Reviewerly/exHarmony huggingface dataset:
A New Video Quality Assessment Dataset for Video Surveillance Applications

In this paper, we propose a new comprehensive Video Surveillance Quality Assessment Dataset (VSQuAD) dedicated to Video Surveillance (VS) systems. In contrast to other public datasets, this one contains many more videos with distortions and diversified content from common video surveillance scenarios. These videos have been artificially degraded with various types of distortions (single distortion or multiple distortions simultaneously) at different severity levels. In order to improve the efficiency of the surveillance systems and the versatility of the video quality assessment dataset, night vision CCTV videos are also included. Furthermore, a comprehensive analysis of the content in terms of diversity and challenging problems is also presented in this study. The interest of such database is twofold. First, it will serve for benchmarking different video distortion detection and classification algorithms. Second, it will be useful for the design of learning models for various challenging VS problems such as identification and removal of the most common distortions. The complete dataset is made publicly available as part of a challenge session in this conference through the following link: https://www.l2ti.univ-paris13.fr/VSQuad/.
Parasitic Egg Detection and Classification with Transformer-Based Architectures

Soil-transmitted helminth infections are one of the most common healthcare problems worldwide and they especially affect to the poorest communities in tropical and subtropical areas. Nowadays, diagnosis of intestinal parasites is performed by highly skilled medical staff, directly examining samples in the laboratory via a microscope, a laborious and time-consuming work. Automatic deep learning-based object detection methods can help to automatically detect and identify intestinal parasitic eggs, or at least reduce the workload. In this work, the application of novel Transformer-based architectures is proposed to solve the parasitic egg detection task in microscopic images. Several detection methods and backbones have been analyzed and compared, obtaining up to 0.875 mIoU score on the dataset used for testing.
Detecting Gan-Generated Images by Orthogonal Training of Multiple CNNs

In the last few years, we have witnessed the rise of a series of deep learning methods to generate synthetic images that look extremely realistic. These techniques prove useful in the movie industry and for artistic purposes. However, they also prove dangerous if used to spread fake news or to generate fake online accounts. For this reason, detecting if an image is an actual photograph or has been synthetically generated is becoming an urgent necessity. This paper proposes a detector of synthetic images based on an ensemble of Convolutional Neural Networks (CNNs). We consider the problem of detecting images generated with techniques not available at training time. This is a common scenario, given that new image generators are published more and more frequently. To solve this issue, we leverage two main ideas: (i) CNNs should provide "orthogonal" results to better contribute to the ensemble; (ii) the original-image class is better defined than the synthetic-image one, thus it should be better trusted at testing time. Experiments show that pursuing these two ideas improves the detector accuracy on NVIDIA's newly generated StyleGAN3 images, never used in training.
Deeblif: Deep Blind Light Field Image Quality Assessment by Extracting Angular and Spatial Information

In the era of immersive media, the high-dimensional Light Field Image (LFI) puts forward higher requirements for Light Field Image Quality Assessment (LF-IQA). However, currently most existing LF-IQA metrics still rely on sophisticated hand-crafted feature extraction, which fail to predict the quality of LFI accurately. In this paper, we propose a patch-based Deep Blind Light Field image quality assessment metric (abbreviated as DeeBLiF), by employing a two-stream Convolutional Neural Network (CNN) model specifically designed for extracting the angular and spatial information of LFI. Firstly, the spatio-angular patches are generated as input data, which effectively reflect the spatio-angular information of LFI. After that, a two-stream CNN model is exploited to extract the patch features and further predict the patch scores. Finally, all the patch scores are pooled into an overall quality score of LFI. Experimental results on the LFI dataset demonstrate that the proposed DeeBLiF outperforms the state-of-the-art LF-IQA metrics. The code will be publicly available at https://github.com/ZhengyuZhang96/DeeBLiF.
Parasitic Egg Detection with a Deep Learning Ensemble

Intestinal parasitic infections are a healthcare problem with a high impact in some areas. While nowadays the assessment performed by experts is mostly manual, it is possible to introduce machine learning techniques to help automating this task, or at least reduce the workload. This can lead to shorter detection times and faster treatment application. In the context of deep learning, several object detection techniques have been proposed and validated on general purpose datasets such as ImageNet or COCO. In this work, an ensemble of these is proposed for this particular task. The merged detections of FasterRCNN, TOOD, YOLOX and Cascade with Swin-Transformers applied to this problem achieved 0.915 IoU, larger than the result that each method obtained independently.
Parasitic Egg Detection and Classification by Utilizing the YOLO Algorithm with Deep Latent Space Image Restoration and GrabCut Augmentation

