Reviewerly/exHarmony huggingface dataset:
Face Denoising and 3D Reconstruction from A Single Depth Image

The reconstruction of 3D face shapes and expressions from a single depth image obtained by a consumer depth camera is a challenging issue considering device-specific noise, the data missing, and the lack of textual constraints. In order to relieve the computationally-intensive nonlinear optimization of traditional template-fitting-based methods, we aim to build an end-to-end regression framework between a depth image and a 3D face encoded by the identity, the expression, and the pose parameters. Concerning the lack of paired depth images and 3D faces, we utilize the unsupervised CycleGAN-based network to adapt the regression model learned from the synthetic data to the real-captured noisy depth images. Instead of separate depth image denoising and 3D face inference, we present a task-specific coupled loss for end-to-end 3D face estimation. We propose a three-tier constraint for the shape consistency in the joint embedding, the depth image, and the surface space to avoid shape distortions in the unsupervised domain adaptation network. We report promising qualitative results for the task of the face denoising and 3D face reconstruction from a single depth image.
FEM: Feature extraction and mapping for radio modulation classification

Due to the stochastic nature of wireless channels, the received radio signal is noised during transmission causing difficulty in classifying radio modulation categories. Deep learning methods are applied to radio modulation classification for its promising performance compared with traditional approaches. However, deep learning models have relied on a large number of labeled data as training data. Collecting and labeling radio signals in various environments require a lot of manpower and material resources, which will limit the application of deep learning models in modulation recognition. To improve the efficiency of labeling radio signals, in this paper, we propose a feature extraction and mapping algorithm (FEM) to extract and map radio signals to images for further classification. Specifically, we propose an LSTM-based feature extraction network to extract radio features from radio signals, which are further mapped to different categories of images via a feature mapping network. The mapping between radio features and images is automatically learned by a generative adversarial network. Experiment results show that different modulation categories can be obviously discriminated though the mapped images. Furthermore, the modulation classification accuracy based on mapped images is slighted improved comparing with the original radio signals.
A Probabilistic Model for Visual Driver Gaze Approximation from Head Pose Estimation

The direction of a vehicle driver's visual attention plays an essential role in the research on Advanced Driving Assistance Systems (ADAS) and autonomous vehicles. How a driver monitors the surrounding environment is at least partially descriptive of the driver's situational awareness. While driver gaze is not explicitly related to head pose due to the interplay between head and eye movements, it may still provide an approximation of the visual attention that is sufficiently accurate for many applications. In this research, we propose a probabilistic method for describing the visual attention of drivers. This method applies a Gaussian Process Regression (GPR) technique that estimates the probability of the driver gaze direction, given head pose. We evaluate our model on real data collected during drives with an experimental vehicle in urban and suburban areas. Our experimental results show that 82.5% of drivers' gaze lies within the 95% confidence interval predicted by our framework.
A computer vision-based object detection and counting for COVID-19 protocol compliance: a case study of Jakarta

The chaotic world situation caused by the SARS-CoV-2 virus (COVID-19 pandemic) has hampered many sectors of human activity, especially in activities that require physical interactions.Thus, requiring social restrictions for those sectors that are affected.This paper reports the analysis of the proposed system for monitoring and supporting public activities in order to carry out social restrictions, specifically in the DKI Jakarta province.The proposed systems are YOLO and MobileNet SSD as its main weight to help this detection system with 30% and 40% confidence, respectively.The results of object counting and physical distancing are expected to be a guideline for public complaints in the future by using several CCTV locations points with better image quality and better angles.
A feedback system using blockchain technology

Blockchain has emerged as a new light in the digital world. Though this technology is one of the newest inventions, it has widened the doors of security and transparency in the technology-based world. Stakeholder's feedback brings out more opportunities in an organization. But it is not easy to find honest feedback since people may feel hesitation to give an honest review for many reasons like safety, bias, nepotism, etc. Today mostly a database system is used to store the data of feedback, but lack of trust in the database system leads to false feedback or partially compromised feedback. In this paper, a digital feedback system will be used where feedback will be ensured with the anonymity of the users. This system will make sure the trust of users is being stalled when feedback is given. Furthermore, it will give an idea of how the people of an organization think about certain topics. This system can be a game-changer in the corporate world as well.
Hierarchical data fusion processes involving the Möbius representation of capacities

