Reviewerly/exHarmony huggingface dataset:
A Quantitative Analysis of Labeling Issues in the CelebA Dataset

Facial attribute prediction is a facial analysis task that describes images using natural language features. While many works have attempted to optimize prediction accuracy on CelebA, the largest and most widely used facial attribute dataset, few works have analyzed the accuracy of the dataset's attribute labels. In this paper, we seek to do just that. Despite the popularity of CelebA, we find through quantitative analysis that there are widespread inconsistencies and inaccuracies in its attribute labeling. We estimate that at least one third of all images have one or more incorrect labels, and reliable predictions are impossible for several attributes due to inconsistent labeling. Our results demonstrate that classifiers struggle with many CelebA attributes not because they are difficult to predict, but because they are poorly labeled. This indicates that the CelebA dataset is flawed as a facial analysis tool and may not be suitable as a generic evaluation benchmark for imbalanced classification.
Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space

Most few-shot image classification methods are trained based on tasks. Usually, tasks are built on base classes with a large number of labeled images, which consumes large effort. Unsupervised few-shot image classification methods do not need labeled images, because they require tasks to be built on unlabeled images. In order to efficiently build tasks with unlabeled images, we propose a novel single-stage clustering method: Learning Features into Clustering Space (LF2CS), which first set a separable clustering space by fixing the clustering centers and then use a learnable model to learn features into the clustering space. Based on our LF2CS, we put forward an image sampling and c-way k-shot task building method. With this, we propose a novel unsupervised few-shot image classification method, which jointly learns the learnable model, clustering and few-shot image classification. Experiments and visualization show that our LF2CS has a strong ability to generalize to the novel categories. From the perspective of image sampling, we implement four baselines according to how to build tasks. We conduct experiments on the Omniglot, miniImageNet, tieredImageNet and CIFARFS datasets based on the Conv-4 and ResNet-12 backbones. Experimental results show that ours outperform the state-of-the-art methods.
Learning to regulate 3D head shape by removing occluding hair from in-the-wild images

Recent 3D face reconstruction methods reconstruct the entire head compared to earlier approaches which only model the face. Although these methods accurately reconstruct facial features, they do not explicitly regulate the upper part of the head. Extracting information about this part of the head is challenging due to varying degrees of occlusion by hair. We present a novel approach for modeling the upper head by removing occluding hair and reconstructing the skin, revealing information about the head shape. We introduce three objectives: 1) a dice consistency loss that enforces similarity between the overall head shape of the source and rendered image, 2) a scale consistency loss to ensure that head shape is accurately reproduced even if the upper part of the head is not visible, and 3) a 71 landmark detector trained using a moving average loss function to detect additional landmarks on the head. These objectives are used to train an encoder in an unsupervised manner to regress FLAME parameters from in-the-wild input images. Our unsupervised 3DMM model achieves state-of-the-art results on popular benchmarks and can be used to infer the head shape, facial features, and textures for direct use in animation or avatar creation.
Effects of Optical See-Through Displays on Self-Avatar Appearance in Augmented Reality

Display technologies in the fields of virtual and augmented reality affect the appearance of human representations, such as avatars used in telepresence or entertainment applications, based on the user's current viewing conditions. With changing viewing conditions, it is possible that the perceived appearance of one's avatar changes in an unexpected or undesired manner, which may change user behavior towards these avatars and cause frustration in using the AR display. In this paper, we describe a user study (N=20) where participants saw themselves in a mirror standing next to their own avatar through use of a HoloLens 2 optical see-through head-mounted display. Participants were tasked to match their avatar's appearance to their own under two environment lighting conditions (200 lux and 2,000 lux). Our results showed that the intensity of environment lighting had a significant effect on participants selected skin colors for their avatars, where participants with dark skin colors tended to make their avatar's skin color lighter, nearly to the level of participants with light skin color. Further, in particular female participants made their avatar's hair color darker for the lighter environment lighting condition. We discuss our results with a view on technological limitations and effects on the diversity of avatar representations on optical see-through displays.
Locating Image Objects With Probability Distributions

