Reviewerly/exHarmony huggingface dataset:
Digital contact-tracing adoption in the COVID-19 pandemic: IT governance for collective action at the societal level

The COVID-19 pandemic has created a need for rapid, population-wide digital contact tracing. One solution, Bluetooth-enabled digital proximity tracing using smartphones, promises to preserve individual privacy while helping to contain society-wide viral outbreaks. However, this digital solution works effectively only if adopted by the majority of the population. This poses a collective action problem: everyone would benefit from wide-spread proximity tracing, but the benefits for the individual are indirect and limited. To facilitate such collective action at the societal level, this paper conceptualises the option space of IT governance actions for proximity tracing adoption along two dimensions: decision-making entities (who will govern the roll-out) and accountability enforcement (how strictly will adoption and use be enforced). Examining coherent governance approaches that arise from the framework, we show that there are no globally ideal approaches but only locally contextualised ones that depend on immediate health risk, prior experience with pandemics, societal values and national culture, role of government, trust in government and trust in technology in each society. The paper contributes specific propositions for governing digital contact tracing in the COVID-19 pandemic and general theoretical implications for IT governance for collective action at the societal level.
Generation of thermofield double states and critical ground states with a quantum computer

Finite-temperature phases of many-body quantum systems are fundamental to phenomena ranging from condensed-matter physics to cosmology, yet they are generally difficult to simulate. Using an ion trap quantum computer and protocols motivated by the quantum approximate optimization algorithm (QAOA), we generate nontrivial thermal quantum states of the transverse-field Ising model (TFIM) by preparing thermofield double states at a variety of temperatures. We also prepare the critical state of the TFIM at zero temperature using quantum-classical hybrid optimization. The entanglement structure of thermofield double and critical states plays a key role in the study of black holes, and our work simulates such nontrivial structures on a quantum computer. Moreover, we find that the variational quantum circuits exhibit noise thresholds above which the lowest-depth QAOA circuits provide the best results.
PF-BTS: A Privacy-Aware Fog-enhanced Blockchain-assisted task scheduling

In recent years, the deployment of Cloud Computing (CC) has become more popular both in research and industry applications, arising form various fields including e-health, manufacturing, logistics and social networking. This is due to the easiness of service deployment and data management, and the unlimited provision of virtual resources (VR). In simple scenarios, users/applications send computational or storage tasks to be executed in the cloud, by manually assigning those tasks to the available computational resources. In complex scenarios, such as a smart city applications, where there is a large number of tasks, VRs, or both, task scheduling is exposed as an NP-Hard problem. Consequently, it is preferred and more efficient in terms of time and effort, to use a task scheduling automation technique. As there are many automated scheduling solutions proposed, new possibilities arise with the advent of Fog Computing (FC) and Blockchain (BC) technologies. Accordingly, such automation techniques may help the quick, secure and efficient assignment of tasks to the available VRs. In this paper, we propose an Ant Colony Optimization (ACO) algorithm in a Fog-enabled Blockchain-assisted scheduling model, namely PF-BTS. The protocol and algorithms of PF-BTS exploit BC miners for generating efficient assignment of tasks to be performed in the cloud’s VRs using ACO, and award miner nodes for their contribution in generating the best schedule. In our proposal, PF-BTS further allows the fog to process, manage, and perform the tasks to enhance latency measures. While this processing and managing is taking place, the fog is enforced to respect the privacy of system components, and assure that data, location, identity, and usage information are not exposed. We evaluate and compare PF-BTS performance, with a recently proposed Blockchain-based task scheduling protocol, in a simulated environment. Our evaluation and experiments show high privacy awareness of PF-BTS, along with noticeable enhancement in execution time and network load.
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark

Despite great recent advances in visual tracking, its further development, including both algorithm design and evaluation, is limited due to lack of dedicated large-scale benchmarks. To address this problem, we present LaSOT, a high-quality Large-scale Single Object Tracking benchmark. LaSOT contains a diverse selection of 85 object classes, and offers 1550 totaling more than 3.87 million frames. Each video frame is carefully and manually annotated with a bounding box. This makes LaSOT, to our knowledge, the largest densely annotated tracking benchmark. Our goal in releasing LaSOT is to provide a dedicated high quality platform for both training and evaluation of trackers. The average video length of LaSOT is around 2500 frames, where each video contains various challenge factors that exist in real world video footage,such as the targets disappearing and re-appearing. These longer video lengths allow for the assessment of long-term trackers. To take advantage of the close connection between visual appearance and natural language, we provide language specification for each video in LaSOT. We believe such additions will allow for future research to use linguistic features to improve tracking. Two protocols, full-overlap and one-shot, are designated for flexible assessment of trackers. We extensively evaluate 48 baseline trackers on LaSOT with in-depth analysis, and results reveal that there still exists significant room for improvement. The complete benchmark, tracking results as well as analysis are available at http://vision.cs.stonybrook.edu/~lasot/ .
Catastrophic forgetting and mode collapse in GANs

In this paper, we show that Generative Adversarial Networks (GANs) suffer from catastrophic forgetting even when they are trained to approximate a single target distribution. We show that GAN training is a continual learning problem in which the sequence of changing model distributions is the sequence of tasks to the discriminator. The level of mismatch between tasks in the sequence determines the level of forgetting. Catastrophic forgetting is interrelated to mode collapse and can make the training of GANs non-convergent. We investigate the landscape of the discriminator's output in different variants of GANs and find that when a GAN converges to a good equilibrium, real training datapoints are wide local maxima of the discriminator. We empirically show the relationship between the sharpness of local maxima and mode collapse and generalization in GANs. We show how catastrophic forgetting prevents the discriminator from making real datapoints local maxima, and thus causes non-convergence. Finally, we study methods for preventing catastrophic forgetting in GANs.
Challenges in Chatbot Development

Chatbots are becoming increasingly popular due to their benefits in saving costs, time, and effort. This is due to the fact that they allow users to communicate and control different services easily through natural language. Chatbot development requires special expertise (e.g., machine learning and conversation design) that differ from the development of traditional software systems. At the same time, the challenges that chatbot developers face remain mostly unknown since most of the existing studies focus on proposing chatbots to perform particular tasks rather than their development.
Enhancing underwater image via color correction and Bi-interval contrast enhancement

Underwater captured images often suffer from color cast and low visibility due to light is scattered and absorbed while it traveling in water. In this paper, we proposed a novel method of color correction and Bi-interval contrast enhancement to improve the quality of underwater images. Firstly, a simple and effective color correction method based on sub-interval linear transformation is employed to address color distortion. Then, a Gaussian low-pass filter is applied to the L channel to decompose the low- and high-frequency components. Finally, the low- and high-frequency components are enhanced by Bi-interval histogram based on optimal equalization threshold strategy and S-shaped function to enhancement image contrast and highlight image details. Inspired by the multi-scale fusion, we employed a simple linear fusion to integrate the enhanced high- and low-frequency components. Comparison with state-of-the-art methods show that the proposed method outputs high-quality underwater images with qualitative and quantitative evaluation well.
Mobilenet-SSDv2: An Improved Object Detection Model for Embedded Systems

Object detection plays an important role in the field of computer vision. Many superior object detection algorithms have been proposed in literature; however, most of them are designed to improve the detection accuracy. As a result, the requirement of reducing computational complexity is usually ignored. To achieve real-time performance, these superior object detectors need to operate with a high-end GPU. In this paper, we introduce a lightweight object detection model, which is developed based on Mobilenet-v2. The proposed real-time object detector can be applied in embedded systems with limited computational resources. This is one of the key features in the design of modern autonomous driving assistance systems (ADAS). Besides, we also integrate a feature pyramid network (FPN) with the proposed object detection model to effectively improve detection accuracy and detection stability. Experimental results show that the proposed lightweight object detection model achieves up to 75.9% mAP in the VOC dataset. Compared with the existing Mobilenet-SSD detector, the detection accuracy of the proposed detector is improved about 3.5%. In addition, when implemented on the Nvidia Jetson AGX Xavier platform, the proposed detector achieves an average of 19 frames per second (FPS) in processing 720p video streams. Therefore, the proposed lightweight object detector has great application prospects.
Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool

To evaluate the benefits of an artificial intelligence (AI)-based tool for two-dimensional mammography in the breast cancer detection process.In this multireader, multicase retrospective study, 14 radiologists assessed a dataset of 240 digital mammography images, acquired between 2013 and 2016, using a counterbalance design in which half of the dataset was read without AI and the other half with the help of AI during a first session and vice versa during a second session, which was separated from the first by a washout period. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and reading time were assessed as endpoints.The average AUC across readers was 0.769 (95% CI: 0.724, 0.814) without AI and 0.797 (95% CI: 0.754, 0.840) with AI. The average difference in AUC was 0.028 (95% CI: 0.002, 0.055, P = .035). Average sensitivity was increased by 0.033 when using AI support (P = .021). Reading time changed dependently to the AI-tool score. For low likelihood of malignancy (< 2.5%), the time was about the same in the first reading session and slightly decreased in the second reading session. For higher likelihood of malignancy, the reading time was on average increased with the use of AI.This clinical investigation demonstrated that the concurrent use of this AI tool improved the diagnostic performance of radiologists in the detection of breast cancer without prolonging their workflow.Supplemental material is available for this article.© RSNA, 2020.
A deep learning based algorithm for multi-criteria recommender systems

Recommender systems have become exceptionally widespread in recent years to deal with the information overload problem by providing personalized recommendations. Multi-criteria recommender systems proved to have more accurate recommendations compared to single-criterion recommender systems as multi-criteria rating reflects the user appreciation of an item in terms of many aspects. On the another hand, deep learning techniques achieve promising performance in many research areas such as image processing, computer vision, pattern recognition and natural language processing. Recently, the application of deep learning in recommender systems have been frequently explored with encouraging results. Accordingly, this paper proposes a deep learning based algorithm for multi-criteria recommender systems in which deep autoencoders are employed to exploit the non-trivial, nonlinear and hidden relations between users with regard to multi-criteria preferences, and generate more accurate recommendations. Experiments on the Yahoo! Movies and TripAdvisor multi-criteria datasets show that the proposed algorithm prove to be very effective in terms of producing more accurate predictions compared with the state-of-the-art recommendation algorithms
Learning Progressive Joint Propagation for Human Motion Prediction

Despite the great progress in human motion prediction, it remains a challenging task due to the complicated structural dynamics of human behaviors. In this paper, we address this problem in three aspects. First, to capture the long-range spatial correlations and temporal dependencies, we apply a transformer-based architecture with the global attention mechanism. Specifically, we feed the network with the sequential joints encoded with the temporal information for spatial and temporal explorations. Second, to further exploit the inherent kinematic chains for better 3D structures, we apply a progressive-decoding strategy, which performs in a central-to-peripheral extension according to the structural connectivity. Last, in order to incorporate a general motion space for high-quality prediction, we build a memory-based dictionary, which aims to preserve the global motion patterns in training data to guide the predictions. We evaluate the proposed method on two challenging benchmark datasets (Human3.6M and CMU-Mocap). Experimental results show our superior performance compared with the state-of-the-art approaches.
SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization

We present a novel framework, Spatial Pyramid Attention Network (SPAN) for detection and localization of multiple types of image manipulations. The proposed architecture efficiently and effectively models the relationship between image patches at multiple scales by constructing a pyramid of local self-attention blocks. The design includes a novel position projection to encode the spatial positions of the patches. SPAN is trained on a generic, synthetic dataset but can also be fine tuned for specific datasets; The proposed method shows significant gains in performance on standard datasets over previous state-of-the-art methods.
Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation

Learning a good image prior is a long-term goal for image restoration and manipulation. While existing methods like deep image prior (DIP) capture low-level image statistics, there are still gaps toward an image prior that captures rich image semantics including color, spatial coherence, textures, and high-level concepts. This work presents an effective way to exploit the image prior captured by a generative adversarial network (GAN) trained on large-scale natural images. As shown in Fig. 1, the deep generative prior (DGP) provides compelling results to restore missing semantics, e.g., color, patch, resolution, of various degraded images. It also enables diverse image manipulation including random jittering, image morphing, and category transfer. Such highly flexible effects are made possible through relaxing the assumption of existing GAN-inversion methods, which tend to fix the generator. Notably, we allow the generator to be fine-tuned on-the-fly in a progressive manner regularized by feature distance obtained by the discriminator in GAN. We show that these easy-to-implement and practical changes help preserve the reconstruction to remain in the manifold of nature image, and thus lead to more precise and faithful reconstruction for real images. Code is at https://github.com/XingangPan/deep-generative-prior .
Zero-Shot Video Object Segmentation with Co-Attention Siamese Networks

