Reviewerly/exHarmony huggingface dataset:
Pyramid Attention Upsampling Module for Object Detection

The core task of object detection is to extract features of various sizes by hierarchically stacking multi-scale feature maps.However, it is not easy to decide whether we should transmit semantic information to the low layers while reducing the loss of semantic information of the high-level features.In this paper, we present a novel method to reduce the loss of semantic information, and at the same time to improve the object detection performance by using the attention mechanism on the high-level layer of the feature pyramid network.The proposed method focuses on the sparse spatial information using deformable convolution v2 (DCNv2) on the lateral connection in the feature pyramid network.Specifically, the upsampling process is divided into two branches.The first one pays attention to the global context information of high-level features, and the other rescales the feature map by interpolation.Finally, by multiplying the results from the two branches, we can obtain upsampling result that pays attention to semantic information of the high-level layer.The proposed pyramid attention upsampling module has three contributions.First, It can be easily applied to any models using feature pyramid network.Second, it is possible to reduce losses in semantic information of the high-level feature map by performing context attention of the high-level layer.Third, it improves the detection performance by stacking layers up to the low layer.We used MS-COCO 2017 detection dataset to evaluate the performance of the proposed method.Experimental results show that the proposed method provided better detection performance comparing with existing feature pyramid network-base methods.
QA-Filter: A QP-Adaptive Convolutional Neural Network Filter for Video Coding

Convolutional neural network (CNN)-based filters have achieved great success in video coding. However, in most previous works, individual models were needed for each quantization parameter (QP) band, which is impractical due to limited storage resources. To explore this, our work consists of two parts. First, we propose a frequency and spatial QP-adaptive mechanism (FSQAM), which can be directly applied to the (vanilla) convolution to help any CNN filter handle different quantization noise. From the frequency domain, a FQAM that introduces the quantization step (Qstep) into the convolution is proposed. When the quantization noise increases, the ability of the CNN filter to suppress noise improves. Moreover, SQAM is further designed to compensate for the FQAM from the spatial domain. Second, based on FSQAM, a QP-adaptive CNN filter called QA-Filter that can be used under a wide range of QP is proposed. By factorizing the mixed features to high-frequency and low-frequency parts with the pair of pooling and upsampling operations, the QA-Filter and FQAM can promote each other to obtain better performance. Compared to the H.266/VVC baseline, average 5.25% and 3.84% BD-rate reductions for luma are achieved by QA-Filter with default all-intra (AI) and random-access (RA) configurations, respectively. Additionally, an up to 9.16% BD-rate reduction is achieved on the luma of sequence BasketballDrill. Besides, FSQAM achieves measurably better BD-rate performance compared with the previous QP map method.
Efficient facial emotion recognition model using deep convolutional neural network and modified joint trilateral filter

Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions and to recognize human emotions. Recently, deep learning models are utilized to improve the performance of facial emotion recognition. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, an efficient deep learning-based facial emotion recognition model is proposed. Initially, contrast-limited adaptive histogram equalization (CLAHE) is applied to improve the visibility of input images. Thereafter, a modified joint trilateral filter is applied to the obtained enhanced images to remove the impact of impulsive noise. Finally, an efficient deep convolutional neural network is designed. Adam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are conducted by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models
Blockchain for Cyber-Physical Systems: Challenges and Applications

The future of industrial systems, as represented principally by Industry 4.0, will be dependent on the convergence of recent research work in the fields of control architectures, cyber-physical systems (CPS), and the Internet of Things (IoT). CPS are sophisticated embedded systems that interact with their surroundings continuously and dynamically. Computing, communication, and physical dimensions are among them. They are crucial to smart grids, vehicle traffic networks, intelligent buildings, cooperative robotics, automotive, and avionics systems. Blockchain (BC) is a realistic method for enabling a secure, decentralized public ledger, enabling a variety of interesting new technological applications in cyber-physical systems such as the IoT, manufacturing, transportation, and supply chain, among many others. This chapter provides an in-depth examination and discussion of numerous CPS applications that have employed blockchain. Many applications, including healthcare, transportation, and cybersecurity, can benefit from blockchain technology, as will be explored in this chapter.
Question Rewriting? Assessing Its Importance for Conversational Question Answering