A parasitic egg could cause infections and become significant diseases, especially in countries with poor sanitation and hygiene. Its detection which is manually conducted by humans using a microscope could be time-consuming and potent to misclassify. The goal of this research is to get a method that has a good accuracy to automatically detect and identify the parasitic egg. Also, we have a challenge here because the dataset has different resolutions, lighting, and setting conditions. Here we try to utilize the YOLO algorithm by doing image augmentation and restoration to the dataset, we use deep latent space translation for restoration and a simple GrabCut algorithm to generate an augmented image. Furthermore, we apply hyperparameter tuning in image resolution and see how we could utilize the YOLO algorithm to do fast and accurate classification. From the result, we get the combination could give better results in classifying the parasitic egg which the mIoU results reached 71.7 percent.
Emerging imbalance in the development of TPACK - A challenge for teacher training

Abstract This research continues the tradition of Technological Pedagogical Content Knowledge (TPACK) studies aimed at better understanding the development of pre-service teachers’ TPACK. The aim of the study is to show what are the areas of TPACK that pre-service teachers perceived as important and relevant for them, from a teacher training course. This course can be seen as a one of the many teacher training courses within teacher training. The course is not specially designed for mere educational technology, instead the course focuses on biology, using inquiry-based learning activities supported with various technologies. This study was conducted using qualitative methods. The research data consists of pre-service teachers’ (n = 165) answers to two short questions focusing on the elements that pre-service teachers gained from a teacher training science course for building their TPACK. The aim of this method was to highlight only the area that pre-service teachers felt important and relevant without providing any guiding structures. Results show the important role of Pedagogical content Knowledge (PCK) as the core area that respondents gained from the course. The results indicate that the role of Technological Pedagogical Knowledge (TPK) remained modest, the number of responses focusing on TPK was low, and the responses remained at a very general level. The results suggest that in order to provide pre-service teachers with better and more balanced support for the development of their TPACK, we need to highlight technology and make its role more explicit, especially from the perspectives of teachers.
Comparison of modeled and measured photovoltaic microinstallation energy productivity

There are many models for determining the theoretical productivity of PV panels, based on weather data and data characterizing PV panels. The main target of this study to contribute the similar studies for Polish conditions for photovoltaic microinstallations. The study analyzed the compliance of weather data from the ERA5 database with the measurement (pyranometer on horizontal surface) on PV installation site. Comparative studies of insolation from the ERA5 model with measurements made with a pyranometer were carried out for 1187 days. Higher values for daily insolation values were observed in most day cases (85 %) for data from ERA5 than from the measuring station. The value of relative mean bias error (rMBE) was 23 %. The second part of the research concerned the verification of the computational model of PV energy production based on the data of photovoltaic panels, configuration of settings and locations as well as ERA5. The differences between the results from the model and the real measurements from the rMBE installation for the first year of operation are 8 % and for the second 0 %.
Automatic sea state estimation with online trust measure based on ship response measurements

The ever-changing sea state constitutes an integral part of situational awareness for vessels at sea. In this paper, a computationally efficient sea state estimation algorithm is extended by (1) automatic gain and tolerance calculation, and (2) trust measure for wave filtering condition identification. The resulting algorithm can run online in a control system without the need of user input, producing estimates of sea state parameters (significant wave height, peak wave frequency and main propagation direction), while simultaneously monitoring the quality of the estimates produced by identifying when wave filtering occurs. The algorithm is developed for application in dynamic positioning (DP) control systems, where the vessel has zero (or low) forward speed. The estimation method is tested with realistic values of measurement noise and sampling in terms of comprehensive time series simulations based on both long- and short-crested wave conditions. The sea state estimates correspond well with a state-of-the-art Bayesian estimation approach. This indicates that the simplifications made in order to enhance the computational speed, has not affected the quality of the estimates to any significant extent. The trust measure correctly identifies when wave filtering occurs in all simulated scenarios.
Comparative Study on Shading Database Construction for Urban Roads Using 3D Models and Fisheye Images for Efficient Operation of Solar-Powered Electric Vehicles

Accounting for shadows on urban roads is a complex task in the operation of solar-powered electric vehicles. There have been few opportunities to compare the methods and tools for the construction of an effective shading database for urban roads. This study quantitatively investigated and compared shading matrices generated from 3D models or fisheye images. Skymaps were formed considering the geometry of nearby shading obstructions. Sun-path diagrams tracking the position of the sun by time and season were overlaid on the skymaps, and month-by-hour shading matrices were calculated. Mean squared error (MSE) was used to clarify the quantitative differences between the shading matrices. The cases were divided into A, B, and C according to the presence of buildings and trees around the survey points. Under case A (trees), case B (buildings and trees), and case C (buildings), the average MSEs between the matrices were 24.5%, 23.9%, and 2.1%, respectively. The shading matrices using either 3D models or fisheye images provided accurate shading effects caused by buildings. In contrast, the shading effects of trees were more accurately analyzed when using fisheye images. The findings of this study provide a background for constructing shading databases of urban road environments for the optimal operation of solar-powered electric vehicles.
Evolutionary Algorithm for Selecting Dynamic Signatures Partitioning Approach

