Reviewerly/exHarmony huggingface dataset:
Text Prior Guided Scene Text Image Super-Resolution

Scene text image super-resolution (STISR) aims to improve the resolution and visual quality of low-resolution (LR) scene text images, while simultaneously boost the performance of text recognition. However, most of the existing STISR methods regard text images as natural scene images, ignoring the categorical information of text. In this paper, we make an inspiring attempt to embed text recognition prior into STISR model. Specifically, we adopt the predicted character recognition probability sequence as the text prior, which can be obtained conveniently from a text recognition model. The text prior provides categorical guidance to recover high-resolution (HR) text images. On the other hand, the reconstructed HR image can refine the text prior in return. Finally, we present a multi-stage text prior guided super-resolution (TPGSR) framework for STISR. Our experiments on the benchmark TextZoom dataset show that TPGSR can not only effectively improve the visual quality of scene text images, but also significantly improve the text recognition accuracy over existing STISR methods. Our model trained on TextZoom also demonstrates certain generalization capability to the LR images in other datasets.
Human Motion Target Posture Detection Algorithm Using Semi-Supervised Learning in Internet of Things

To address the problem that the traditional human motion attitude detection process is easy to ignore the data calibration, which leads to the problems of long running time, low accuracy and poor detection effect, a human motion target attitude detection algorithm based on semi-supervised learning in the Internet of things environment is proposed.Firstly, human motion target images are collected using the Internet of things (IoT), human motion attitude features are extracted based on the eight-star model, and multi-features are fused to form image blocks of 17-dimensional feature vectors.Then, random fern classifiers are optimized and semi-supervised learning is used to calculate a large number of uncalibrated data in time domain, spatial domain and data.The classifier is trained to complete image block classification.Finally, the classifier parameters are updated iteratively to complete the attitude detection of human motion target.The results show that the proposed algorithm has high accuracy in human motion attitude extraction and multi-feature fusion, and has a high correct classification rate for different feature poses, as high as 92.5%.The average F value of human motion attitude detection is 0.95, the overlap ratio is high and the time is short.The overall performance is good.
Climate Change Communication in HCI: a Visual Analysis of the Past Decade

Climate change is one of the most urgent issues of our time, and the HCI community has shown a growing interest and application in this topic. Considering the communication and interaction challenges it poses, now is an opportune time to analyse how the academic community has explored interaction addressing climate change. Following this premise, we present a visual analysis of the past decade, focusing on HCI research projects that target the general public. For this survey we used the grounded theory review method, resulting in a final corpus of 40 projects, with data visualization as a key step in our investigation.
HP-VCS: A high-quality and printer-friendly visual cryptography scheme

Visual Cryptography Scheme (VCS) is a secret-sharing scheme which aims to encrypt a secret message into multiple shares and transmit them to participants over an untrusted communication channel. Although human vision can easily reveal the secret message by stacking a sufficient number of shares, this scheme reduces the visual quality of recovered images. This paper presents a novel high-quality and printer-friendly VCS. When providing high-quality recovery, this scheme keeps the size of the shares the same as the secret image. Experimental results show that, compared with previous work, the proposed scheme significantly improves the performance of recovered images.
Nonlinear Exposure Intensity Based Modification Histogram Equalization for Non-Uniform Illumination Image Enhancement

Non-uniform illumination image is often generated owing to various factors, such as an improper setting in the image acquisition device and absorption or reflectance of the objects that results in the existence of different exposure regions in the image. Although Histogram Equalization (HE) is well known and widely used in image enhancement, existing HE-based methods often generate washed-out effects and show unnatural appearance due to the over-enhancement phenomenon, which limits the capabilities of achieving illumination uniformity of an image. Therefore, this study proposes a modified HE method for non-uniform illumination image, namely Nonlinear Exposure Intensity-Based Modification Histogram Equalization (NEIMHE). The proposed NEIMHE method divides the non-uniform illumination image into five sub-regions and modifies the histogram of each sub-region by setting a nonlinear weight into their cumulative density function (CDF) of histogram in each sub-region. Each modified histogram is then equalized using modified HE equations that provide the intensity expansion and different intensity mapping directions for under-exposed and over-exposed sub-regions. A total of 354 non-uniform illuminated sample images were used to evaluate the performance of the proposed NEIMHE method, qualitatively and quantitatively. The proposed NEIMHE method was compared qualitatively with five state-of-the-art methods: Backlit, Adaptive Fuzzy Exposure Local Contrast Enhancement (AFELCE), Visual Contrast Enhancement Algorithm Based on Histogram Equalization (VCEA), Exposure Region-based Multi Histogram Equalization (ERMHE); and Exposure based Sub-Image Histogram Equalization (ESIHE). The proposed NEIMHE method produced an enhanced image with more uniform illumination, better preservation of image details, and high capability of maintaining image naturalness. Quantitatively, the proposed NEIMHE method achieved the highest scores in Discrete Entropy (DE), Measure of Enhancement (EME), Measure of Enhancement by Entropy (EMEE), and Peak Signal to Noise Ratio (PSNR); it attained second-best in Absolute Mean Brightness Error (AMBE) and Lightness Order Error (LOE). From both analyses, the proposed NEIMHE method has shown its capability of enhancing different exposure regions that exist in non-uniform illumination images.
Learning a Non-blind Deblurring Network for Night Blurry Images

