Reviewerly/exHarmony huggingface dataset:
SSFlow: Style-guided Neural Spline Flows for Face Image Manipulation

Significant progress has been made in high-resolution and photo-realistic image generation by Generative Adversarial Networks (GANs). However, the generation process is still lack of control, which is crucial for semantic face editing. Furthermore, it remains challenging to edit target attributes and preserve the identity at the same time. In this paper, we propose SSFlow to achieve identity-preserved semantic face manipulation in StyleGAN latent space based on conditional Neural Spline Flows. To further improve the performance of Neural Spline Flows on such task, we also propose Constractive Squash component and Blockwise 1 x 1 Convolution layer. Moreover, unlike other conditional flow-based approaches that require facial attribute labels during inference, our method can achieve label-free manipulation in a more flexible way. As a result, our methods are able to perform well-disentangled edits along various attributes, and generalize well for both real and artistic face image manipulation. Qualitative and quantitative evaluations show the advantages of our method for semantic face manipulation over state-of-the-art approaches.
Weighted Gaussian Loss based Hamming Hashing

Recently, deep Hamming hashing methods have been proposed for Hamming space retrieval which enables constant-time search by hash table lookups instead of linear scan. When carrying out Hamming space retrieval, for each query datapoint, there is a Hamming ball centered on the query datapoint, and only the datapoints within the Hamming ball are returned as the relevant ones, while those beyond are discarded directly. Thus, to further enhance the retrieval performance, it is a key point for the Hamming hashing methods to decrease the dissimilar datapoints within the Hamming ball. However, nearly all existing Hamming hashing methods cannot effectively penalize the dissimilar pairs within the Hamming ball to push them out. To tackle this problem, in this paper, we propose a novel Weighted Gaussian Loss based Hamming Hashing, called WGLHH, which introduces a weighted Gaussian loss to optimize hashing model. Specifically, the weighted Gaussian loss consists of three parts: a novel Gaussian-distribution based loss, a novel badly-trained-pair attention mechanism and a quantization loss. The Gaussian-distribution based loss is proposed to effectively penalize the dissimilar pairs within the Hamming ball. The badly-trained-pair attention mechanism is proposed to assign a weight for each data pair, which puts more weight on data pairs whose corresponding hash codes cannot preserve original similarity well, and less on those having already handled well. The quantization loss is used to reduce the quantization error. By incorporating the three parts, the proposed weighted Gaussian loss will penalize significantly on the dissimilar pairs within the Hamming ball to generate more compact hashing codes. Extensive experiments on two benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in image retrieval task.
Image Forgeries Detection through Mosaic Analysis: the Intermediate Values Algorithm

Cameras sample each image pixel in one color channel only.The remaining channels are interpolated from neighboring pixels during demosaicing.This operation leaves traces, that can be exploited to authentify images and detect forgeries.This paper describes the method introduced by Choi et al. that exploits the fact that interpolated pixels are more prone to be intermediate values, to detect in which pattern an image has been sampled.We then use this information to find regions that are inconsistent with the global image.We attribute a confidence score to each detection, which can then be thresholded to provide a binary map of detected forgeries.
An Overview of Sparsity Exploitation in CNNs for On-Device Intelligence With Software-Hardware Cross-Layer Optimizations

This paper presents a detailed overview of sparsity exploitation in deep neural network (DNN) accelerators. Despite the algorithmic advancements which drove DNNs to become the standard of artificial intelligence (AI), DNNs’ computational and memory overhead limits the deployment of off-the-shelf models on edge devices. Numerous optimizations have been widely studied to efficiently run DNN models on performance and energy-limited mobile devices, from both software’s and hardware’s perspective. Sparsity exploitation one of the mainstream optimization techniques, whose objective is to achieve higher efficiency and speed through avoiding redundant multiply-and-accumulate (MAC) operations cause by zero operands. This paper overviews previous contributions on sparsity exploitation from both the software and hardware side of views, with the newly proposed taxonomy to categorize and analyze the works. On the software side, different sparsification algorithms are explained, including pruning and output speculation. From the hardware’s perspective, advancements in architectures to efficiently handle sparse DNN computation are elaborated. The proposed taxonomy will help classify previous accelerators easily, by which sparsity they exploit and how. In addition, related works on sparse processing-in-memory (PIM) architectures and similarity exploitation are briefly introduced.
Multi-Scale Cost Volumes Cascade Network for Stereo Matching

