Reviewerly/exHarmony huggingface dataset:
Age-VOX-Celeb: Multi-Modal Corpus for Facial and Speech Estimation

Estimating a speaker’s age from their speech is more challenging than age estimation from their face because of insufficiently available public corpora. To tackle this problem, we construct a new audio-visual age corpus named AgeVoxCeleb by annotating age labels to VoxCeleb2 videos. AgeVoxCeleb is the first large-scale, balanced, and multi-modal age corpus that contains both video and speech of the same speakers from a wide age range. Using AgeVox-Celeb, our paper makes the following contributions: (i) A facial age estimation model can outperform a speech age estimation model by comparing the state-of-the-art models in each task. (ii) Facial age estimation is more robust against the difference between training and test sets. (iii) We developed cross-modal transfer learning from face to speech age estimation, showing that the estimated age with a facial age estimation model can be used to train a speech age estimation model. Proposed AgeVoxCeleb will be published in https://github.com/nttcslab-sp/agevoxceleb.
Multivariate Time-Series Prediction Using LSTM Neural Networks

In this paper, we analyzed different models of LSTM neural networks on the multi-step time-series dataset. The purpose of this study is to express a clear and precise method using LSTM neural networks for sequence datasets. These models can be used in other similar datasets, and the models are composed to be developed for various multi-step datasets with the slightest adjustment required. The principal purpose and question of this study were whether it is possible to provide a model to predict the amount of electricity consumed by a house over the next seven days. Using the specified models, we have made a prediction based on the dataset. We also made a comprehensive comparison with all the results obtained from the methods among different models. In this study, the dataset is household electricity consumption data gathered over four years. We have been able to achieve the desired prediction results with the least amount of error among the existing state-of-the-art models.
Dual Set Membership Filter With Minimizing Nonlinear Transformation of Ellipsoid

In this article, we propose a dual set membership filter for nonlinear dynamic systems with additive unknown but bounded noises, and it has three distinct advantages. First, the nonlinear system is translated into the linear system by leveraging a semi-infinite programming, rather than linearizing the nonlinear function. The semi-infinite programming is to find an ellipsoid bounding the nonlinear transformation of an ellipsoid, which aims to compute a tight ellipsoid to cover the state. Second, the duality result of the semi-infinite programming is derived by rigorous analysis; then, a first-order Frank–Wolfe method is developed to efficiently solve it with a lower computation complexity. Third, the proposed filter enjoys stability for some special nonlinear dynamic systems and succeeds the advantages of the classic linear set membership filter. Finally, two illustrative examples in the simulations reveal the effectiveness of the dual set membership filter.
VAIM: Verifiable Anonymous Identity Management for Human-Centric Security and Privacy in the Internet of Things

The human internet of things (HIoT) is a promising trend that adopts a user-centered vision to improve life quality by interacting with heterogeneous physical and virtual entities and the internet. However, It refers to exchanging contextual data between collaborative entities that raise privacy concerns. Emerging blockchain technology allows a digital identity management system (IDM) to be deployed in it, which largely alleviates the problems caused by the centralized third party. Still, its inherent transparency and lack of privacy pose a considerable challenge to IDM. We propose verifiable anonymous identity management (VAIM) connecting privacy channels between users by constructing identity verification and access control provisioning via user-centric decisions and an anonymous identity management system. This work has the following contributions: (1) We establish a novel IDM system by analyzing the existing scheme. In this regard, we improve the traditional claim identity model in blockchain by implementing zero-knowledge proof (ZKP) algorithms to achieve identity unlinkability, essentially preventing the disclosure of attribute ownership. (2) We implement a system that includes blind ordered multi-signature (BOMS) protocol, which allows users to processes efficiently and trusts the verification of anonymous transactions. (3) Finally, specific ZKP-based algorithm (commonly used practical ZKP such as Camenisch and Lysyanskaya signature (CL-Signature) and zero-knowledge succinct non-interactive argument of knowledge (ZK-SANRKS)) implementation and various environment performance evaluation and security analysis show that our scheme achieves efficient privacy protection and a broader application scope compared with the prior model. To the best of our knowledge, existing zero-knowledge proof-based IDM has not developed or compared each scheme before.
Joint obfuscation of location and its semantic information for privacy protection

