Reviewerly/exHarmony huggingface dataset:
End-to-end weakly supervised semantic segmentation with reliable region mining

Weakly supervised semantic segmentation is a challenging task that only takes image-level labels as supervision but produces pixel-level predictions for testing. To address such a challenging task, most current approaches generate pseudo pixel masks first that are then fed into a separate semantic segmentation network. However, these two-step approaches suffer from high complexity and being hard to train as a whole. In this work, we harness the image-level labels to produce reliable pixel-level annotations and design a fully end-to-end network to learn to predict segmentation maps. Concretely, we firstly leverage an image classification branch to generate class activation maps for the annotated categories, which are further pruned into tiny reliable object/background regions. Such reliable regions are then directly served as ground-truth labels for the segmentation branch, where both global information and local information sub-branches are used to generate accurate pixel-level predictions. Furthermore, a new joint loss is proposed that considers both shallow and high-level features. Despite its apparent simplicity, our end-to-end solution achieves competitive mIoU scores (val: 65.4%, test: 65.3%) on Pascal VOC compared with the two-step counterparts. By extending our one-step method to two-step, we get a new state-of-the-art performance on the Pascal VOC 2012 dataset(val: 69.3%, test: 69.2%). Code is available at: https://github.com/zbf1991/RRM.
RankSum—An unsupervised extractive text summarization based on rank fusion

In this paper, we propose Ranksum, an approach for extractive text summarization of single documents based on the rank fusion of four multi-dimensional sentence features extracted for each sentence: topic information, semantic content, significant keywords, and position. The Ranksum obtains the sentence saliency rankings corresponding to each feature in an unsupervised way followed by the weighted fusion of the four scores to rank the sentences according to their significance. The scores are generated in completely unsupervised way, and a labeled document set is required to learn the fusion weights. Since we found that the fusion weights can generalize to other datasets, we consider the Ranksum as an unsupervised approach. To determine topic rank, we employ probabilistic topic models whereas semantic information is captured using sentence embeddings. To derive rankings using sentence embeddings, we utilize Siamese networks to produce abstractive sentence representation and then we formulate a novel strategy to arrange them in their order of importance. A graph-based strategy is applied to find the significant keywords and related sentence rankings in the document. We also formulate a sentence novelty measure based on bigrams, trigrams, and sentence embeddings to eliminate redundant sentences from the summary. The ranks of all the sentences – computed for each feature – are finally fused to get the final score for each sentence in the document. We evaluate our approach on publicly available summarization datasets — CNN/DailyMail and DUC 2002. Experimental results show that our approach outperforms other existing state-of-the-art summarization methods.
Histogram modification in adaptive bi-histogram equalization for contrast enhancement on digital images

In this paper, a novel histogram modification-based bi histogram equalization (HE) approach for contrast enhancement on digital images is presented. At first, a power-logarithm transformation function is used to change the histogram of the input image. The logarithm operation reduces the input histogram’s excessive peaks, while the power function restores the histogram structure. The adjusted histogram is then separated into two sub-histograms at the threshold limit, using the input image’s minimum intensity and standard deviation. Sub-histograms are clipped based on their individual plateau limit parameters, which are based on the median value of the individual sub-histogram, to control the over-enhanced outcomes. The clipped pixels are then redistributed evenly across the histogram’s non-empty bins. Finally, the intended outcome is achieved by applying the HE procedure on the updated individual sub-histogram. Simulation results show that the proposed HE approach successfully improves the visual quality of the images. Quantitative measurements such as entropy, feature similarity index measure (FSIM), spectral residual similarity index measure (SR-SIM), gradient magnitude similarity deviation (GMSD), visual saliency-induced index (VSI) and multi scale structural similarity index measure (MS-SSIM) efficiently confirm the effectiveness of the suggested method when compared with the existing enhancement techniques. Furthermore, a comparative study of the different approaches is performed using a dataset of 300 images, demonstrating the superiority of the suggested method over the other state-of-the-art techniques.
Comparison of Deepfake Detection Techniques through Deep Learning

