Reviewerly/exHarmony huggingface dataset:
User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation

Building predictive models for human-interactive systems is a challenging task. Every individual has unique characteristics and behaviors. A generic human–machine system will not perform equally well for each user given the between-user differences. Alternatively, a system built specifically for each particular user will perform closer to the optimum. However, such a system would require more training data for every specific user, thus hindering its applicability for real-world scenarios. Collecting training data can be time consuming and expensive. For example, in clinical applications it can take weeks or months until enough data is collected to start training machine learning models. End users expect to start receiving quality feedback from a given system as soon as possible without having to rely on time consuming calibration and training procedures. In this work, we build and test user-adaptive models (UAM) which are predictive models that adapt to each users’ characteristics and behaviors with reduced training data. Our UAM are trained using deep transfer learning and data augmentation and were tested on two public datasets. The first one is an activity recognition dataset from accelerometer data. The second one is an emotion recognition dataset from speech recordings. Our results show that the UAM have a significant increase in recognition performance with reduced training data with respect to a general model. Furthermore, we show that individual characteristics such as gender can influence the models’ performance.
Tiny Video Networks

Abstract Automatic video understanding is becoming more important for applications where real‐time performance is crucial and compute is limited: for example, automated video tagging, robot perception, activity recognition for mobile devices. Yet, accurate solutions so far have been computationally intensive. We propose efficient models for videos—Tiny Video Networks—which are video architectures, automatically designed to comply with fast runtimes and, at the same time are effective at video recognition tasks. The TVNs run at faster‐than‐real‐time speeds and demonstrate strong performance across several video benchmarks. These models not only provide new tools for real‐time video applications, but also enable fast research and development in video understanding. Code and models are available.
SMP Challenge

"SMP Challenge" aims to discover novel prediction tasks for numerous data on social multimedia and seek excellent research teams. Making predictions via social multimedia data (e.g. photos, videos or news) is not only helps us to make better strategic decisions for the future, but also explores advanced predictive learning and analytic methods on various problems and scenarios, such as multimedia recommendation, advertising system, fashion analysis etc.
Blockchain and the Tokenization of the Individual: Societal Implications

We are living in a world where the very systems upon which trust is based are being challenged by new and exciting paradigm shifts. Centralization whether in the form of governments, financial institutions, enterprises and organizations is simply being challenged because of the lack of trust associated with data governance often experienced in the form of data breaches or simply a monetization of our data without our permission and/or incentives to participate in this emerging decentralization of structures. We see this trust deficit challenging the very institutions we have depended on including but not limited to financial institutions, private enterprises or government bodies. A new “social contract” is required as we continuously evolve into more decentralized and self-governing (or semi self-governing) entities. We will see more development in digital sovereignty with the caveat that a governance model will need to be defined. This position paper will present evidence that supports the premise that blockchain and individual tokenization could provide a new social contract.
Capability Based Outsourced Data Access Control with Assured File Deletion and Efficient Revocation with Trust Factor in Cloud Computing

Cloud computing has introduced a paradigm which support data outsourcing to third parties for processing using commodity clusters. It allows the owner to outsource sensitive data and share it with the authorized user while reducing the computation and management cost. Since owners store sensitive data over the cloud, the requirements of access control and data security have also been increasing. To alleviate all the problem requirements, the need has arisen for providing a safe, secure, and sound model. The existing solutions for these problems use pure cryptographic techniques, which increases the computation cost. In this article, the security problems are solved by using a trusted third party and a quorum of key managers. A service provider is responsible for capability-based access control to ensure that only authorized users will be able to access the data. Whenever any data revocation is required, the data owner simply updates this information to the master key manager to revoke a specific number of shares. The model for the proposed work has been presented and its analysis shows how it introduces security features.
Plane Completion and Filtering for Multi-View Stereo Reconstruction

Multi-View Stereo (MVS)-based 3D reconstruction is a major topic in computer vision for which a vast number of methods have been proposed over the last decades showing impressive visual results. Long-since, benchmarks like Middlebury [32] numerically rank the individual methods considering accuracy and completeness as quality attributes. While the Middlebury benchmark provides low-resolution images only, the recently published ETH3D [31] and Tanks and Temples [19] benchmarks allow for an evaluation of high-resolution and large-scale MVS from natural camera configurations. This benchmarking reveals that still only few methods can be used for the reconstruction of large-scale models. We present an effective pipeline for large-scale 3D reconstruction which extends existing methods in several ways: (i) We introduce an outlier filtering considering the MVS geometry. (ii) To avoid incomplete models from local matching methods we propose a plane completion method based on growing superpixels allowing a generic generation of high-quality 3D models. (iii) Finally, we use deep learning for a subsequent filtering of outliers in segmented sky areas. We give experimental evidence on benchmarks that our contributions improve the quality of the 3D model and our method is state-of-the-art in high-quality 3D reconstruction from high-resolution images or large image sets.
An Efficient Federated Learning Scheme with Differential Privacy in Mobile Edge Computing

