Reviewerly/exHarmony huggingface dataset:
Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features

Imaging of facial affects may be used to measure psychophysiological attributes of children through their adulthood for applications in education, healthcare, and entertainment, among others. Deep convolutional neural networks show promising results in classifying facial expressions of adults. However, classifier models trained with adult benchmark data are unsuitable for learning child expressions due to discrepancies in psychophysical development. Similarly, models trained with child data perform poorly in adult expression classification. We propose domain adaptation to concurrently align distributions of adult and child expressions in a shared latent space for robust classification of either domain. Furthermore, age variations in facial images are studied in age-invariant face recognition yet remain unleveraged in adult-child expression classification. We take inspiration from multiple fields and propose deep adaptive FACial Expressions fusing BEtaMix SElected Landmark Features (FACE-BE-SELF) for adult-child expression classification. For the first time in the literature, a mixture of Beta distributions is used to decompose and select facial features based on correlations with expression, domain, and identity factors. We evaluate FACE-BE-SELF using 5-fold cross validation for two pairs of adult-child data sets. Our proposed FACE-BE-SELF approach outperforms transfer learning and other baseline domain adaptation methods in aligning latent representations of adult and child expressions.
An End-to-End Deep Generative Network for Low Bitrate Image Coding

Generative adversarial network (GAN)-based image compression approaches reconstruct images with highly realistic quality at low bit rates. However, currently there is no published GAN-based image compression approach that utilizes advanced GAN losses, such as the Wasserstein GAN with gradient penalty loss (WGAN-GP), to improve the quality of reconstructed images. Meanwhile, existing deep learning-based image compression approaches require extra convolution layers to estimate and constrain the entropy during training, which makes the network larger and may require extra bits to send information to the decoder. In this paper, we propose a new GAN for image compression with novel discriminator and generator loss functions and a simple entropy estimation approach. Our new loss functions outperform the current GAN loss for low bitrate image compression. Our entropy estimation approach does not require extra convolution layers but still works well to constrain the number of bits during training.
SCSS: An Intelligent Security System to Guard City Public Safe

Traditional security surveillance detection relies on post-event forensics or is hosted on a backend server, making it impossible to identify behaviors filmed in the field online. This paper proposes the Smart City Security System (SCSS) for detecting anomalous activity in public locations online. SCSS combines the DeepSORT and YOLOv4 algorithms to generate the DS-YOLO aberrant behavior detection algorithm, which compares and matches the target detected in the previous picture frame with the target detected in the following frame to achieve detection and tracking. SCSS is equipped with GPS, WIFI, and Uninterruptible Power Supply (UPS). When a risky behavior is detected, the system will upload the abnormal event as well as the latitude and longitude that occurred to the cloud via the WIFI and notify the user. The recognition accuracy of three deviant behaviors, including Fight, Car Accident, and Fall, was examined using diverse situations, and the results were 89%, 90%, and 90.33% respectively. The findings demonstrate that SCSS has successfully made the transition from passive monitoring to active identification, offsetting the flaws of conventional security systems that can only post-mordem forensics, and bridging the gap of the construction of national smart cities.
Design and Evaluation of Inclusive Email Security Indicators for People with Visual Impairments

Due to the challenges to detect and filter phishing emails, it is inevitable that some phishing emails can still reach a user’s inbox. As a result, email providers such as Gmail have implemented phishing warnings to help users to better recognize phishing attempts. Existing research has primarily focused on phishing warnings for sighted users and yet it is not well understood how people with visual impairments interact with phishing emails and warnings. In this paper, we worked with a group of users (N=41) with visual impairments to study the effectiveness of existing warnings and explore more inclusive designs (using Gmail warning designs as a baseline for comparison). We took a multipronged approach including an exploratory study (to understand the challenges faced by users), user-in-the-loop design and prototyping, and the main study (to assess the impact of design choices). Our results show that users with visual impairments often miss existing Gmail warnings because the current design (e.g., warning position, HTML tags used) does not match well with screen reader users’ reading habits. The inconsistencies of the warnings (e.g., across the Standard and HTML view) also create obstacles to users. We show that an inclusive design (combining audio warning, shortcut key, and warning page overlay) can effectively increase the warning noticeability. Based on our results, we make a number of recommendations to email providers.
Computational and Exploratory Landscape Analysis of the GKLS Generator

