Reviewerly/exHarmony huggingface dataset:
Optimized communication strategies with binary coherent states over phase noise channels

Abstract The achievable rate of information transfer in optical communications is determined by the physical properties of the communication channel, such as the intrinsic channel noise. Bosonic phase noise channels, a class of non-Gaussian channels, have emerged as a relevant noise model in quantum information and optical communication. However, while the fundamental limits for communication over Gaussian channels have been extensively studied, the properties of communication over Bosonic phase noise channels are not well understood. Here we propose and demonstrate experimentally the concept of optimized communication strategies for communication over phase noise channels to enhance information transfer beyond what is possible with conventional methods of modulation and detection. Two key ingredients are generalized constellations of coherent states that interpolate between standard on-off keying and binary phase-shift keying formats, and non-Gaussian measurements based on photon number resolving detection of the coherently displaced signal. For a given power constraint and channel noise strength, these novel strategies rely on joint optimization of the input alphabet and the measurement to provide enhanced communication capability over a non-Gaussian channel characterized in terms of the error rate as well as mutual information.
Employing Multi-estimations for Weakly-Supervised Semantic Segmentation

Image-level label based weakly-supervised semantic segmentation (WSSS) aims to adopt image-level labels to train semantic segmentation models, saving vast human labors for costly pixel-level annotations. A typical pipeline for this problem is first to adopt class activation maps (CAM) with image-level labels to generate pseudo-masks (a.k.a. seeds) and then use them for training segmentation models. The main difficulty is that seeds are usually sparse and incomplete. Related works typically try to alleviate this problem by adopting many bells and whistles to enhance the seeds. Instead of struggling to refine a single seed, we propose a novel approach to alleviate the inaccurate seed problem by leveraging the segmentation model’s robustness to learn from multiple seeds. We managed to generate many different seeds for each image, which are different estimates of the underlying ground truth. The segmentation model simultaneously exploits these seeds to learn and automatically decides the confidence of each seed. Extensive experiments on Pascal VOC 2012 demonstrate the advantage of this multi-seeds strategy over previous state-of-the-art.
Learning Canonical Representations for Scene Graph to Image Generation

Generating realistic images of complex visual scenes becomes challenging when one wishes to control the structure of the generated images. Previous approaches showed that scenes with few entities can be controlled using scene graphs, but this approach struggles as the complexity of the graph (the number of objects and edges) increases. In this work, we show that one limitation of current methods is their inability to capture semantic equivalence in graphs. We present a novel model that addresses these issues by learning canonical graph representations from the data, resulting in improved image generation for complex visual scenes (The project page is available at https://roeiherz.github.io/CanonicalSg2Im/ ). Our model demonstrates improved empirical performance on large scene graphs, robustness to noise in the input scene graph, and generalization on semantically equivalent graphs. Finally, we show improved performance of the model on three different benchmarks: Visual Genome, COCO, and CLEVR.
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampler

Vision-and-Language Navigation (VLN) is a task where agents must decide how to move through a 3D environment to reach a goal by grounding natural language instructions to the visual surroundings. One of the problems of the VLN task is data scarcity since it is difficult to collect enough navigation paths with human-annotated instructions for interactive environments. In this paper, we explore the use of counterfactual thinking as a human-inspired data augmentation method that results in robust models. Counterfactual thinking is a concept that describes the human propensity to create possible alternatives to life events that have already occurred. We propose an adversarial-driven counterfactual reasoning model that can consider effective conditions instead of low-quality augmented data. In particular, we present a model-agnostic adversarial path sampler (APS) that learns to sample challenging paths that force the navigator to improve based on the navigation performance. APS also serves to do pre-exploration of unseen environments to strengthen the model’s ability to generalize. We evaluate the influence of APS on the performance of different VLN baseline models using the room-to-room dataset (R2R). The results show that the adversarial training process with our proposed APS benefits VLN models under both seen and unseen environments. And the pre-exploration process can further gain additional improvements under unseen environments.
Deep Hough-Transform Line Priors

Classical work on line segment detection is knowledge-based; it uses carefully designed geometric priors using either image gradients, pixel groupings, or Hough transform variants. Instead, current deep learning methods do away with all prior knowledge and replace priors by training deep networks on large manually annotated datasets. Here, we reduce the dependency on labeled data by building on the classic knowledge-based priors while using deep networks to learn features. We add line priors through a trainable Hough transform block into a deep network. Hough transform provides the prior knowledge about global line parameterizations, while the convolutional layers can learn the local gradient-like line features. On the Wireframe (ShanghaiTech) and York Urban datasets we show that adding prior knowledge improves data efficiency as line priors no longer need to be learned from data.
The adoption of contact tracing applications of COVID-19 by European governments