Location-based social networks (LBSNs) such as Foursquare and Facebook enable users to share with each other, their (geographical) locations together with the semantic information associated with their locations. The semantic information captures the type of a location and is usually represented by a semantic tag like “restaurant”, “museum”, “school”, etc. Semantic tag sharing increases the threat to users’ location privacy (which is already at risk because of location sharing) and it also puts users’ semantic location privacy at risk. The existing solution to protect the location privacy and the semantic location privacy of users in such LBSNs is to obfuscate the location and the semantic tag independently of each other in a so called disjoint obfuscation approach. Thus, in this approach, the semantic tag is obfuscated i.e., replaced by a more general tag. Also, the location is obfuscated i.e., replaced by a generalized area (called the cloaking area) made of the actual location and some of its nearby locations. However, since in this approach the location obfuscation is performed in a semantic-oblivious manner, an adversary can still increase his chance to infer the actual location and the actual semantic tag by filtering out the locations in the cloaking area that are not semantically compatible with the obfuscated semantic tag. In this work, we address this issue by proposing a joint obfuscation approach in which the location obfuscation is performed based on the result of the semantic tag obfuscation. We also provide a formal framework for evaluation and comparison of our joint approach with the disjoint approach. By running an experimental evaluation on a dataset of real-world user traces collected from six different cities, we show that in almost all cases (i.e., in different cities and with different obfuscation parameters), the joint approach outperforms the disjoint approach in terms of location privacy protection and the semantic location privacy protection. Based on the evaluation results, we also discuss how different obfuscation parameters and the choice of the city can affect the performance of the obfuscation approaches. In particular, we show how changing these parameters can improve the performance of the joint approach.
Federated Learning for Distributed Reasoning on Edge Computing

The development of the Internet of Things over the last decade has led to large amounts of data being generated at the network edge. This highlights the importance of local data processing and reasoning. Machine learning is most commonly used to automate tasks and perform complex data processing and reasoning. Collecting such data in a centralized location has become increasingly problematic in recent years due to network bandwidth and data privacy concerns. The easy-to-change behavior of edge infrastructure enabled by software-defined networking (SDN) allows IoT data to be gathered on edge servers and gateways, where federated learning (FL) can be performed: creating a centralized model without uploading data to the cloud. In this paper, we analyze the use of edge computing and federated learning, a decentralized machine learning methodology that increases the amount and variety of data used to train deep learning models. To the best of our knowledge, this paper reports the first use of federated learning to help the Microgrid Energy Management System (EMS) predict load and obtain promising results. Simulations were performed using TensorFlow Federated with data from a modified version of the Dataport site.
Granular Data Access Control with a Patient-Centric Policy Update for Healthcare

Healthcare is a multi-actor environment that requires independent actors to have a different view of the same data, hence leading to different access rights. Ciphertext Policy-Attribute-based Encryption (CP-ABE) provides a one-to-many access control mechanism by defining an attribute’s policy over ciphertext. Although, all users satisfying the policy are given access to the same data, this limits its usage in the provision of hierarchical access control and in situations where different users/actors need to have granular access of the data. Moreover, most of the existing CP-ABE schemes either provide static access control or in certain cases the policy update is computationally intensive involving all non-revoked users to actively participate. Aiming to tackle both the challenges, this paper proposes a patient-centric multi message CP-ABE scheme with efficient policy update. Firstly, a general overview of the system architecture implementing the proposed access control mechanism is presented. Thereafter, for enforcing access control a concrete cryptographic construction is proposed and implemented/tested over the physiological data gathered from a healthcare sensor: shimmer sensor. The experiment results reveal that the proposed construction has constant computational cost in both encryption and decryption operations and generates constant size ciphertext for both the original policy and its update parameters. Moreover, the scheme is proven to be selectively secure in the random oracle model under the q-Bilinear Diffie Hellman Exponent (q-BDHE) assumption. Performance analysis of the scheme depicts promising results for practical real-world healthcare applications.
Leaving the Field: Designing a Socio-Material Toolkit for Teachers to Continue to Design Technology with Children

Leaving the field when conducting research in situated contexts, and finding ways to sustain project outcomes beyond academia, is an ongoing struggle in HCI. In our research project, we co-designed technologies with children in classroom contexts for three years. Nearing the projects' end, we focused on creating resources that enable teachers to continue our work with their pupils. In collaboration with teachers, we developed socio-material tools that support them in empowering neurodiverse children to engage with technology in creative ways and create their own technologies. While the majority of technology design toolkits are stand-alone artefacts, part of our toolkit is an infrastructure to keep guiding and supporting teachers beyond the project's end. In this paper, we discuss the teachers expectations and requirements for a toolkit and argue that an infrastructure must be part of a toolkit. We present a set of guidelines for researchers planning for a project's end.
Image Retrieval using Weighted Fusion of GLCM and TSBTC Features