Stereo matching is essential for robot navigation. However, the accuracy of current widely used traditional methods is low, while methods based on CNN need expensive computational cost and running time. This is because different cost volumes play a crucial role in balancing speed and accuracy. Thus we propose MSCVNet, which combines traditional methods and neural networks to improve the quality of cost volume. Concretely, our network first generates multiple 3D cost volumes with different resolutions and then uses 2D convolutions to construct a novel cascade hourglass network for cost aggregation. Meanwhile, we design an algorithm to distinguish and calculate the loss for discontinuous areas of the disparity result. According to the KITTI official website, our network is much faster than most top-performing methods (24than CSPN, 44than GANet, etc.). Meanwhile, compared to traditional methods (SPS-St, SGM) and other real-time stereo matching networks (Fast DS-CS, DispNetC, and RTSNet, etc.), our network achieves a big improvement in accuracy, demonstrating the feasibility and capability of the proposed method.
Webcam Eye Tracking for Monitoring Visual Attention in Hypothetical Online Shopping Tasks

Online retailers are challenged to present their products in an appropriate way to attract customers’ attention. To test the impact of product presentation features on customers’ visual attention, webcam eye tracking might be an alternative to infrared eye tracking, especially in situations where face-to-face contact is difficult. The aim of this study was to examine whether webcam eye tracking is suitable for investigating the influence of certain exogenous factors on customers’ visual attention when visiting online clothing shops. For this purpose, screenshots of two websites of two well-known online clothing retailers were used as stimuli. Linear regression analyses were conducted to determine the influence of the spatial position and the presence of a human model on the percentage of participants visiting a product depiction. The results show that products presented by human models and located in the upper middle area of a website were visited by more participants. From this, we were able to derive recommendations for optimising product presentation in online clothing shops. Our results fit well with those of other studies on visual attention conducted with infrared eye tracking, suggesting that webcam eye tracking could be an alternative to infrared eye tracking, at least for similar research questions.
RL-DARTS: Differentiable neural architecture search via reinforcement-learning-based meta-optimizer

Differentiable search approaches have attracted extensive attention recently due to their advantages in effectively finding novel neural architectures. However, these methods suffer from shortcomings on heavy computation consumption and low robustness in some cases. In this work, we propose a novel differentiable search method based on reinforcement learning, to further improve the computation efficiency, network precision, and robustness in the neural architecture search area. Our method constructs a reinforcement learning-based meta-optimizer to solve the architecture-parameter optimization problem, which is superior in properties of adaptability and robustness to fixed optimizers. This learnable meta-optimizer can alter its model parameters along with the search process to adapt the optimization procedure, making it possible to find out better structures and parameters with less time. Specifically, we formulate a double-loop algorithm to address the optimization problem in the searched super-network. Through switching between the external and internal loops, our method alternately optimizes the super-network and the meta-optimizer, which converges to the optimal location more rapidly and robustly.
Learning Transferrable and Interpretable Representations for Domain Generalization

Conventional machine learning models are often vulnerable to samples with different distributions from the ones of training samples, which is known as domain shift. Domain Generalization (DG) challenges this issue by training a model based on multiple source domains and generalizing it to arbitrary unseen target domains. In spite of remarkable results made in DG, a majority of existing works lack a deep understanding of the feature representations learned in DG models, resulting in limited generalization ability when facing domainsout-of-distribution. In this paper, we aim to learn a domain transformation space via a domain transformer network (DTN) which explicitly mines the relationship among multiple domains and constructs transferable feature representations for down-stream tasks by interpreting each feature as a semantically weighted combination of multiple domain-specific features. Our DTN is encouraged to meta-learn the properties and characteristics of domains during the training process based on multiple seen domains, making transformed feature representations more semantical, thus generalizing better to unseen domains. Once the model is constructed, the feature representations of unseen target domains can also be inferred adaptively by selectively combining the feature representations from the diverse set of seen domains. We conduct extensive experiments on five DG benchmarks and the results strongly demonstrate the effectiveness of our approach.
Secured Vehicle Life Cycle Tracking Using Blockchain and Smart Contract

Life Cycle Tracking (LCT) involves continuous monitoring and analysis of various activities associated with a vehicle. The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain phases are involved and the data is assessed by multiple stakeholders. Frauds and swindling activities can be prevented if the history of the vehicles is made available to the interested parties. Blockchain provides a way of enforcing trustworthiness to the supply chain participants and the data associated with the various actions performed. Machine learning techniques when combined decentralized nature of blockchains can be used to develop a robust Vehicle LCT model. In the proposed work, Harmonic Optimized Gradient Descent and Łukasiewicz Fuzzy (HOGD-LF) Vehicle Life Cycle Tracking in Cloud Environment is proposed and it involves three stages. First, the Progressive Harmonic Optimized User Registration and Authentication model is designed for computationally efficient registration and authentication. Next, for the authentic user, the Gradient Descent Blockchain-based SVM Data Encryption model is designed with minimum CPU utilization. Finally, Łukasiewicz Fuzzy Smart Contract Verification is performed with encrypted data to ensure accurate and precise fraudulent activity deduction. The experimental analysis shows that the proposed method achieves significant performance in terms of life cycle's prediction time, overhead, and accuracy for a different number of users.
Hate Hurts: Exploring the Impact of Online Hate on LGBTQ+ Young People