The use of the Choquet integral in data fusion processes allows for the effective modelling of interactions and dependencies between data features or criteria. Its application requires identification of the defining capacity (also known as fuzzy measure) values. The main limiting factor is the complexity of the underlying parameter learning problem, which grows exponentially in the number of variables. However, in practice we may have expert knowledge regarding which of the subsets of criteria interact with each other, and which groups are independent. In this paper we study hierarchical aggregation processes, architecturally similar to feed-forward neural networks, but which allow for the simplification of the fitting problem both in terms of the number of variables and monotonicity constraints. We note that the Möbius representation lets us identify a number of relationships between the overall fuzzy measure and the data pipeline structure. Included in our findings are simplified fuzzy measures that generalise both k-intolerant and k-interactive capacities.
Label-guided Attention Distillation for lane segmentation

Contemporary segmentation methods are usually based on deep fully convolutional networks (FCNs). However, the layer-by-layer convolutions with a growing receptive field is not good at capturing long-range contexts such as lane markers in the scene. In this paper, we address this issue by designing a distillation method that exploits label structure when training segmentation network. The intuition is that the ground-truth lane annotations themselves exhibit internal structure. We broadcast the structure hints throughout a teacher network, i.e., we train a teacher network that consumes a lane label map as input and attempts to replicate it as output. Then, the attention maps of the teacher network are adopted as supervisors of the student segmentation network. The teacher network, with label structure information embedded, knows distinctly where the convolution layers should pay visual attention into. The proposed method is named as Label-guided Attention Distillation (LGAD). It turns out that the student network learns significantly better with LGAD than when learning alone. As the teacher network is deprecated after training, our method do not increase the inference time. Note that LGAD can be easily incorporated in any lane segmentation network.
Pseudo fourth-order moment based bearing fault feature reconstruction and diagnosis

In this paper, a novel bearing fault diagnosis approach based on pseudo fourth-order moment is proposed and verified. Based on the wavelet decomposition of different vibration signals, the pseudo fourth-order moment is calculated, and a new fault feature-the feature angle is proposed. Feature angle between these pseudo fourth-order moments is obtained, and corresponding working condition maybe characterized by this feature angle. The ranges of these feature angles are determined by simulation experiments with a large amount of data. An assessment index (namely selection index of optimal data size) is constructed to select the optimal amount of computational data. Meanwhile, extreme learning machine (ELM) model is established to classify different working conditions. According to the ELM model obtained from training data, the accuracy of test data classification reaches 95.42%, which proves the effectiveness of present bearing fault diagnosis method.
Thresholdizing HashEdDSA: MPC to the Rescue

Following recent comments in a NIST document related to threshold cryptographic standards, we examine the case of thresholdizing the HashEdDSA signature scheme. This is a deterministic signature scheme based on Edwards elliptic curves. Unlike DSA, it has a Schnorr-like signature equation, which is an advantage for threshold implementations, but it has the disadvantage of having the ephemeral secret obtained by hashing the secret key and the message. We show that one can obtain relatively efficient implementations of threshold HashEdDSA with no modifications to the behaviour of the signing algorithm; we achieve this using a doubly authenticated bit (daBit) generation protocol tailored for $$\mathcal {Q}_2$$ access structures that is more efficient than prior work. However, if one was to modify the standard algorithm to use an MPC-friendly hash function, such as Rescue, the performance becomes very fast indeed.
Towards Geometry Guided Neural Relighting with Flash Photography

Previous image based relighting methods require capturing multiple images to acquire high frequency lighting effect under different lighting conditions, which needs nontrivial effort and may be unrealistic in certain practical use scenarios. While such approaches rely entirely on cleverly sampling the color images under different lighting conditions, little has been done to utilize geometric information that crucially influences the high-frequency features in the images, such as glossy highlight and cast shadow. We therefore propose a framework for image relighting from a single flash photograph with its corresponding depth map using deep learning. By incorporating the depth map, our approach is able to extrapolate realistic high-frequency effects under novel lighting via geometry guided image decomposition from the flashlight image, and predict the cast shadow map from the shadow-encoding transformed depth map. Moreover, the single-image based setup greatly simplifies the data capture process. We experimentally validate the advantage of our geometry guided approach over state-of-the-art image-based approaches in intrinsic image decomposition and image relighting, and also demonstrate our performance on real mobile phone photo examples.
Image-based rock typing using local homogeneity filter and Chan-Vese model