In this paper, we consider a mobile edge computing (MEC) system that multiple users participate in the federated learning protocol by jointly training a deep neural network (DNN) with their private training datasets. The main challenges of applying federated learning to MEC are: (1) it incurs tremendous computational cost by carrying out the deep neural network training phase on the resource-constraint mobile edge devices; (2) existing literature demonstrates that the parameters of a DNN trained on a dataset can be exploited to partially reconstruct the training samples in original dataset. To address the aforementioned issues, we introduce an efficiently private federated learning scheme in mobile edge computing, named FedMEC, with model partition technique and differential privacy method in this work. The experimental results demonstrate that our proposed FedMEC scheme can achieve high model accuracy under different perturbation strengths.
Feature Enhancement with Deep Feature Losses for Speaker Verification

Speaker Verification still suffers from the challenge of generalization to novel adverse environments. We leverage on the recent advancements made by deep learning based speech enhancement and propose a feature-domain supervised denoising based solution. We propose to use Deep Feature Loss which optimizes the enhancement network in the hidden activation space of a pre-trained auxiliary speaker embedding network. We experimentally verify the approach on simulated and real data. A simulated testing setup is created using various noise types at different SNR levels. For evaluation on real data, we choose BabyTrain corpus which consists of children recordings in uncontrolled environments. We observe consistent gains in every condition over the state-of-the-art augmented Factorized-TDNN x-vector system. On BabyTrain corpus, we observe relative gains of 10.38% and 12.40% in minDCF and EER respectively.
“The Early Bird Catches the Worm - First Mover Advantage through IoT Adoption for Indian Public Sector Retail Oil Outlets”

(2019). "The Early Bird Catches the Worm - First Mover Advantage through IoT Adoption for Indian Public Sector Retail Oil Outlets". Journal of Global Information Technology Management: Vol. 22, No. 4, pp. 280-308.
Deep Multi-Scale Recurrent Network for Synthetic Aperture Radar Images Despeckling

For the existence of speckles, many standard optical image processing methods, such as classification, segmentation, and registration, are restricted to synthetic aperture radar (SAR) images. In this work, an end-to-end deep multi-scale recurrent network (MSR-net) for SAR image despeckling is proposed. The multi-scale recurrent and weights sharing strategies are introduced to increase network capacity without multiplying the number of weights parameters. A convolutional long short-term memory (convLSTM) unit is embedded to capture useful information and helps with despeckling across scales. Meanwhile, the sub-pixel unit is utilized to improve the network efficiency. Besides, two criteria, edge feature keep ratio (EFKR) and feature point keep ratio (FPKR), are proposed to evaluate the performance of despeckling capacity for SAR, which can assess the retention ability of the despeckling algorithm to edge and feature information more effectively. Experimental results show that our proposed network can remove speckle noise while preserving the edge and texture information of images with low computational costs, especially in the low signal noise ratio scenarios. The peak signal to noise ratio (PSNR) of MSR-net can outperform traditional despeckling methods SAR-BM3D (Block-Matching and 3D filtering) by more than 2 dB for the simulated image. Furthermore, the adaptability of optical image processing methods to real SAR images can be enhanced after despeckling.
Level set based shape prior and deep learning for image segmentation

Deep convolutional neural network can effectively extract hidden patterns in images and learn realistic image priors from the training set. And fully convolutional networks (FCNs) have achieved state-of-the-art performance in the image segmentation. However, these methods have the disadvantages of noise, boundary roughness and no prior shape. Therefore, this study proposes a level set with the deep prior method for the image segmentation based on the priors learned by FCNs. The FCNs can learn high-level semantic patterns from the training set. Also, the output of the FCNs represents the high-level semantic information as a probability map and the global affine transformation can obtain the optimal affine transformation of the intrinsic prior shape. Moreover, the improved level set method integrates the information of the original image, the probability map and the corrected prior shape to achieve the image segmentation. Compared with the traditional level set method of simple scenes, the proposed method solves the disadvantage of FCNs by using the high-level semantic information to segment images of complex scenes. Finally, Portrait data set are used to verify the effectiveness of the proposed method. The experimental results show that the proposed method can obtain more accurate segmentation results than the traditional FCNs.
MALL in Collaborative Learning as a Vocabulary-Enhancing Tool for EFL Learners: A Study Across Two Universities in Saudi Arabia

