Reviewerly/exHarmony huggingface dataset:
End-to-End Binaural Speech Synthesis

In this work, we present an end-to-end binaural speech synthesis system that combines a low-bitrate audio codec with a powerful binaural decoder that is capable of accurate speech binauralization while faithfully reconstructing environmental factors like ambient noise or reverb. The network is a modified vector-quantized variational autoencoder, trained with several carefully designed objectives, including an adversarial loss. We evaluate the proposed system on an internal binaural dataset with objective metrics and a perceptual study. Results show that the proposed approach matches the ground truth data more closely than previous methods. In particular, we demonstrate the capability of the adversarial loss in capturing environment effects needed to create an authentic auditory scene.
Fake profile identification using ANN

Social life of everyone has become linked with online social networks in today&apos;s generation. These websites have drastically affected the way we do our social lives. Making new friends and staying in touch with them has become much easier. However, as a result of their rapid growth, plenty of new issues have emerged, including malicious users, fraudulent profiles, and online impersonation. There are no practical ways for resolving these issues. In this research paper, we use an artificial neural network to detect fake profiles automatically and effectively. We determine the risk that a Facebook friend request is genuine or not. This can be used by online social networks with millions of profiles that can&apos;t be manually reviewed.
PF-MOT: Probability Fusion Based 3D Multi-Object Tracking for Autonomous Vehicles

3D Multi-Object Tracking (MOT) plays a crucial role in efficient and safe operation of automatic driving, especially in scenarios of occlusion or poor visibility. Most 3D MOT methods leverage only positional distance, which is insufficient for scenes with high density of objects or drastic changes in the motion state. In order to address this, we propose a new 3D MOT model which fuses information pertaining to positional distance and geometric similarity. Our proposed solution comprises of four parts: a) a feature extraction mechanism integrated into a commonly used detector to extract individual features for each detection, b) computation of two distance matrices based on Euclidean distance and feature similarity, c) conversion of the distance matrices to probability matrices by a cluster based Earth-Mover Distance (EMD) algorithm, and d) a data association method that fuses both sources to boost the tracking accuracy. Our proposed model demonstrates state-of-the-art performance on the nuScenes tracking dataset, with extensive experiments attesting to an improved tracking accuracy over baselines that operate solely on positional distance.
Incremental Translation Averaging

Translation averaging is known to be more difficult than rotation averaging due to scale ambiguity, estimation sensitivity, and solution uncertainty. Existing approaches have exposed their limitations in terms of accuracy, robustness, simplicity, or efficiency. To tackle this tough problem, a simple yet effective translation averaging pipeline, termed as Incremental Translation Averaging (ITA), is proposed in this paper. It combines the advantages of high accuracy and robustness in incremental parameter estimation pipeline and the advantages of high simplicity and efficiency in global motion averaging approach. Unlike the traditional translation averaging methods which estimate all the absolute camera locations simultaneously and suffer from inaccuracy in parameter estimation and incompleteness in scene reconstruction, our ITA computes them novelly in an incremental way with higher accuracy and robustness. Thanks to the introduction of incremental parameter estimation thought into the translation averaging pipeline, 1) our ITA is robust to measurement outliers and accurate in parameter estimation; and 2) our ITA is simple and efficient because of its less dependency on complicated optimization, carefully-designed preprocessing, or additional information. Comprehensive evaluations on the 1DSfM dataset demonstrate the effectiveness of our ITA and its advantages over several state-of-the-art translation averaging approaches.
Hyper-Parameter Optimization in Support Vector Machine on Unbalanced Datasets Using Genetic Algorithms

Hyper-parameter optimization and class imbalance are two challenging problems for machine learning in many real-world applications. A hyper-parameter is a parameter whose value is used to control the learning process and it has to be tuned in order to reach good performance. The class imbalance occurs when one class contains significantly fewer instances than the other class. Common approaches for dealing with the class imbalance problems involve modifying the data distribution or modifying the classifier. This paper presents an optimization framework that considers two evaluation measures, i.e., accuracy and G-mean, by optimizing a cost-sensitive Support Vector Machine and its hyper-parameter by a genetic algorithm. Experimental results on two benchmark datasets show that the proposed method is effective and efficient in comparison with the commonly used grid search method.
Application of Forensic Audio-Video Steganography Technique to Improve Security, Robustness, and Authentication of Secret Data

