Reviewerly/exHarmony huggingface dataset:
Controllable speech synthesis by learning discrete phoneme-level prosodic representations

In this paper, we present a novel method for phoneme-level prosody control of F0 and duration using intuitive discrete labels. We propose an unsupervised prosodic clustering process which is used to discretize phoneme-level F0 and duration features from a multispeaker speech dataset. These features are fed as an input sequence of prosodic labels to a prosody encoder module which augments an autoregressive attention-based text-to-speech model. We utilize various methods in order to improve prosodic control range and coverage, such as augmentation, F0 normalization, balanced clustering for duration and speaker-independent clustering. The final model enables fine-grained phoneme-level prosody control for all speakers contained in the training set, while maintaining the speaker identity. Instead of relying on reference utterances for inference, we introduce a prior prosody encoder which learns the style of each speaker and enables speech synthesis without the requirement of reference audio. We also fine-tune the multispeaker model to unseen speakers with limited amounts of data, as a realistic application scenario and show that the prosody control capabilities are maintained, verifying that the speaker-independent prosodic clustering is effective. Experimental results show that the model has high output speech quality and that the proposed method allows efficient prosody control within each speaker’s range despite the variability that a multispeaker setting introduces.
The Sentiment Analysis for Hindi Language Using Convolution Neural Network and Deep Belief Network

It is simple for people to share their ideas, feelings and thoughts on the widespread social media network. The changes in technology and the creation of social platforms have allowed individuals to express their ideas both in the English language and in the mother tongue for such platforms as Hindi, Marathi etc. These are good, negative and neutral ideas. These phrases function as a reservoir of information that may be used to obtain knowledge. In the analysis of sentiment from regional language such as Hindi, study becomes vital and motivating. The goal is therefore to utilize Neural Networks (NNs) to analyses the feelings of Hindi words. The Machine Learning (ML) model will be formed and compared with the use of Deep Belief Network (DBN), Convolution Neural Network (CNN) and Long Short Time Memory (LSTM) to organize the data into constructive and undesirable feelings in Hindi.
Automated data validation: An industrial experience report

There has been a massive explosion of data generated by customers and retained by companies in the last decade. However, there is a significant mismatch between the increasing volume of data and the lack of automation methods and tools. The lack of best practices in data science programming may lead to software quality degradation, release schedule slippage, and budget overruns. To mitigate these concerns, we would like to bring software engineering best practices into data science. Specifically, we focus on automated data validation in the data preparation phase of the software development life cycle. This paper studies a real-world industrial case and applies software engineering best practices to develop an automated test harness called RESTORE. We release RESTORE as an open-source R package. Our experience report, done on the geodemographic data, shows that RESTORE enables efficient and effective detection of errors injected during the data preparation phase. RESTORE also significantly reduced the cost of testing. We hope that the community benefits from the open-source project and the practical advice based on our experience.
Towards Intuitive HMI for UAV Control

In the last decade, UAVs have become a widely used technology. As they are used by both professionals and amateurs, there is a need to explore different control modalities to make control intuitive and easier, especially for new users. In this work, we compared the most widely used joystick control with a custom human pose control. We used human pose estimation and arm movements to send UAV commands in the same way that operators use their fingers to send joystick commands. Experiments were conducted in a simulation environment with first-person visual feedback. Participants had to traverse the same maze with joystick and human pose control. Participants' subjective experience was assessed using the raw NASA Task Load Index.
Improvement of driver night vision in foggy environments by structured light projection

Fog visibility scene Fog and light scattering Veiling luminance Fog chamber Smart lighting Simulated fog scene Nowadays, fog is still a natural phenomenon that hinders our ability to detect targets, particularly in the field of driving where accidents are increasing.In the literature we find different studies determining the range of visibility, improving the quality of an image, determining the characteristics of fog, etc.In this work we propose the possibility of using a structured lighting system, on which we project the light towards the target, limiting the field lighting.We have developed a scattering light propagation model to simulate and subsequently study the veil luminance, generated by backscattering, to predict the decrease in visibility.This simulation considers the type of fog, the relative orientation of various elements (observer, light source and targets).We have built a fog chamber to validate the experimental params of the described system.The results obtained from both the simulation and the experimental measurements demonstrate that it is possible to obtain a high contrast enhancement for viewing a target when illuminated as described.Clearly, this kind of lighting technology will improve the road safety in foggy night environments.The results of this work can also be extrapolated to any situation in which the visibility of an observer is compromised by the environment, such as heavy rain, smoke from fires, among others.
Audio-Visual Kinship Verification: A New Dataset and a Unified Adaptive Adversarial Multimodal Learning Approach