Innovation in imaging technology, the widespread use of smartphones and social media, along with the boost in networking and storage technology has resulted in huge image databases. Exploring and searching similar visual images has become a key topic of research. This research article presents weighted feature fusion of gray level co-occurrence matrix (GLCM) based texture features and n-ary Thepade's Sorted Block Truncation Coding (TSBTC) based color features for image retrieval. Query image feature vector is compared with dataset image feature vector. Related images having a minimum mean squared error (MSE) are retrieved. The experimental results demonstrate that the weighted fusion of GLCM and TSBTC 8-ary features with weights 0.3 and 0.7, respectively give an Average Retrieval Accuracy (ARA) of 44.74% for augmented Wang dataset and, the weighted fusion of GLCM and TSBTC 4-ary features with 0.4 and 0.6 weights, respectively gives an ARA of 74.36% for modified COIL dataset. The proposed technique performs better as compared to the existing techniques studied and proved through statistical evaluations.
Speech Recognition by Simply Fine-Tuning Bert

We propose a simple method for automatic speech recognition (ASR) by fine-tuning BERT, which is a language model (LM) trained on large-scale unlabeled text data and can generate rich contextual representations. Our assumption is that given a history context sequence, a powerful LM can narrow the range of possible choices and the speech signal can be used as a simple clue. Hence, comparing to conventional ASR systems that train a powerful acoustic model (AM) from scratch, we believe that speech recognition is possible by simply fine-tuning a BERT model. As an initial study, we demonstrate the effectiveness of the proposed idea on the AISHELL dataset and show that stacking a very simple AM on top of BERT can yield reasonable performance.
Detection of Breast Cancer using Machine Learning Algorithms – A Survey

A common disease affecting millions of women in today’s world is breast cancer. To minimize the high number of superfluous breast biopsies, a few computer supported frameworks have been proposed somewhat recently. Machine learning provides potentially large opportunities for computer-aided diagnosis of a disease. Machine Learning gives possibly huge freedoms to computer helped finding of an illness. This paper investigates the informational indexes accessible to prepare the Machine Learning [ML] models and give a far reaching correlation of various kinds of models applied to anticipate breast cancer.
A Comparative Study on PSO with Other Metaheuristic Methods

The research and development of metaheuristic methods are critical issues in computer science. In the past decade, metaheuristic algorithms have been used in many engineering applications such as optimization of engineering problems, telecommunications, information security, and image processing. Many metaheuristic algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) are recently becoming very popular.There are many studies conducted in the literature on the comparison of PSO with other metaheuristic algorithms. In this chapter, various studies carried out between the years of 2010 and 2020 about the comparison of PSO with the other metaheuristic algorithms will be examined. The metaheuristic algorithms to be considered are simulated annealing (SA), genetic algorithm (GA), differential evolution (DE), ant colony optimization (ACO), artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), tabu search (TS), harmony search (HS), firefly algorithm (FF), cuckoo search (CS), bat-inspired algorithm (BA), water wave optimization (WWO), clonal selection algorithm (CLONALG), chemical reaction optimization (CRO), sine cosine algorithm (SCA), glowworm swarm optimization (GSO), and grey wolf optimizer (GWO). This study aims to evaluate and analyze the covered papers according to several criteria such as (a) rates of studies according to publishing years, (b) the metaheuristic algorithms that are compared to PSO, (c) performance evaluation of compared algorithms, (d) the metaheuristic algorithms with their inspirational approaches and their initial proposed studies and years, (e) the field of subjects where the algorithms are applied in the reviewed studies, and (f) used databases in the examined studies.This study is a comprehensive literature review of the comparison of PSO with the most popular metaheuristic algorithms. The intention of this review is to be useful for researchers who want to conduct a survey on this area of the subject as this chapter will cover the essential and helpful analysis of the related research.
VMAF Oriented Perceptual Coding Based on Piecewise Metric Coupling

It has been recognized that videos have to be encoded in a rate-distortion optimized manner for high coding performance. Therefore, operational coding methods have been developed for conventional distortion metrics such as Sum of Squared Error (SSE). Nowadays, with the rapid development of machine learning, the state-of-the-art learning based metric Video Multimethod Assessment Fusion (VMAF) has been proven to outperform conventional ones in terms of the correlation with human perception, and thus deserves integration into the coding framework. However, unlike conventional metrics, VMAF has no specific computational formulas and may be frequently updated by new training data, which invalidates the existing coding methods and makes it highly desired to develop a rate-distortion optimized method for VMAF. Moreover, VMAF is designed to operate at the frame level, which leads to further difficulties in its application to today's block based coding. In this paper, we propose a VMAF oriented perceptual coding method based on piecewise metric coupling. Firstly, we explore the correlation between VMAF and SSE in the neighborhood of a benchmark distortion. Then a rate-distortion optimization model is formulated based on the correlation, and an optimized block based coding method is presented for VMAF. Experimental results show that 3.61% and 2.67% bit saving on average can be achieved for VMAF under the low_delay_p and the random_access_main configurations of HEVC coding respectively.
Designing for and with People with Dementia using a Human Rights-Based Approach