The GKLS generator is one of the most used testbeds for benchmarking global optimization algorithms. In this paper, we conduct both a computational analysis and the Exploratory Landscape Analysis (ELA) of the GKLS generator. We utilize both canonically used and newly generated classes of GKLS-generated problems and show their use in benchmarking three state-of-the-art methods (from evolutionary and deterministic communities) in dimensions 5 and 10. We show that the GKLS generator produces "needle in a haystack" type problems that become extremely difficult to optimize in higher dimensions. We also conduct the ELA on the GKLS generator and then compare it to the ELA of two other widely used benchmark sets (BBOB and CEC 2014), and discuss the results.
Self-Supervised Digital Histopathology Image Disentanglement for Arbitrary Domain Stain Transfer

Diagnosis of cancerous diseases relies on digital histopathology images from stained slides. However, the staining varies among medical centers, which leads to a domain gap of staining. Existing generative adversarial network (GAN) based stain transfer methods highly rely on distinct domains of source and target, and cannot handle unseen domains. To overcome these obstacles, we propose a self-supervised disentanglement network (SDN) for domain-independent optimization and arbitrary domain stain transfer. SDN decomposes an image into features of content and stain. By exchanging the stain features, the staining style of an image is transferred to the target domain. For optimization, we propose a novel self-supervised learning policy based on the consistency of stain and content among augmentations from one instance. Therefore, the process of training SDN is independent on the domain of training data, and thus SDN is able to tackle unseen domains. Exhaustive experiments demonstrate that SDN achieves the top performance in intra-dataset and cross-dataset stain transfer compared with the state-of-the-art stain transfer models, while the number of parameters in SDN is three orders of magnitude smaller parameters than that of compared models. Through stain transfer, SDN improves AUC of downstream classification model on unseen data without fine-tuning. Therefore, the proposed disentanglement framework and self-supervised learning policy have significant advantages in eliminating the stain gap among multi-center histopathology images.
Authenticated Public Key Elliptic Curve Based on Deep Convolutional Neural Network for Cybersecurity Image Encryption Application

The demand for cybersecurity is growing to safeguard information flow and enhance data privacy. This essay suggests a novel authenticated public key elliptic curve based on a deep convolutional neural network (APK-EC-DCNN) for cybersecurity image encryption application. The public key elliptic curve discrete logarithmic problem (EC-DLP) is used for elliptic curve Diffie–Hellman key exchange (EC-DHKE) in order to generate a shared session key, which is used as the chaotic system’s beginning conditions and control parameters. In addition, the authenticity and confidentiality can be archived based on ECC to share the parameters between two parties by using the EC-DHKE algorithm. Moreover, the 3D Quantum Chaotic Logistic Map (3D QCLM) has an extremely chaotic behavior of the bifurcation diagram and high Lyapunov exponent, which can be used in high-level security. In addition, in order to achieve the authentication property, the secure hash function uses the output sequence of the DCNN and the output sequence of the 3D QCLM in the proposed authenticated expansion diffusion matrix (AEDM). Finally, partial frequency domain encryption (PFDE) technique is achieved by using the discrete wavelet transform in order to satisfy the robustness and fast encryption process. Simulation results and security analysis demonstrate that the proposed encryption algorithm achieved the performance of the state-of-the-art techniques in terms of quality, security, and robustness against noise- and signal-processing attacks.
Cyberbiosecurity in high-containment laboratories

High-containment laboratories (HCLs) conduct critical research on infectious diseases, provide diagnostic services, and produce vaccines for the world’s most dangerous pathogens, often called high-consequence pathogens (HCPs). The modernization of HCLs has led to an increasingly cyber-connected laboratory infrastructure. The unique cyberphysical elements of these laboratories and the critical data they generate pose cybersecurity concerns specific to these laboratories. Cyberbiosecurity, the discipline devoted to the study of cybersecurity risks in conjunction with biological risks, is a relatively new field for which few approaches have been developed to identify, assess, and mitigate cyber risks in biological research and diagnostic environments. This study provides a novel approach for cybersecurity risk assessment and identification of risk mitigation measures by applying an asset-impact analysis to the unique environment of HCLs. First, we identified the common cyber and cyberphysical systems in HCLs, summarizing the typical cyber-workflow. We then analyzed the potential adverse outcomes arising from a compromise of these cyber and cyberphysical systems, broadly categorizing potential consequences as relevant to scientific advancement, public health, worker safety, security, and the financial wellbeing of these laboratories. Finally, we discussed potential risk mitigation strategies, leaning heavily on the cybersecurity materials produced by the Center for Internet Security (CIS), including the CIS Controls ® , that can serve as a guide for HCL operators to begin the process of implementing risk mitigation measures to reduce their cyberbiorisk and considering the integration of cyber risk management into existing biorisk management practices. This paper provides a discussion to raise awareness among laboratory decision-makers of these critical risks to safety and security within HCLs. Furthermore, this paper can serve as a guide for evaluating cyberbiorisks specific to a laboratory by identifying cyber-connected assets and the impacts associated with a compromise of those assets.
Semi-Supervised Learning Matting Algorithm Based on Semantic Consistency of Trimaps