Facial kinship verification refers to automatically determining whether two people have a kin relation from their faces. It has become a popular research topic due to potential practical applications. Over the past decade, many efforts have been devoted to improving the verification performance from human faces only while lacking other biometric information, for example, speaking voice. In this article, to interpret and benefit from multiple modalities, we propose for the first time to combine human faces and voices to verify kinship, which we refer it as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">audio-visual kinship verification</i> study. We first establish a comprehensive audio-visual kinship dataset that consists of familial talking facial videos under various scenarios, called TALKIN-Family. Based on the dataset, we present the extensive evaluation of kinship verification from faces and voices. In particular, we propose a deep-learning-based fusion method, called unified adaptive adversarial multimodal learning (UAAML). It consists of the adversarial network and the attention module on the basis of unified multimodal features. Experiments show that audio (voice) information is complementary to facial features and useful for the kinship verification problem. Furthermore, the proposed fusion method outperforms baseline methods. In addition, we also evaluate the human verification ability on a subset of TALKIN-Family. It indicates that humans have higher accuracy when they have access to both faces and voices. The machine-learning methods could effectively and efficiently outperform the human ability. Finally, we include the future work and research opportunities with the TALKIN-Family dataset.
Leverage Edu: caught between novelty and efficiency

Learning outcomes The learning outcomes are as follows: 1. enable students to appreciate how a platform company can navigate through diminishing network effects; 2. enable students to foresee the downside of scaling up a platform business; 3. enable students to appreciate the trade-off between an efficiency-centric and a novelty-centric business model for platform businesses; and 4. enable students to create a platform business model canvas for a company. Case overview/synopsis The teaching case discusses the dilemma of Akshay Chaturvedi, the founder of Leverage Edu, an artificial intelligence-enabled platform for students seeking admission to foreign universities. It had received nearly US$9.6m in funding until December 2021. Chaturvedi wanted to make the best use of his funds, but was torn between turning Leverage Edu into an “efficient platform” and transforming it into a “novelty-centric platform”. The teaching note attempts to resolve Chaturvedi’s dilemma by analyzing competitors using the platform canvas model and determining how Chaturvedi could create and use network effects to Leverage Edu’s advantage. The case is based on secondary data that is freely available in the public domain. Complexity academic level This case is intended for MBA Entrepreneurship students taking a platform business elective. It can also be used in faculty and management development programs under the banner “Technology and Platform Businesses”. Supplementary materials Teaching notes are available for educators only. Subject code CSS 3: Entrepreneurship.
The development and application of artificial intelligence in the field of robotics

With the continuous development of science and technology, robots and artificial intelligence have made great progress and progress. The scope of application in the production and life practice of today's society is also more and more extensive. How to perfectly combine artificial intelligence and intelligent robots to create more wealth and convenience for society and life is the focus of people's attention and research. The State Council's "New Generation Artificial Intelligence Development Plan" clearly puts forward the goal of reaching the world's leading level of artificial intelligence theory, technology and application by 2030. This paper firstly introduces the research background and significance of robotics and artificial intelligence. Then it analyzes the connotation, development status and main applications of robots and artificial intelligence. Finally, the development trend and development prospect of robots and artificial intelligence are prospected.
A Survey on Multimedia Ontologies for a Semantic Annotation of Cinematographic Resources for the Web of Data

The Semantic Web provides approaches and tools that allow for the processing and analysis of online content, including multimedia resources.Multimedia resources like videos, audios, and photos are increasingly common in contemporary Web content.Cinematographic works (also known as film contents) stand out among these resources as one of the most recent attractions on the Internet.An important tool employed recently in the semantic indexation of digital resources and film content is ontological annotation.This paper studies the current multimedia ontologies related to the film contents on the web.The relevant indicators were discussed comparatively, and some open issues were reviewed in details.In this way, the authors managed to integrate the metadata related to online films practically into the web of data.
A Model Theory for the Potential Infinite

We present the model theoretic concepts that allow mathematics to be developed with the notion of the potential infinite instead of the actual infinite. The potential infinite is understood as a dynamic notion, being an indefinitely extensible finite. The main adoption is the interpretation of the universal quantifier, which has an implicit reflection principle. Each universal quantification refers to an indefinitely large, but finite set. The quantified sets may increase, so after a reference by quantification, a further reference typically uses a larger, still finite set. We present the concepts for classical first-order logic and show that these dynamic models are sound and complete with respect to the usual inference rules. Moreover, a finite set of formulas requires a finite part of the increasing model for a correct interpretation.
Gridless 3D Recovery of Image Sources From Room Impulse Responses

