Reviewerly/exHarmony huggingface dataset:
Exploring Inequalities in the Social, Spatial and Material Practices of Teaching and Learning in Pandemic Times

Abstract This article conceptualises higher education as a complex and dynamic set of entangled social, spatial and material practices — enacted, adapted and contested across spaces and technologies as these interact with diverse learners, teachers, curricula and contexts. Using modes of enquiry that start from this inherent complexity and intersecting these with contemporary disability and education studies, I ask how some of the normative social and spatial practices of higher education are being surfaced by the pandemic. Rather than framing Covid-19 as a massive shift from ‘normal’ (face-to-face) to ‘abnormal’ (virtual) delivery modes, I propose that its impact both continues and alters assumptions about what constitutes ‘proper’ university education, and both perpetuates and disrupts what is ‘noticed’, valued and supported in conventional teaching and learning processes. To do this, I will focus on two themes in current HE practices in the UK, as examples of what such an approach can open-up to view. This starts from the already existing tensions, complexities and contradictions as to what should constitute appropriate teacher and student behaviours and settings, and how this ‘normality’ is often being perceived as being lost because of the pandemic. By engaging with existing literature about longer-term patterns of inequalities in access and inclusion across physical and virtual HE learning environments, I hope to show some underlying problems in how student competency is being evidenced in virtual as compared to physical space and some ways the pandemic has exposed the unevenness of diverse student and staff relationships to space, time and technologies and the differential impacts on their educational experiences.
Scaling Down Power Utilization with Optimal Virtual Machine Placement Scheme for Cloud Data Center Resources: A Performance Evaluation

Cloud computing provides IT Services to the user throughout the world according to their needs. There is a huge usage of electric power dues to the accelerating demand for large scale data centres required for processing potential, by scientific web operations. To provide high-level computation in large scale for a distributed system such as clouds an enormous amount of energy is consumed and handling this energy by applying an optimal virtual machine scheme is a major task. We have defined an architectural framework for energy conscious mapping of available virtual machines to appropriate data center resources in addition to dynamic combination of virtual machine scheduling scheme in this paper. Clusters of the server are contained by the cloud infrastructure modelled. It also represents the cluster management and the factors inside the clusters. Also, the data recovery is presented in a best possible way, taking in consideration that backup not only act as a backup but also helpful in the energy consideration process, by using two space backup (one in guiding cluster and one at secondary cluster) making model more powerful and resistible in case of data recovery. Experiments results demonstrate that the performance of the proposed scheme is found to be remarkable in comparison to the other state-of-the-art competing scheme of its class.
Semantic-Aware Dehazing Network With Adaptive Feature Fusion

Despite that convolutional neural networks (CNNs) have shown high-quality reconstruction for single image dehazing, recovering natural and realistic dehazed results remains a challenging problem due to semantic confusion in the hazy scene. In this article, we show that it is possible to recover textures faithfully by incorporating semantic prior into dehazing network since objects in haze-free images tend to show certain shapes, textures, and colors. We propose a semantic-aware dehazing network (SDNet) in which the semantic prior is taken as a color constraint for dehazing, benefiting the acquisition of a reasonable scene configuration. In addition, we design a densely connected block to capture global and local information for dehazing and semantic prior estimation. To eliminate the unnatural appearance of some objects, we propose to fuse the features from shallow and deep layers adaptively. Experimental results demonstrate that our proposed model performs favorably against the state-of-the-art single image dehazing approaches.
Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-Speech

Personalizing a speech synthesis system is a highly desired application, where the system can generate speech with the user’s voice with rare enrolled recordings. There are two main approaches to build such a system in recent works: speaker adaptation and speaker encoding. On the one hand, speaker adaptation methods fine-tune a trained multi-speaker text-to-speech (TTS) model with few enrolled samples. However, they require at least thousands of fine-tuning steps for high-quality adaptation, making it hard to apply on devices. On the other hand, speaker encoding methods encode enrollment utterances into a speaker embedding. The trained TTS model can synthesize the user’s speech conditioned on the corresponding speaker embedding. Nevertheless, the speaker encoder suffers from the generalization gap between the seen and unseen speakers. In this paper, we propose applying a meta-learning algorithm to the speaker adaptation method. More specifically, we use Model Agnostic Meta-Learning (MAML) as the training algorithm of a multi-speaker TTS model, which aims to find a great meta-initialization to adapt the model to any few-shot speaker adaptation tasks quickly. Therefore, we can also adapt the meta-trained TTS model to unseen speakers efficiently. Our experiments compare the proposed method (Meta-TTS) with two baselines: a speaker adaptation method baseline and a speaker encoding method baseline. The evaluation results show that Meta-TTS can synthesize high speaker-similarity speech from few enrollment samples with fewer adaptation steps than the speaker adaptation baseline and outperforms the speaker encoding baseline under the same training scheme. When the speaker encoder of the baseline is pre-trained with extra 8371 speakers of data, Meta-TTS can still outperform the baseline on LibriTTS dataset and achieve comparable results on VCTK dataset.
Variational Bayesian-Based Robust Cubature Kalman Filter With Application on SINS/GPS Integrated Navigation System

