Reviewerly/exHarmony huggingface dataset:
Multiple Face Detection Tracking and Recognition from Video Sequence

Face detection and recognition are a cutting-edge biometric technology. Numerous approaches and systems have been extensively studied in this subject. Face recognition is gaining popularity, and the majority of us use it without recognizing it. This detects many faces from a video sequence that has been tracked and identified. Face detection is performed here using the Viola–Jones technique and neural networking. By integrating the Viola–Jones approach with a neural network, it is possible to determine the computing time and develop a resilient algorithm (this system will adapt and learn new data very fast and system more robust). The system becomes more robust and capable of detecting more faces, while reducing false positives. Neural networking is a method for detecting faces that is image-based. For image tracking, the KLT tracking algorithm is employed, and for recognition, the eigenface detection approach is used. The purpose of this study is to detect many faces in a video sequence, follow the face and recognize it.
Query Complexity of Unitary Operation Discrimination

Discrimination of unitary operations is fundamental in quantum computation and information. A lot of quantum algorithms including the well-known DeutschJozsa algorithm, Simon’s algorithm, and Grover’s algorithm can essentially be regarded as discriminating among individual, or sets of unitary operations (oracle operators). The problem of discriminating between two unitary operations U and V can be described as: Given X ∈ { U,V }, determine which one X is. If X is given with multiple copies, then one can design an adaptive procedure that takes multiple queries to X to output the identification result of X . In this paper, we consider the problem: How many queries are required for achieving a desired failure probability ϵ of discrimination between U and V . We prove in a uniform framework: (i) if U and V are discriminated with bound error ϵ , then the number of queries T must satisfy, and (ii) if they are discriminated with one-sided error ϵ , then there is, where Θ( W ) denotes the length of the smallest arc containing all the eigenvalues of W on the unit circle.
Dialog Act Segmentation and Classification in Vietnamese

Natural Language Understanding (NLU) is a critical component in building a conversational system. So far, most systems have processed the user inputs at the utterance-level and assumed single dialog act (DA) per utterance. In fact, one utterance might contain more than one DA which are denoted by different continuous text spans inside it (a.k.a functional segments). As a step towards achieving natural and flexible interaction between human and machine especially in poor-resource languages, this paper presents a work for dialog segmentation (DS) and DA classification in Vietnamese. We first introduce the corpus and then systematically investigate different pipeline and joint learning approaches to deal the two tasks. Experimental results show that the joint learning approach is superior in boosting the performance of both tasks. It outperforms the conventional pipeline approach which looked at the two tasks separately. Moreover, to further enhance the final performance, this paper proposes a technique to enrich the models with useful DA knowledge. Compared to the standard models which don’t use DA knowledge, we achieve considerably better results for two tasks. Specifically, we achieved an F1 score of 86% in segmenting dialogues, and an F1-micro score of 74.75% in classifying DAs. This provides a strong foundation for future research on this interesting field.
ExMo: Explainable AI Model Using Inverse Frequency Decision Rules

In this paper, we present a novel method to compute decision rules to build a more accurate interpretable machine learning model, denoted as ExMo. The ExMo interpretable machine learning model consists of a list of IF...THEN... statements with a decision rule in the condition. This way, ExMo naturally provides an explanation for a prediction using the decision rule that was triggered. ExMo uses a new approach to extract decision rules from the training data using term frequency-inverse document frequency (TF-IDF) features. With TF-IDF, decision rules with feature values that are more relevant to each class are extracted. Hence, the decision rules obtained by ExMo can distinguish the positive and negative classes better than the decision rules used in the existing Bayesian Rule List (BRL) algorithm, obtained using the frequent pattern mining approach. The paper also shows that ExMo learns a qualitatively better model than BRL. Furthermore, ExMo demonstrates that the textual explanation can be provided in a human-friendly way so that the explanation can be easily understood by non-expert users. We validate ExMo on several datasets with different sizes to evaluate its efficacy. Experimental validation on a real-world fraud detection application shows that ExMo is $$\approx $$ 20% more accurate than BRL and that it achieves accuracy similar to those of deep learning models.
CV Based Person Detection System for Smart Transportation