In this letter, we predict the locations as probability distributions for the tasks of image object detection. We adopt the Kullback-Leibler divergences as the regression losses to train the deep neural networks. Since most existing evaluations label the objects with rectangular bounding boxes, we propose the Nearest Distribution Converter to find the closest uniform distributions from the predicted ones. Our proposed method can improve the detected accuracy measured in mAP by 0.57%, 0.75%, and 0.48% on the models YOLOv3, the YOLOv4-tiny, and the YOLOv4, respectively.
The Adjusted Reality Vision - Emerging Technology for Digital Transformation of Disruptive Environmental Stimuli

Through a novel approach, which we call Adjusted Reality, it should become possible to virtually adapt physical reality by shrinking it virtually. You could also call it the reverse approach of Augmented Reality (AR). We see the main application in the medical field, for example with Highly Sensitive Persons (HSP). These individuals experience immense sensory and psychological overload. Due to the stressful situations caused by the disruptive stimuli, mental illnesses such as depression, anxiety disorders or somatoform disorders can develop and manifest. Up to now, there are only a few possibilities to offer help to these persons and thus prevent the symptoms. Therapies or participation in self-help groups can be mentioned as means of help. Interestingly, there are no digital aids for HSP so far, neither wearables nor mobile applications. By using Adjusted Reality, the aforementioned consequences should at best be avoided, or at least mitigated, by reducing the physical stimuli before they reach the sensory cells. The functionality of Adjusted Reality is therefore designed as an intelligent, AI-assisted and multimodal wearable system. Sensory input is personalized in the system, digitally modified, and delivered in an attenuated version to the sensory cells and finally to the brain. Through the use of Adjusted Reality, individuals are expected to relax and feel more comfortable with themselves, including their situation within society. In general, due to its omnipotent possibilities, Adjusted Reality can be relevant whenever physical experiences can be specifically reduced. In this work, these possibilities will be further explored.
CESAMMO: Categorical Encoding by Statistical Applied Multivariable Modeling

Categorical attributes are present in datasets used in machine learning (ML) tasks. Since most ML algorithms only accept numeric inputs, categorical instances must be converted to numbers. There are different encoding techniques to accomplish this task. During this conversion, it is important to preserve the underlying pattern in the dataset. Otherwise, there may be a loss of information that can negatively affect the performance of supervised learning algorithms. In this paper, we present an encoding technique based on finding those numbers or codes that preserve the relationship between the categorical attribute and the other variables of the dataset. We solved six supervised classification problems using the proposed technique with five different ML algorithms. Additionally, we compare the performance of the proposed technique with other ten encoding techniques. We found that the proposed technique outperforms the most commonly used encoding techniques for certain trained ML algorithms. On average, CESAMMO remained within the top 5 techniques in terms of performance of the 12 encoders tested.
Online QoS estimation for vehicular radio environments

Quality of service (QoS) estimation is a key enabler in wireless networks. This has been facilitated by the increasing capabilities of machine learning (ML). However, ML algorithms often underperform when presented with non-stationary data, which is typically the case for radio environments. In such environments, ML schemes might require extra signaling for retraining. In this paper, we propose an approach to online QoS estimation, where a trained model can be taken as a base estimator and fine-tuned with information from the user equipment (UE) and the cell itself. The proposed approach is based on the Adaptive Random Forest (ARF) algorithm, which uses streaming data and reacts on changes under concept drift, i.e., to changes in the data's statistical properties. This effectively allows to retrain parts of the ML model as vehicular UEs visit diverse radio environments. We evaluate this method with real data from an extensive measurement campaign in a cellular test network that covered diverse radio environments.
Augmented Reality for Windy Cities: 3D Visualization of Future Wind Nature Analysis in City Planning

Abstract Effective government management, convenient public services, and sustainable industrial development are achieved by the thorough utilization and management of green, renewable resources. The research and the study of meteorological data and its effect on devising renewable solutions as a replacement for nonrenewable ones is the motive of researchers and city planners. Sources of energy like wind and solar are free, green, and popularly being integrated into sustainable development and city planning to preserve environmental quality. Sensor networks have become a convenient tool for environmental monitoring. Wind energy generated through the use and maintenance of wind turbines requires knowledge of wind parameters such as speed and direction for proper maintenance. An augmented reality (AR) tool for interactive visualization and exploration of future wind nature analyses for experts is still missing. Existing solutions are limited to graphs, tabular data, two-dimensional space (2D) maps, globe view, and GIS tool designed for the desktop and not adapted with AR for easy, interactive mobile use. This work aims to provide a novel AR-based mobile supported application (App) that serves as a bridge between three-dimensional space (3D) temporal wind dataset visualization and predictive analysis through machine learning (ML). The proposed development is a dynamic application of AR supported with ML. It provides a user interactive designed approach, presenting a multilayered infrastructure process accessed through a mobile AR platform that supports 3D visualization of temporal wind data through future wind analysis. Thus, a novel AR visualization App with the prediction of wind nature using ML algorithms would provide city planners with advanced knowledge of wind conditions and help in easy decision-making with interactive 3D visualization.
Towards Learning to Play Piano with Dexterous Hands and Touch