In this article, we focus on addressing the nonlinear filtering problem with unknown measurement noise covariance and measurement outliers, which may be encountered in the application in strapdown inertial navigation system/global positioning system integrated navigation system. Although the existing methods, such as the adaptive Kalman filter, are widely used in the integrated navigation system, their estimation accuracy is poor, this paper proposes a variational Bayesian-based robust cubature Kalman filter to address this problem, which not only retains the adaptivity when the measurement noise is unknown but also exhibits robustness in the presence of measurement outliers, First, the variational Bayesian method is applied to the estimation of measurement noise, then the maximum correntropy criterion is introduced to replace the minimum mean square error criterion, which improves the robust performance of the filter. The numerical simulation demonstrates that the proposed filter outperforms the existing filters both in estimation accuracy and robustness, and the effectiveness of the proposed filter is verified on the integrated navigation system.
On the TOCTOU Problem in Remote Attestation

Much attention has been devoted to verifying software integrity of remote embedded (IoT) devices. Many techniques, with different assumptions and security guarantees, have been proposed under the common umbrella of so-called Remote Attestation (RA). Aside from executable's integrity verification, RA serves as a foundation for many security services, such as proofs of memory erasure, system reset, software update, and verification of runtime properties. Prior RA techniques verify the remote device's binary at the time when RA functionality is executed, thus providing no information about the device's binary before current RA execution or between consecutive RA executions. This implies that presence of transient malware (in the form of modified binary) may be undetected. In other words, if transient malware infects a device (by modifying its binary), performs its nefarious tasks, and erases itself before the next attestation, its temporary presence will not be detected. This important problem, called Time-Of-Check-Time-Of-Use ( TOCTOU ), is well-known in the research literature and remains unaddressed in the context of hybrid RA.
DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories

Recent advancements in Deep Neural Networks (DNNs) have enabled widespread deployment in multiple security-sensitive domains. The need for resource-intensive training and the use of valuable domain-specific training data have made these models the top intellectual property (IP) for model owners. One of the major threats to DNN privacy is model extraction attacks where adversaries attempt to steal sensitive information in DNN models. In this work, we propose an advanced model extraction framework DeepSteal that steals DNN weights remotely for the first time with the aid of a memory side-channel attack. Our proposed DeepSteal comprises two key stages. Firstly, we develop a new weight bit information extraction method, called HammerLeak, through adopting the rowhammer-based fault technique as the information leakage vector. HammerLeak leverages several novel system-level techniques tailored for DNN applications to enable fast and efficient weight stealing. Secondly, we propose a novel substitute model training algorithm with Mean Clustering weight penalty, which leverages the partial leaked bit information effectively and generates a substitute prototype of the target victim model. We evaluate the proposed model extraction framework on three popular image datasets (e.g., CIFAR-10/100/GTSRB) and four DNN architectures (e.g., ResNet-18/34/Wide-ResNetNGG-11). The extracted substitute model has successfully achieved more than 90% test accuracy on deep residual networks for the CIFAR-10 dataset. Moreover, our extracted substitute model could also generate effective adversarial input samples to fool the victim model. Notably, it achieves similar performance (i.e., ~1-2% test accuracy under attack) as white-box adversarial input attack (e.g., PGD/Trades).
Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition

With the rapid growth of the Internet of Things (IoT), smart systems and applications are equipped with an increasing number of wearable sensors and mobile devices. These sensors are used not only to collect data but, more importantly, to assist in tracking and analyzing the daily human activities. Sensor-based human activity recognition is a hotspot and starts to employ deep learning approaches to supersede traditional shallow learning that rely on hand-crafted features. Although many successful methods have been proposed, there are three challenges to overcome: (1) deep model’s performance overly depends on the data size; (2) deep model cannot explicitly capture abundant sample distribution characteristics; (3) deep model cannot jointly consider sample features, sample distribution characteristics, and the relationship between the two. To address these issues, we propose a meta-learning-based graph prototypical model with priority attention mechanism for sensor-based human activity recognition. This approach learns not only sample features and sample distribution characteristics via meta-learning-based graph prototypical model, but also the embeddings derived from priority attention mechanism that mines and utilizes relations between sample features and sample distribution characteristics. What is more, the knowledge learned through our approach can be seen as a priori applicable to improve the performance for other general reasoning tasks. Experimental results on fourteen datasets demonstrate that the proposed approach significantly outperforms other state-of-the-art methods. On the other hand, experiments of applying our model to two other tasks show that our model effectively supports other recognition tasks related to human activity and improves performance on the datasets of these tasks.
Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data