Image matting methods based on deep learning have made tremendous success. However, the success of previous image matting methods typically relies on a massive amount of pixel-level labeled data, which are time-consuming and costly to obtain. This paper first proposes a semi-supervised deep learning matting algorithm based on semantic consistency of trimaps (Tri-SSL), which uses trimaps to provide weakly supervised signals for the unlabeled data, to reduce the labeling cost. Tri-SSL is a single-stage semi-supervised algorithm that consists of a supervised branch and a weakly supervised branch that share the same network in one iteration during training. The supervised branch is consistent with standard supervised matting methods. In the weakly supervised branch, trimaps of different granularities are used as weakly supervised signals for unlabeled images, and the two trimaps are naturally perturbed samples. Orientation consistency constraints are imposed on the prediction results of trimaps of different granuliarty and the intermediate features of the network. Experimental results show that Tri-SSL improves model performance by effectively utilizing unlabeled data.
BIBLIOMETRIC ANALYSIS ON AUDITOR PERFORMANCE RESEARCH

Auditor performance is a general topic that has been extensively researched worldwide. However, of course, some aspects are still minimally discussed in previous research. Therefore, this study applied a bibliometric analysis to find out the overall picture and aspects or matters that were still rarely discussed about auditor performance. The data source is obtained from Scopus with a publication time span of 2013-2022 and the keyword "Auditor Performance". The number of studies that became the data in this study was 506 studies. These data were analyzed using the VOS Viewer software. Based on the results of the bibliometric analysis, it is concluded that the variable or aspect that has been widely studied in the auditor's performance keyword is audit quality. Then, the aspect or variable that can be a novelty idea is big data. Further researchers can explore or discuss more deeply related to big data and its relationship to auditor performance.
Faster RCNN based robust vehicle detection algorithm for identifying and classifying vehicles

Abstract Deep convolutional neural networks (CNNs) have shown tremendous success in the detection of objects and vehicles in recent years. However, when using CNNs to identify real-time vehicle detection in a moving context remains difficult. Many obscured and truncated cars, as well as huge vehicle scale fluctuations in traffic photos, provide these issues. To improve the performance of detection findings, we used multiscale feature maps from CNN or input pictures with numerous resolutions to adapt the base network to match different scales. This research presents an enhanced framework depending on Faster R-CNN for rapid vehicle recognition which presents better accuracy and fast processing time. Research results on our custom dataset indicate that our recommended methodology performed better in terms of detection efficiency and processing time, especially in comparison to the earlier age of Faster R-CNN models.
Exploitation of Subharmonic Injection Locking for Solving Combinatorial Optimization Problems with Coupled Oscillators using VO<sub>2</sub> based devices

Coupled nano-oscillators are attracting increasing interest because of their potential to perform computation efficiently, enabling new applications in computing and information processing. The potential of phase transition devices for such dynamical systems has recently been recognized. This paper investigates the implementation of coupled VO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> based oscillators networks for solving Max-Cut combinatorial optimization problems. The Max-Cut model is mapped to an Ising model which is solved by the synchronization dynamics of the system. The impact of subharmonic injection locking on the probability of the system to reach the ground state of the Ising Hamiltonian and so the optimum solution to the corresponding optimization problem is analyzed.
A type-augmented knowledge graph embedding framework for knowledge graph completion

Knowledge graphs (KGs) are of great importance to many artificial intelligence applications, but they usually suffer from the incomplete problem. Knowledge graph embedding (KGE), which aims to represent entities and relations in low-dimensional continuous vector spaces, has been proved to be a promising approach for KG completion. Traditional KGE methods only concentrate on structured triples, while paying less attention to the type information of entities. In fact, incorporating entity types into embedding learning could further improve the performance of KG completion. To this end, we propose a universal Type-augmented Knowledge graph Embedding framework (TaKE) which could utilize type features to enhance any traditional KGE models. TaKE automatically captures type features under no explicit type information supervision. And by learning different type representations of each entity, TaKE could distinguish the diversity of types specific to distinct relations. We also design a new type-constrained negative sampling strategy to construct more effective negative samples for the training process. Extensive experiments on four datasets from three real-world KGs (Freebase, WordNet and YAGO) demonstrate the merits of our proposed framework. In particular, combining TaKE with the recent tensor factorization KGE model SimplE can achieve state-of-the-art performance on the KG completion task.
Learning and Adapting Agile Locomotion Skills by Transferring Experience