Abstract In the verification of identity, the aim is to increase effectiveness and reduce involvement of verified users. A good compromise between these issues is ensured by dynamic signature verification. The dynamic signature is represented by signals describing the position of the stylus in time. They can be used to determine the velocity or acceleration signal. Values of these signals can be analyzed, interpreted, selected, and compared. In this paper, we propose an approach that: (a) uses an evolutionary algorithm to create signature partitions in the time and velocity domains; (b) selects the most characteristic partitions in terms of matching with reference signatures; and (c) works individually for each user, eliminating the need of using skilled forgeries. The proposed approach was tested using Biosecure DS2 database which is a part of the DeepSignDB, a database with genuine dynamic signatures. Our simulations confirmed the correctness of the adopted assumptions.
Rock alteration mapping in and around fossil shallow intrusions at Mt. Ruapehu New Zealand with laboratory and aerial hyperspectral imaging

Diagnostic absorption features in hyperspectral data can be used to identify a specific mineral or mineral associations. However, it is unknown how accurate hyperspectral mapping can be for identifying alteration mineral compositions at the resolution required to describe structures such as fossil intrusions, or whether it can accurately quantify the alteration present. This study compared petrographic observation with visible, near-infrared (VNIR), and shortwave infrared (SWIR) hyperspectral remote sensing at laboratory- (centimetre-scale) and aerial- (metre-scale) scales to characterise the abundance of surface hydrothermal rock alteration in and around a shallow fossil intrusion on Pinnacle Ridge, Mt. Ruapehu, New Zealand. We classified a high-resolution aerial hyperspectral image to develop a new surface alteration map using Spectral Angle Mapper (SAM) algorithm. The petrographic thin-section and the laboratory and aerial hyperspectral imaging revealed a spectrum of hydrous alteration phases as indicated by the presence of an absorption feature at 2207 nm. Moderate correlation exists between the depth of the absorption feature at 2207 nm and the point counting-derived alteration percent values, indicating reliability of laboratory-based hyperspectral analytical methods. In contrast, aerial hyperspectral data failed to provide any clear correlations to field-mapped alteration using a band-depth approach, and we interpret this due to ‘oversampling’ of surface (supergene) alteration, spectral mixing, and sensor limitations (e.g., bandwidth, signal-to-noise ratio). The hyperspectral image-derived alteration map, created using supervised image classification, can loosely be translated to a geotechnical map where porosity and permeability play a major role in localizing hydrothermal fluid flow and the formation of alteration mineral associations.
What’s wrong with the rainbow? An interdisciplinary review of empirical evidence for and against the rainbow color scheme in visualizations

Rainbow color scheme is popularly used across the board in many scientific disciplines for visualizing data, yet heavily debated in visualization literature. In this paper, we first report an interdisciplinary visualization survey examining the prevalence rainbow color scheme use in scientific publications including a temporal analysis. Then we consolidate findings from empirical studies on the subject to better understand why and precisely when the rainbow color scheme might impair human performance with visualizations. Consequently, we systematically document and analyze the consequences of using the rainbow color scheme based on over 37,000 figures in 11,808 papers in information visualization, neuroscience, hydrology, geography, remote sensing and planetary science. Our findings reveal that while the rainbow scheme appears less and less in visualization-related publications, it remains popular in other scientific domains including remote sensing and planetary sciences (strikingly fourteen times more frequent in remote sensing and planetary sciences than in information visualization outlets). We also find that conflicting findings about human performance with the rainbow color scheme is most likely explained by what the users are asked to do with it (i.e., task type). We detail and typify tasks used in the related empirical work in an effort to organize the current understanding on the subject, and translate it to practicable recommendations. We believe our review and analyses bring clarity and nuance to “the rainbow debate” and enable better-informed visualization advice.
Artificial intelligence applied to the classification of eight middle Eocene species of the genus <i>Podocyrtis</i> (polycystine radiolaria)

