Reviewerly/exHarmony huggingface dataset:
Statistically Evolving Fuzzy Inference System for Non-Gaussian Noises

Non-Gaussian noises always exist in the nonlinear system, which usually lead to inconsistency and divergence of the regression and identification applications. The conventional evolving fuzzy systems (EFSs) in common sense have succeeded to conquer the uncertainties and external disturbance employing the specific variable structure characteristic. However, non-Gaussian noises would trigger the frequent changes of structure under the transient criteria, which severely degrades performance. Statistical criterion provides an informed choice of the strategies of the structure evolution, utilizing the approximation uncertainty as the observation of model sufficiency. The approximation uncertainty can be always decomposed into model uncertainty term and noise term, and is suitable for the non-Gaussian noise condition, especially relaxing the traditional Gaussian assumption. In this article, a novel incremental statistical evolving fuzzy inference system (SEFIS) is proposed, which has the capacity of updating the system parameters, and evolving the structure components to integrate new knowledge in the new process characteristic, system behavior, and operating conditions with non-Gaussian noises. The system generates a new rule based on the statistical model sufficiency which gives so insight into whether models are reliable and their approximations can be trusted. The nearest rule presents the inactive rule under the current data stream and further would be deleted without losing any information and accuracy of the subsequent trained models when the model sufficiency is satisfied. In our article, an adaptive maximum correntropy extend Kalman filter is derived to update the parameters of the evolving rules to cope with the non-Gaussian noises problems to further improve the robustness of parameter updating process. The parameter updating process shares an estimate of the uncertainty with the criteria of the structure evolving process to make the computation less of a burden dramatically. The simulation studies show that the proposed SEFIS has faster learning speed and is more accurate than the existing evolving fuzzy systems (EFSs) in the case of noise free and noisy conditions.
High throughput novel architectures of TEA family for high speed IoT and RFID applications

The current era of ubiquitous computing has led to the emergence of a sub-domain in cryptography called Lightweight Cryptography, which deals with imparting adequate security to resource constraint devices like IoT devices, RFID tags with a suitable choice of design metrics. The advancement in network connectivity and data handling capabilities shows the tremendous growth of IoT in physical life. The number of connected devices have been increasing throughout IoT applications, leaving severe security concerns behind. There is a need of secure communication which can be fulfilled with lightweight algorithms. The motive of this work is to implement the optimized lightweight ciphers and model its design metrics. Design has to be simulated to implement the cipher in hardware from which different metrics can be measured. TEA, XTEA and XXTEA ciphers have been used to fulfil the objective stated and were modelled, implemented and optimized on specific hardware technology like Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC) platform. Designs have been implemented to examine numerous properties like block sizes, rounds of implementations and key scheduling part. This paper presents four hardware architectures namely TEA (T1), XTEA (T2), XXTEA (T3) and hybrid model (T4). T1, T2 and T3 have been implemented using pipelined method with percentage improvement in frequency is 75.9%, 162% and 89%. Similarly, the enhancement in terms of area has been 85.43%, 57.08% and 90.79%. T4 presents a hybrid model which is also a pipelined architecture that combines TEA family (TEA, XTEA and XXTEA) in a single design. The percentage improvement of gate equivalent (GE) for T2 is 47.50%. The hybrid model (T4) has the same throughput as that of T1, T2 & T3 and has less GE when compared to combined GE of all three. Efficiency improvement of all the novel architectures are more than eighteen times as compared to the existing literature.
Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.
An Optimized Architecture for Binary Huff Curves With Improved Security

In order to compute the elliptic curve point addition and doubling, the unified point addition law provides better protection against side channel attacks. One of the elliptic curve models, providing the unified point addition, is Binary Huff Curves (BHC). Recently, a new unified method for BHC with improved security against side channel attacks, has been proposed. Therefore, an optimized hardware architecture in terms of higher clock frequency and throughput for the new unified point addition method is required. Consequently, this article has three major contributions: The first contribution is to modify the unified addition law from hardware implementation point of view, resulting 31% decrease in memory requirements. The second contribution is the analysis of memory optimized unified law in the context of pipelining. In this regard, this article proposes an instruction scheduling policy for a 2-stage pipelined architecture which provides 36% reduction in the number of required clock cycles, 14.3% improvement in clock frequency, 53.2% decrease in computation time and 54.5% improvement in throughput/area. Finally, the third contribution is to propose a 4-stage pipelined architecture. The proposed 4-stage pipelined architecture provides an additional 19% improvement in clock frequency and 10% increase in throughput, as compared to the 2-stage pipelined architecture.
Intelligent Data Collaboration in Heterogeneous-device IoT Platforms