Research has demonstrated how LGBTQ+ hate is widespread on the internet. The nature of the online world is such that the permanence and desistance of hate is greater than its offline counterpart. However, comparatively little attention has been paid to the impacts of this type of behavior. Drawing on the findings of a survey involving 175 LGBTQ+ respondents aged 13–25, and 15 follow-up interviews, this paper addresses this gap by exploring the range of significant impacts that LGBTQ+ young people experience on their well-being and relationships with others. Given the ubiquitous nature of online abuse, this paper demonstrates the need for a targeted criminal justice response. Consequently, this paper discusses the implications of the findings with respect to future research.
Motion-Encoded Electric Charged Particles Optimization for Moving Target Search Using Unmanned Aerial Vehicles

In this paper, a new optimization algorithm called motion-encoded electric charged particles optimization (ECPO-ME) is developed to find moving targets using unmanned aerial vehicles (UAV). The algorithm is based on the combination of the ECPO (i.e., the base algorithm) with the ME mechanism. This study is directly applicable to a real-world scenario, for instance the movement of a misplaced animal can be detected and subsequently its location can be transmitted to its caretaker. Using Bayesian theory, finding the location of a moving target is formulated as an optimization problem wherein the objective function is to maximize the probability of detecting the target. In the proposed ECPO-ME algorithm, the search trajectory is encoded as a series of UAV motion paths. These paths evolve in each iteration of the ECPO-ME algorithm. The performance of the algorithm is tested for six different scenarios with different characteristics. A statistical analysis is carried out to compare the results obtained from ECPO-ME with other well-known metaheuristics, widely used for benchmarking studies. The results found show that the ECPO-ME has great potential in finding moving targets, since it outperforms the base algorithm (i.e., ECPO) by as much as 2.16%, 5.26%, 7.17%, 14.72%, 0.79% and 3.38% for the investigated scenarios, respectively.
Online Adaptive Kalman Filter for Target Tracking With Unknown Noise Statistics

Considering that the external hostile environment will lead to rapid attenuation of sensor signals, which will make the noise parameters we set different from the actual noise parameters. In this letter, a novel online adaptive Kalman filter (AKF) is investigated with the main focus on inaccurate nonzero mean Gaussian white noise inherent in the filtering model. In the proposed AKF, we employed the expectation maximization algorithm to construct the noise parameter iteration expressions and obtain an approximate solution of the noise parameter. Finally, the derived AKF can effectively estimate the one-step prediction mean vector, the one-step prediction error covariance matrix, and the measurement noise covariance matrix. A classical target tracking simulation results show the effectiveness and stability of the derived AKF.
A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA

Background: The outbreak of the novel coronavirus disease 2019 (COVID-19) has been raging around the world for more than 1 year. Analysis of previous COVID-19 data is useful to explore its epidemic patterns. Utilizing data mining and machine learning methods for COVID-19 forecasting might provide a better insight into the trends of COVID-19 cases. This study aims to model the COVID-19 cases and perform forecasting of three important indicators of COVID-19 in the United States of America (USA), which are the adjusted percentage of daily admitted hospitalized COVID-19 cases (hospital admission), the number of daily confirmed COVID-19 cases (confirmed cases), and the number of daily death cases caused by COVID-19 (death cases). Materials and Methods: The actual COVID-19 data from March 1, 2020 to August 5, 2021 were obtained from Carnegie Mellon University Delphi Research Group. A novel forecasting algorithm was proposed to model and predict the three indicators. This algorithm is a hybrid of an unsupervised time series anomaly detection technique called matrix profile and an attention-based long short-term memory (LSTM) model. Several classic statistical models and the baseline recurrent neural network (RNN) models were used as the baseline models. All models were evaluated using a repeated holdout training and test strategy. Results: The proposed matrix profile-assisted attention-based LSTM model performed the best among all the compared models, which has the root mean square error (RMSE) = 1.23, 31612.81, 467.17, mean absolute error (MAE) = 0.95, 26259.55, 364.02, and mean absolute percentage error (MAPE) = 0.25, 1.06, 0.55, for hospital admission, confirmed cases, and death cases, respectively. Conclusion: The proposed model is more powerful in forecasting COVID-19 cases. It can potentially aid policymakers in making prevention plans and guide health care managers to allocate health care resources reasonably.
Simulation of Condensed-Phase Spectroscopy with Near-Term Digital Quantum Computers