In conversational question answering, systems must correctly interpret the interconnected interactions and generate knowledgeable answers, which may require the retrieval of relevant information from a background repository. Recent approaches to this problem leverage neural language models, although different alternatives can be considered in terms of modules for (a) representing user questions in context, (b) retrieving the relevant background information, and (c) generating the answer. This work presents a conversational question answering system designed specifically for the Search-Oriented Conversational AI (SCAI) shared task, and reports on a detailed analysis of its question rewriting module. In particular, we considered different variations of the question rewriting module to evaluate the influence on the subsequent components, and performed a careful analysis of the results obtained with the best system configuration. Our system achieved the best performance in the shared task and our analysis emphasizes the importance of the conversation context representation for the overall system performance.
Robust and Accurate Superquadric Recovery: a Probabilistic Approach

Interpreting objects with basic geometric primitives has long been studied in computer vision. Among geometric primitives, superquadrics are well known for their ability to represent a wide range of shapes with few parameters. However, as the first and foremost step, recovering superquadrics accurately and robustly from 3D data still remains challenging. The existing methods are subject to local optima and sensitive to noise and outliers in real-world scenarios, resulting in frequent failure in capturing geometric shapes. In this paper, we propose the first probabilistic method to recover superquadrics from point clouds. Our method builds a Gaussian-uniform mixture model (GUM) on the parametric surface of a superquadric, which explicitly models the generation of outliers and noise. The superquadric recovery is formulated as a Maximum Likelihood Estimation (MLE) problem. We propose an algorithm, Expectation, Maximization, and Switching (EMS), to solve this problem, where: (1) outliers are predicted from the posterior perspective; (2) the superquadric parameter is optimized by the trust-region reflective algorithm; and (3) local optima are avoided by globally searching and switching among parameters encoding similar superquadrics. We show that our method can be extended to the multi-superquadrics recovery for complex objects. The proposed method outperforms the state-of-the-art in terms of accuracy, efficiency, and robustness on both synthetic and real-world datasets. The code is at http://github.com/bmlklwx/EMS-superquadric_fitting.git.
Efficient Implementation of AES-CTR and AES-ECB on GPUs With Applications for High-Speed FrodoKEM and Exhaustive Key Search

The Advanced Encryption Standard (AES) is a standardized block cipher widely used to protect data confidentiality. Besides that, it can be used to generate pseudo-random numbers, which has many important applications. Recently, several works demonstrated the efficient implementations of AES electronics code book (ECB) and counter (CTR) mode on GPU platforms, achieving high throughput. In this brief, we set a speed record of AES implementation, which outperformed previous implementations. In particular, the proposed AES implementation achieved throughput 9% (CTR) and 7% (ECB) higher than the state-of-the-art, bit-sliced implementation. Moreover, the proposed technique does not require round keys to be embedded into the code during compilation, which is a serious limitation found in earlier work. The proposed technique also achieved up to 63% higher throughput compared to another technique presented recently. Two use cases are presented here to verify the efficiency of the proposed AES implementation. Firstly, AES is used to generate random samples in a NIST post-quantum key encapsulation mechanism (KEM), achieving 3,350, 1,503 and 7,716 key exchanges per second on V100, T4, and RTX3080 GPUs respectively. This allows the proposed FrodoKEM implementation to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.99\times $ </tex-math></inline-formula> faster than the state-of-the-art performance. The proposed AES implementation was also used in an exhaustive key search application, achieving 11,428, 3,969, and 9,998 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 10^{6}$ </tex-math></inline-formula> encryptions per second on V100, T4, and RTX3080 GPUs, respectively.
Review of applications and user perceptions of smart home technology for health and environmental monitoring

Abstract In recent decades, smart home technology has advanced, improving the well-being and quality of life of its users. Thus, its applications have expanded, particularly in health and environmental monitoring. Numerous devices have been developed to accommodate user requirements of monitoring; however, the adoption of monitoring devices is closely related to user perception. User perception can be considered from different perspectives. One method of understanding different user perceptions is comparing wearable and nonwearable devices, owing to the differences in their obtrusiveness. The aim of this study was to systematically review the applications and user perceptions of health and environmental monitoring devices, emphasizing on the wearable and nonwearable distinction. We conducted a focused search of articles related to smart home technology and its user perceptions based on its applications. The inclusion criteria were original and peer-reviewed articles centered on health and environmental monitoring devices. We identified and analysed 159 of the 4476 relevant articles and divided the articles into two categories. The first category comprised health and environmental monitoring and their applications by the type of device. The second category comprised user perceptions of monitoring devices. The devices were grouped into wearable and nonwearable devices for our analysis. We identified user perceptions based on usefulness, ease of use, and privacy. Because wearable and nonwearable devices complement their limitations, we recommend their integration for improving user perception.
An Efficient Adaptive Differential Grouping Algorithm for Large-Scale Black-Box Optimization

