Reviewerly/exHarmony huggingface dataset:
Face-to-camera distance estimation using machine learning

Distance estimation of moving objects from the camera with accuracy is a challenging task in the digital era where human interaction increases with smart systems and state-of-the-art applications. The wide-ranging applications of distance estimation include the “zooming” effect in a document reader and finding the power of the correction lens for eyesight (eyesight testing). A lot of research has already been done on different methods of distance estimation like the most widely-used methods of "mono-vision" and "stereo-vision". The purpose of this study is to introduce a novel approach to finding the distance between the face and the camera with a high degree of accuracy and speed. The proposed method is based on detecting, measuring, and calculating the size of irises on an image of a human face obtained from a single camera, and a Supervised Machine Learning algorithm. The open-source Mediapipe Python package has been employed to extract the irises from the images. The proposed method has given the results of the estimated distance with an average accuracy of 95.6%.
Visual-Lexical Alignment Constraint for Continuous Sign Language Recognition

The aim of Continuous Sign Language Recognition (CSLR) is to recognize unsegmented signs from image streams and convert them to gloss sequences. Data collection and annotation of sign language is costly, most recent CSLR works handle this problem in a weakly supervised manner and adopt network architectures consisting of a feature extractor and an alignment module. In their design, the gloss-level semantic feature is used to align the gesture-level representational feature, which may suppress the activation of non-key frames. To further enhance the generalization of the visual extractor, this paper proposes a Visual-Lexical Alignment Constraint (VLAC) with an improved self-distillation based alignment supervision. Specifically, the proposed model adopts a visual-lexical module to supervise the visual and the lexical modules separately. In addition, we modify the traditional lexical extractor from Bi-LSTM to lite-transformer encoder, which is beneficial in terms of parallelization and computational efficiency. Experimental results on the RWTH-PHOENIX-Weather-2014 dataset show that the proposed model exceeds the current state-of-the-art models.
Size Measurement of Stone Images Based on Improved UNet

To address the current problem of calculating stone grain size in the field of sand and gravel aggreg es, image segmentation of stone targets is achieved by stone images, and the grain length of stone targets is finally obtained. By pre-processing the target stone images, the pre-processed stone images are segmented and predicted using deep learning image processing techniques, and the predicted result maps are subjected to morphological and image binarization operations for subsequent stone particle size calculation. The algorithm is implemented to delineate the assignment of individual stone regions and to find the boundary coordinate points of individual stone image regions, and to calculate the image grain size length of stones from them. The true grain length of the stone is calculated by the proportional mapping relationship between the camera and the pixel length of the stone taken and the real stone length. Through experiments, this operation procedure can segment and calculate the grain length of stones quickly and accurately.
Movie Recommendation System with Sentimental Analysis Using Cosine Similarity Technique

Recommender systems importance arises in such situations when each online service provider always aims at having many clients. Movie recommendation system is one of the top research areas, currently. It helps the movie enthusiasts by suggesting what movie to watch without going into such a long way to opt from such a large database of movies that is much time consuming and confusing. In this paper, a content-based movie recommendation system is proposed. Along with that, the system is also equipped with sentimental analysis where the sentiments of dynamic users come and helpful for suggesting content-based movies. More importantly, the model is implemented by using the cosine similarity technique, which is highly used in the recommendation systems. Cosine Similarity is such a powerful algorithm that it also has some part use in Netflix. Cosine similarity helps in driving out the ratings of users and aiming to build a powerful recommendation system based upon User based recommender, Item based and Genres recommender. In this model, we proposed the solution of recommending movies with the help of sentiments because movies were previously recommended with the help of user ratings and genres in the content bases system. Although, this model is too eloquent to recommend movies by employing the data of crew members, directors plus main lead top cast actors. Datasets for movie sets are considered from TMDB. However, the movie ratings, genres, release date, runtime and status are scrapped from IMDB. In short, this system is way too good to provide low-rated movie recommendations and predictions according to available datasets.
Image Retrieval Based on Dynamic Weighted Patterns

In this paper we present Dynamic Pattern Weighting (DPW), a novel method for Content-Based Image Retrieval (CBIR). This method has the capability to reduce the semantic gap by giving dynamically an appropriate weight to each pattern of the image according to the image class and the importance of the pattern in the image. After an offline optimization phase using a metaheuristic algorithm, a weight vector is obtained for each class of the image database. Thereafter, to choose the proper weight vector for the query image, an assumed class is determined by applying K-nearest neighbors algorithm. Furthermore, for each individual pattern a different importance is determined adaptively, depending on the occurrences number of the pattern in the image. The proposed method has been evaluated using four local pattern methods to extract image texture features. Experiments on Corel-1K database reveals that the performance of the dynamic weighted methods outperforms the other methods.
U-Shaped Transformer-Based 360-Degree No Reference Image Quality Assessment