Vocabulary, as important as it is, is largely relegated to the domain of memorization in the English as a foreign language (EFL) situations in the Kingdom of Saudi Arabia (KSA). Assessment and achievement tests have proven time and again that this strategy is not doing any good for the learners’ proficiency. This study was conceived to suggest means of improving critical knowledge application. In pilot studies with intermediate EFL learners at Majmaah University and Qassim University, preliminary findings after using mobile-assisted language learning (MALL) as a tool of collaborative learning indicated improved vocabulary retention. The study employed a mixed-method approach by comparing pre- and posttest results across genders and obtaining direct teacher–learner feedback using questionnaires, with 80 participants from two Universities over a period of 3 months. The questions were centered around seeking their opinion on collaborative learning and using smart devices as study tools on the campuses. Results showed that, the experimental group’s use of mobile devices for collaboration helped them for better retention of vocabulary, postintervention, and group performance was improved drastically with more learners scoring closer to the mean value, while the control group showed no remarkable difference in performance.
Pedestrian Detection for Autonomous Driving within Cooperative Communication System

The ability to perceive and understand surrounding road-users behaviors is crucial for self-driving vehicles to correctly plan reliable reactions. Computer vision that relies mostly on machine learning techniques enables autonomous vehicles to perform several required tasks such as pedestrian detection. Furthermore, within a fully autonomous driving environment, driverless vehicle has to communicate and share perceived data with its neighboring vehicles for more safe navigation. In this context, our paper proposes a warning notification diffusion solution related to real-time pedestrian presence detection, through an inter-vehicle communication system. To achieve this purpose, pedestrian and vehicle recognition is required. Thus, we implemented intended detectors. We used Histogram of Oriented Gradients (HOG) descriptor with the linear Support Vector Machine (SVM) classifier for the pedestrian detector, and Haar feature-based cascade classifier to reach vehicle detection. The performance evaluation of our solution leads to fairly good detection accuracy around 90% for pedestrian and 88% for vehicle.
Ship Detection under Complex Backgrounds Based on Accurate Rotated Anchor Boxes from Paired Semantic Segmentation

It is still challenging to effectively detect ship objects in optical remote-sensing images with complex backgrounds. Many current CNN-based one-stage and two-stage detection methods usually first predefine a series of anchors with various scales, aspect ratios and angles, and then the detection results can be outputted by performing once or twice classification and bounding box regression for predefined anchors. However, most of the defined anchors have relatively low accuracy, and are useless for the following classification and regression. In addition, the preset anchors are not robust to produce good performance for other different detection datasets. To avoid the above problems, in this paper we design a paired semantic segmentation network to generate more accurate rotated anchors with smaller numbers. Specifically, the paired segmentation network predicts four parts (i.e., top-left, bottom-right, top-right, and bottom-left parts) of ships. By combining paired top-left and bottom-right parts (or top-right and bottom-left parts), we can take the minimum bounding box of these two parts as the rotated anchor. This way can be more robust to different ship datasets, and the generated anchors are more accurate and have fewer numbers. Furthermore, to effectively use fine-scale detail information and coarse-scale semantic information, we use the magnified convolutional features to classify and regress the generated rotated anchors. Meanwhile, the horizontal minimum bounding box of the rotated anchor is also used to combine more context information. We compare the proposed algorithm with state-of-the-art object-detection methods for natural images and ship-detection methods, and demonstrate the superiority of our method.
Xyreum: A High-Performance and Scalable Blockchain for IIoT Security and Privacy

As cyber attacks to Industrial Internet of Things (IIoT) remain a major challenge, blockchain has emerged as a promising technology for IIoT security due to its decentralization and immutability characteristics. Existing blockchain designs, however, introduce high computational complexity and latency challenges which are unsuitable for IIoT. This paper proposes Xyreum, a new high-performance and scalable blockchain for enhanced IIoT security and privacy. Xyreum uses a Time-based Zero-Knowledge Proof of Knowledge (T-ZKPK) with authenticated encryption to perform Mutual Multi-Factor Authentication (MMFA). T-ZKPK properties are also used to support Key Establishment (KE) for securing transactions. Our approach for reaching consensus, which is a blockchain group decision-making process, is based on lightweight cryptographic algorithms. We evaluate our scheme with respect to security, privacy, and performance, and the results show that, compared with existing relevant blockchain solutions, our scheme is secure, privacy-preserving, and achieves a significant decrease in computation complexity and latency performance with high scalability. Furthermore, we explain how to use our scheme to strengthen the security of the REMME protocol, a blockchain-based security protocol deployed in several application domains.
Heterogeneity-aware Gradient Coding for Straggler Tolerance