This paper describes an implementation method for a person counter system which detects people using a fixed single camera. Here the system counts the number of people sitting in a transportation bus and displays the count at the next station so that the crowd can be reduced significantly. This computer vision based crowd detection system updates the real-time seat availability in the public buses. Also maintaining the live tracking can cost a lot of money. Our solution provides the answers of these queries. Using computer vision based automated techniques we are receiving optimum output.
The Integration and Optimization of British and American Literary Information Resources Based on Web 3.0

The rapid development of information technology is an important context for the development of contemporary literature and culture. Based on Web 3.0, this paper analyzes the necessity and feasibility of the optimization and integration of British and American literature curriculum resources for English Majors by using mash up, open ID, open API, semantic web technology, intelligent search, cloud computing and other technologies. Relying on Web 3.0 technology, this paper constructs the curriculum resource platform of British and American literature, which can make the construction of curriculum resources of British and American literature systematic, scientific and personalized, so as to change the lagging and messy status of teaching resources of British and American literature.
Ambient Light Tolerant Laser-Pen Based Interaction with Curved Multi-projector Displays

Large multi-projector displays allow users to be immersed in the data at a grand scale without wearing AR/VR headsets. There has been tremendous advancement in automated projection mapping that uses multiple feedback cameras to stitch and blend images from multiple projectors on curved surfaces of different shapes and sizes. Yet, there is still no easy way to interact with such displays. In this paper, we present a scalable technique that uses the same feedback cameras used for projection mapping to enable multi-user interaction with these displays using multi-colored laser-pens. We also devise methods to achieve the projection mapping and the laser based interaction in the presence of ambient light. This results in an interactive multi-projector system that can be set up and used in an interactive manner in regular illuminated conditions without any disruptions.
Divergence of an Observed User Profile and a Simulated Real State of User Due to Social Communication

In this paper, we extend our previous modular model of a social collective with hierarchical knowledge structure, and we propose an additional step in tuning information retrieval systems after dataset experiments and before testing them with real users. For this purpose, we add a simulation of the group of users as a social collective model and run the information retrieval system on it. Here, we take first steps in that direction, by focusing on a subsystem of a recommender system and simulating what type of social collective it is most effective with. We present details on the social collective model and the information retrieval subsystem model, as well as how to put them together in one simulation. We run several experiments and present some initial findings. In our opinion, this overall approach could be used to greatly enhance further tests with real users.
Toffa-Das: Design-Time Trade-Off Analysis for Dynamically Adaptable Software

Context: Contemporary software systems, known as Dynamic Adaptive Systems (DAS), sense the changes in the surrounding environment, and promote self-adaptations accordingly. To accomplish this, such systems must then deal with both system and context variability from design time to runtime. Different approaches have been proposed to deal with the configuration selection problem of this kind of system. However, most of the existing studies do not consider the satisfaction of contexts and Non-functional requirements (NFR) at the same time. Objective: This paper presents an approach of trade-off analysis for DAS at design-time, named ToffA-DAS. It uses the Integer Linear Program (ILP) technique to deal with the configuration selection process considering interactions between NFRs and contexts. In addition, ToffA-DAS enables software engineers to identify design faults and define adaptation models from optimal configurations suggested by the trade-off analysis.Method: We also propose a new release of our approach, called ToffA-DAS+. It uses a genetic algorithm (GA) to ensure configurations that satisfy the DAS constraints and optimize the satisfaction of NFRs. In terms of evaluation, we performed a simulation-based exploratory study to compare both releases: ToffA-DAS and Toffa-DAS+.Results: The configurations generated by the ToffA-DAS execution provide higher satisfaction levels of NFRs than those generated by the ToffA-DAS+ execution. On the other hand, ToffA-DAS+ provides more optimal solutions and they are more balanced concerning the positive and negative contributions to the NFRs.Conclusion: The evidence gathered shows that ToffA-DAS+ generates a bigger set of configurations, providing, thus, more optimal solutions to meet the requirements specification, and needs of their stakeholders. This variety of configurations is essential to leverage the DAS adaptability while satisfying multiple and even conflicting NFRs.
Platform System and Method of Cloud Component Framework Configuration in Computer Software System