Thanks to creative rendering and display techniques, 360-degree images can provide a more immersive and interactive experience for streaming users. However, such features make the perceptual characteristics of 360-degree images more complex than those of fixed-view images, making it impossible to achieve a comprehensive and accurate image quality assessment (IQA) task using a simple stack of pre-processing, post-processing, compression, and rendering tasks. In order to thoroughly learn global and local features in 360-degree images, reduce the complexity of multichannel neural network models and simplify the training process, this paper proposes a user-aware joint architecture and an efficient converter dedicated to 360-degree no-reference (NR) IQA. The input of the proposed method is a 360-degree cubic mapping projection (CMP) image. In addition, the proposed 360-degree NRIQA method includes a non-overlapping self-attentive selection module based on a dominant map and a feature extraction module based on a U-shaped transformer (U-former) to address perceptual region significance and projection distortion. The transformer-based architecture and the weighted averaging technique are jointly used to predict local perceptual quality. Experimental results obtained on widely used databases show that the proposed model outperforms other state-of-the-art methods in the case of NR 360-degree image quality assessment. In addition, cross-database evaluation and ablation studies demonstrate the intrinsic robustness and generalization of the proposed model.
Particle swarm optimization algorithm with dual population adaptive mutation

The particle swarm optimization (PSO) algorithm has the characteristics of fewer parameters, fast convergence speed and easy implementation. However, the algorithm has the problem of low accuracy and tends to fall into local optimization. In this paper, a particle swarm optimization algorithm with dual population adaptive mutation (DPAMPSO) is proposed. In this algorithm, the whole population is divided into a general subpopulation and an elite subpopulation. In order to balance the global exploration and local exploitation ability of particles, a chaos-based inertia weight is proposed in this paper. Among them, the ordinary subpopulation uses an adaptive mutation strategy. By setting the probability parameter to make the common subpopulation randomly choose to mutate in the direction of the superior particle or still keep its own direction. The elite subpopulation then uses an exemplar learning strategy. The historical optimal and global optimal individuals of the particles are crossed to generate examples, thus guiding the particles to perform a local search for the region of possible solutions. To verify the effectiveness of the algorithm, the DPAMPSO algorithm is tested on ten benchmark problem. It is also compared with other recent variants of particle swarm optimization algorithms. The experimental results show that the DPAMPSO algorithm is superior to other variations of PSO in terms of solving accuracy and convergence speed.
Flow of Information in Hopfield Neural Networks

Deep learning and neural networks have been used in various machine learning applications in the past decades. Developing a precise understanding of the underling behavior of neural networks is crucial for their efficient deployment. In this paper, we use an information theoretic approach to study the flow of information in a discrete Hopfield neural network. While discrete Hopfield networks, are used as associative memories, study of their dynamic behavior is important in general, as they represent the family of recurrent neural networks. We determine lower and upper bounds for the entropy and the conditional entropy. We also study the mutual information between the input patterns and the final output of the network. Experimental results support the theoretical conclusions of the paper.
Real-time Violence Detection using Deep Learning Techniques

The subject of violence detection plays a significant role in tackling threats and abuses in society. It is the key element of any security enforcing system. The widespread deployment of video surveillance has facilitated the law enforcement agencies to visually monitor environments and take prompt action in case of any alerting situation. This task requires manual interaction for continuously overseeing the live streams of CCTVs. This paper presents an efficient approach for detecting violence in real-time using different deep learning methods which diminishes the element of human supervision to a higher extent. The existing research on the topic of violence detection using machine learning is either based on specially created videos or immensely relies upon less accurate algorithms and infeasible assumptions. The presented system in the paper is premised on a hybrid approach of employing different algorithms for assessing all distinct aspects of the problem in a viable and effective manner. The proposed system is reliant upon YOLO for real-time object detection and Long Short-Term Memory for developing the classification module. DeepSort algorithm in the proposed approach further augments the efficiency. The model was trained using a relevant violence detection dataset and integrated with different software frameworks for enhancing the interface. As an outcome of the paper, we developed a fully-fledged violence detection system based on deep learning algorithms which passed different tests and evaluations.
Stochastic Sensitivity Regularized Autoencoder for Robust Feature Learning