Multiplexed tissue imaging facilitates the diagnosis and understanding of complex disease traits. However, the analysis of such digital images heavily relies on the experience of anatomical pathologists for the review, annotation and description of tissue features. In addition, the wider use of data from tissue atlases in basic and translational research and in classrooms would benefit from software that facilitates the easy visualization and sharing of the images and the results of their analyses. In this Perspective, we describe the ecosystem of software available for the analysis of tissue images and discuss the need for interactive online guides that help histopathologists make complex images comprehensible to non-specialists. We illustrate this idea via a software interface (Minerva), accessible via web browsers, that integrates multi-omic and tissue-atlas features. We argue that such interactive narrative guides can effectively disseminate digital histology data and aid their interpretation.
Secure Graph Analysis at Scale

We present a highly-scalable secure computation of graph algorithms, which hides all information about the topology of the graph or other input values associated with nodes or edges. The setting is where all nodes and edges of the graph are secret-shared between multiple servers, and a secure computation protocol is run between these servers. While the method is general, we demonstrate it in a 3-server setting with an honest majority, with either semi-honest security or full security. A major technical contribution of our work is replacing the usage of secure sort protocols with secure shuffles, which are much more efficient. Full security against malicious behavior is achieved by adding an efficient verification for the shuffle operation, and computing circuits using fully secure protocols. We demonstrate the applicability of this technology by implementing two major algorithms: computing breadth-first search (BFS), which is also useful for contact tracing on private contact graphs, and computing maximal independent set (MIS). We implement both algorithms, with both semi-honest and full security, and run them within seconds on graphs of millions of elements.
Sharding-Hashgraph: A High-Performance Blockchain-Based Framework for Industrial Internet of Things With Hashgraph Mechanism

In recent years, with the development and widespread use of blockchain, many projects have introduced blockchain technology to solve the increasingly serious security problems of the Industrial Internet of Things (IIoT). However, due to the conflict between the operational performance and security of the blockchain system, the conflict between transparency and privacy, and the compatibility issues with a large number of IIoT devices running together, the mainstream blockchain system cannot be applied to IIoT scenarios. In order to solve these problems, in this article, we propose an IIoT distributed data system based on blockchain technology. We provide a novel system architecture for different IIoT devices to deploy high-performance blockchain systems in many scenarios, such as smart factory networks. To improve the performance of the blockchain network, we adopt the sharding hashgraph consensus mechanism and introduce a node evaluation mechanism based on the state of the node, which is applied to divide a large number of nodes into many shards dynamically. We abstract the node sharding problem as a joint optimization problem and use deep reinforcement learning to solve it. Finally, we compared with asynchronous Byzantine consensus algorithms, such as HoneybadgerBFT and BEAT, which validated the performance of this system architecture.
Chaotical PRNG based on composition of logistic and tent maps using deep-zoom

We propose the deep-zoom analysis of the composition of the logistic map and the tent map, which are well-known discrete one-dimensional chaotic maps. The deep-zoom technique transforms each point of a given chaotic orbit by removing the first k-digits after decimal separator. We found that the pseudo-random qualities of the composition map as a pseudo-random number generator (PRNG) improve as the k parameter increases. This was evidenced by the fact that it successfully passed the randomness tests and even outperformed the k-logistic map and the k-tent map PRNG. These dynamic properties show that the application of deep-zoom to the composition of chaotic maps, at least to these two well-known maps, is suitable for better randomization for PRNG purposes as well as for cryptographic systems.
With a Little Help from My Friends

Anonymous credentials (ACs) are a powerful cryptographic tool for the secure use of digital services, when simultaneously aiming for strong privacy guarantees of users combined with strong authentication guarantees for providers of services. They allow users to selectively prove possession of attributes encoded in a credential without revealing any other meaningful information about themselves. While there is a significant body of research on AC systems, modern use-cases of ACs such as mobile applications come with various requirements not sufficiently considered so far. These include preventing the sharing of credentials and coping with resource constraints of the platforms (e.g., smart cards such as SIM cards in smartphones). Such aspects are typically out of scope of AC constructions, and, thus AC systems that can be considered entirely practical have been elusive so far.
An Energy Efficient Agent Aware Proactive Fault Tolerance for Preventing Deterioration of Virtual Machines Within Cloud Environment