Deblurring night blurry images is difficult, because the common-used blur model based on the linear convolution operation does not hold in this situation due to the influence of saturated pixels. In this paper, we propose a non-blind deblurring network (NBDN) to restore night blurry images. To mitigate the side effects brought by the pixels that violate the blur model, we develop a confidence estimation unit (CEU) to estimate a map which ensures smaller contributions of these pixels in the deconvolution steps which are optimized by the conjugate gradient (CG) method. Moreover, unlike the existing methods using manually tuned hyper-parameters in their frameworks, we propose a hyper-parameter estimation unit (HPEU) to adaptively estimate hyper-parameters for better image restoration. The experimental results demonstrate that the proposed network performs favorably against state-of-the-art algorithms both quantitatively and qualitatively.
Holdout-Based Empirical Assessment of Mixed-Type Synthetic Data

AI-based data synthesis has seen rapid progress over the last several years and is increasingly recognized for its promise to enable privacy-respecting high-fidelity data sharing. This is reflected by the growing availability of both commercial and open-sourced software solutions for synthesizing private data. However, despite these recent advances, adequately evaluating the quality of generated synthetic datasets is still an open challenge. We aim to close this gap and introduce a novel holdout-based empirical assessment framework for quantifying the fidelity as well as the privacy risk of synthetic data solutions for mixed-type tabular data. Measuring fidelity is based on statistical distances of lower-dimensional marginal distributions, which provide a model-free and easy-to-communicate empirical metric for the representativeness of a synthetic dataset. Privacy risk is assessed by calculating the individual-level distances to closest record with respect to the training data. By showing that the synthetic samples are just as close to the training as to the holdout data, we yield strong evidence that the synthesizer indeed learned to generalize patterns and is independent of individual training records. We empirically demonstrate the presented framework for seven distinct synthetic data solutions across four mixed-type datasets and compare these then to traditional data perturbation techniques. Both a Python-based implementation of the proposed metrics and the demonstration study setup is made available open-source. The results highlight the need to systematically assess the fidelity just as well as the privacy of these emerging class of synthetic data generators.
Predicting Community Smells’ Occurrence on Individual Developers by Sentiments

Community smells appear in sub-optimal software development community structures, causing unforeseen additional project costs, e.g., lower productivity and more technical debt. Previous studies analyzed and predicted community smells in the granularity of community sub-groups using socio-technical factors. However, refactoring such smells requires the effort of developers individually. To eliminate them, supportive measures for every developer should be constructed according to their motifs and working states. Recent work revealed developers' personalities could influence community smells' variation, and their sentiments could impact productivity. Thus, sentiments could be evaluated to predict community smells' occurrence on them. To this aim, this paper builds a developer-oriented and sentiment-aware community smell prediction model considering 3 smells such as Organizational Silo, Lone Wolf, and Bottleneck. Furthermore, it also predicts if a developer quitted the community after being affected by any smell. The proposed model achieves cross- and within-project prediction F-Measure ranging from 76% to 93%. Research also reveals 6 sentimental features having stronger predictive power compared with activeness metrics. Imperative and indicative expressions, politeness, and several emotions are the most powerful predictors. Finally, we test statistically the mean and distribution of sentimental features. Based on our findings, we suggest developers should communicate in a straightforward and polite way.
Generative Zero-shot Network Quantization

Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration [66]. We show that, for high-level image recognition tasks, we can further reconstruct "realistic" images of each category by leveraging intrinsic Batch Normalization (BN) statistics without any training data. Inspired by the popular VAE/GAN methods, we regard the zero-shot optimization process of synthetic images as generative modeling to match the distribution of BN statistics. The generated images serve as a calibration set for the following zero-shot network quantizations. Our method meets the needs for quantizing models based on sensitive information, e.g., due to privacy concerns, no data is available. Extensive experiments on benchmark datasets show that, with the help of generated data, our approach consistently outperforms existing data-free quantization methods.
A User Study on View-sharing Techniques for One-to-Many Mixed Reality Collaborations

In a one-to-many mixed reality collaboration environment, where multiple local users wearing AR headsets are supervised by a remote expert wearing a VR HMD, we evaluated three view-sharing techniques: 2D video, 360 video, and 3D model augmented with 2D video. Through a pilot test, the weaknesses of the techniques were identified, and additional features were integrated into them. Then, their performances were compared in two different collaboration scenarios based on search and assembling. In the first scenario, a local user performed both search and assembling. In the second scenario, two local users had dedicated roles, one for search and the other for assembling. The experiment results showed that the 3D model augmented with 2D video was time-efficient, usable, less demanding and most preferred in one-to-many mixed reality collaborations.
Low Bandwidth Video-Chat Compression using Deep Generative Models

To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver’s device using facial landmarks extracted at the sender’s side and transmitted over the network. In this context, we discuss and evaluate the benefits and disadvantages of several deep adversarial approaches. In particular, we explore quality and bandwidth trade-offs for approaches based on static landmarks, dynamic landmarks or segmentation maps. We design a mobile-compatible architecture based on the first order animation model of Siarohin et al. In addition, we leverage SPADE blocks to refine results in important areas such as the eyes and lips. We compress the networks down to about 3 MB, allowing models to run in real time on iPhone 8 (CPU). This approach enables video calling at a few kbits per second, an order of magnitude lower than currently available alternatives.
Improving Heterogeneous Network Knowledge Transfer Based on the Principle of Generative Adversarial

Deep learning requires a large amount of datasets to train deep neural network models for specific tasks, and thus training of a new model is a very costly task. Research on transfer networks used to reduce training costs will be the next turning point in deep learning research. The use of source task models to help reduce the training costs of the target task models, especially heterogeneous systems, is a problem we are studying. In order to quickly obtain an excellent target task model driven by the source task model, we propose a novel transfer learning approach. The model linearly transforms the feature mapping of the target domain and increases the weight value for feature matching to realize the knowledge transfer between heterogeneous networks and add a domain discriminator based on the principle of generative adversarial to speed up feature mapping and learning. Most importantly, this paper proposes a new objective function optimization scheme to complete the model training. It successfully combines the generative adversarial network with the weight feature matching method to ensure that the target model learns the most beneficial features from the source domain for its task. Compared with the previous transfer algorithm, our training results are excellent under the same benchmark for image recognition tasks.
Research on Path Planning of AUV Based on Improved Ant Colony Algorithm

In order to solve the problem of local optimal solution and slow convergence speed of traditional ant colony algorithm,initial pheromone, transfer probability and pheromone update strategy are improved in the paper. Combined with the practical application of Autonomous Underwater Vehicle (AUV), the path planning of traditional ant colony algorithm and improved ant colony algorithm are compared by simulation. The simulation results show that the improved ant colony algorithm can improve the convergence speed of the algorithm effectively.
Beyond Algorithmic Fairness in Recommender Systems

Fairness is one of the crucial aspects of modern Recommender Systems which has recently drawn substantial attention from the community. Many recent works have addressed this aspect by studying the fairness of the recommendation through different forms of evaluation methodologies and metrics. However, the majority of these works have mainly concentrated on the recommendation algorithms and hence measured the fairness from the algorithmic viewpoint. While such viewpoint may still play an important role, it does not necessarily project a comprehensive picture of how the users may perceive the overall fairness of a recommender system.
Teaching robotics to future teachers as part of education activities

Abstract One of the most effective ways to implement STEM education in full secondary education is through research activities. It is implemented by performing certain projects. Professional activity of teachers of natural and mathematical disciplines in STEM education is aimed at the students’ mental, cognitive, and personal qualities formation and development. Their level determines the possibility of further mastering by students of a promising specialty STEM industry. It also involves the formation of the ability and willingness to solve complex problems, which is possible with the appropriate level of critical thinking, creativity, cognitive flexibility, teamwork, as well as the ability to implement research activities. In article determines the nature of the relationship between disciplines and the degree of integration. The place of project activity on robotics in school training is considered, describes the competencies that are formed as a result of such project activities. the possibility of teaching robotics to future teachers within the framework of existing curricula is also considered. The concept of STEM education has a broad interpretation: from a simple list of “exact” or “engineering” disciplines to inventive activity. In Ukraine, this area has become synonymous with scientific and research work: research and experiments. From this point of view, robotics is an effective means of developing STEM education. But this is a new tool and schools have a low level of interest in its study. Therefore, our article focuses on finding opportunities to implement the basics of robotics in education. The article formulated course requirements in accordance with the educational needs of the future teachers of science, mathematics and the level of technology development. The paper presents an example of project implementation in robotics, describes the stages of its implementation and achieved educational results.
Scalable Differential Privacy with Sparse Network Finetuning