We present a new regularized autoencoder for robust feature learning. The regularization, implying stochastic sensitivity, is defined as the sum of entries of the absolute covariance matrix of the output perturbation at each layer of the autoencoder. The advantages of the stochastic sensitivity regularization are two-fold. Firstly, we show that the classical Frobenius norm regularization effectively enforces the network to be insensitive to input perturbation and that the Frobenius norm regularization is a special case of the proposed stochastic sensitivity regularization which enables the proposed method to train an autoencoder for robust feature learning. Secondly, we also show that the stochastic sensitivity regularization attempts to drive the network to learn a set of decorrelated feature maps which removes redundant information and thus improves generalization capabilities. These two properties enable the autoencoder to learn a set of robust and diverse feature maps. Finally, the efficacy and the robustness of the proposed regularization method are confirmed a nd quantified by comparing it against existing regularized auto encoders over a range of tasks.
Application of artificial intelligence in the banking sector

Artificial intelligence has been attracting the attention of the scientific and professional public for decades. Today, in the digital age, it is more and more present in business and projects. The creation and implementation, but also the use of innovations created as a result of working with artificial intelligence, requires a multidisciplinary approach. One such innovation is REA Raiffeisen Bank's Electronic Assistant. REA has been created on the Weaver platform and uses certain artificial intelligence-based solutions, participating in the service business and communicating in a manner that respects the key points of the same. Simple and communicative for the user, REA is an example of the complexity that is a prerequisite for its creation and implementation. Therefore, this set of knowledge and skills, as well as the final outcomes of work on this project, are the inspiration both for work on such and similar innovations, and on the effects that can be monitored by research. Analyses show that business communication is dominated by telephone conversation, e-mail, website, while the Chatbot's share in business communication shows a score of 15%. The Chatbot is expected to be used in emergencies, when quick responses are needed: in resolving complaints, getting comprehensive answers, finding assistants to help people, booking hotels or restaurants, paying bills, etc. Although only partly present (15%), the Chatbot managed to attract the attention of the business public. The mentioned case study and desk analysis applied in this paper offer arguments which prove that the use of innovative systems in banking, based on artificial intelligence, is justified. Cyber security (trust) is the basic support in preserving privacy of the actors operating in that environment. Since security is the basic support for an innovative approach, the paper discusses the ISO 27001 international standard for the protection and security of information, which implements the Information Security Management System.
Virtual Grid Mapping for Visual Size Measurements

This paper proposes a novel, more robust method for single-image visual size measurements, named Virtual Grid Mapping (VGM). VGM requires just a single image as input; however, it does not require any prior information concerning the scene like the horizon line or reference objects. Unlike current methods, the VGM approach uses a grid of virtual 3D points projected to the 2D image plane, along with the fusion of probable depth values indicated by 2D-3D point correspondences, it manages to reduce the uncertainty originating from the calibration and the positioning of the camera; thus, providing more accurate measurements. Given the geometric properties of the camera, a novel approach of VGM is that it automatically generates and projects a grid of virtual 3D points to the 2D image plane, enabling the establishment of approximative correspondences between 3D points of the real world and 2D points of the image plane. Then, by considering these initial 3D-2D point correspondences as known, a range of possible depth values is estimated, and through an adaptive fusion process, the size of an object of interest can be accurately approximated. Another advantage of VGM over the state-of-the-art deep learning -based methods, is that it requires a simple training process. Experiments performed on simulated and real image datasets, captured in controlled conditions and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-the-wild</i> , show that the mean absolute percentage error (MAPE) of the proposed method ranges between 3.13% and 11.66%.
Multisensor Anomaly Detection and Interpretable Analysis for Linear Induction Motors

In this paper, a graph neural network anomaly detection framework is proposed to improve the safety of linear induction motors, a key component of high-speed maglev trains. In our framework, each sensor sequence is treated as a separate feature. The similarity and correlation between multi-dimensional features are learned as prior knowledge for graph structure learning. The spatial-temporal graph attention network incorporates prior knowledge to learn complex correlations between nodes. Furthermore, the framework optimises a joint model for anomaly detection, avoiding the trap of falling into either local or global optimisation and thus achieving the most stable detection. Experimental results of our method on four real-world datasets show that it is more accurate than other state-of-the-art methods in detecting anomalies and capturing inter-sensor correlations. Further analysis of graph attention weights and visualization subgraphs show that our framework is well interpretable and allowing users to locate the root cause of anomalies.
Multisensor Intelligent Fall Perception Algorithm considering Precise Classification of Human Behavior Characteristics

