed public ledger technology and decentralized computing paradigm make Blockchain an appropriate alternative to resolve these issues and to achieve profitability & trust for all its stakeholders. In this research, we intend to propose a Blockchain-based mechanism viz. AgriOnBlock which would address the issues mentioned in the agriculture sector by connecting various stakeholders through the usage of IoT devices and smart contracts in Ethereum. We further discuss implications, constraints (methodological, awareness related, regulatory, etc.), and potential for actual adoption of AgriOnBlock.
Fairness in Biometrics: A Figure of Merit to Assess Biometric Verification Systems

Machine learning-based (ML) systems are being largely deployed since the last decade in a myriad of scenarios impacting several instances in our daily lives. With this vast sort of applications, aspects of fairness start to rise in the spotlight due to the social impact that this can get in some social groups. In this work aspects of fairness in biometrics are addressed. First, we introduce a figure of merit that is able to evaluate and compare fairness aspects between multiple biometric verification systems, the so-called Fairness Discrepancy Rate (FDR). A use case with two synthetic biometric systems is introduced and demonstrates the potential of this figure of merit in extreme cases of demographic differentials. Second, a use case using face biometrics is presented where several systems are evaluated compared with this new figure of merit using three public datasets exploring gender and race demographics.
Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model

The creation of digital marketing has enabled companies to adopt personalized item recommendations for their customers. This process keeps them ahead of the competition. One of the techniques used in item recommendation is known as item-based recommendation system or item–item collaborative filtering. Presently, item recommendation is based completely on ratings like 1–5, which is not included in the comment section. In this context, users or customers express their feelings and thoughts about products or services. This paper proposes a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users’ reviews and comments, without disrupting it. We have implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with [Formula: see text] accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of [Formula: see text], precision at [Formula: see text], recall at [Formula: see text] and F1-Score at [Formula: see text]. Our model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall. Results of this experiment indicate that the similarity recommendation algorithm performs better than the conventional algorithm and enhances recommendation accuracy.
Blockchain-Enabled Tripartite Anonymous Identification Trusted Service Provisioning in Industrial IoT

The integration of Internet of Things (IoT) and industry reveals the industrial manufacturing developments, resulting in Industry IoT (IIoT), which is to provide a general interconnect system for the access of various industry devices. However, as the amount and type of terminal increase, the creditability and privacy protection of terminal devices are hard to be guaranteed in IIoT, since the data and digital identity of access devices are nearly transparent for more devices in networks. It is a critical issue for the security of IIoT whether the access and service of device are trustworthy. In this article, we present a novel private blockchain-enabled trusted anonymous access (BlockTrust) architecture for IIoT, where the distributed cloud radio and optical access networks (C-RONs) are considered to provide a risk reduction of privacy leakage. Based on the BlockTrust architecture, a blockchain-enabled tripartite anonymous identification trusted service provisioning (TriTrustServ) scheme is further proposed to guarantee a balanced tradeoff among the credibility, confidentiality, and efficiency in IIoT, including digital identity generation, anonymous access identification, and trusted resource provisioning. Note that for the sake of a high credibility in IIoT networks, a tripartite authentication is presented in this article with the first time among device manufacturer, devices, and network operator for the access process of device in IIoT networks. The feasibility and efficiency of BlockTrust architecture are experimentally verified in the realistic testbed, and the performances of the TriTrustServ scheme are evaluated by comparing with two benchmark schemes in the terms of average mistrust rate, resource utilization, and identification cost.
Hidden Cosets and Applications to Unclonable Cryptography

In 2012, Aaronson and Christiano introduced the idea of hidden subspace states to build public-key quantum money [STOC '12]. Since then, this idea has been applied to realize several other cryptographic primitives which enjoy some form of unclonability. In this work, we propose a generalization of hidden subspace states to hidden coset states. We study different unclonable properties of coset states and several applications:
Detection and Classification of Different Weapon Types Using Deep Learning

Today, with the increasing number of criminal activities, automatic control systems are becoming the primary need for security forces. In this study, a new model is proposed to detect seven different weapon types using the deep learning method. This model offers a new approach to weapon classification based on the VGGNet architecture. The model is taught how to recognize assault rifles, bazookas, grenades, hunting rifles, knives, pistols, and revolvers. The proposed model is developed using the Keras library on the TensorFlow base. A new model is used to determine the method required to train, create layers, implement the training process, save training in the computer environment, determine the success rate of the training, and test the trained model. In order to train the model network proposed in this study, a new dataset consisting of seven different weapon types is constructed. Using this dataset, the proposed model is compared with the VGG-16, ResNet-50, and ResNet-101 models to determine which provides the best classification results. As a result of the comparison, the proposed model’s success accuracy of 98.40% is shown to be higher than the VGG-16 model with 89.75% success accuracy, the ResNet-50 model with 93.70% success accuracy, and the ResNet-101 model with 83.33% success accuracy.
APPLICATIONS OF GUDERMANNIAN NEURAL NETWORK FOR SOLVING THE SITR FRACTAL SYSTEM