Gradient descent algorithms are widely used in machine learning. In order to deal with huge volume of data, we consider the implementation of gradient descent algorithms in a distributed computing setting where multiple workers compute the gradient over some partial data and the master node aggregates their results to obtain the gradient over the whole data. However, its performance can be severely affected by straggler workers. Recently, some coding-based approaches are introduced to mitigate the straggler problem, but they are efficient only when the workers are homogeneous, i.e., having the same computation capabilities. In this paper, we consider that the workers are heterogenous which are common in modern distributed systems. We propose a novel heterogeneity-aware gradient coding scheme which can not only tolerate a predetermined number of stragglers but also fully utilize the computation capabilities of heterogenous workers. We show that this scheme is optimal when the computation capabilities of workers are estimated accurately. A variant of this scheme is further proposed to improve the performance when the estimations of the computation capabilities are not so accurate. We conduct our schemes for gradient descent based image classification on QingCloud clusters. Evaluation results show that our schemes can reduce the whole computation time by up to 3× compared with a state-of-the-art coding scheme.
Data Capsule: A New Paradigm for Automatic Compliance with Data Privacy Regulations

The increasing pace of data collection has led to increasing awareness of privacy risks, resulting in new data privacy regulations like General data Protection Regulation (GDPR). Such regulations are an important step, but automatic compliance checking is challenging. In this work, we present a new paradigm, Data Capsule, for automatic compliance checking of data privacy regulations in heterogeneous data processing infrastructures. Our key insight is to pair up a data subject's data with a policy governing how the data is processed. Specified in our formal policy language: PrivPolicy, the policy is created and provided by the data subject alongside the data, and is associated with the data throughout the life-cycle of data processing (e.g., data transformation by data processing systems, data aggregation of multiple data subjects' data). We introduce a solution for static enforcement of privacy policies based on the concept of residual policies, and present a novel algorithm based on abstract interpretation for deriving residual policies in PrivPolicy. Our solution ensures compliance automatically, and is designed for deployment alongside existing infrastructure. We also design and develop PrivGuard, a reference data capsule manager that implements all the functionalities of Data Capsule paradigm .
<scp>DIY</scp> gerontechnology: circumventing mismatched technologies and bureaucratic procedure by creating care technologies of one's own

Abstract This study analyses ‘Do‐It‐Yourself’ ( DIY ) gerontechnologies and shows that they can be viable and valuable alternatives to ‘ready‐made’ gerontechnologies. Using the concept of innosumption, we analyze the work of care workers in gerontechnology showrooms in Norway. We show how and why care workers will sometimes advice older adults to assemble DIY ‐gerontechnologies. Such DIY ‐gerontechnologies are not high‐tech solutions made by technology producers, but creative solutions that older adults’ suit to their specific needs and assemble for themselves from mundane objects that are available in shops. So far, analyses of the design, implementation and use of gerontechnologies have almost exclusively focused on professionally designed and produced ‘ready‐made’ gerontechnologies. But for various reasons, ready‐made gerontechnologies often do not fit in well with the lives of older people. In such cases, care workers guide older people to the innosumption of DIY ‐gerontechnologies that offer workable solutions that are useful, quickly implemented, easily understandable and often cheap. We show that and how the existence of DIY ‐gerontechnologies questions the reasons behind the strong and widely accepted assumption that only high‐tech innovations are a proper solution to the needs of older people.
Quantum trajectory theory of few-photon cavity-QED systems with a time-delayed coherent feedback

We describe an efficient approach to modeling cavity quantum electrodynamics (QED) with a time-delayed coherent feedback using quantum trajectory simulations. An analytical set of equations is derived to exploit the advantages of trajectories in the presence of the non-Markovian dynamics, where adjustments to the standard stochastic dynamics are discussed. In the weak excitation regime, we first verify that our approach recovers known results obtained with other simulation methods and demonstrate how a coherent feedback loop can increase the photon lifetime in typical cavity-QED systems. We also highlight the underlying stochastic dynamics. We then explore the nonlinear few-photon regime of cavity QED, under the restriction of at most one photon at a time in the feedback loop. In particular, we show how feedback affects the cavity photoluminescence (populations versus laser detuning) and describe how one must account for conditioning in the presence of feedback; specifically, the system observables must be conditioned on no photon detections at the feedback output channel occurring.