We introduce a novel network, called CO-attention siamese network (COSNet), to address the zero-shot video object segmentation task in a holistic fashion. We exploit the inherent correlation among video frames and incorporate a global co-attention mechanism to further improve the state-of-the-art deep learning based solutions that primarily focus on learning discriminative foreground representations over appearance and motion in short-term temporal segments. The co-attention layers in COSNet provide efficient and competent stages for capturing global correlations and scene context by jointly computing and appending co-attention responses into a joint feature space. COSNet is a unified and end-to-end trainable framework where different co-attention variants can be derived for capturing diverse properties of the learned joint feature space. We train COSNet with pairs (or groups) of video frames, and this naturally augments training data and allows increased learning capacity. During the segmentation stage, the co-attention model encodes useful information by processing multiple reference frames together, which is leveraged to infer the frequently reappearing and salient foreground objects better. Our extensive experiments over three large benchmarks demonstrate that COSNet outperforms the current alternatives by a large margin. Our implementations are available at https://github.com/carrierlxk/COSNet.
Learning to Balance Specificity and Invariance for In and Out of Domain Generalization

We introduce Domain-specific Masks for Generalization, a model for improving both in-domain and out-of-domain generalization performance. For domain generalization, the goal is to learn from a set of source domains to produce a single model that will best generalize to an unseen target domain. As such, many prior approaches focus on learning representations which persist across all source domains with the assumption that these domain agnostic representations will generalize well. However, often individual domains contain characteristics which are unique and when leveraged can significantly aid in-domain recognition performance. To produce a model which best generalizes to both seen and unseen domains, we propose learning domain specific masks. The masks are encouraged to learn a balance of domain-invariant and domain-specific features, thus enabling a model which can benefit from the predictive power of specialized features while retaining the universal applicability of domain-invariant features. We demonstrate competitive performance compared to naive baselines and state-of-the-art methods on both PACS and DomainNet (Our code is available at https://github.com/prithv1/DMG ).
"I Like Your Suggestion!" the role of humanlikeness and parasocial relationship on the website versus voice shopper's perception of recommendations

Abstract Voice assistants are changing the way consumers shop. Guided by the anthropomorphism literature and parasocial interaction theory, this study investigated how the new unique relationship between consumers and artificial intelligence‐powered voice assistants may affect the way consumers evaluate the recommended products through two experiments. Study 1 ( n = 85, students) employed a 2 (shopping medium type: voice assistant vs. website) × 2 (interaction style: task‐oriented vs. socially‐oriented) between‐subjects design lab experiment. Study 2 ( n = 418, Mechanical Turk) employed a 2 (shopping medium type: voice assistant vs. website) × 2 (product type: search vs. experience) between‐subjects online experiment. The results suggested that consumers may perceive voice assistants as pseudohuman agents detached from the service provider while perceiving websites as a tool or interface used by the provider, resulting in a more positive perception and evaluation of websites. As one of the few studies investigating voice assistants from the consumer perspective, this study contributes to the growing body of research in voice assistants. The study also contributes to anthropomorphism literature and parasocial interaction theory by confirming the causal relationship between humanlikeness and parasocial relationships.
A deep reinforcement learning based long-term recommender system

Recommender systems aim to maximize the overall accuracy for long-term recommendations. However, most of the existing recommendation models adopt a static view, and ignore the fact that recommendation is a dynamic sequential decision-making process. As a result, they fail to adapt to new situations and suffer from the cold-start problem. Although sequential recommendation methods have been gaining attention recently, the objective of long-term recommendation still has not been explicitly addressed since these methods are developed for short-term prediction situations. To overcome these problems, we propose a novel top-N interactive recommender system based on deep reinforcement learning. In our model, the processes of recommendation are viewed as Markov decision processes (MDP), wherein the interactions between agent (recommender system) and environment (user) are simulated by the recurrent neural network (RNN). In addition, reinforcement learning is employed to optimize the proposed model for the purpose of maximizing long-term recommendation accuracy. Experimental results based on several benchmarks show that our model significantly outperforms previous top-N methods in terms of Hit-Rate and NDCG for the long-term recommendation, and can be applied to both cold-start and warm-start scenarios.