Steganography is the art to conceal any type of secret data into digital media. Its main aim is to maintain data security and authentication. Existing data embedding approaches does not provide information security, authentication and its robustness of secret data. To avoids these limitations this paper uses the Forensic Exploiting Modification Direction (FEMD) algorithm for audio video steganography to increase data security, authentication and its robustness. It uses 3m2 + 3m + 3 ary notation to embed three pixels at a time for enhancing embedding capacity and security of secret data. The different types of image processing attacks have been verified on stego video which produces the very good recovery of original and secret data without any distortion. The observed and verified software results confirm that the implemented security model provides better authentication, imperceptibility, robustness, CC (Concealed Capacity), and CR (Concealed Rate) as compared to any existing EMD methods.
Monocular Robust 3D Human Localization by Global and Body-Parts Depth Awareness

Learning the human depth localization in camera coordinate space plays a crucial role in understanding the behavior and activities of multi-person in 3D scenes. However, existing monocular-based methods rarely combine the global image features and the human body-parts features effectively, resulting in a large gap from the actual location in some cases, e.g., the special body-sized persons and mutual occlusion between humans in the image. This paper presents a novel Robust 3D Human Localization (R3HL) network consisting of two stages: global depth awareness and body-parts depth awareness, to significantly improve the robustness and accuracy of the 3D location. In the first stage, the front-back and far-near relationship estimation module based on multi-person are proposed to make the network extract depth features from the global perspective. In the second stage, the network focuses on the target human. We propose a Pose-guided Multi-person Repulsion (PMR) module to enhance the target human’s features and reduce the interference features produced by the background and other people. In addition, an Adaptive Body-parts Attention (ABA) module is designed to assign different feature weights to each joint. Finally, the human’s absolute depth is obtained through global pooling and fully connected layers. The experimental results show that the attention from the whole image to a single person helps find the absolute location of different body-sized and poses people from diverse scenes. Our method can achieve better performance than other state-of-the-art methods on both indoor and outdoor 3D multi-person datasets.
Formation control of multiple UAVs via pigeon inspired optimisation

A distributed coordinated control is proposed for the group of UAVs to map the behaviour rule for the pigeon group. Moreover, on the basis of pigeon inspired optimisation (PIO), the UAV groups are clustered and synchronised through the coordinated control algorithm. To control the speed, direction of nth UAVs for the desired formation of flight are controlled by proportional integral (PI) and proportional integral differential (PID) controller. A high degree of convergence control method is presented to control the UAV groups at the same level of height during formation of flight. On the basis of pigeon flight obstacle avoidance, a method of UAV obstacle avoidance is designed, and a method of obstacle obstructing is also designed along with the improved artificial physical method. The proposed methodology provides obstacle avoidance for a group of UAV and overcome traditional physical methods, and can easily make the desired formation in which sudden obstacles lie in the way of formation through the arrow channel.
Hierarchical Storyline Generation Based on Event-centric Temporal Knowledge Graph

As the main channel for people to obtain information and express their opinions, online media generate a huge amount of unstructured news data every day, which bring great difficulties for people to perceive social events and grasp the development of events. Event-centric knowledge graph has been used to facilitate the reconstruction of news to form structured event information. Most existing studies generate timeline based on event-centric knowledge graphs without considering the complex relations between events. This paper collects news data from Sina platform, constructs event ontology, and builds event-centric knowledge graph with temporal attribute. Afterwords, we propose a novel storyline generation framework with constraints of coherence and coverage. Experiment results show that our method significantly outperforms two baseline approaches.
Generalized Net Model of a Serial Composition of Services with Intuitionistic Fuzzy Estimations of Uncertainty

Generalized Nets (GNs) are used in the conceptual modelling of service systems. In the present paper, a GN model of a serial composition of services is proposed. The model includes intuitionistic fuzzy estimations of uncertainty in the form of intuitionistic fuzzy pairs. The intuitionistic fuzzy pairs are represented as characteristics of some tokens of the GN. A naming system for the places and the transitions of the GN is proposed which facilitates the analytical modelling. The proposed GN approach can be used in the construction of GNs representing more complex compositions of services. It can also be used to define new Quality of Service (QoS) indicators as a function of QoS indicators of the composed services.
The Relationship Between the Scientific Activities in Research Laboratories with Webometrics Ranking of Algerian Universities: An Empirical Investigation