Agile skills learned with our proposed method enable the A1 robot to jump repeatedly (left) and walk to a goal location
Deep Reference Frame Generation Method for VVC Inter Prediction Enhancement

In video coding, inter prediction aims to reduce temporal redundancy by using previously encoded frames as references. The quality of reference frames is crucial to the performance of inter prediction. This paper presents a deep reference frame generation method to optimize the inter prediction in Versatile Video Coding (VVC). Specifically, reconstructed frames are sent to a well-designed frame generation network to synthesize a picture similar to the current encoding frame. The synthesized picture serves as an additional reference frame inserted into the reference picture list (RPL) to provide a more reliable reference for subsequent motion estimation (ME) and motion compensation (MC). The frame generation network employs optical flow to predict motion precisely. Moreover, an optical flow reorganization strategy is proposed to enable bi-directional and uni-directional predictions with only a single network architecture. To reasonably apply our method to VVC, we further introduce a normative modification of the temporal motion vector prediction (TMVP). Integrated into the VVC reference software VTM-15.0, the deep reference frame generation method achieves coding efficiency improvements of 5.22%, 3.61%, and 3.83% for the Y component under random access (RA), low delay B (LDB), and low delay P (LDP) configurations, respectively. The proposed method has been discussed in Joint Video Exploration Team (JVET) meeting and is currently part of Exploration Experiments (EE) for further study.
Learning Hierarchical Semantic Information for Efficient Low-Light Image Enhancement

Low-light environments can cause a variety of complex degradation problems, which result in poor visibility in images. As a classical vision task, low-light image enhancement has attracted an increasing interest in the research community. However, the existing methods tend to require a large number of parameters, making them difficult to implement and optimize, especially on resource-constrained devices. In this paper, we mainly focus on the lightweight of the method and propose a novel end-to-end two-stage CNN-ViT architecture (HSINet) to learn hierarchical semantic information (HSI) from low-light images efficiently. The HSINet consists of two stages: the first stage is a CNN-based low-level semantic (LS) Stage, and the second stage is ViT-based high-level semantic (HS) Stage. The LS Stage contains an efficient multi-scale convolution block, MLS Block, for low-level semantic information extraction. The HS stage, on the other hand, aims to learn the high-level semantic features via ViT's excellent global-learning capability. We propose a hierarchical Swin Transformer-based block, HS Block, to gradually enlarge Swin Transformer's window size as the network becomes deeper, to learn hierarchical high-level semantic information. Benefiting from the efficient architecture, our model only contains 0.6M parameters, far fewer than the existing SOTAs. We evaluated the method on three challenging benchmark datasets: LOL, VE-LOL, and MIT-Adobe FiveK, using three popular evaluation metrics. The quantitative and qualitative results both show that the proposed method not only outperforms the state of the arts in terms of PSNR, SSIM, LPIPS, and visual effects, but also with better efficiency.
Dual adversarial network with meta-learning for domain-generalized few-shot text classification

Meta-learning-based methods prevail in few-shot text classification. Current methods perform meta-training and meta-testing on two parts of a dataset in the same or similar domains. This results in a significant limit in model performance when faced with data from different domains, limiting the generalization of few-shot models. To solve this problem, this study proposes a new setting, namely, domain-generalized few-shot text classification. First, meta-training is conducted on a multi-domain dataset to learn a generalizable model. Subsequently, the model is meta-tested on a target dataset. In addition, a domain-generalized model, namely, a dual adversarial network, is designed to improve the meta-learning-based methods under domain drift between different datasets and domains. Unlike previous meta-learning methods, two N-way-K-shot tasks were input from different domains for a dual adversarial network at each episode. Dual adversarial networks leverage the features from two different domains for adversarial training to improve the domain adaptability of the model. The proposed model utilizes a domain-knowledge generator during adversarial training to produce domain-specific knowledge, and a domain discriminator to recognize the domain label of the produced knowledge. Extensive experiments are conducted to verify the effectiveness of the proposed settings and model. The experimental results show that the model performance in our proposed setting is improved by an average of 3.84% compared to that in cross-domain few-shot text classification. Furthermore, the dual adversarial network significantly outperforms the five competitive baseline models, with an average improvement of 7.20%. The proposed model achieves an average performance improvement of 2.69% compared with the best baseline method.