As Liszt once said “(a virtuoso) must call up scent and blossom, and breathe the breath of life”, a virtuoso plays the piano with passion, poetry, and extraordinary technical ability. Hence, piano playing, being a task that is quintessentially human, becomes a hallmark for roboticians and artificial intelligence researchers to pursue. In this paper, we advocate an end-to-end reinforcement learning (RL) paradigm to demonstrate how an agent can learn directly from machine-readable music score to play the piano with touch-augmented dexterous hands on a simulated piano. To achieve the desired tasks, we design useful touch- and audio-based reward functions and a series of tasks. Empirical results show that the RL agent can not only find the correct key position but also deal with the various rhythmic, volume, and fingering requirements. As a result, the agent demonstrates its effectiveness in playing simple pieces that have different musical requirements which show the potential of leveraging reinforcement learning approach for the piano playing tasks.
Video Frame Interpolation With Learnable Uncertainty and Decomposition

Video frame interpolation can flexibly increase the temporal resolution of low frame-rate videos by generating the missing intermediate frames at any time. Existing methods generally train a renderer to predict rgb frames based on estimated clues. It tends to generate blurry outputs with unpleasing artifacts due to false clues and the neural network's bias in favor of low-frequency information. To address this problem, we propose a novel two-stage supervised framework. The inaccuracy of clues is modeled as uncertainty which can be estimated by training implicitly with a parameterized loss function in stage one. The bias is alleviated in stage two by regressing a lossless decomposition of frames instead of the raw rgbs. The decomposition can be achieved by several invertible cross-coupling layers, motivating the network to synthesize high-frequency details. Moreover, the proposed framework is equipped with a time-varying neural network that is adaptive to the timestamp of any intermediate frame, bringing benefits to multiple-frame interpolation. Both qualitative and quantitative experiments demonstrate the superiority of our proposed approach.
Infinite Families of Cyclic and Negacyclic Codes Supporting 3-Designs

Interplay between coding theory and combinatorial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -designs has been a hot topic for many years for combinatorialists and coding theorists. Some infinite families of cyclic codes supporting infinite families of 3-designs have been constructed in the past 50 years. However, no infinite family of negacyclic codes supporting an infinite family of 3-designs has been reported in the literature. This is the main motivation of this paper. Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q=p^{m}$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> is an odd prime and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m \geq 2$ </tex-math></inline-formula> is an integer. The objective of this paper is to present an infinite family of cyclic codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {GF}}(q)$ </tex-math></inline-formula> supporting an infinite family of 3-designs and two infinite families of negacyclic codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {GF}}(q^{2})$ </tex-math></inline-formula> supporting two infinite families of 3-designs. The parameters and the weight distributions of these codes are determined. The subfield subcodes of these negacyclic codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {GF}}(q)$ </tex-math></inline-formula> are studied. Three infinite families of almost MDS codes are also presented. A constacyclic code over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {GF}}(4)$ </tex-math></inline-formula> supporting a 4-design and seven open problems are also presented in this paper.
Multi-view Heterogeneous Temporal Graph Neural Network for “Click Farming” Detection

