Reviewerly/exHarmony huggingface dataset:
Structure-Aware Face Clustering on a Large-Scale Graph with 10<sup>7</sup>Nodes

Face clustering is a promising method for annotating un-labeled face images. Recent supervised approaches have boosted the face clustering accuracy greatly, however their performance is still far from satisfactory. These methods can be roughly divided into global-based and local-based ones. Global-based methods suffer from the limitation of training data scale, while local-based ones are difficult to grasp the whole graph structure information and usually take a long time for inference. Previous approaches fail to tackle these two challenges simultaneously. To address the dilemma of large-scale training and efficient inference, we propose the STructure-AwaRe Face Clustering (STAR-FC) method. Specifically, we design a structure-preserved subgraph sampling strategy to explore the power of large-scale training data, which can increase the training data scale from 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> . During inference, the STAR-FC performs efficient full-graph clustering with two steps: graph parsing and graph refinement. And the concept of node intimacy is introduced in the second step to mine the local structural information. The STAR-FC gets 91.97 pairwise F-score on partial MS1M within 310s which surpasses the state-of-the-arts. Furthermore, we are the first to train on very large-scale graph with 20M nodes, and achieve superior inference results on 12M testing data. Overall, as a simple and effective method, the proposed STAR-FC provides a strong baseline for large-scale face clustering. Code is available at https://sstzal.github.io/STAR-FC/.
Feasibility of full immersive virtual reality video game on balance and cybersickness of healthy adolescents

Various virtual reality (VR) games with head-mounted displays (HMDs) can provide immersive experiences, but are often accompanied by negative experiences. We investigated the feasibility of immersive virtual reality game on balance and cybersickness in healthy adolescents. This is cross-over design. This study included 11 healthy adolescents. Balance and cybersickness were measured in the following three conditions. First, as a baseline, only balance was measured while no immersive virtual reality game was being played. Second, balance and cybersickness were measured while the participants played an immersive virtual reality game with a fixed background. Third, balance and cybersickness were measured while the participants played an immersive virtual reality game with a moving background. A force plate was used to measure balance, while the Questionnaire (VRSQ) and Simulator Sickness Questionnaire (SSQ) were used to measure cybersickness. Sway velocity and length significantly increased during the game with a moving background compared to baseline and a fixed background game (p < 0.05). VRSQ and SSQ scores significantly increased during the game with a fixed and moving background compared to baseline (p < 0.05) and were significantly higher with use of the moving versus fixed background (p < 0.05). This study demonstrated that playing an immersive virtual reality game with a moving background could negatively affect balance and cybersickness. These results will help to select game contents that can reduce side effects when applying VR HMD to various fields in the future.
Ternary Feature Masks: zero-forgetting for task-incremental learning

We propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Using masks prevents both catastrophic forgetting and backward transfer. We argue – and show experimentally – that avoiding the former largely compensates for the lack of the latter, which is rarely observed in practice. In contrast to earlier works, our masks are applied to the features (activations) of each layer instead of the weights. This considerably reduces the number of mask parameters for each new task; with more than three orders of magnitude for most networks. The encoding of the ternary masks into two bits per feature creates very little overhead to the network, avoiding scalability issues. To allow already learned features to adapt to the current task without changing the behavior of these features for previous tasks, we introduce task-specific feature normalization. Extensive experiments on several finegrained datasets and ImageNet show that our method outperforms current state-of-the-art while reducing memory overhead in comparison to weight-based approaches.
Shadow Removal with Paired and Unpaired Learning

Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photorealistic restoration of the image contents. Decades of research produced a multitude of hand-crafted restoration techniques and, more recently, learned solutions from shadowed and shadow-free training image pairs. In this work, we propose a single image shadow removal solution via self-supervised learning by using a conditioned mask. We rely on self-supervision and jointly learn deep models to remove and add shadows to images. We derive two variants for learning from paired images and unpaired images, respectively. Our validation on the recently introduced ISTD and USR datasets demonstrate large quantitative and qualitative improvements over the state-of-the-art for both paired and unpaired learning settings.
Hotel virtual reality advertising: a presence-mediated model and gender effects