User-centered design is typically framed around meeting the preferences and needs of populations involved in the design process. However, when designing technology for people with disabilities, in particular dementia, there is also a moral imperative to ensure that human rights of this segment of the population are consciously integrated into the process and respectfully included in the product. We introduce a human rights-based user-centered design process which is informed by the United Nations Convention on the Rights of Persons with Disabilities (CRPD). We conducted two editions of a three-day-long design workshop during which undergraduate students and dementia advocates came together to design technology for people with dementia. This case study demonstrates our novel approach to user-centered design that centers human rights through different stages of the workshop and actively involves people with dementia in the design process.
Cross-Modality Fusion and Progressive Integration Network for Saliency Prediction on Stereoscopic 3D Images

Traditional 2D image-based saliency prediction models suffer from unsatisfactory performance when dealing with stereoscopic 3D (S3D) images because eye movements in the case of freely viewing S3D images are demonstrated to be guided by both RGB and depth features. This paper studies the problem of saliency prediction on S3D images, where the interactions between RGB and depth modalities are both taken into account. Specifically, we design a novel deep neural network named Cross-modality Fusion and Progressive Integration Network (CFPI-Net) to address this problem. It consists of a Multi-level Cross-modality Feature Fusion (MCFF) module and a Multi-stage Progressive Feature Integration (MPFI) module. The MCFF module first captures hierarchical contexture features from each modality and then effectively fuses the hierarchical contexture features from different modalities at each level. The MPFI module involves multiple cascaded deeply supervised feature integration (DSFI) blocks in which the low-level and high-level cross-modality features are progressively integrated using the integrated features in the previous stage as a guidance. Our proposed CFPI-Net benefits from the advantages of multi-level feature representation, cross-modality feature fusion, and multi-stage progressive feature integration, which hereby fully boost the performance. Experimental results on two benchmark datasets demonstrate that CFPI-Net outperforms state-of-the-art saliency prediction methods both quantitatively and qualitatively. All the results and relevant codes will be made available to the public.
Bifurcation control and sound intensities in musical art

Qualitative changes in sound intensities frequently occur in musical signals and Hopf bifurcation control is the most natural mathematical approach to study them. We propose a natural formulation for investigating and generating varieties of pitch simultaneity and sound intensity using Eulerian flows with n-tuple Hopf singularity for a sufficiently large n. Chord leaves and chord limit sets are defined as two critical flow-invariant manifolds. The reduction of flows on chord leaves facilitate the study and bifurcation control through a generic codimension-three (a three-parametric family) scalar bifurcation problem. There is a one-to-one correspondence between bifurcations of positive equilibria in the scalar equation and bifurcations of invariant chord limit sets. An ordered set of double saddle-node and pitchfork bifurcation control of subcritical and supercritical types can describe and generate the sound intensity bifurcations of a sheet music. These scalar bifurcations are interpreted in terms of appearances and disappearances of various chord leaf-stable and unstable flow-invariant chord limit sets. We apply our proposed method to the first three bars of a sheet music. This is implemented in Matlab so that one can hear the smooth dynamic effect of bifurcations using a computer through a classical sheet music. Then, one can hear how pitchfork (subcritical and supercritical) and double fold-bifurcations of invariant hypertori sound in music.
Multi-Density Convolutional Neural Network for In-Loop Filter in Video Coding

As the latest video coding standard, Versatile Video Coding (VVC) achieves up to 40% Bjontegaard delta bit-rate (BD-rate) reduction compared with High Efficiency Video Coding (HEVC). Recently, Convolutional Neural Network (CNN) has attracted tremendous attention and shows great potential in video coding. In this paper, we design a Multi-Density Convolutional Neural Network (MDCNN) as an integrated in-loop filter to improve the quality of the reconstructed frames. The core of our approach is the multi-density block (MDB), which contains two branches: (a) the basic branch maintaining full resolution for capturing spatially-precise representations, (b) the density branch for learning rich spatial correlation with larger receptive field through down-sampling and upsampling. The feature maps of two branches will be fused into one stream repeatedly. Benefiting from this architecture, spatially-precise representations and density correlations in larger receptive field are utilized to improve the model performance and promote the model's robustness to different input resolutions. Experimental results show that, in terms of BD-rate savings for the (Y, U, V) components compared to the state-of-the-art VVC standard, the proposed MDCNN filter can achieve (5.06%, 13.86%, 13.76%) and (4.36%, 10.85%, 10.91 %) coding gain for the Random Access (RA) configuration and the All Intra (AI) configuration, respectively.