Abstract. This study evaluates the application of artificial intelligence (AI) to the automatic classification of radiolarians and uses as an example eight distinct morphospecies of the Eocene radiolarian genus Podocyrtis, which are part of three different evolutionary lineages and are useful in biostratigraphy. The samples used in this study were recovered from the equatorial Atlantic (ODP Leg 207) and were supplemented with some samples coming from the North Atlantic and Indian Oceans. To create an automatic classification tool, numerous images of the investigated species were needed to train a MobileNet convolutional neural network entirely coded in Python. Three different datasets were obtained. The first one consists of a mixture of broken and complete specimens, some of which sometimes appear blurry. The second and third datasets were leveled down into two further steps, which excludes broken and blurry specimens while increasing the quality. The convolutional neural network randomly selected 85 % of all specimens for training, while the remaining 15 % were used for validation. The MobileNet architecture had an overall accuracy of about 91 % for all datasets. Three predicational models were thereafter created, which had been trained on each dataset and worked well for classification of Podocyrtis coming from the Indian Ocean (Madingley Rise, ODP Leg 115, Hole 711A) and the western North Atlantic Ocean (New Jersey slope, DSDP Leg 95, Hole 612 and Blake Nose, ODP Leg 171B, Hole 1051A). These samples also provided clearer images since they were mounted with Canada balsam rather than Norland epoxy. In spite of some morphological differences encountered in different parts of the world's oceans and differences in image quality, most species could be correctly classified or at least classified with a neighboring species along a lineage. Classification improved slightly for some species by cropping and/or removing background particles of images which did not segment properly in the image processing. However, depending on cropping or background removal, the best result came from the predictive model trained on the normal stacked dataset consisting of a mixture of broken and complete specimens.
Model compression and privacy preserving framework for federated learning

Federated learning (FL) as a collaborative learning paradigm has attracted extensive attention due to its characteristic of privacy preserving, in which the clients train a shared neural network model collaboratively by their local dataset and upload their model parameters merely instead of original data by wireless network in the whole training process. Because FL reduces transmission significantly, it can further meets the efficiency and security of the next generation wireless system. Although FL has reduced the size of information that needs to be transmitted, the update of model parameters still suffers from privacy leakage and communication bottleneck especially in wireless networks. To address the problem of privacy and communication, this paper proposes a model compression based FL framework. Firstly, the designed model compression framework provides effective support for efficient and secure model parameters updating in FL while keeping the personalization of all clients. Then, the proposed perturbed model compression method can further reduce the size of the model and protect the privacy of the model without sacrificing much accuracy. Besides, it also facilitates the simultaneous execution of decryption and decompressing operations by reconstruction algorithm on encrypted and compressed model parameters which is obtained by the proposed perturbed model compression method. Finally, the illustrative results demonstrate that the proposed model compression based FL framework can significantly reduce the number of model parameters for uploading with a strong privacy preservation property. For example, when the compression ratio is 0.0953 (i.e., only 9.53% of the parameters are uploaded), the accuracy of MNIST achieves 97% while the accuracy without compression is 98%.
“I am not a YouTuber who can make whatever video I want. I have to keep appeasing algorithms”: Bureaucracy of Creator Moderation on YouTube

Recent HCI studies have recognized an analogy between bureaucracy and algorithmic systems; given platformization of content creators, video sharing platforms like YouTube and TikTok practice creator moderation, i.e., an assemblage of algorithms that manage not only creators' content but also their income, visibility, identities, and more. However, it has not been fully understood as to how bureaucracy manifests in creator moderation. In this poster, we present an interview study with 28 YouTubers (i.e., video content creators) to analyze the bureaucracy of creator moderation from their moderation experiences. We found participants wrestled with bureaucracy as multiple obstructions in re-examining moderation decisions, coercion to appease different algorithms in creator moderation, and the platform's indifference to participants' labor. We discuss and contribute a conceptual understanding of how algorithmic and organizational bureaucracy intertwine in creator moderation, laying a solid ground for our future study.
StolenEncoder

Pre-trained encoders are general-purpose feature extractors that can be used for many downstream tasks. Recent progress in self-supervised learning can pre-train highly effective encoders using a large volume of unlabeled data, leading to the emerging encoder as a service (EaaS). A pre-trained encoder may be deemed confidential because its training often requires lots of data and computation resources as well as its public release may facilitate misuse of AI, e.g., for deepfakes generation. In this paper, we propose the first attack called StolenEncoder to steal pre-trained image encoders. We evaluate StolenEncoder on multiple target encoders pre-trained by ourselves and three real-world target encoders including the ImageNet encoder pre-trained by Google, CLIP encoder pre-trained by OpenAI, and Clarifai's General Embedding encoder deployed as a paid EaaS. Our results show that the encoders stolen by StolenEncoder have similar functionality with the target encoders. In particular, the downstream classifiers built upon a target encoder and a stolen encoder have similar accuracy. Moreover, stealing a target encoder using StolenEncoder requires much less data and computation resources than pre-training it from scratch. We also explore three defenses that perturb feature vectors produced by a target encoder. Our evaluation shows that these defenses are not enough to mitigate StolenEncoder.