Purpose This paper aims to examine the effectiveness of two types of hotel commercials [virtual reality (VR) versus traditional commercials] by proposing and testing a presence-mediated model along with gender effects. Design/methodology/approach An experimental design is used to test the proposed hypotheses. Data were collected in China, and the Shangri-La hotel brand was chosen. University students were randomly assigned to watch either a hotel VR commercial or a hotel traditional video commercial. One HTC Vive VR headset was provided to those participants in the hotel VR commercial group. Findings The study revealed that VR commercials generate a higher level of vividness and interactivity among customers, which positively influence attitudes toward the ad, brand attitude and booking intention. Moreover, the findings showed that in the virtual environment, women, as compared to men, are influenced more by VR. Originality/value This research is the first to bring the presence model and gender effects together to better understand the effectiveness of VR in hospitality advertising research.
Credit Card Fraud Detection Based on Deep Neural Network Approach

Currently, the financial companies' reliance has become on the new technologies and developments, in which these companies provide the customers with various services, electronically. One the most common financial services is the credit cards, where the customers can complete any financial payments using a credit card, such as; Visa card and MasterCard. However, criminals started to find ways to attack customers' cards for fraud purposes, and that has caused a big issue for both of companies and customers. This work aim to propose a new for credit card fraud detection based on a dataset for customers' transactions of a well-known company called Vesta, where the utilization of deep learning approaches has achieved a method with efficient performance in identifying transactions that was classified as fraud with 99.1% score of area under ROC curve.
Quantum Safe Lightweight Cryptography with Quantum Permutation Pad

Quantum permutation pad or QPP was first proposed by Kuang and Bettenburg in 2020 [15]. QPP is a generic quantum algorithm consisting of multiple n-qubits quantum permutation gates. As a quantum algorithm, QPP can be implemented both in a quantum computing system as a quantum circuit operating on n-qubits' state for transformation and in a classical computing system represented by a pad of n-bit permutation matrices. QPP has two unique characteristics: huge Shannon information entropy and non-commutativity between permutation matrices or the generalized uncertainty principal. Permutation transformation is bijective mapping between input information space and output ciphertext space. That means, QPP has the property of Shannon perfect secrecy with reusability due to the uncertainty relationship. QPP is the generalization of One-Time-Pad or OTP over Hilbert space and OTP is the simplification of QPP over a Galois field. Based on those, this paper explores a variant of AES for a quantum safe lightweight cryptography by incorporating AES ShiftRows and MixColumns with QPP or called AES-QPP. AES-QPP unifies the SubBytes and AddRoundKey with the same QPP of 16 8-bit permutation matrices, essentially SubBytes to be a special 8-bit permutation matrix and AddRoundKey to be 16 8-bit permutation matrices selected from XOR operations. By randomly selecting 16 permutation matrices with a secret key material, AES-QPP could hold a total equivalent 26,944 bits of Shannon entropy. It not only improves the security against differential and linear attacks but also largely reduces the number of rounds to 5 rounds. AES-QPP could be a good candidate for quantum safe lightweight cryptography.
XAuth: Efficient Privacy-Preserving Cross-Domain Authentication

It is well known that each Public Key Infrastructure (PKI) system forms a closed security domain and only recognizes certificates in its own domain (such as medical systems, financial systems, and 5G networks). When users need to access services in other domains, their identities often cannot be recognized or PKI systems require extremely complex operations to authenticate the users’ identities. This is the cross-domain authentication problem. The distributed consensus feature of blockchain provides a technical approach to solve this problem. However, there are some unresolved problems in existing blockchain-based schemes. On one hand, due to the low throughput of blockchain systems, the response speed may be insufferable when the number of cross-domain authentication requirements becomes enormous. On the other hand, these schemes insufficiently consider the privacy risk in the cross-domain scenario. In this article, we propose an efficient privacy-preserving cross-domain authentication scheme called XAuth that is integrated naturally with the existing PKI and Certificate Transparency (CT) systems. Specifically, we design a lightweight correctness verification protocol based on Multiple Merkle Hash Tree for rapid response. To protect users’ privacy, we present an anonymous authentication protocol for cross-domain authentication. The security analysis and experimental results demonstrate that XAuth is secure and efficient.
DSRNA: Differentiable Search of Robust Neural Architectures

In deep learning applications, the architectures of deep neural networks are crucial in achieving high accuracy. Many methods have been proposed to search for high-performance neural architectures automatically. However, these searched architectures are prone to adversarial attacks. A small perturbation of the input data can render the architecture to change prediction outcomes significantly. To address this problem, we propose methods to perform differentiable search of robust neural architectures. In our methods, two differentiable metrics are defined to measure architectures’ robustness, based on certified lower bound and Jacobian norm bound. Then we search for robust architectures by maximizing the robustness metrics. Different from previous approaches which aim to improve architectures’ robustness in an implicit way: performing adversarial training and injecting random noise, our methods explicitly and directly maximize robustness metrics to harvest robust architectures. On CIFAR-10, ImageNet, and MNIST, we perform game-based evaluation and verification-based evaluation on the robustness of our methods. The experimental results show that our methods 1) are more robust to various norm-bound attacks than several robust NAS baselines; 2) are more accurate than baselines when there are no attacks; 3) have significantly higher certified lower bounds than baselines.
Rethinking the Self-Attention in Vision Transformers