Spectroscopy is an indispensable tool for understanding the structures and dynamics of molecular systems. However, computational modeling of spectroscopy is challenging due to the exponential scaling of computational complexity with system sizes unless drastic approximations are made. Quantum computers could potentially overcome these classically intractable computational tasks, but the existing approaches using quantum computers to simulate spectroscopy can only handle isolated and static molecules. In this work, we develop a workflow that combines multi-scale modeling and a time-dependent variational quantum algorithm to compute the linear spectroscopy of systems interacting with their condensed-phase environment via the relevant time correlation function. We demonstrate the feasibility of our approach by numerically simulating the UV-vis absorption spectra of organic semiconductors. We show that our dynamical approach captures several spectral features that are otherwise overlooked by static methods. Our method can be directly used for other linear condensed-phase spectroscopy and could potentially be extended to nonlinear multi-dimensional spectroscopy.
Rolling-Shutter-stereo-aware motion estimation and image correction

The vast majority of modern consumer-grade cameras employ the Rolling Shutter (RS) mechanism, which often produces geometrically distorted images due to inter-row delay during imaging, that needs to be properly handled. While several models and solutions have been proposed for the motion estimation problem of the monocular RS camera, the corresponding motion estimation model and algorithm have not been developed for the standard RS stereo setup. This is primarily due to the temporal-dynamic nature of the RS camera, which results in scanline-varying camera poses. This paper fills in this gap by developing two tractable RS stereo models under constant velocity motion and constant acceleration motion respectively, which only need to simply scale the optical flow vector. We further propose two corresponding RS stereo motion estimation algorithms to accurately compute the relative pose of the RS stereo camera between two consecutive frames. As a result of utilizing these RS stereo models associated with the parallel visual rays, our proposed methods offer better disambiguation of the translation and rotation, hence leading to significantly improved performance compared with the Global Shutter (GS) stereo model. Furthermore, we develop an RS stereo image correction method to remove RS distortions and recover high-quality GS images by conducting motion compensation. Experimental results on both simulated data and real RS stereo images show the superiority of our proposed models and methods.
The effect of knowledge management capacity on firm performance through sequential mediations of strategic HRM, administrative and technical innovations

Purpose This study aims to explore the relationship between knowledge management (KM) and firm performance through mediating effects of strategic human resource management (HRM) and organizational innovations. Design/methodology/approach Usable responses from 638 employees belonging to 230 organizations operating in India were collected via questionnaires. To determine the hypothesized relationships between the study variables, namely, KM capacity, strategic HRM, administrative innovation, technical innovation and firm performance, bootstrapping method via PROCESS was put into use. Findings The findings strengthen the notion that effective KM in firms leads to better innovation capabilities and higher firm performance. Further, the study reveals that the effects of KM capacity on firm performance go through a relationship chain (serial mediation) consisting of strategic HRM, administrative innovation and technical innovation. Practical implications The results undeniably infer that the effective management of knowledge is critical for leveraging innovations and other performance indicators in organizations. More specifically, it does not only result in enhanced firm performance but also in proper utilization of strategic HR practices for the benefits of employees, with a boost in administrative and technical innovations in organizations. Originality/value By confirming several mediation paths through strategic HRM, administrative and technical innovations, this study offers some noteworthy insights in expounding the relationship between KM and firm performance.
Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder

Several works have demonstrated the use of variational autoencoders (VAEs) for generating levels in the style of existing games and blending levels across different games. Further, quality-diversity (QD) algorithms have also become popular for generating varied game content by using evolution to explore a search space while focusing on both variety and quality. To reap the benefits of both these approaches, we present a level generation and game blending approach that combines the use of VAEs and QD algorithms. Specifically, we train VAEs on game levels and run the MAP-Elites QD algorithm using the learned latent space of the VAE as the search space. The latent space captures the properties of the games whose levels we want to generate and blend, while MAP-Elites searches this latent space to find a diverse set of levels optimizing a given objective such as playability. We test our method using models for 5 different platformer games as well as a blended domain spanning 3 of these games. We refer to using MAP-Elites for blending as Blend-Elites. Our results show that MAP-Elites in conjunction with VAEs enables the generation of a diverse set of playable levels not just for each individual game but also for the blended domain while illuminating game-specific regions of the blended latent space.