Algeria has paid attention to the higher education and scientific research sector through the five-year scientific research and technological development programs (1998-2002, 2008-2012, and 2014-2019). The study aims to evaluate the scientific research activities in Algerian universities within the framework of scientific research programs, to study the relationship of scientific activities by researchers in Algerian research laboratories with Webometrics ranking of Algerian universities. It is eluted what are the scientific activities of Algerian researchers which have the most impact on the ranking of Algerian universities in the Webometrics ranking. We adopted the experimental, exploratory approach by collecting, exploring, and analyzing the correlation between of data from the Algerian university rankings according to the number of researchers in the research laboratories, the number of citations, the number of patents, published papers, and the ranking of each Algerian university in Webometrics ranking, by Spearman’s correlation coefficient to test the strength and signification of the correlation between these rankings. The results showed development in the various components of scientific, material, human and financial research, but it is not enough. Also, despite many research laboratories, researchers at these laboratories, and the improvement in the number of internationally published research in each Algerian university, this did not contribute to improving its global Webometrics ranking due to the weakness of the relationship with activities of researchers in Algerian research laboratories. The result showed that the Algerian university is not interested in its virtual presence on the web, which negatively affected its Webometrics ranking. The researchers conclude that to improve the ranks of Algerian universities in various aspects, they must improve their global ranking according to the number of citations. Accordingly, to improve the ranking, international visibility of Algerian universities, decision-makers must adopt indicators of ‘research quality’ (number of citations), ‘research production’ (publications) in financing and accrediting research laboratories.
The Human Interface Device (HID) Attack on Android Lock Screen Non-Biometric Protections and Its Computational Complexity

Nowadays, information obtained from mobile phones is often the subject of evidence in front of a court. ForensicNowadays, information obtained from mobile phones is often the subject of evidence in front of a court. Forensicanalysts often come across smartphones about which they have no prior information. However, they need to extract data fromthem. The main prerequisite to extract the data is to bypass Android lock screen protection. The HID attack is a promisingmethod to break Android lock screen protection. In many cases, this is the only way how to break the smartphone´s nonbiometriclock screen protections on newer Android OS versions. The article contains examples of three non-biometric typesof Android smartphone lock screen protections and their computational complexity. The paper describes hardware and softwarerequirements for implementation of HID attack.
Factors influencing users' satisfaction when using Ehteraz app: the case of Qatar

Individuals in Qatar are required to download and use a digital contact tracing (DCT) app (known as Ehteraz app) to be granted the accessibility to all public facilities and shopping malls. The study aims to identify the users' satisfaction when using Ehteraz app. An integrated research model was utilised based on two major theories in the literature. 550 respondents were randomly surveyed to examine the hypotheses of the research model and answer the research questions. The results indicated that performance expectancy, effort expectancy, system quality and trust are significant factors when using Ehteraz, while information quality, technical experience, and privacy were not significant predictors of satisfaction. There is no substantial evidence to indicate a relationship between user satisfaction and either chronic disease or voluntariness. Overall, the research provides useful insights for the designers and developers of Ehteraz, the Qatari Government, and health authorities.
EDUCATIONAL LEARNING METHODS WITH GAMIFICATION ASPECTS FOR INMATES DURING PANDEMIC

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 5746-5751ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.1351Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
From intentions to services in modular process plants

In the context of industrial cyber-physical systems in the chemical and pharmaceutical industry, services and service-oriented architectures are being increasingly used. Services are mainly used in modular automation in the context of modular process plants. The design of services requires a high level of expertise in the field of automation as well as domain knowledge from the field of process engineering. This contribution presents an approach for the derivation of services in modular process plants from descriptions of intentions. The approach includes different ontological models for the modeling of intentions and services. The intention ontology describes goals, implementations, and requirements of the system from the viewpoint of the domain expert. To bridge the gap between the abstract objectives described by the intentions and the technically detailed services, functionality descriptions are used. A workflow for the design and selection of services from intentions is presented using the aforementioned ontological models. This approach supports the systematic design of services in the industrial context of modular process plants.
Deep Learning Based Model for Multi-class Classification of Cervical Cells Using Pap Smear Images

Cervical cancer is the second most prevalent cancer among women in India and has a high fatality rate. Cervical cancer is currently diagnosed via a Pap Smear test followed by a biopsy. The cells from the cervix are obtained and examined for abnormal cells manually in this procedure, which is quite strenuous. Moreover, given the ratio of healthcare professionals to patients, a computer-assisted approach would certainly help pathologists automate the screening process and make predictions much faster. This research paper proposes a system that uses a deep learning model that implements transfer learning with EfficientNet architecture to classify the pap smear cell images. When compared to alternative models for the same accuracy, the EfficientNet architecture has the advantage of using fewer parameters. The work uses the data set &#x201C;Liquid based-cytology Pap smear data set for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions&#x201D; that contain images collected from 490 patients from a hospital so as to deal with real-world data. These images are classified into four categories: NILM, LSIL, HSIL, and SCC, with the proposed model achieving a state-of-the-art accuracy of 98.85%.
Handwritten Logic Circuits Analysis Using the YOLO Network and a New Boundary Tracking Algorithm