Image-based rock typing is carried out to classify an image of the heterogeneous rock sample into different rock types where each rock type can be treated as a homogeneous porous medium. In this study, we propose an innovative method for rock typing of the heterogeneous rock sample via three steps. First, the target image, a segmented binary image with two phases of pore and solid, is consecutively inputted into two filters of a local homogeneity filter and an average filter to increase the contrast between different rock types and decrease the contrast within each single rock type. Second, Chan-Vese model is applied to classify the filtered image into different rock types. Third, a thresholding is used to remove the particles, which are treated as noisy particles, smaller than a given preset size. The main idea of the local homogeneity filtering introduced in this study is undertaken by counting the number of pixels that possess the same phases as the center pixel within a 3 × 3 pixels neighborhood. This process is carried out iteratively, which means the previously estimated pixel will be used in the estimation of its neighbor unprocessed pixels. We demonstrate the application of the proposed method in several heterogeneous images and present good performance.
UAV Path Planning based on Improved Whale optimization Algorithm

In order to solve the problem of path planning for Unmanned Aerial Vehicle (UAV) in complex battlefield environment, an improved whale optimization algorithm (IWOA) is proposed in this paper. The algorithm is improved by adding adaptive weights. Then, the linear convergence factor of the algorithm is modified into a nonlinear convergence factor, which is beneficial to balance the global searching ability and local development ability. The simulation results show that the IWOA algorithm has better convergence than the other three algorithms.
Scaled Document Clustering and Word Cloud-Based Summarization on Hindi Corpus

Managing a large number of textual documents is a critical and significant task and supports many applications ranging from information retrieval to clustering search engine results. The multilinguistic facility provided by websites makes Hindi a major language in the digital domain of information technology today. This work focuses on document management through document clustering for a big corpus and summarization of clusters. The objective is to overcome the scalability problem while managing the documents and summarizing the Hindi corpus by extracting tokens. The work is better in terms of scalability and supports the consistent quality of cluster for incremental dataset. Most of the past and contemporary research works have targeted English corpus document management. Hindi corpus has been mostly exploited by the researchers for exploring stemming, single-document summarization, and classifier design. Implementing unsupervised learning on the Hindi corpus for summarization of multiple documents through Word Cloud is still an untouched area. Technically speaking, the current work is an application of TF-IDF, cosine-based document similarity measures, and cluster dendrograms, in addition to various other Natural Language Processing (NLP) activities. Entropy and precision are used to evaluate the experiments carried on different live and available/tested datasets and results prove the robustness of the proposed approach for Hindi Corpus.
iSAM: Personalizing an Artificial Intelligence Model for Emotion with Pleasure-Arousal-Dominance in Immersive Virtual Reality

Emotion, a crucial element of mental health, is not often explored in the field of immersive Virtual Reality (iVR). Enabling personalized affective iVR experiences may be incredibly useful for the expansion and evaluation of serious games. To further this direction of research, we present a playable iVR experience in which the user evaluates the emotion of images through an immersive Self-Assessment Manikin (iSAM). This game explores a pilot system for enabling efficient online fine-tuning of a user's Pleasure-Arousal-Dominance (PAD) emotional model using personalized deep-learning. We discuss adapting the International Affective Picture system (IAPs), in which our Artificial Intelligence (AI) model responds with a personalized image after learning from ten user supplied answers during an iVR session. Lastly, we evaluated our iVR experience with an initial pilot study of four users. Our preliminary results suggest that iSAM can successfully learn from user affect to better predict a `happy' personalized image than static base model.
Thinking About Causation: A Causal Language with Epistemic Operators

In this paper we propose a formal framework for modeling the interaction of causal and (qualitative) epistemic reasoning. To this purpose, we extend the notion of a causal model [11, 16, 17, 26] with a representation of the epistemic state of an agent. On the side of the object language, we add operators to express knowledge and the act of observing new information. We provide a sound and complete axiomatization of the logic, and discuss the relation of this framework to causal team semantics.
Generate and Purify: Efficient Person Data Generation for Re-Identification

Generating person images has been a promising approach to enhance the input richness for re-identification (reID) tasks in recent works. A key challenge is that the generated data often contains noise, which is caused by identity inconsistency between the generated person and the original input and failure cases in generative adversarial networks (GAN). Directly training using generated images may greatly affect learning good feature embeddings, resulting in unsatisfactory reID performance. This work presents a two-stage framework that can generate high-quality person images and purify failure cases for reID training. Experimental results demonstrate that our proposed generative model can produce person images with superior appearance consistency comparing with other state-of-the-art methods. Furthermore, we show that our method yields a significant improvement in re-identification (reID) task on public datasets with insufficient training data.
Adaptive modem and interference suppression based on deep learning