Decomposition plays a significant role in cooperative coevolution (CC), which shows great potential in large-scale black-box optimization (LSBO). However, current learning-based decomposition algorithms require many fitness evaluations (FEs) to detect variable interdependencies and encounter the difficulty of threshold setting. To address these issues, this study proposes an efficient adaptive differential grouping (EADG) algorithm. Instead of homogeneously tackling different types of LSBO instances, EADG first identifies the instance type by detecting the interdependencies of a few pairs of variable subsets. Only if the instance is partially separable dose EADG further engages with it by converting its decomposition process into a search process in a binary tree. This facilitates the systematic reutilization of evaluated solutions so that half the interdependencies can be directly deduced without extra FEs. To promote the decomposition accuracy, EADG specially designs a normalized interdependency indicator that can adaptively generate a decomposition threshold according to its ordinal distribution. Theoretical analysis and experimental results show that EADG outperforms current popular decomposition algorithms. Further tests indicate that it can help CC achieve highly competitive optimization performance.
Self-Supervised Video Representation Learning Using Improved Instance-Wise Contrastive Learning and Deep Clustering

Instance-wise contrastive learning (Instance-CL), which learns to map similar instances closer and different instances farther apart in the embedding space, has achieved considerable progress in self-supervised video representation learning. However, canonical Instance-CL does not handle properly the temporal similarities between different videos, limiting the representation capabilities of learned models. This paper presents a novel two-stage framework that combines Instance-CL and unsupervised clustering to progressively learn desirable temporal representations with high intra-class compactness. Specifically, (a) we first introduce a new consistency-preserving sampling strategy to generate positive/negative pairs. Compared to the traditional sampling methods, our sampling strategy focuses more on motion dynamics, resulting in more temporal-related feature representations. (b) To further explore the temporal similarities between videos so as to encourage intra-class compactness, we set temporal representations extracted from Instance-CL as an initializer, and iteratively use k-means clustering to generate pseudo-labels for training the encoder. We term our method as Improved Instance-CL with Deep Clustering (ICDC) and apply it to two downstream tasks, including action recognition and video retrieval. Extensive experimental results show that ICDC gains considerable improvements compared to the existing self-supervised methods.
Subjective and Objective Video Quality Assessment for Windowed-6DoF Synthesized Videos

Video Quality Assessment (VQA) for windowed six Degree of Freedom (windowed-6DoF) synthesized viewpoints is conducive to the development and broadcasting of 6DoF immersive video system. To explore this new field, we propose the subjective and objective VQA studies for windowed-6DoF synthesized videos. Stage one, a pioneering windowed-6DoF synthesized video quality database with compressed distortion, rendering distortion, and viewing path discomfort are presented, in which overall 128 videos are synthesized from four sequences with different distortion combinations. Stage two, a no-reference objective VQA method is proposed. Firstly, the video is converted into one traditional spatio-temporal domain (XY-T domain) and two new spatio-temporal domains (XT-Y and TY-X domains). Secondly, the emerging rendering distortion and viewing path discomfort are measured in the XT-Y and TY-X domains based on their respective characteristic statistical models, and the common compressed distortion is measured in XY-T domain. Thirdly, the image numbers of new domains are accurately determined through optical flow based depth estimation. Finally, the regression model plus weighting strategy is used to obtain the ultimate quality score of windowed-6DoF synthesized video. Experimental results on three synthesized video databases illustrate that the proposed objective NR VQA method has a good performance in evaluating various synthesized videos, compared with other classic and state-of-the-art 2D/3D IQA/VQA metrics.
Assessment of COVID-19 data reporting in 100+ websites and apps in India

India is among the top three countries in the world both in COVID-19 case and death counts. With the pandemic far from over, timely, transparent, and accessible reporting of COVID-19 data continues to be critical for India's pandemic efforts. We systematically analyze the quality of reporting of COVID-19 data in over one hundred government platforms (web and mobile) from India. Our analyses reveal a lack of granular data in the reporting of COVID-19 surveillance, vaccination, and vacant bed availability. As of 5 June 2021, age and gender distribution are available for less than 22% of cases and deaths, and comorbidity distribution is available for less than 30% of deaths. Amid rising concerns of undercounting cases and deaths in India, our results highlight a patchy reporting of granular data even among the reported cases and deaths. Furthermore, total vaccination stratified by healthcare workers, frontline workers, and age brackets is reported by only 14 out of India's 36 subnationals (states and union territories). There is no reporting of adverse events following immunization by vaccine and event type. By showing what, where, and how much data is missing, we highlight the need for a more responsible and transparent reporting of granular COVID-19 data in India.
Domain-Adversarial-Guided Siamese Network for Unsupervised Cross-Domain 3-D Object Retrieval