Self-attention is a corner stone for transformer models. However, our analysis shows that self-attention in vision transformer inference is extremely sparse. When applying a sparsity constraint, our experiments on image (ImageNet- 1K) and video (Kinetics-400) understanding show we can achieve 95% sparsity on the self-attention maps while main-taining the performance drop to be less than 2 points. This motivates us to rethink the role of self-attention in vision transformer models.
A Comprehensive Application of Machine Learning Techniques for Short-Term Solar Radiation Prediction

Forecasting the output power of solar PV systems is required for the good operation of the power grid and the optimal management of energy fluxes occurring in the solar system. Before forecasting the solar system’s output, it is essential to focus on the prediction of solar irradiance. In this paper, the solar radiation data collected for two years in a certain place in Jiangsu in China are investigated. The objective of this paper is to improve the ability of short-term solar radiation prediction. Firstly, missing data are recovered through the means of matrix completion. Then the completed data are denoised via robust principal component analysis. To reduce the influence of weather types on solar radiation, spectral clustering is adopted by fusing sparse subspace representation and k-nearest-neighbor to partition the data into three clusters. Next, for each cluster, four neural networks are established to predict the short-term solar radiation. The experimental results show that the proposed method can enhance the solar radiation accuracy.
One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking

Despite remarkable progress achieved, most neural architecture search (NAS) methods focus on searching for one single accurate and robust architecture. To further build models with better generalization capability and performance, model ensemble is usually adopted and performs better than stand-alone models. Inspired by the merits of model ensemble, we propose to search for multiple diverse models simultaneously as an alternative way to find powerful models. Searching for ensembles is non-trivial and has two key challenges: enlarged search space and potentially more complexity for the searched model. In this paper, we propose a one-shot neural ensemble architecture search (NEAS) solution that addresses the two challenges. For the first challenge, we introduce a novel diversity-based metric to guide search space shrinking, considering both the potentiality and diversity of candidate operators. For the second challenge, we enable a new search dimension to learn layer sharing among different models for efficiency purposes. The experiments on ImageNet clearly demonstrate that our solution can improve the supernet’s capacity of ranking ensemble architectures, and further lead to better search results. The discovered architectures achieve superior performance compared with state-of-the-arts such as MobileNetV3 and EfficientNet families under aligned settings. Moreover, we evaluate the generalization ability and robustness of our searched architecture on the COCO detection benchmark and achieve a 3.1% improvement on AP compared with MobileNetV3. Codes and models are available here.
Is Segmentation Uncertainty Useful?

Probabilistic image segmentation encodes varying prediction confidence and inherent ambiguity in the segmentation problem. While different probabilistic segmentation models are designed to capture different aspects of segmentation uncertainty and ambiguity, these modelling differences are rarely discussed in the context of applications of uncertainty. We consider two common use cases of segmentation uncertainty, namely assessment of segmentation quality and active learning. We consider four established strategies for probabilistic segmentation, discuss their modelling capabilities, and investigate their performance in these two tasks. We find that for all models and both tasks, returned uncertainty correlates positively with segmentation error, but does not prove to be useful for active learning.
Real-parameter constrained optimization using enhanced quality-based cultural algorithm with novel influence and selection schemes

Hybridization in context to Evolutionary Computation (EC) strives to combine operators, components, and the best merits of different EC paradigms, to form a new evolutionary algorithm that enjoys a statistically superior performance, compared to its ancestors, over a wide range of application-specific optimization problems. In this paper, we propose a simple yet powerful amalgam composed of a modified Cultural Algorithm (CA) that is supported with an Enhanced Levy Flight Search (ELFS) to guide the search and further promote the harmony between the explorative and exploitative capacities of the conventional techniques. The novel amalgam, denoted by q-aCA + mIS, utilizes a balanced search scheme where it employs an adapted Influence Function (IF) with a novel quality function that establishes a harmony between the Knowledge Sources (KSs) in the Belief Space (BS), and between the BS and other components in the hybrid to produce the most suitable knowledge needed for a certain search mode. The CA framework is reinforced with an updated Selection Function (SF) that employs a successful selection strategy that uses the extended situational knowledge for the future selection of individuals. The proposed algorithm is tested using more than 50 benchmark functions that are taken from the IEEE CEC’06, and the IEEE CEC’19 competitions on constrained real-parameter optimization. Moreover, three well-known engineering design problems are used to test the validity of the algorithm for the solution of complex real-life problems. The comparative study indicates that the q-aCA + mIS algorithm was able to obtain a statistically superior performance and scalability behavior over most of the considered functions in comparison with other state-of-the-art algorithms.
Evaluation of online emoji description resources for sentiment analysis purposes