Deepfakes are realistic-looking fake media generated by deep-learning algorithms that iterate through large datasets until they have learned how to solve the given problem (i.e., swap faces or objects in video and digital content). The massive generation of such content and modification technologies is rapidly affecting the quality of public discourse and the safeguarding of human rights. Deepfakes are being widely used as a malicious source of misinformation in court that seek to sway a court’s decision. Because digital evidence is critical to the outcome of many legal cases, detecting deepfake media is extremely important and in high demand in digital forensics. As such, it is important to identify and build a classifier that can accurately distinguish between authentic and disguised media, especially in facial-recognition systems as it can be used in identity protection too. In this work, we compare the most common, state-of-the-art face-detection classifiers such as Custom CNN, VGG19, and DenseNet-121 using an augmented real and fake face-detection dataset. Data augmentation is used to boost performance and reduce computational resources. Our preliminary results indicate that VGG19 has the best performance and highest accuracy of 95% when compared with other analyzed models.
Deep contrastive learning based tissue clustering for annotation-free histopathology image analysis

Deep convolutional neural networks (CNNs) have yielded promising results in automatic whole slide images (WSIs) processing for digital pathology in recent years. Training supervised CNNs usually requires a large amount of annotated samples. However, manual annotation of gigapixel WSIs is labor-intensive and error-prone, i.e., the shortage of annotations has become the major bottleneck of WSI diagnosis model development. In this work, we aim to develop a deep learning based self-supervised histopathology image analysis workflow that can classify tissues without any annotation.Inspired by the contrastive learning methods that have achieved state-of-the-art results on unsupervised representation learning for natural images, we adopt the self-supervised training scheme to generate discriminative embeddings from annotation-free WSI patches and simultaneously obtain initial clusters, which are further refined by a silhouette coefficient based recursive scheme to divide tissue mixture clusters. A multi-scale encoder network is specifically designed to extract pathology-specific contextual features. A tissue dictionary composed by the tissue clusters is then built for cancer diagnosis.Experiments show that our method can identify different tissues in annotation-free conditions with competitive results (achieving the accuracy of 0.9364/0.9325 in human colorectal/sentinel lymph WSIs) as the supervised methods (with the corresponding accuracy of 0.9806/0.9494) and surpass other unsupervised baselines. Our method is also evaluated in a cohort of 20 clinical patients and get an AUC score of 0.99 to distinguish benign/malignant polyps.Our proposed deep contrastive learning based tissue clustering method can learn from raw WSIs without annotation to distinguish different tissues. The method are tested in three different datasets and show the potential to help pathologists diagnosing diseases as a quantitative and qualitative tool.
Thermal quantum correlations in a two-qubit Heisenberg model under Calogero–Moser and Dzyaloshinsky–Moriya interactions

In this paper, we investigate the pairwise quantum correlations in a two-qubit anisotropic Heisenberg model under the interplay of Calogero–Moser (CM) and Dzyaloshinsky–Moriya (DM) interactions in the presence of the homogeneous and inhomogeneous magnetic fields. We employ, respectively, the logarithmic negativity and local quantum uncertainty (LQU) to characterize the degree of entanglement and the amount of quantum correlations between the two parties of the considered system in equilibrium with a thermal reservoir. We analyze and compare the behaviors of the two quantum correlation quantifiers in the thermal state of the chain spin system and we discuss how relative distance between spins, equilibrium temperature, DM interaction coupling parameter and the external magnetic fields strengths influence the variations of both quantum correlation measures in such system.
Nested Named Entity Recognition: A Survey

With the rapid development of text mining, many studies observe that text generally contains a variety of implicit information, and it is important to develop techniques for extracting such information. Named Entity Recognition (NER), the first step of information extraction, mainly identifies names of persons, locations, and organizations in text. Although existing neural-based NER approaches achieve great success in many language domains, most of them normally ignore the nested nature of named entities. Recently, diverse studies focus on the nested NER problem and yield state-of-the-art performance. This survey attempts to provide a comprehensive review on existing approaches for nested NER from the perspectives of the model architecture and the model property, which may help readers have a better understanding of the current research status and ideas. In this survey, we first introduce the background of nested NER, especially the differences between nested NER and traditional (i.e., flat) NER. We then review the existing nested NER approaches from 2002 to 2020 and mainly classify them into five categories according to the model architecture, including early rule-based, layered-based, region-based, hypergraph-based, and transition-based approaches. We also explore in greater depth the impact of key properties unique to nested NER approaches from the model property perspective, namely entity dependency, stage framework, error propagation, and tag scheme. Finally, we summarize the open challenges and point out a few possible future directions in this area. This survey would be useful for three kinds of readers: (i) Newcomers in the field who want to learn about NER, especially for nested NER. (ii) Researchers who want to clarify the relationship and advantages between flat NER and nested NER. (iii) Practitioners who just need to determine which NER technique (i.e., nested or not) works best in their applications.
Distance Perception in the Oculus Quest and Oculus Quest 2