In order to improve the accuracy and efficiency of human motion perception, a multisensor intelligent fall perception algorithm considering the precise classification of human behavior characteristics is proposed. Multisensor devices (smart watches, smart phones) collect data such as acceleration and heart rate of the human body to obtain human behavior data. On the basis of human behavior data collection, the acceleration characteristics of a falling state are extracted, and the SVM method is used to classify human behavior characteristics. Cuckoo search is used to optimize the width of the SVM kernel and improve the accuracy of human behavior recognition. Finally, based on the behavior recognition results, the intelligent perception of human falling behavior is realized through the exercise preparation potential. The experimental results show that the perceptual accuracy of this method is high, which has reached 90%, and the perception efficiency is higher. The minimum perception time is only 0.56 s, which fully verifies the effectiveness of this method. It can be widely used in human-computer interaction, machine vision, and other fields.
The Copyright Protection and Enforcement of Musical Works on Internet in Vietnam

Copyright is one branch of intellectual property rights, which is the right of organizations and individuals to the works, including the musical works. The protection of copyright for musical works for both moral rights and property rights is very necessary for the economic development. In the development of Industrial Revolution 4.0, with the process of international integration and innovation, Vietnam has been launching legal rules to satisfy the copyright protection requirements from Berne Convention, Geneva Convention, Rome Convention, TRIPS, CTTPP, and EVFTA agreements, etc. However, the copyright protection is more complicated with the development of Internet. In particular, its infringement is very serious in Vietnam nowadays because the spread of data or musical works on the Internet is the transmission of susceptible information. Awareness of most Internet users and Internet service providers about copyright issues is still limited. The enforcement of intellectual property rights is still weak. Downloading songs online and using these songs without paying the author’s royalty is more serious, which harms the psychology of composers and they no longer motivate to continue composing and don’t receive enough money to develop their creations. The chapter will define firstly the copyright protection for musical works on Internet then analyse the current relevant legal rules and its enforcement of these rules in Vietnam to find out some deficiencies and recommend some solutions to overcome these challenges.
Menger or Marx? The political ontology of cryptocurrency

Abstract One of the perennial fault-lines in monetary theory is that between commodity and credit theories of money. The emergence of alternative payment systems based on blockchain and distributed ledger technologies, of which Bitcoin is the most prominent example, has raised a host of important questions in relation to this debate. This article considers two. The first is ontological: Are Bitcoin and similar ‘cryptocurrencies’ best conceived of as money? The second is political: Do these money candidates represent an emancipatory development over state-backed fiat currency? The ontological question, we will argue, invites the political one. If it is the case, as Chartalists maintain, that (i) for some X to be money it must have certain properties which can only be imparted by political authority (broadly understood) and if (ii) political authority ought to be subject to public control, then attempts by private actors to usurp a social ‘money function’ cannot count as legitimate political developments. We will argue in support of this position. This discussion is limited to Bitcoin, though its implications generalize for relevantly similar cryptocurrencies. Our method involves considering, first, claims made by Bitcoin’s defenders about its status as money, and what accounts for that status. While these claims are often thought to extend Mengerite or generally Austrian lines of economic argument, they resonate more with Marx’s theory of monetary value. Moreover, a close assessment of that theory’s defects yields specific normative conclusions that potentially undermine the notion that Bitcoin constitutes a valid means of resisting state monetary authority.
Selective and Efficient Quantum Process Tomography for Non-Trace-Preserving Maps: Implementation with a Superconducting Quantum Processor

Alternatively to the full reconstruction of an unknown quantum process, the so-called selective and efficient quantum process tomography (SEQPT) allows estimating, individually and up to the required accuracy, a given element of the matrix that describes such an operation with a polynomial amount of resources. The implementation of this protocol has been carried out with success to characterize the evolution of a quantum system that is well described by a trace-preserving quantum map. Here, we deal with a more general type of quantum process that does not preserve the trace of the input quantum state, which naturally arises in the presence of imperfect devices and system-environment interactions, in the context of quantum information science or quantum dynamics control. In that case, we show with the aid of a priori information on the losses structure of the quantum channel that the SEQPT reconstruction can be adapted to reconstruct the non-trace-preserving map. We explicitly describe how to implement the reconstruction in an arbitrary Hilbert space of finite dimension $d$. The method is experimentally verified on a superconducting quantum processor provided by IBM Quantum services, by estimating several non-trace-preserving quantum processes in dimensions up to $d=6$. Our results show that it is possible to efficiently reconstruct non-trace-preserving processes, with high precision, and with significantly higher fidelity than when the process is assumed to be trace preserving.