Multi-purpose Messaging Mobile App (MMMA) combines several functionalities in a single APP to provide integrated service that brings tremendous convenience to users. Therefore, MMMAs become more and more popular. However, the prevalence of MMMAs also makes them a hotbed for cybercrime. Among them, “Click Farming” fraud requires special attention, as it causes substantial pecuniary losses and is challenging to detect. In this paper, we describe Multi-view Heterogeneous Temporal Graph Neural Network (MHT-GNN), a framework for detecting “Click Farming” fraudsters in a popular MMMA called WeChat. We first adopt a Heterogeneous Temporal Graph (HTG) to model spatial, heterogeneous and temporal information contained in MM-MA data. We then extract two different types of user history sequences as two “views” of user behavior patterns from HTG. MHT-GNN contains a pretraining phase and a detection phase. The main components in MHT-GNN include Inductive Heterogeneous GNN Encoder, Temporal Snapshot Sequence Encoder, and User Relation Sequence Encoder. The first encoder aims to capture spatial information and the heterogeneity in each snapshot of HTG. The later two encoders are designed to incorporate temporal information to better reveal user’s behavior patterns and MHT-GNN leverages them to capture the two different views of user history behavior data. We conduct experiments on a real-world, million-scale dynamic graph extracted from WeChat. Experimental results demonstrate the effectiveness of MHT-GNN: it significantly exceeds existing detection methods, and it is able to block “Click Farming” fraud activities.
Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning

Coronavirus disease has a crisis with high spread throughout the world during the COVID19 pandemic period. This disease can be easily spread to a group of people and increase the spread. Since it is a worldly disease and not plenty of vaccines available, social distancing is the only best approach to defend against the pandemic situation. All the affected countries' governments declared locked-down to implement social distancing. This social separation and persons not being in a mass group can slow down the spread of COVID19. It reduces the physical contact between infected persons and normal healthy persons. Almost every health organization tells that to follow social distancing people should maintain at least 6 feet of distance from each other. This research proposes a deep learning approach for social distancing which is developed for tracking and detecting people who are in indoor as well as outdoor scenarios using YOLO V3 video analytic technique. This approach focuses to inspect whether the people are maintaining social distancing in many areas, using surveillance video with measuring the distance in real-time performance. Most of the early studies of detecting social distance monitoring were based on GPS for tracking the movements of people where the signals could be lost. On the other hand, some countries use drones to detect large gatherings of people who cannot have a clear view at night times [10]. In the future, the proposed system can be used fully for detecting threats in the public crowded or it can detect any person affected by critical situations (ie fainting, Cordia arrest) or planting the crops in the forms evenly with a uniform measurement. This proposal could be used in many fields like crowd analysis, autonomous vehicles, and human action recognition and could help the government authorities to redesign the public place layout and take precautionary action in the risk zones. This system analyses the social distancing of people by calculating the distance between people to slow downing the spread of the COVID 19 virus.
Schema Formalism for Semantic Summary Based on Labeled Graph from Heterogeneous Data

Graphs are used in various applications and to model real world objects. To understand the underlying characteristics of large graphs, graph summarization becomes a hot topic aiming to facilitate the identification of structure and meaning in data. The problem of graph summarization has been studied in the literature and many approaches for static contexts are proposed to summarize the graph in terms of its communities. These approaches typically produce groupings of nodes which satisfy or approximate some optimization function. Nevertheless, they fail to characterize the subgraphs and do not summarize both the structure and the content in the same approach. Existing approaches are only suitable for a static context, and do not offer direct dynamic counterparts. This means that there is no framework that provides summarization of mixed-source and information with the goal of creating a dynamic, syntactic, and semantic data summary. In this paper, the main contribution relies on summarizing data into a single graph model for heterogeneous sources. It's a schema-driven approach based on labeled graph. Our approach allows also to link the graph model to the relevant domain knowledge to find relevant concepts to provide meaningful and concise summary. After extracting relevant domain, we provide a personalized visualization model capable of summarize graphically both the structure and the content of the data from databases, devices, and sensors to reduce cognitive barriers related to the complexity of the information and its interpretation. We illustrate this approach through a case study on the use of E-health domain.
Towards a Blockchain-Based Smart Farm Agricultural Revolution in Sub-Saharan Africa.

Africa is considered one of the agricultural superpowers, with 60% of the global arable land. As a result of the increase in the population and the growing demand for agricultural products, the agri-food sector is resorting to the use of New Technologies of Information and Communication. With the fourth industrial revolution, it is now possible to integrate technologies such as the Blockchain and the Internet of Things into the activities of the agricultural sector. This study aims to show how the blockchain can improve the practice of intelligent agriculture in Africa. For these, two research questions will guide this reflection. The results obtained a state where Blockchain technology has begun to make its first appearances in some African states thanks to its advantages. Nevertheless, it should be noted that the blockchain is still facing various internal and external challenges in the African States, where the proposal for some slopes to make the blockchain a real asset for the African agri-food sector.