Contact tracing can be defined as the identification and the monitoring of each person who has been in contact with an infected person. However, the effectiveness of manual contact tracing is hindered by low responsiveness, limited data processing, respondent omissions or the inability to identify individuals in a crowd. Faced with these limitations, research on digital contact tracing has been carried out. Digital contact tracing, especially smartphone contact tracing apps, has progressively appeared as a solution to slow the spread of the SARS-CoV-2 pandemic. Such a technological solution allows to track, in real-time, a massive number of (potentially) infected individuals within a given population. Despite high acceptability rates among the population and positive evaluations regarding its effectiveness, the implementation of these digital tracing applications has raised many technological and political questions. By conducting a thematic analysis, this research identifies the technological and policy issues with regard to digital tracing in three European countries.
Integrate domain knowledge in training multi-task cascade deep learning model for benign–malignant thyroid nodule classification on ultrasound images

The automatic and accurate diagnosis of thyroid nodules in ultrasound images is of great significance to reduce the workload and radiologists' misdiagnosis rate. Although deep learning has shown strong image classification performance, the inherent limitations of medical images small dataset and time-consuming access to lesion annotations, leaving this work still facing challenges. In our study, a multi-task cascade deep learning model (MCDLM) was proposed, which integrates radiologists' various domain knowledge (DK) and uses multimodal ultrasound images for automatic diagnosis of thyroid nodules. Specifically, we transfer the knowledge learned by U-net from the source domain to the target domain under the guidance of radiologist' marks to obtain more accurate nodules' segmentation results. We then quantify the nodules' ultrasound features (UF) as conditions to assist the dual-path semi-supervised conditional generative adversarial network (DScGAN) in generating higher quality images obtaining more powerful discriminators. After that, we concatenate DScGAN learning's image representation to train a supervised support vector machine (S3VM) for thyroid nodule classification. The experiment results on ultrasound images of 1030 patients suggest that the MCDLM model can achieve almost the same classification performance as the fully supervised learning (an accuracy of 90.01% and an AUC of 91.07%) using only about 35% of the full labeled dataset, which saves a lot of time and effort compared to traditional methods.
Constraining dense hand surface tracking with elasticity

Many of the actions that we take with our hands involve self-contact and occlusion: shaking hands, making a fist, or interlacing our fingers while thinking. This use of of our hands illustrates the importance of tracking hands through self-contact and occlusion for many applications in computer vision and graphics, but existing methods for tracking hands and faces are not designed to treat the extreme amounts of self-contact and self-occlusion exhibited by common hand gestures. By extending recent advances in vision-based tracking and physically based animation, we present the first algorithm capable of tracking high-fidelity hand deformations through highly self-contacting and self-occluding hand gestures, for both single hands and two hands. By constraining a vision-based tracking algorithm with a physically based deformable model, we obtain an algorithm that is robust to the ubiquitous self-interactions and massive self-occlusions exhibited by common hand gestures, allowing us to track two hand interactions and some of the most difficult possible configurations of a human hand.
COVID-19 Case Investigation and Contact Tracing: Early Lessons Learned and Future Opportunities

Case investigation and contact tracing are fundamental public health strategies for controlling and preventing the spread of infectious diseases. Although the principles behind these strategies are not new, the capacity and operational requirements needed to support disease investigation during the SARS-CoV-2 (COVID-19) pandemic are unprecedented. This article analyzes the implementation of case investigation and contact tracing in controlling COVID-19 transmission during the early stages of the US pandemic response (January 20 through August 31, 2020).Governmental public health agencies mobilized to expand case investigation and contact tracing programs in the early months of the pandemic. In doing so, they encountered a range of challenges that included rapidly scaling up the workforce; developing and subsequently revising guidance and protocols specific to COVID-19 as more was learned about the virus over time; defining job functions; encouraging public acceptance of and participation in case investigation and contact tracing; and assessing the utility of these activities during both the containment and mitigation phases of outbreak response. COVID-19 case investigation and contact tracing programs presented an array of opportunities for health departments to innovate, especially around technology to support public health efforts, as well as opportunities to address health equity and advance community resilience.Lessons learned from disease intervention specialists, guidance and resources from federal agencies and national partners, and peer-to-peer exchange of promising practices can support jurisdictions encountering early implementation challenges. Further research is needed to assess COVID-19 case investigation and contact tracing program models and innovations, as well as strategies for implementing these activities during containment and mitigation phases.
A Blockchain Ethereum Technology-Enabled Digital Content: Development of Trading and Sharing Economy Data