This study is related to explore the Gudermannian neural network (GNN) for solving a nonlinear SITR COVID-19 fractal system by using the optimization efficiencies of a genetic algorithm (GA), a global search technique and sequential quadratic programming (SQP) and a quick local search scheme, i.e. GNN-GA-SQP. The nonlinear SITR COVID-19 fractal system is dependent on four collections: “susceptible”, “infected”, “treatment” and “recovered”. For the optimization procedures through the GNN-GA-SQP, a merit function is constructed using the nonlinear SITR COVID-19 fractal system and its corresponding initial conditions. The description of each collection of the nonlinear SITR COVID-19 fractal system is provided along with comprehensive detail. The comparison of the achieved numerical result performances of each collection of the nonlinear SITR COVID-19 fractal system is performed with the Adams results to verify the exactness of the designed computational GNN-GA-SQP. The statistical processes based on different operators are presented for 30 independent trials using 5 neurons to authenticate the consistency of the designed computational GNN-GA-SQP. Moreover, the graphs of absolute error (AE), performance indices, and convergence measures along with the boxplots and histograms are also plotted to check the stability, exactness and reliability of the designed computational GNN-GA-SQP.
Using M-ary decomposition and virtual bits for visually meaningful image encryption

A visually meaningful image encryption algorithm is presented. More specifically, a strategy of M-ary decomposition is first presented to be used to represent a positive integer in multi base. Then the concept of virtual bits is presented and the relationship between it and the M-ary decomposition is discussed. Based on the M-ary decomposition strategy, a non-standard image pre-encryption algorithm is proposed where each pixel value of the pre-encrypted image is limited to a specified range thus saving a small amount of virtual bits which can also be used to improve the visual quality of cipher images. A Generalized Embedding Model is proposed, where the Generalized-LSB (G-LSB) method is used to embed virtual bits into the host domain and an error-correcting strategy is also presented. The optimal static virtual embedded bits (SVEB) are found and a set of rules for dynamic virtual embedded bits (DVEB) are constructed. Experimental simulations show that the visual quality of cipher images is better than that using existing visually meaningful image encryption algorithms.
Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs

Gas condensate reservoirs display unique phase behavior and are highly sensitive to reservoir pressure changes. This makes it difficult to determine their PVT characteristics, including their condensate viscosity, which is a key variable in determining their flow behavior. In this study, a novel machine learning approach is developed to predict condensate viscosity in the near wellbore regions (μc) from five input variables: pressure (P), temperature (T), initial gas to condensate ratio (RS), gas specific gravity (γg), and condensate gravity (API). Due to the absence of accurate recombination methods for determining μc machine learning methods offer a useful alternative approach. Nine machine learning and hybrid machine learning algorithms are evaluated including novel multiple extreme learning machine (MELM), least squares support vector machine (LSSVM) and multi-layer perceptron (MLP) and each hybridized with a particle swarm optimizer (PSO) and genetic algorithm (GA). The new MELM algorithm has some advantages including 1) rapid execution, 2) high accuracy, 3) simple training, 4) avoidance of overfitting, 5) non-linear conversion during training, 6) no trapping at local optima, 6) fewer optimization steps than SVM and LSSVM. Combining MELM with PSO, to find the best control parameters, further improves its performance. Analysis indicates that the MELM-PSO model provides the highest μc prediction accuracy achieving a root mean squared error (RMSE) of 0.0035 cP and a coefficient of determination (R2) of 0.9931 for a dataset of 2269 data records compiled from gas-condensate fields around the world. The MELM-PSO algorithm generates no outlying data predictions. Spearman correlation coefficient analysis identifies that P, γg and Rs are the most influential variables in terms of condensate viscosity based on the large dataset studied.
Boosting convolutional image captioning with semantic content and visual relationship

Image captioning aims to display automatically the natural language sentence for the image by the computer, which is an important but a challenging task which covers the fields of computer vision and natural language processing. This task is dominated by Long-short term memory (LSTM) based solutions. Although many progresses have been made based on LSTM in recent years, the model based on LSTM relies on serialized generation of descriptions, which cannot be processed in parallel and pay less attentions to the hierarchical structure of the captions. In order to solve this problem, we propose a framework using a CNN-based generation model to generate image captions with the help of conditional generative adversarial training (CGAN). Furthermore, multi-modal graph convolution network(MGCN) is used to exploit visual relationships between objects for generating the captions with semantic meanings, in which the scene graph is used as the bridge to connect objects, attributes and visual relationship information together to generate better captions. Extensive experiments are conducted on MSCOCO database and the results show that our method could achieve better or comparable scores compared with state-of-the-art methods. Ablation experimental results show that CGAN and MGCN can reflect a better visual relationships between objects in image and thus can generate better captions with richer semantic content.