The merging boundaries between edge computing and deep learning are forging a new blueprint for the Internet of Things (IoT). However, the low-quality of data in many IoT platforms, especially those composed of heterogeneous devices, is hindering the development of high-quality applications for those platforms. The solution presented in this article is intelligent data collaboration, i.e., the concept of deep learning providing IoT with the ability to adaptively collaborate to accomplish a task. Here, we outline the concept of intelligent data collaboration in detail and present a mathematical model in general form. To demonstrate one possible case where intelligent data collaboration would be useful, we prepared an implementation called adaptive data cleaning (ADC), designed to filter noisy data out of temperature readings in an IoT base station network. ADC primarily consists of a denoising autoencoder LSTM for predictions and a four-level data processing mechanism to perform the filtering. Comparisons between ADC and a maximum slop method show ADC with the lowest false error and the best filtering rates.
ALA-Net: Adaptive Lesion-Aware Attention Network for 3D Colorectal Tumor Segmentation

Accurate and reliable segmentation of colorectal tumors and surrounding colorectal tissues on 3D magnetic resonance images has critical importance in preoperative prediction, staging, and radiotherapy. Previous works simply combine multilevel features without aggregating representative semantic information and without compensating for the loss of spatial information caused by down-sampling. Therefore, they are vulnerable to noise from complex backgrounds and suffer from misclassification and target incompleteness-related failures. In this paper, we address these limitations with a novel adaptive lesion-aware attention network (ALA-Net) which explicitly integrates useful contextual information with spatial details and captures richer feature dependencies based on 3D attention mechanisms. The model comprises two parallel encoding paths. One of these is designed to explore global contextual features and enlarge the receptive field using a recurrent strategy. The other captures sharper object boundaries and the details of small objects that are lost in repeated down-sampling layers. Our lesion-aware attention module adaptively captures long-range semantic dependencies and highlights the most discriminative features, improving semantic consistency and completeness. Furthermore, we introduce a prediction aggregation module to combine multiscale feature maps and to further filter out irrelevant information for precise voxel-wise prediction. Experimental results show that ALA-Net outperforms state-of-the-art methods and inherently generalizes well to other 3D medical images segmentation tasks, providing multiple benefits in terms of target completeness, reduction of false positives, and accurate detection of ambiguous lesion regions.
Video-Based Sign Language Translation System Using Machine Learning

The development of an interactive real-time video-based sign language translation system powered by efficient machine learning algorithms which is commonly developed for deaf-dumb people who are not able to hear or speak and is difficult for them to communicate among themselves or with normal people. Gesture and human activity recognition both are crucial for detecting the sign language as well as the behavior of an individual. These components are rapidly growing domains, enabling higher automation in households as well as in industries. Since extracting the features from continuous hand movements is complex and traditional sign language recognition gloves are costly, the combination of two deep learning algorithms, CNN and RNN can be used for automated sign language recognition. When both of these algorithms are used accuracy of the system also increases (the estimated accuracy is noted to be 92.4% on dynamic hand gestures studied on most of the available datasets). The system will then be able to translate the recognized sign language to desired text and then to speech for further communication using open-source Text-To-Speech API with python. This type of system has the potential in the future to enable a person to give the presentation or join a video conference in business or educational platform in which image or video-based representation of sign language can be projected as the person is speaking on a real-time basis. This architecture is well constructed and hence can solve many difficulties in communication purposes.
Towards High Resolution Video Quality Assessment in the Crowd