The idea of a shared economy becomes one of the companies as an enterprise type.Especially with the advanced development of digital smart devices and the internet, several forms of the mutual economy have been advanced in accord with the need for sharing of separate income.Shareable commodity and digital content are also seeking to utilize.When digital content is used as a sharing economy, various possible threats may arise in the course of transactions, the potential for theft, alteration, and hacking of contents.This paper presents a comprehensive overview of the security and privacy of Blockchain.Blockchain promise transparent, tamper-proof and secure systems that can enable novel solutions, especially when combined with smart contracts.In this research, we proposed a content protection and transaction method using Blockchain Ethereum Technology.The encryption algorithm is incorporated in proposed system to make transparent transactions and it is also implemented on content itself to prevent from smart forgery and hacking.The experimental results signify that the proposed method has strong potential to enhance transactions transparency by minimizing the security threats in digital content transactions.
Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches

Sketch-based image editing aims to synthesize and modify photos based on the structural information provided by the human-drawn sketches. Since sketches are difficult to collect, previous methods mainly use edge maps instead of sketches to train models (referred to as edge-based models). However, human-drawn sketches display great structural discrepancy with edge maps, thus failing edge-based models. Moreover, sketches often demonstrate huge variety among different users, demanding even higher generalizability and robustness for the editing model to work. In this paper, we propose Deep Plastic Surgery, a novel, robust and controllable image editing framework that allows users to interactively edit images using hand-drawn sketch inputs. We present a sketch refinement strategy, as inspired by the coarse-to-fine drawing process of the artists, which we show can help our model well adapt to casual and varied sketches without the need for real sketch training data. Our model further provides a refinement level control parameter that enables users to flexibly define how “reliable” the input sketch should be considered for the final output, balancing between sketch faithfulness and output verisimilitude (as the two goals might contradict if the input sketch is drawn poorly). To achieve the multi-level refinement, we introduce a style-based module for level conditioning, which allows adaptive feature representations for different levels in a singe network. Extensive experimental results demonstrate the superiority of our approach in improving the visual quality and user controllablity of image editing over the state-of-the-art methods. Our project and code are available at https://github.com/TAMU-VITA/DeepPS .
Modelling High-Order Social Relations for Item Recommendation

The prevalence of online social network makes it compulsory to study how social relations affect user choice. However, most existing methods leverage only first-order social relations, that is, the direct neighbors that are connected to the target user. The high-order social relations, e.g., the friends of friends, which are very informative to reveal user preference, have been largely ignored. In this work, we focus on modeling the indirect influence from the high-order neighbors in social networks to improve the performance of item recommendation. Distinct from mainstream social recommenders that regularize the model learning with social relations, we instead propose to directly factor social relations in the predictive model, aiming at learning better user embeddings to improve recommendation. To address the challenge that high-order neighbors increase dramatically with the order size, we propose to recursively “propagate” embeddings along the social network, effectively injecting the influence of high-order neighbors into user representation. We conduct experiments on two real datasets of Yelp and Douban to verify our <i>High-Order Social Recommender</i> (HOSR) model. Empirical results show that our HOSR significantly outperforms recent graph regularization-based recommenders NSCR and IF-BPR <inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> , and graph convolutional network-based social influence prediction model DeepInf, achieving new state-of-the-arts of the task.
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions

This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering. A potential drawback of using pseudo labels is that errors may accumulate and it is challenging to estimate the number of pseudo IDs. We introduce a different unsupervised method that allows us to learn pedestrian embeddings from raw videos, without resorting to pseudo labels. The goal is to construct a self-supervised pretext task that matches the person re-ID objective. Inspired by the data association concept in multi-object tracking, we propose the Cycle Association (CycAs) task: after performing data association between a pair of video frames forward and then backward, a pedestrian instance is supposed to be associated to itself. To fulfill this goal, the model must learn a meaningful representation that can well describe correspondences between instances in frame pairs. We adapt the discrete association process to a differentiable form, such that end-to-end training becomes feasible. Experiments are conducted in two aspects: We first compare our method with existing unsupervised re-ID methods on seven benchmarks and demonstrate CycAs’ superiority. Then, to further validate the practical value of CycAs in real-world applications, we perform training on self-collected videos and report promising performance on standard test sets.
A polynomial algorithm for best-subset selection problem