Recent advances in 3-D sensors and 3-D modeling have led to the availability of massive amounts of 3-D data. It is too onerous and time consuming to manually label a plentiful of 3-D objects in real applications. In this article, we address this issue by transferring the knowledge from the existing labeled data (e.g., the annotated 2-D images or 3-D objects) to the unlabeled 3-D objects. Specifically, we propose a domain-adversarial guided siamese network (DAGSN) for unsupervised cross-domain 3-D object retrieval (CD3DOR). It is mainly composed of three key modules: 1) siamese network-based visual feature learning; 2) mutual information (MI)-based feature enhancement; and 3) conditional domain classifier-based feature adaptation. First, we design a siamese network to encode both 3-D objects and 2-D images from two domains because of its balanced accuracy and efficiency. Besides, it can guarantee the same transformation applied to both domains, which is crucial for the positive domain shift. The core issue for the retrieval task is to improve the capability of feature abstraction, but the previous CD3DOR approaches merely focus on how to eliminate the domain shift. We solve this problem by maximizing the MI between the input 3-D object or 2-D image data and the high-level feature in the second module. To eliminate the domain shift, we design a conditional domain classifier, which can exploit multiplicative interactions between the features and predictive labels, to enforce the joint alignment in both feature level and category level. Consequently, the network can generate domain-invariant yet discriminative features for both domains, which is essential for CD3DOR. Extensive experiments on two protocols, including the cross-dataset 3-D object retrieval protocol (3-D to 3-D) on PSB/NTU, and the cross-modal 3-D object retrieval protocol (2-D to 3-D) on MI3DOR-2, demonstrate that the proposed DAGSN can significantly outperform state-of-the-art CD3DOR methods.
Understanding the Resilience of Neural Network Ensembles against Faulty Training Data

Machine learning is becoming more prevalent in safety-critical systems like autonomous vehicles and medical imaging. Faulty training data, where data is either misla-belled, missing, or duplicated, can increase the chance of misclassification, resulting in serious consequences. In this paper, we evaluate the resilience of ML ensembles against faulty training data, in order to understand how to build better ensembles. To support our evaluation, we develop a fault injection framework to systematically mutate training data, and introduce two diversity metrics that capture the distribution and entropy of predicted labels. Our experiments find that ensemble learning is more resilient than any individual model and that high accuracy neural networks are not necessarily more resilient to faulty training data. Further, we find that simple majority voting suffices in most cases for resilience in ML ensembles. Finally, we observe diminishing returns for resilience as we increase the number of models in an ensemble. These findings can help machine learning developers build ensembles that are both more resilient and more efficient.
Blockchain-Based Privacy-Preserving Authentication Model Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) have gained popularity due to smart services and applications to facilitate the users on the roads. The increasing growth of users in these networks created new and complex data processing, storage, security, and privacy concerns. These networks are using centralized edge, fog, or cloud architecture for data management. User privacy is compromised in these networks due to the increasing demands and service provider’s services. To ensure the data privacy, the centralized architectures are used without privacy regulations. In this paper, we present a Blockchain-based Privacy-Preserving Authentication (BPPAU) model for ITS networks to ensures users privacy and security. The proposed model provides data storage, data accessing, and processing management by using a blockchain smartcontract system, access control policy and on demand based functions. The proposed model is tested in a simulation environment to check its performance in terms of transaction cost with data size, transaction per second analysis with block time, and computational time analysis with several transactions.
Design of logic gates using reversible gates with reduced quantum cost

Reversible logic is also called information lossless logic, since the information embedded in the circuits can be recovered, if lost. Research carried out by Landauer and Bennett proved that the energy dissipation would not occur if computation is made reversible. With this aim a number of reversible gates were designed and invented. As examples like- the Fredkin gate, the Toffoli gate, the Peres gate, and the Feynman gate. Reversible logic has extensive applications and is considered as one of the futuristic technologies. But the logic circuit designing is based on logic gates, which are non-reversible. This paper presents design of logic gates using reversible gates. These logic gates help in future implementation of higher end circuits. In this paper an attempt is made to design logic gates using reversible gates and some of the higher end circuits are also designed such as Binary-to-Grey, grey-to-Binary, Adder, Subtractor etc.