Handwriting analysis has been addressed by researchers for decades, and many advances were achieved in understanding handwritten texts so far. However, some applications have been rarely discussed. One of these applications that has received less attention is the understanding and analyzing of handwritten circuits. Today, with the widespread use of intelligent tools in engineering and educational processes, the need for new and accurate solutions for processing such handwritings is felt more than ever. This paper presents a new method to analyze handwritten logic circuits. In this method, circuit components are first identified using a deep neural network based on YOLO. Then, the connection among these components is recognized using a new simple boundary tracking method. Then, the binary function related to the handwritten circuit is obtained. Finally, the truth table of the logic circuit is generated. We have also created a set of various handwritten logic circuits called JSU-HWLC. The results of the experiments show the proper performance of the proposed method on the collected dataset. The experiments demonstrated that the YOLO algorithm achieved better results than other deep learning methods such as faster R-CNN, Detectron2, and RetinaNet. For this reason, YOLO has been used to identify logic gates in the proposed system.
Artificial intelligence and hospitality industry: systematic review using TCCM and bibliometric analysis

This study gives an extensive review of the literature that exists concerning artificial intelligence. This paper helps identify the recent trends in AI in the hospitality industry and research gaps. The use of primary data for qualitative analysis is still low due to the industry's newness of AI applications. The theory development, characteristics, context and methodology (TCCM) framework has been employed for theory development and to identify gaps in existing research and future research directions. This paper provides a qualitative analysis of the relationship between varied use of AI in the hospitality industry holistically using bibliometric analysis and TCCM. Results show that 'consumer experience', 'robots', 'forecasting', 'willingness to accept', 'technology development', 'human-robot interaction' are the main domains. The perception of customers, experience, and technology development are a few factors that impact a firm's competitiveness immensely. This analysis provides a picture of the current state of AI in the hospitality domain.
Versatile kernel reactivation for deep convolutional neural networks

Several networks exhibiting excellent performance in the field of computer vision still suffer from the challenge of over-parameterization. Over-parameterized networks have several parameters that cannot be trained, owing to the poor backpropagation process caused by a small L1 norm, and these parameters suppress the potential for network performance improvement. This study proposes a kernel reactivation method to improve network performance by reusing invalid kernels that cannot be utilized for training because of the small L1 norm. The results indicate that the accuracy of Cifar-10 in ResNet-110 is improved by 0.94% compared to the baseline, and the top-1 and top-5 accuracies of Tiny-ImageNet in ResNet-50 are improved by 1.87% and 1.03% compared to the baseline, respectively.
Using search engines as a retrieval tool for translating newly coined expressions and terminology between Chinese and English

Abstract Translating terminology and newly coined expressions is a daunting challenge for technical translators. Translation of such words or phrases between, e.g. Chinese and English has received little help from dictionaries. With this as motivation, this study explores four methods by analysing results from an experiment with search engines as a retrieval tool for tackling such translation tasks. Specifically, taking 700 new terminologies from The Oxford English Dictionary as the research objects, this study experimentally examines the four terminology translation extraction methods. For each method, the effectiveness is assessed. This article finds that template-based and dictionary-based translation extraction methods have the highest accuracy rates of 86 and 89%, respectively. The semantic prediction search method proves to be the most flexible with the highest retrieval rate of 95%, although its results are not always accurate. Based on the results, this study proposes a model for quantitatively and qualitatively verifying the accuracy of translation results obtained from search engines. The quantitative analysis examines the use frequency of all translations obtained, while the qualitative analysis looks into their contexts. These findings have implications for translating, via search engine retrieval tools, terminology between other language pairs.
Memes and the Future of Pop Culture

Abstract Pop culture, as a distinct form of culture with its own historical, artistic and textual categories, crystallized in the first decades of the twentieth century as a reaction to the restrictive social traditions of colonial America. It spread quickly and broadly throughout the bustling urban centers of the 1920s—an era when it formed a partnership with technology and the business world. This coalition gave pop culture its identity, allowing it to thrive and form alliances with artistic and literary movements. But pop culture may have run its course with the rise of meme culture—a culture that has evolved on the Internet. This essay revisits the social, psychic, and aesthetic roots of pop culture, suggesting that meme culture has fragmented its historical flow, thus threatening to bring about its demise.