Significance Best-subset selection is a benchmark optimization problem in statistics and machine learning. Although many optimization strategies and algorithms have been proposed to solve this problem, our splicing algorithm, under reasonable conditions, enjoys the following properties simultaneously with high probability: 1) its computational complexity is polynomial; 2) it can recover the true subset; and 3) its solution is globally optimal.
Conflux LSTMs Network: A Novel Approach for Multi-View Action Recognition

Multi-view action recognition (MVAR) is an optimal technique to acquire numerous clues from different views data for effective action recognition, however, it is not well explored yet. There exist several challenges to MVAR domain such as divergence in viewpoints, invisible regions, and different scales of appearance in each view require better solutions for real world applications. In this paper, we present a conflux long short-term memory (LSTMs) network to recognize actions from multi-view cameras. The proposed framework has four major steps; 1) frame level feature extraction, 2) its propagation through conflux LSTMs network for view self-reliant patterns learning, 3) view inter-reliant patterns learning and correlation computation, and 4) action classification. First, we extract deep features from a sequence of frames using a pre-trained VGG19 CNN model for each view. Second, we forward the extracted features to conflux LSTMs network to learn the view self-reliant patterns. In the next step, we compute the inter-view correlations using the pairwise dot product from output of the LSTMs network corresponding to different views to learn the view inter-reliant patterns. In the final step, we use flatten layers followed by SoftMax classifier for action recognition. Experimental results over benchmark datasets compared to state-of-the-art report an increase of 3% and 2% on northwestern-UCLA and MCAD datasets, respectively.
Blockchain and Supply Chain Management: A New Paradigm for Supply Chain Integration and Collaboration

Despite there are some arguments about blockchain, it has been highlighted as an important distributed secure technology in the 21 st century.It is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value.Dobrovnik et al. (2018) suggest that blockchain is a revolutionising technology that would change industries at an international level, add values to firms and supply chain networks, improve commerce, and drive economy.Although blockchain has attracted attentions, very few blockchain studies have been focussed on supply chain integration and collaboration areas.This study illustrates the possibilities of applying blockchain technology in the coordination of activities for effective and efficient supply chain management.The study takes a closer look at the use of blockchain in supply chains beyond cryptocurrency, payment, and finance via the use of smart contract and consensus algorithm (i.e., imposing constraints).The key attributes of blockchain are discussed and potential questions were identified in New Zealand.The expected outcome of this study will advance the understanding of the blockchain and supply chain literature, besides inspire both researchers and practitioners to consider the use of blockchain in different context-aware future studies.
Customer perception of the deceptiveness of online product reviews: A speech act theory perspective

With the presence of fake reviews on e-commerce platforms, the reliability of reviews becomes questionable. The extant literature demonstrates the impact of fake reviews on product sales and proposes several algorithms to prevent fake reviews from being displayed on the platform. However, what has largely remained uninvestigated is how customers perceive reviews present on the e-commerce platform. Based on the speech act theory, we develop a theoretical framework that explains how the linguistic style (both at the word and the structural level) acts as a cue for assessing a reviewer's (in)sincere intentions. We evaluate the framework on a corpus of 120 online product reviews – each examined by at least 50 customers – using the fractional logit model. Results suggest that the communication style of a speaker reflects his/her intention. Reviews with less contextual embedding, argument structuring, and flattering through non-verbal cues trigger customers towards perceiving a review as deceptive.
A fractional-order CNN hyperchaotic system for image encryption algorithm

Abstract This paper proposes a fractional-order cellular neural network (CNN) chaotic system for image encryption algorithm to explore the application of fractional-order CNN hyperchaotic system in chaotic secure communication. Firstly, a fractional-order CNN hyperchaotic system is defined based on CNN hyperchaotic system. The numerical solutions of the fractional-order CNN hyperchaotic system are calculated by Adomian decomposition algorithm. The dynamic characteristics of the of the fractional-order CNN hyperchaotic system are analyzed. Then to verify the image encryption application of the fractional-order CNN hyperchaotic system, we designed an image encryption scheme through fractional-order CNN hyperchaotic sequence, the principle of symmetry of main diagonal of matrix and XOR operation. Finally, the results illustrate that the fractional-order CNN hyperchaotic sequence has good randomness, which show that the fractional-order CNN hyperchaotic system more suitable for chaotic secure communication applications. The security performances of the algorithm show that the designed algorithm can effectively encrypt and decrypt image, and has better security performance.