Abstract With the increasingly fierce competition of electromagnetic spectrum, developing intelligent communication systems that can reconfigure its waveform can effectively improve the communication system's ability to adapt to the complex electromagnetic environment. In this paper, an adaptive modem based on a deep autoencoder network (DAN) is designed, and the demodulation performance that is close to, consistent with, or better than traditional MPSK or QAM is achieved. This DAN can be trained by a unified loss function and a unified optimization algorithm to implement a variety of modems. For high‐order modems, the constellation diagrams generated by the DAN are radically different from the traditional modulation and very difficult to distinguish linearly, which is beneficial to improve the anti‐interception ability of the communication systems. Additionally, based on the trained DAN, a convolutional neural network (CNN) with a single convolutional layer is incorporated to suppress a variety of interferences. By using transfer learning, the new deep learning (DL) model with the CNN converges fast with a few epochs of training. Training and test results verify that the new DL model can improve the anti‐interference ability of the communication systems by learning and suppressing the interferences in both frequency and power domains.
Large-scale zero-shot learning in the wild: Classifying zoological illustrations

In this paper we analyse the classification of zoological illustrations. Historically, zoological illustrations were the modus operandi for the documentation of new species, an Currently, they serve as crucial sources for long-term ecological and biodiversity research. By employing computational methods for classification, the illustrations can be made amenable to research. Automated species identification is challenging due to the long-tailed nature of the data, and the millions of possible classes in the species taxonomy. Success commonly depends on large training sets with many examples per class, but images from only a subset of classes are digitally available, and many images are unlabelled, since labelling requires domain expertise. We explore zero-shot learning to address the problem, where features are learned from classes with medium to large samples, which are then transferred to recognise classes with few or no training samples. We specifically explore how distributed, multimodal background knowledge from data providers, such as the Global Biodiversity Information Facility (GBIF), iNaturalist, and the Biodiversity Heritage Library (BHL), can be used to share knowledge between classes for zero-shot learning. We train a prototypical network for zero-shot classification, and introduce fused prototypes (FP) and hierarchical prototype loss (HPL) to optimise the model. Finally, we analyse the performance of the model for use in real-world applications. The experimental results are encouraging, indicating potential for use of such models in an expert support system, but also express the difficulty of our task, showing a necessity for research into computer vision methods that are able to learn from small samples.
Closing the chasm between virtual and physical delivery for innovative learning spaces using learning analytics

Purpose One of the misconceptions of teaching and learning for practical-based programmes, such as engineering, sciences, architecture, design and arts, is the necessity to deliver via face-to-face physical modality. This paper refutes this claim by providing case studies of best practices in delivering such courses and their hands-on skillsets using completely online virtual delivery that utilises different formats of 2D and 3D media and tools, supported by evidence of efficiency using learning analytics. Design/methodology/approach The case studies were designed using pedagogical principles of constructivism and deep learning, conducted within a mixture of 2D and 3D virtual learning environments with flexible interface and tools capabilities. State-of-the-art coding and scripting techniques were also used to automate different student tasks and increase engagement. Regression and descriptive analysis methods were used for Learning Analytics. Findings Learning analytics of all case studies demonstrated the capability to achieve course/project learning outcomes, with high engagement from students amongst peers and with tutors. Furthermore, the diverse virtual learning tools used, allowed students to display creativity and innovation efficiently analogous to physical learning. Originality/value The synthesis of utilised media and tools within this study displays innovation and originality in combining different technology techniques to achieve an effectual learning experience. That would usually necessitate face-to-face, hands-on physical contact to perform practical tasks and receive feedback on them. Furthermore, this paper provides suggestions for future research using more advanced technologies.
Fragmentary label distribution learning via graph regularized maximum entropy criteria

Label distribution learning (LDL) is a new learning paradigm, which assumes that the labels are related to each instance to some degree. It has been successfully applied in many scenarios due to its ability to tackle label ambiguity. Nevertheless, LDL has also enlarged the labeling costs and difficulties. In many real application areas, such as pattern recognition and image classification, the labeling information is often incomplete, i.e., we cannot determine each label degree to each instance. The performance of traditional LDL algorithm will degrade since they often require complete supervised information. In this paper, we have proposed fragmentary LDL algorithm via Graph Regularized Maximum Entropy criteria (GRME) to solve this problem. It explores the relationship among the labels to recover missing label factors, together with a classifier for categorization. The integration of these two components facilitate the performance enhancement of GRME. Besides, compared with most of traditional methods which are transductive, another advantage of our method is the inductive nature to predict for new coming data directly. We have also extended ADMM algorithm to fit this particular nonconvex problem. The effectiveness of our method is verified by the experimental results on real-world data sets with different settings.