With the continuous popularization of cloud computing, more and more users begin to run applications on cloud platforms. These applications on cloud platforms are usually composed of distributed components deployed on many virtual hosts. In this architecture, it is quite complex to dynamically deploy, configure and maintain applications on cloud platforms. However, the mainstream technologies now pay more attention to static deployment, Lack of effective support for application dynamic management on cloud platform. In view of the deficiency of the existing work on the cloud platform application dynamic management support, we combine the model driven engineering technology and configuration management technology, through modeling the application on the cloud platform and manipulating its run-time model, realize the model-based cloud application dynamic management system, and deploy and manage the mainstream distributed applications, and verify the effectiveness of the method.
Deep Learning-based Out-of-Distribution Detection and Recognition of Human Activities with IMU Sensors

Human activity classification has several applications in smart homes, healthcare and human-machine interaction systems. Several human activity recognition systems using IMU sensors, namely accelerometer and gyroscope, have been proposed in literature. In this paper, we propose an improved integrated solution involving two stages with detection network followed by recognition network for classification of human activities. In a practical deployed environment, the IMU sensor is expected to encounter out-of-distribution (OoD) samples due to sensor degradation, alien operating environment or unknown activities. To handle such adverse examples we propose to use generalized out-of-distribution detector for Neural Networks (ODIN), which also acts to engage the following recognition network only when a valid activity example is encountered leading to power saving. Furthermore, compared to conventional cross-entropy based loss function, we propose quadruplet-loss to train our activity recognition network leading to improved classification and clustering scores. We demonstrate the performance of our proposed solution using commercial off-the-shelf IMU sensors.
Harnessing Technology for Hospitality and Tourism

The origin of hospitality is deeply rooted in the origin of man and the nature of man. By the very nature of man, human beings need to care for and be cared for by other beings who are at the same level of dignity. The service and hospitality industry was founded on the human touch, and this must not be forgotten. Yet, certain countries already face an ageing population, followed by a younger generation not interested in pursuing roles involving repetitive tasks (Ivanov & Webster in Robots, artificial intelligence and service automation in travel, tourism and hospitality. Emerald, 2019). Therefore, technological innovation comes in handy, with peculiar needs in different countries. In making choices about adopting technology, hospitality industries could focus on protecting one core aspect of human dignity—work. The main concerns raised about adopting technology and AI relate to human work and privacy or security. In order to dissect and elucidate some of these concerns, this book chapter is an attempt to situate technology with the hospitality industry from various perspectives: historical, philosophical, socio-cultural, economic, policy, and so on, arranged around four broad themes: (1) History of Technology in the Hospitality Industry, (2) Various Aspects of Technology and their Influence in the Hospitality Industry (3) Designing Technology For the Future and the Ethical Consequences (4) Artificial Intelligence in the Hospitality Industry.The impact of this paper should prompt the reader to translate the contents to matter for intuitive thought and reflection. This is achieved with the use of study questions. The hospitality industry will continue to transform with the assistance of technology, and many decisions will have to be made by stakeholders. Hopefully, the contents therein should help the reader make decisions based on deep-rooted convictions and universal principles.﻿
Principal Component Analysis based on Correlation Matrix with Asynchronous and Noisy High Frequency Data

This paper developed the feasible estimation methodology for the principal component analysis (PCA) based on correlation matrix, which is robust to the noisy and asynchronous high frequency data. The general framework of our methodology is constructed based on the estimation of realized spectral functions with respect to the spot correlation matrix. A new asymptotic representation for the element-wise estimation error of the spot correlation matrix estimate has been derived, resulting in a new bias correction term which is much more complex than that of the PCA based covariance matrix. Central limit theorem and rate of convergence have been developed for the bias-corrected estimator. The standard error estimator has also been proposed. As the empirical study of our methodology, we have constructed the first eigen-portfolio based on the eigenvector estimate corresponding to the largest eigenvalue in the spot correlation matrix. We regress the returns of first eigen-portfolio against that of the market ETF, which obtained non-significant alpha estimate and significant beta estimate which is very close to one.
MHGEE: Event Extraction via Multi-granularity Heterogeneous Graph