Distances in virtual environments (VEs) viewed on a head-mounted display (HMD) are typically underperceived relative to the intended distance. This paper presents an experiment comparing perceived egocentric distance in a real environment with that in a matched VE presented in the Oculus Quest and Oculus Quest 2. Participants made verbal judgments and blind walking judgments to an object on the ground. Both the Quest and Quest 2 produced underperception. Verbal judgments in the VE were 82% and 75% of the object distance, in contrast with real world judgments that were 94% of the object distance. Blind walking judgments were 68% and 70% of object distance in the Quest and Quest 2, respectively, compared to 88% in the real world. This project shows that significant underperception of distance persists even in modern HMDs.
Solar Photovoltaic Tree: Urban PV power plants to increase power to land occupancy ratio

With the day-by-day modernization, increasing electricity demand, and the restriction of climate change, more pressure is to search the renewable energy sources (Solar, Wind, etc.) and draw maximum power from them as it increases the need to develop Smart Cities, which have heavy electricity demand. For renewable energy power plant installations, one of the major difficulties is availability of land, as power plants like Solar photovoltaics have higher demand of open land, which is scarce in urban landscapes. In context of the problem statement of generating same electric power using less land, new models of Solar Photovoltaic Trees have been proposed, which can be used instead of conventional Solar PV plants. Simulations have been done on different SPV Tree models, concluding that very less land area is required to generate the same amount of electric power in comparison to conventional SPV plants, for example if a conventional ground – mounted model requires 300 m2 area, but one of our proposed models; Daisy SPV Tree requires only about 13 m2 area for generating the same electric power. Out of the total land area, other available space can be utilized for various purposes, which can be a solution for switching to renewables and can become very useful in urban landscapes. The possible designs are studied to get optimum “Power-to-Land occupancy Ratio” and “Land Coverage Ratio” with no additional power loss as compared to conventional ground-mounted Solar PV power plant and generate same amount of power using very marginal amount of land.
Multi-level U-net network for image super-resolution reconstruction

Deep neural networks have shown better effects for super-resolution. However, it is difficult to extract multi-level features of LR images to reconstruct more clear images. To solve this problem, we present a multi-level U-Net network (MUN) for image super-resolution reconstruction. Specifically, we present a multi-level U-Net residual structure, which is composed of two different levels U-Net structures, to extract multi-level features of LR images. Meanwhile, we present a multi-scale residual block, which extracts multi-level features by dual-branch convolutional layers with different kernels and uses long and short skip connections to bypass a large amount of low-frequency information. Extensive experimental results demonstrate that our presented MUN outperforms other state-of-the-art super-resolution methods.
Random Reconstructed Unpaired Image-to-Image Translation

The goal of unpaired image-to-image translation is to learn a mapping from a source domain to a target domain without using any labeled examples of paired images. This problem can be solved by learning the conditional distribution of source images in the target domain. A major limitation of existing unpaired image-to-image translation algorithms is that they generate untruthful images which are overcolored and lack details, while the translation of realistic images must be rich in details. To address this limitation, in this article, we propose a random reconstructed unpaired image-to-image translation (RRUIT) framework by generative adversarial network, which uses random reconstruction to preserve the high-level features in the source and adopts an adversarial strategy to learn the distribution in the target. We update the proposed objective function with two loss functions. The auxiliary loss guides the generator to create a coarse image, while the coarse-to-fine block next to the generator block produces an image that obeys the distribution of the target domain. The coarse-to-fine block contains two submodules based on the densely connected atrous spatial pyramid pooling, which enriches the details of generated images. We conduct extensive experiments on photorealistic stylization and artistic stylization. The experimental results confirm the superiority of the proposed RRUIT.
Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias

In the world of Big Data, a tool capable of filtering data and providing choice support is crucial. Recommender Systems have this aim. These have evolved further through the use of information that would improve the ability to suggest. Among the possible exploited information, the context is widely used in literature and leads to the definition of the Context-Aware Recommender System. This paper proposes a Context-Aware Recommender System based on the concept of embedded context. This technique has been tested on different datasets to evaluate its accuracy. In particular, the use of multiple datasets allows a deep analysis of the advantages and disadvantages of the proposed approach. The numerical results obtained are promising.
QGTC