Emoji sentiment analysis is a relevant research topic nowadays, for which emoji sentiment lexica are key assets. Manual annotation affects directly their quality (where high quality usually corresponds to high self-agreement and inter-agreement). In this work we present an unsupervised methodology to evaluate emoji sentiment lexica generated from online resources, based on a correlation analysis between a gold standard and the scores resulting from the sentiment analysis of the emoji descriptions in those resources. We consider in our study four such online resources of emoji descriptions: Emojipedia, Emojis.wiki, CLDR emoji character annotations and iEmoji. These resources provide knowledge about real (intended) emoji meanings from different author approaches and perspectives. We also present the automatic creation of a joint lexicon where the sentiment of a given emoji is obtained by averaging its scores from the unsupervised analysis of all the resources involved. The results for the joint lexicon are highly promising, suggesting that valuable subjective information can be inferred from authors’ descriptions in online resources.
Single shot detection for detecting real-time flying objects for unmanned aerial vehicle

This world of rapidly changing technology has an impact on the infrastructure of worldwide transportation systems. These changes are becoming more and more clear with time. As the field of Robotics and Artificial Intelligence is booming, every gadget is getting smarter. The concept of manual vehicles is shifting toward autonomous vehicles. For example, manual delivery is shifting toward autodelivery by autonomous drones. However, the Unmanned Aerial Vehicle (UAV) is one of the recent hot topics in research. There are various challenges that we need to overcome on the questions of safety and reliability. One such challenge for UAVs is detecting flying objects in real time. Our work focuses on detecting real-time objects using the Single Shot Detector (SSD) algorithm. The objective is to train the labeled images of flying objects with SSD and evaluate the efficiency of the algorithm in terms of confidence value and accuracy.
Blockchain based secure smart city architecture using low resource IoTs

Internet of Things (IoT) is growing exponentially in research and industry. Although, many standard and conventional security solutions have been provided for IoT, it suffers from many privacy and security concerns. Standard security protocols are not suitable for majority of IoT devices because of its decentralized topology and resource-constraints. Blockchain (BC) finds its efficient application in IoT to preserve the five basic cryptographic primitives, such as confidentiality, authenticity, integrity, availability and non-repudiation. Adoption of conventional BC in IoT causes high energy consumption, delay and computational overheads which are not appropriate for various resource constrained IoT devices. To mitigate these problems, this work proposes a smart access control framework in a public and a private BC for a smart city application which makes it more efficient and secure as compared to the existing IoT applications. The proposed IoT based smart city architecture adopts BC technology for preserving all the cryptographic security and privacy issues. Moreover, proposed BC has minimal overhead as well. This work investigates the existing threat models and critical access control issues which handle multiple permissions of various processing nodes of IoT environment and detects relevant inconsistencies. Comparison in terms of all security issues with existing literature shows that the proposed architecture is competitively efficient in terms of security access control. The primary goal of this research article is to explore the possibility of BC as an alternative to standard security solutions for low resource IoT applications.
Single Image Dehazing Using Bounded Channel Difference Prior

The single image dehazing task has made significant progress recently, aiming to recover the contrast and color of the scattered image. Many patch prior based dehazing methods have been explored, and this paper proposes another single image dehazing method by analyzing the prior information of local dehazed patches. With our observation, when the estimated transmission value varies from the ground-truth transmission value to 1, the output value of a metric function decrease correspondingly, which is defined based on the difference maps among three RGB channels of local dehazed patches normalized using global atmospheric light. Under additional bounding, the local transmission value can be estimated accurately. To reduce computation time, the whole image is divided into many small patches, and within each patch, we estimate a transmission value accurately. We further use weighted interpolation and guided filtering to refine the edges and details of the rough transmission map. Finally, we evaluate the proposed method using Fattal’s synthetic haze images, SOTS dataset, and a wide variety of real-world haze images. Experiments show that our method outperforms other state-of-the-art dehazing algorithms by a large margin, especially on synthetic noisy haze images.