Event extraction is a key task of information extraction. Existing methods are not effective due to two challenges of this task: 1) Most of previous methods only consider a single granularity information and they are often insufficient to distinguish ambiguity of triggers for some types of events. 2) The correlation among intra-sentence and inter-sentence event is non-trivial to model. Previous methods are weak in modeling interdependency among the correlated events and they have never modeled this problem for the whole event extraction task. In this paper, we propose a novel Multi-granularity Heterogeneous Graph-based event extraction model (MHGEE) to solve the two problems simultaneously. For the first challenge, MHGEE constructs multi-granularity nodes, including word, entity and context and captures interactions among nodes by R-GCN. It can strengthen semantic and distinguish ambiguity of triggers. For the second, MHGEE uses heterogeneous graph neural network to aggregating the information of relevant events and hence capture the interdependency among the events. The experiment results on ACE 2005 dataset demonstrate that our proposed MHGEE model achieves competitive results compared with state-of-the-art methods in event extraction. Then we demonstrate the effectiveness of our model in ambiguity of triggers and event interdependency.
Risks, Advantages and Obstacles Associated with Bitcoin: The Perspective of Palestinian Banks

The study aimed to identify the advantages, obstacles and risks of dealing with Bitcoin from the perspective of Palestinian banks. The study used the descriptive analytical method through a questionnaire that was prepared in accordance with previous studies and the theoretical framework. The questionnaire then distributed to the study sample consisting of 250 male and female employees working in Palestinian banks. The study reached numerous results, the most important of which are: that the advantages of Bitcoin were low, and one of its most important features is high privacy and confidentiality. While the obstacles came to a moderate degree. One of the most important obstacles is the inability to mine it. With regard to its risks, it was also moderate, the most important of which was its exposure to intense competition from other currencies. The results showed that there were no statistically significant differences for the advantages, risks and obstacles of dealing with Bitcoin due to the variable of gender, age and educational qualification. While there were statistically significant differences due to the variable of job title and experience. The study recommended the necessity of the initiative of international monetary authorities in general, especially the International Monetary Fund. This helps to take steps that provide practitioners, the business sector and banks with the rules and controls to ensure the integration of this new technology with the global monetary system. The Palestinian Monetary Authority issued educational directives showing the dangers of the bitcoin currency, while issuing mandatory instructions to prevent the circulation of this currency, until international controls are reached on it.
Video Representation Learning

Video representation learning generates visual semantic representations from given videos, which is vital for video-related tasks, including human action understanding in videos and video question answering. Video representations can be categorized into handcrafted local features and deep-learned features. Handcrafted local features are video features extracted by handcrafted formulas, and deep-learned features are extracted automatically through neural networksNeural networks. In this chapter, we discuss video representation learning from the two aspects of handcrafted features and deep architecture-generated features.
Automatic Localization of the Left Ventricle from Short-Axis MR Images Using Circular Hough Transform

The localization of the left ventricle (LV) automatically from cardiac magnetic resonance images (CMRI) is an important initial step during segmentation to quantify global and regional volumetric functions of LV such as volume, mass, and ejection fraction. The exact quantification of these indicators results in an accurate system for cardiac disease detection. Conventionally, LV localization in magnetic resonance (MR) images is implemented manually by experts, which is a tedious task and time-consuming. This paper proposes an automated localization method for LV from the cardiac short-axis MR images. The technique applies a circular Hough transform (CHT) to localize the LV with optimized performance. The results revealed that the accuracy of the proposed technique is 89.5% based on the range radius of the LV circle.
Analysis of the Relationship Between Food and the Writer's Emotions Using a Meal Diary

The purpose of this study is to gain useful marketing insights by estimating the writer's emotions from the text and clarifying the relationship between the text's characteristics and food. First, each day's emotional evaluation, taste evaluation, and the presence or absence of an event are clarified from the text, and each text is evaluated in three items. Next, the overall score is calculated from the average value of the three items, and the characteristics of sentences with high and low scores are clarified. The result of our analysis, the characteristics of the text reveal the relationship between emotions of the day and food choices.
Exact Affine Histogram Matching by Cumulants Transformation

This paper proposes an exact solution to histogram matching under affine transformation, i.e., it shows how to retrieve an unknown affine transformation between the colors of two images. The key is the use of the third central moment (skewness) in addition to covariance and mean. These three moments (a.k.a. cumulants) are sufficient to determine all the d.o.f. of an affine mapping of the RGB space.
Steganographic Approaches for Carrier Related Information Hiding

In this paper we’ll define new steganographic techniques for information hiding, which depend on the carrier. In traditional steganographic solutions the way of secret hiding is usually not dependent on the content or features of container, in which new information is placed. Most approaches modify parameters in spatial or frequency domains, and not consider local carrier parameters. Such parameters can also determine the way of secret hiding. Such techniques allow to hide particular information in different manner, especially when we consider containers with different graphical features.