Over the most recent years, quantized graph neural network (QGNN) attracts lots of research and industry attention due to its high robustness and low computation and memory overhead. Unfortunately, the performance gains of QGNN have never been realized on modern GPU platforms. To this end, we propose the first Tensor Core (TC) based computing framework, QGTC, to support any-bitwidth computation for QGNNs on GPUs. We introduce a novel quantized low-bit arithmetic design based on the low-bit data representation and bit-decomposed computation. We craft a novel TC-tailored CUDA kernel design by incorporating 3D-stacked bit compression, zero-tile jumping, and non-zero tile reuse technique to improve the performance systematically. We incorporate an effective bandwidth-optimized subgraph packing strategy to maximize the transferring efficiency between CPU host and GPU device. We integrate QGTC with Pytorch for better programmability and extensibility. Extensive experiments demonstrate that QGTC can achieve evident inference speedup (on average 2.7X) compared with the state-of-the-art DGL framework across diverse settings.
CGNet: Detecting computer-generated images based on transfer learning with attention module

The rapid development of digital image processing technology and the popularization of image editors have made computer-generated images more realistic so that it is more challenging to distinguish computer-generated images from natural images with the naked eye. Simultaneously, the malicious potential of highly realistic computer-generated images has made the detection of the authenticity of digital images a significant research area. In this work, we propose a computer-generated image detection algorithm through the application of transfer learning and Convolutional Block Attention Module. More specifically, our method uses the feature transfer module and feature fusion module to consider both the shallow content features and the deep semantic features of the image, thereby improving the accuracy of identifying computer-generated images. We validate our method through extensive experiments on various datasets, and the experimental results show that our model outperformed state-of-the-art methods and achieved an accuracy of 0.963. We also show that the proposed model has strong robustness and high generalization ability.
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users’ local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless environment; however, training models in a time-efficient manner can be a challenging task due to intermittent connectivity of devices, heterogeneous connection quality, and non-i.i.d. data. In this paper, we provide a novel convergence analysis of non-convex loss functions using FL on both i.i.d. and non-i.i.d. datasets with arbitrary device selection probabilities for each round. Then, using the derived convergence bound, we use stochastic optimization to develop a new client selection and power allocation algorithm that minimizes a function of the convergence bound and the average communication time under a transmit power constraint. We find an analytical solution to the minimization problem. One key feature of the algorithm is that knowledge of the channel statistics is not required and only the instantaneous channel state information needs to be known. Using the FEMNIST and CIFAR-10 datasets, we show through simulations that the communication time can be significantly decreased using our algorithm, compared to uniformly random participation.
Implicit Motion Handling for Video Camouflaged Object Detection

We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames. An essential property of camouflaged objects is that they usually exhibit patterns similar to the background and thus make them hard to identify from still images. Therefore, effectively handling temporal dynamics in videos becomes the key for the VCOD task as the camouflaged objects will be noticeable when they move. However, current VCOD methods often leverage homography or optical flows to represent motions, where the detection error may accumulate from both the motion estimation error and the segmentation error. On the other hand, our method unifies motion estimation and object segmentation within a single optimization framework. Specifically, we build a dense correlation volume to implicitly capture motions between neighbouring frames and utilize the final segmentation supervision to optimize the implicit motion estimation and segmentation jointly. Furthermore, to enforce temporal consistency within a video sequence, we jointly utilize a spatio-temporal transformer to refine the short-term predictions. Extensive experiments on VCOD benchmarks demonstrate the architectural effectiveness of our approach. We also provide a large-scale VCOD dataset named MoCA-Mask with pixel-level handcrafted ground-truth masks and construct a comprehensive VCOD bench-mark with previous methods to facilitate research in this direction. Dataset Link: https://xueliancheng.github.io/SLT-Net-project.
CEBA: A Data Lake for Data Sharing and Environmental Monitoring

This article presents a platform for environmental data named "Environmental Cloud for the Benefit of Agriculture" (CEBA). The CEBA should fill the gap of a regional institutional platform to share, search, store and visualize heterogeneous scientific data related to the environment and agricultural researches. One of the main features of this tool is its ease of use and the accessibility of all types of data. To answer the question of data description, a scientific consensus has been established around the qualification of data with at least the information "when" (time), "where" (geographical coordinates) and "what" (metadata). The development of an on-premise solution using the data lake concept to provide a cloud service for end-users with institutional authentication and for open data access has been completed. Compared to other platforms, CEBA fully supports the management of geographic coordinates at every stage of data management. A comprehensive JavaScript Objet Notation (JSON) architecture has been designed, among other things, to facilitate multi-stage data enrichment. Data from the wireless network are queried and accessed in near real-time, using a distributed JSON-based search engine.