Nowadays, Cloud Computing is widely adopted in current scenario for both personal and professional usage. With advent of technology, large scale usage and increasing growth of cloud computing resources has led to an important issue which is cloud service reliability and high energy consumption. The major issue in cloud computing is data availability, backup replication, data efficiency and reliability because of the failures encountered during the execution. So, for ensuring reliability and availability and limit the use of energy in cloud a technique for fault tolerance must be developed. Presently two main types of fault tolerant techniques such as proactive and reactive fault tolerance are available. In the existing fault tolerant techniques, the focus is mainly on the way to tolerate fault but co-ordination between VMs is missing. Reactive fault tolerance techniques work only after fault is encountered i.e., the system is diagnosed after fault occurs and these techniques lower the effects of faults. To overcome this problem, we first allocate tasks to the virtual machines by using concept of Hot Queues and Cold Queues to reduce total energy consumption and allocating the load among VMs in such a manner that no task goes into starvation state. In this paper agent-based monitoring mechanism is proposed to dynamically monitor the running statistics of all jobs running on VMs. Moreover, our agent-based monitoring system is able to resolve the problem of deteriorating Virtual Machine. In case deterioration is detected, migration is initiated causing high fault tolerance rate and Performance optimization in terms of overhead and execution time, energy efficiency is observed.
Design of the color classification system for sunglass lenses using PCA-PSO-ELM

Color deviation of the sunglass lens brings many problems to the pairing of sunglasses. In order to accurately classify the sunglass lens by color depth, a data acquisition system based on spectral analysis method is developed, which is composed of reflection integrating sphere, optical fiber spectrometer and optical fiber. Besides, the classification algorithm based on Principal Component Analysis, with Particle Swarm Optimization and Extreme Learning Machine is proposed. In which, PCA reduces the dimensions of the spectral reflectance data, PSO optimizes the input weights and hidden layer bias values of ELM, and the optimized ELM obtains a satisfactory classification through certain learning and training. This algorithm avoids the lengthy formula calculations in the traditional color classification method, and requires fewer hidden layer neurons to achieve high and stable classification accuracy in ELM. The classification accuracy of PCA-PSO-ELM and PCA-ELM, LM-BP, LS-SVM is compared by the experiments. It is proved that the adoption of the proposed PCA-PSO-ELM in the color classification of sunglass lenses is feasible and effective.
Health Record Security Through Multiple Watermarking on Fused Medical Images

Nowadays, there is an increasing tendency to fuse multi-modal medical image due to the high diagnostic accuracy and assessment. However, the distribution of these images and more generally, health records may bring serious copy-violation issues in healthcare applications. Further, a massive amount of electronic health records is uploaded on the cloud due to the low cost and high usability. However, outsourcing these records to the cloud may greatly increase security and privacy issues. The present article develops medical image fusion-based watermarking scheme in dual tree complex wavelet transform (DTCWT)-singular value decomposition (SVD) domain. Here, computed tomography (CT) and magnetic resonance imaging (MRI) images are partitioned into a sequence of high frequencies and low frequency by non-subsampled shearlet transform (NSST) to obtain a fused image as a carrier. To enhance the authenticity, spatial and transform domain-based embedding is conducted to conceal multi-marks into carrier image. Finally, improved encryption scheme is employed to obtain a secure marked carrier image. Objective and subjective evaluations indicate a strong robustness effect (43.64%) of the proposed algorithm compared to other methods with remarkable invisibility, payload, and security. To the best of our knowledge, studies of image fusion-based dual watermarking and encryption have not yet been developed in combination with spatial and transform domain schemes for copy protection and ownership of health records, since it is also suitable for applications in the cloud.
To talk or to touch: Unraveling consumer responses to two types of hotel in-room technology

This research reveals a novel downside effect of the emerging AI-powered voice assistant (vs. the prevalent touch panel) in the hotel in-room technology context. Building on the automated social presence theory, this research comprises two studies to explore whether, how, and why using voice assistant (vs. touch panel) affects consumers’ satisfaction levels and their behavioral intentions differently. This research documents that using AI-powered voice assistant (vs. touch panel) leads to a lower level of satisfaction due to a deficiency in perceived control, especially among consumers with independent self-construal tendency. Serial mediation tests confirm the downstream effects via the causal link of psychological mechanism (perceived control) to service evaluation (satisfaction) and to future behavioral intentions (consumption preference and WOM). This research contributes to the burgeoning literature on the AI-powered voice assistant by elucidating the phenomenon and investigating why consumers may respond differently to this emerging technology with practical implications.