We propose a novel method for privacy-preserving training of deep neural networks leveraging public, out-domain data. While differential privacy (DP) has emerged as a mechanism to protect sensitive data in training datasets, its application to complex visual recognition tasks remains challenging. Traditional DP methods, such as Differentially-Private Stochastic Gradient Descent (DP-SGD), perform well only on simple datasets and shallow networks, while recent transfer learning-based DP methods often make unrealistic assumptions about the availability and distribution of public data. In this work, we argue that minimizing the number of trainable parameters is the key to improving the privacy-performance tradeoff of DP on complex visual recognition tasks. Inspired by this argument, we also propose a novel transfer learning paradigm that finetunes a very sparse subnetwork with DP. We conduct extensive experiments and ablation studies on two visual recognition tasks: CIFAR-100 → CIFAR-10 (standard DP setting) and the CD-FSL challenge (few-shot, multiple levels of domain shifts) and demonstrate competitive experimental performance.
Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements

Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost importance in smart grids. The deployment of STPF techniques provides fast dispatching in the case of sudden variations due to stochastic weather conditions. This paper presents an efficient data-driven method based on enhanced Random Forest (RF) model. The proposed method employs an ensemble of attribute selection techniques to manage bias/variance optimization for STPF application and enhance the forecasting quality results. The overall architecture strategy gathers the relevant information to constitute a voted feature-weighting vector of weather inputs. The main emphasis in this paper is laid on the knowledge expertise obtained from weather measurements. The feature selection techniques are based on local Interpretable Model-Agnostic Explanations, Extreme Boosting Model, and Elastic Net. A comparative performance investigation using an actual database, collected from the weather sensors, demonstrates the superiority of the proposed technique versus several data-driven machine learning models when applied to a typical distributed PV system.
Pseudo 3D Auto-Correlation Network for Real Image Denoising

The extraction of auto-correlation in images has shown great potential in deep learning networks, such as the self-attention mechanism in the channel domain and the self-similarity mechanism in the spatial domain. However, the realization of the above mechanisms mostly requires complicated module stacking and a large number of convolution calculations, which inevitably increases model complexity and memory cost. Therefore, we propose a pseudo 3D auto-correlation network (P3AN) to explore a more efficient way of capturing contextual information in image de-noising. On the one hand, P3AN uses fast 1D convolution instead of dense connections to realize criss-cross interaction, which requires less computational resources. On the other hand, the operation does not change the feature size and makes it easy to expand. It means that only a simple adaptive fusion is needed to obtain contextual information that includes both the channel domain and the spatial domain. Our method built a pseudo 3D auto-correlation attention block through 1D convolutions and a lightweight 2D structure for more discriminative features. Extensive experiments have been conducted on three synthetic and four real noisy datasets. According to quantitative metrics and visual quality evaluation, the P3AN shows great superiority and surpasses state-of-the-art image denoising methods.
A more compact object detector head network with feature enhancement and relational reasoning

Modeling feature interaction patterns is of significant importance to object detection tasks. However, reasoning about the relationship between instance features is challenging in two-stage detectors due to the overuse of hand-crafted components. To tackle this problem, we analyze three different levels of feature interaction relationships, namely, the dependency relationship between the cropped local features and global features, the feature autocorrelation within the instance, and the cross-correlation relationship between the instances. To this end, we propose a more compact object detector head network (CODH), which can not only preserve global context information and condense the information density, but also allows instance-wise feature enhancement and relational reasoning in a larger matrix space. Without bells and whistles, our method can effectively improve the detection performance while significantly reducing the parameters of the model, e.g., with our method, the parameters of the head network is 0.6× smaller than the state-of-the-art Cascade R-CNN. Yet, the performance boost is 1.3% on COCO test-dev. Without losing generality, we can also build a lighter head network for other multi-stage detectors by assembling our method, including Faster R-CNN, Libra R-CNN, and Double Head R-CNN.