Given a sound field generated by a sparse distribution of impulse image sources, can the continuous 3D positions and amplitudes of these sources be recovered from discrete, bandlimited measurements of the field at a finite set of locations, e.g., a multichannel room impulse response? Borrowing from recent advances in super-resolution imaging, it is shown that this nonlinear, non-convex inverse problem can be efficiently relaxed into a convex linear inverse problem over the space of Radon measures in R3. The linear operator introduced here stems from the fundamental solution of the free-field inhomogenous wave equation combined with the receivers' responses. An adaptation of the Sliding Frank-Wolfe algorithm is proposed to numerically solve the problem off-the-grid, i.e., in continuous 3D space. Simulated experiments show that the approach achieves near-exact recovery of hundreds of image sources using an arbitrarily placed compact 32-channel spherical microphone array in random rectangular rooms. The impact of noise, sampling rate and array diameter on these results is also examined.
An enhanced heterogeneous public key searchable encryption scheme supporting multiple keywords

Searchable encryption (SE) technology allows users to use keywords to retrieve encrypted data and ensure that useful plaintext information about encrypted data will not be disclosed. For a secure SE scheme, if it is able to meet the multi-trapdoor privacy (MTP), the security will be improved compared with the traditional SE scheme. However, there are few searchable encryption schemes that can meet the requirements of MTP. In addition to the security of the SE scheme, we should also strive to improve its practicability. Nowadays, many existing SE schemes use a single keyword to generate the keyword ciphertext and trapdoor for retrieving ciphertext, which will greatly reduce the accuracy of the search result. Another phenomenon deserves our attention. In recent years, public key cryptosystems such as certificateless cryptography (CLC) and public key infrastructure (PKI) have been widely used. If a SE scheme satisfies heterogeneity means that both sides of communication parties do not need to restrict the use of the same cryptosystems, the practicability of this scheme will be improved. Therefore, we propose a heterogeneous SE scheme that provides MTP. The new scheme supports multi-keyword search and allows the sender and receiver to be worked in different cryptosystems. Furthermore, it would obviously be impractical to use the same cryptographic system parameters between heterogeneous systems, so the communication parties in our scheme operate in different cryptosystems with different cryptographic system parameters. With the use of the random oracle model (ROM), we demonstrate the security of the proposed scheme, and we show the excellent performance of the proposed scheme at the end of the article.
Spiking Behaviour in Laterally-Coupled Pairs of VCSELs With Applications in Neuromorphic Photonics

We report a theoretical study on laterally-coupled pairs of vertical-cavity surface-emitting lasers (VCSELs) operated under conditions that generate or suppress high-speed optical spiking regimes, and show their potential in exemplar functionalities for use in photonic neuromorphic computing systems. The VCSEL numerical analysis is based on a system of five coupled mode equations, which, for the case of weak coupling, are reduced to a set of three equations that predict the saddle-node stability boundary in terms of device parameters and operating conditions. These results guide numerical simulation to demonstrate multiple neuron-like dynamics, including single- and multiple-spike emission, spiking inhibition, and rebound spiking directly in the optical domain. Importantly, these behaviours are obtained at sub-nanosecond rates, hence multiple orders of magnitude faster than the millisecond timescales of biological neurons. The mechanisms responsible are explained by reference to appropriate phase portraits. The coupled VCSELs model is then used for demonstration of high-speed, all-optical digital-to-spiking encoding and for representation of digital image data using rate-coded spike trains.
HayCAM: A Novel Visual Explanation for Deep Convolutional Neural Networks

Explaining the decision mechanism of Deep Convolutional Neural Networks (CNNs) is a new and challenging area because of the “Black Box” nature of CNN's. Class Activation Mapping (CAM) as a visual explainable method is used to highlight important regions of input images by using classification gradients. The lack of the current methods is to use all of the filters in the last convolutional layer which causes scattered and unfocused activation mapping. HayCAM as a novel visualization method provides better activation mapping and therefore better localization by using dimension reduction. It has been shown with mask detection use case that input images are fed into the CNN model and bounding boxes are drawn over the generated activation maps (i.e. weakly-supervised object detection) by three different CAM methods. IoU values are obtained as 0.1922 for GradCAM, 0.2472 for GradCAM++, 0.3386 for EigenCAM, and 0.3487 for the proposed HayCAM. The results show that HayCAM achieves the best activation mapping with dimension reduction.
SECURITY OF AUTONOMOUS VEHICLES: 5G IOV (INTERNET OF VEHICLES) ENVIRONMENT