Assessing high resolution video quality is usually performed using controlled, defined, and standardized lab tests. This method of acquiring human ratings in a lab environment is time-consuming and may also not reflect the typical viewing conditions. To overcome these disadvantages, crowd testing paradigms have been used for assessing video quality in general. Crowdsourcing-based tests enable a more diverse set of participants and also use a realistic hardware setup and viewing environment of typical users. However, obtaining valid ratings for high-resolution video quality poses several problems. Example issues are that streaming of such high-bandwidth content may not be feasible for some users, or that crowd participants lack an appropriate, high-resolution display device. In this paper, we propose a method to overcome such problems and conduct a crowd test using for higher resolution content by using a 540 p cutout from the center of the original 2160p video. To this aim, we use the videos from Test#1 of the publicly available dataset AVT-VQDB-UHD-1, which contains videos up to a resolution of UHD-1. The quality-labels available from that lab test allow us to compare the results with the crowd test presented in this paper. It is shown that there is a Pearson correlation of 0.96 between the lab and crowd tests and hence such crowd tests can reliably be used for video assessment of higher resolution content. The overall implementation of the crowd test framework and the results are made publicly available for further research and reproducibility <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A study of dynamic fuzzy cognitive map model with group consensus based on linguistic variables

A fuzzy cognitive map (FCM) is an analysis tool that uses a graph structure to show the causal relationships of influence factors in a decision-making system. During the decision-making process, it is reasonable for experts to use linguistic variables to express their subjective opinions. However, few studies have discussed methods for aggregating the linguistic opinions of experts to reach a group consensus in FCM. In addition, the interaction weights among the factors in FCM will usually change over time in a real environment. Therefore, we applied the learning algorithm to adjust the interaction weights among the factors in the steps of FCM, after which we proposed a dynamic fuzzy cognitive map model with group consensus based on the linguistic evaluations in this study. Finally, we presented a case study using the proposed model to illustrate the development possibility of the Internet of Things (IoT) industry in Taiwan. According to the analysis results, we found that the development prospects for the IoT industry in Taiwan are optimistic. The four key factors for IoT industry development were found to be the degree of authorization and trust, the development of application technologies, the complexity of systems and equipment and cross-platform possibility.
Family and Friend Communication over Distance in Canada During the COVID-19 Pandemic

During the COVID-19 pandemic, communication technologies have allowed people to maintain connections with their loved ones over distance. At the same time, we do not yet have a deep understanding of if and how communication needs amongst family and friends change as a result of physical distancing and travel restrictions and how technologies could be better designed to support these needs. For these reasons, we conducted an exploratory study to investigate the use of communication technologies and family communication needs during the first fourth months of the COVID-19 pandemic in Canada. We used contextual interviews with 18 participants and an open-ended survey with 12 respondents. Our results show that people began the pandemic with a period of shifting and trialing new communication practices; this included increased communications with family and friends. People tried to recreate in-person situations with large group video calls beyond the typical two-household connection found pre-pandemic. This created challenges related to control and participation, and saw people explore ways to increase a sense of shared atmosphere over distance with efforts to increase physicality. Yet large amounts of technology use generally did not persist as participants abandoned many forms of online interaction over time in a form of technology detachment and sometimes cleanse. These results point to design lessons for times of extreme disconnection between family and friends, such as during a pandemic, where control, participation, and atmosphere receive deep consideration.
Channel pruning guided by spatial and channel attention for DNNs in intelligent edge computing

Deep Neural Networks (DNNs) have achieved remarkable success in many computer vision tasks recently, but the huge number of parameters and the high computation overhead hinder their deployments on resource-constrained edge devices. It is worth noting that channel pruning is an effective approach for compressing DNN models. A critical challenge is to determine which channels are to be removed, so that the model accuracy will not be negatively affected. In this paper, we first propose Spatial and Channel Attention (SCA), a new attention module combining both spatial and channel attention that respectively focuses on “where” and “what” are the most informative parts. Guided by the scale values generated by SCA for measuring channel importance, we further propose a new channel pruning approach called Channel Pruning guided by Spatial and Channel Attention (CPSCA). Experimental results indicate that SCA achieves the best inference accuracy, while incurring negligibly extra resource consumption, compared to other state-of-the-art attention modules. Our evaluation on two benchmark datasets shows that, with the guidance of SCA, our CPSCA approach achieves higher inference accuracy than other state-of-the-art pruning methods under the same pruning ratios.
Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency

A video can be represented by the composition of appearance and motion. Appearance (or content) expresses the information invariant throughout time, and motion describes the time-variant movement. Here, we propose self-supervised approaches for video Generative Adversarial Networks (GANs) to achieve the appearance consistency and motion coherency in videos. Specifically, the dual discriminators for image and video individually learn to solve their own pretext tasks; appearance contrastive learning and temporal structure puzzle. The proposed tasks enable the discriminators to learn representations of appearance and temporal context, and force the generator to synthesize videos with consistent appearance and natural flow of motions. Extensive experiments in facial expression and human action public benchmarks show that our method outperforms the state-of-the-art video GANs. Moreover, consistent improvements regardless of the architecture of video GANs confirm that our framework is generic.
A Tale of Two CILs: The Connections between Class Incremental Learning and Class Imbalanced Learning, and Beyond

Catastrophic forgetting, the main challenge of Class Incremental Learning, is closely related to the classifier’s bias due to imbalanced data, and most researchers resort to empirical techniques to remove the bias. Such anti-bias tricks share many ideas with the field of Class Imbalanced Learning, which encourages us to reflect on why these tricks work, and how we can design more principled solutions from a different perspective. In this paper, we comprehensively analyze the connections and seek possible collaborations between these two fields, i.e. Class Incremental Learning and Class Imbalanced Learning. Specifically, we first provide a panoramic view of recent bias correction tricks from the perspective of handling class imbalance. Then, we show that an adapted post-scaling technique which originates from Class Imbalanced Learning is on par with or even outperforms SOTA Class Incremental Learning method. Visualization via violin plots and polar charts further sheds light on how SOTA methods address the class imbalance problem from a more intuitive geometric perspective. These findings may encourage further infiltration between the two closely connected fields, but also raise concerns about whether it is correct that Class Incremental Learning degenerates into a class imbalance problem.
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems.A recent study showed that manual summarization of consumer health questions brings significant improvement in retrieving relevant answers.However, the automatic summarization of long questions is a challenging task due to the lack of training data and the complexity of the related subtasks, such as the question focus and type recognition.In this paper, we introduce a reinforcement learning-based framework for abstractive question summarization.We propose two novel rewards obtained from the downstream tasks of (i) question-type identification and (ii) question-focus recognition to regularize the question generation model.These rewards ensure the generation of semantically valid questions and encourage the inclusion of key medical entities/foci in the question summary.We evaluated our proposed method on two benchmark datasets and achieved higher performance over state-of-theart models.The manual evaluation of the summaries reveals that the generated questions are more diverse and have fewer factual inconsistencies than the baseline summaries.
Least kth-Order and Rényi Generative Adversarial Networks

Abstract We investigate the use of parameterized families of information-theoretic measures to generalize the loss functions of generative adversarial networks (GANs) with the objective of improving performance. A new generator loss function, least kth-order GAN (LkGAN), is introduced, generalizing the least squares GANs (LSGANs) by using a kth-order absolute error distortion measure with k≥1 (which recovers the LSGAN loss function when k=2). It is shown that minimizing this generalized loss function under an (unconstrained) optimal discriminator is equivalent to minimizing the kth-order Pearson-Vajda divergence. Another novel GAN generator loss function is next proposed in terms of Rényi cross-entropy functionals with order α&amp;gt;0, α≠1. It is demonstrated that this Rényi-centric generalized loss function, which provably reduces to the original GAN loss function as α→1, preserves the equilibrium point satisfied by the original GAN based on the Jensen-Rényi divergence, a natural extension of the Jensen-Shannon divergence. Experimental results indicate that the proposed loss functions, applied to the MNIST and CelebA data sets, under both DCGAN and StyleGAN architectures, confer performance benefits by virtue of the extra degrees of freedom provided by the parameters k and α, respectively. More specifically, experiments show improvements with regard to the quality of the generated images as measured by the Fréchet inception distance score and training stability. While it was applied to GANs in this study, the proposed approach is generic and can be used in other applications of information theory to deep learning, for example, the issues of fairness or privacy in artificial intelligence.