Abstract. An autonomous vehicle is designed to move partially or totally without the intervention of the driver. It is a system equipped with sensors, communication and processing units that make it able to monitor and analyse traffic information in real time to improve road safety. In addition, Internet of Vehicles is the latest technology dedicated to autonomous vehicles, the integration of this technology with 5G serves as a platform to interconnect sensors, vehicles, infrastructure, pedestrian, and network. Hence, the 5G Internet of Vehicles environment provides significant benefits, including increased security, high reliability, wide communication coverage and low service latency. On the other hand, due to the ubiquity of network connectivity, it also presents serious confidentiality, integrity, and availability issues for autonomous vehicles. This paper provides an overview of the autonomous vehicle concept by highlighting its technologies and the 5G IoV environment, presenting some susceptible attacks that can touch the security of this environment, and some good practices to ensure the autonomous vehicle security.
Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors

Although machine learning based algorithms have been extensively used for detecting phishing websites, there has been relatively little work on how adversaries may attack such "phishing detectors" (PDs for short).In this paper, we propose a set of Gray-Box attacks on PDs that an adversary may use which vary depending on the knowledge that he has about the PD.We show that these attacks severely degrade the effectiveness of several existing PDs.We then propose the concept of operation chains that iteratively map an original set of features to a new set of features and develop the "Protective Operation Chain" (POC for short) algorithm.POC leverages the combination of random feature selection and feature mappings in order to increase the attacker's uncertainty about the target PD.Using 3 existing publicly available datasets plus a fourth that we have created and will release upon the publication of this paper 1 , we show that POC is more robust to these attacks than past competing work, while preserving predictive performance when no adversarial attacks are present.Moreover, POC is robust to attacks on 13 different classifiers, not just one.These results are shown to be statistically significant at the p < 0.001 level.
Research on Man-machine Environment Optimization Design Strategy of Military Cabin Based on Deep Learning

In order to enhance the adaptability of operators and military cabin environment and create a comfortable, efficient and safe cabin environment, in this paper, from the perspective of industrial design and artificial intelligence technology, the human-machine environment design method and strategy of military cabin are studied innovatively. The deep learning theory is introduced into the design of military cabin environment. The comfort model strategy of military cabin environment under human-machine efficiency load, the comfort model strategy of military cabin operators under situational awareness, the grey correlation model strategy of military cabin environment and operator comfort, and the intelligent recommendation system strategy of military cabin environment and operator optimal comfort matching under deep learning are studied, which provides new ideas and new directions for military cabin designers, in order to improve the military cabin human-machine environment and operator cognitive adaptation, manipulation efficiency and environmental comfort to reduce cabin human error and ensure system environmental safety.
Crime Prevention Strategies in Tennessee State, USA

Crime has become one of the most serious security concerns confronting many nations in the World. Crime is widespread in both rural and urban areas, but it is less prevalent in both. Hence, crime prevention strategies are key in any country. In Tennessee State, Crime has been a growing problem. Hence, the study examined the crime prevention strategies employed to combat its widespread. The study was literature based. The study found that some crime prevention strategies include using ICT, holding meetings and increasing government public participation. The importance of increasing government public participation is that criminals are within the society and, in some instances, known. It becomes easy to find criminals if society is involved in the process. Security meetings reduce deviant activities and encourage compliant behaviours of their members through effective socialization about criminal awareness. The research concluded that security meetings, the use of ICT and increasing government public participation are essential components of effective crime prevention. Leaders travelling from one location to another and raising understanding of the role of communities, government policing agencies and the community in security reduces the chances of crime. The study recommended effective strategies be employed to reduce crime in Tennessee State. There should be no use of a single strategy when combating crime. Residents in local areas should be directly involved in policing exercises. A culture of shared responsibility and decision-making, and persistent commitment from both government policing agencies and the community, is required. Through the relevant Ministry, the government should fully understand the mechanisms that create, alter, and lower the insecurity issue through security meetings. Identifying which social group is severely affected by the problem is also critical. Keywords: Crime, Prevention Strategies, Tennessee State, USA