Reviewerly/exHarmony huggingface dataset:
LedgerDB

The emergence of Blockchain has attracted widespread attention. However, we observe that in practice, many applications on permissioned blockchains do not benefit from the decentralized architecture. When decentralized architecture is used but not required, system performance is often restricted, resulting in low throughput, high latency, and significant storage overhead. Hence, we propose LedgerDB on Alibaba Cloud, which is a centralized ledger database with tamper-evidence and non-repudiation features similar to blockchain, and provides strong auditability. LedgerDB has much higher throughput compared to blockchains. It offers stronger auditability by adopting a TSA two-way peg protocol, which prevents malicious behaviors from both users and service providers. LedgerDB supports verifiable data removals demanded by many real-world applications, which are able to remove obsolete records for storage saving and hide records for regulatory purpose, without compromising its verifiability. Experimental evaluation shows that LedgerDB's throughput is 80X higher than state-of-the-art permissioned blockchain (i.e., Hyperledger Fabric). Many blockchain customers (e.g., IP protection and supply chain) on Alibaba Cloud have switched to LedgerDB for its high throughput, low latency, strong auditability, and ease of use.
Finite Versus Infinite Neural Networks: an Empirical Study

We perform a careful, thorough, and large scale empirical study of the correspondence between wide neural networks and kernel methods. By doing so, we resolve a variety of open questions related to the study of infinitely wide neural networks. Our experimental results include: kernel methods outperform fully-connected finite-width networks, but underperform convolutional finite width networks; neural network Gaussian process (NNGP) kernels frequently outperform neural tangent (NT) kernels; centered and ensembled finite networks have reduced posterior variance and behave more similarly to infinite networks; weight decay and the use of a large learning rate break the correspondence between finite and infinite networks; the NTK parameterization outperforms the standard parameterization for finite width networks; diagonal regularization of kernels acts similarly to early stopping; floating point precision limits kernel performance beyond a critical dataset size; regularized ZCA whitening improves accuracy; finite network performance depends non-monotonically on width in ways not captured by double descent phenomena; equivariance of CNNs is only beneficial for narrow networks far from the kernel regime. Our experiments additionally motivate an improved layer-wise scaling for weight decay which improves generalization in finite-width networks. Finally, we develop improved best practices for using NNGP and NT kernels for prediction, including a novel ensembling technique. Using these best practices we achieve state-of-the-art results on CIFAR-10 classification for kernels corresponding to each architecture class we consider.
A Scoping Review on Virtual Reality-Based Industrial Training

The fourth industrial revolution has forced most companies to technologically evolve, applying new digital tools, so that their workers can have the necessary skills to face changing work environments. This article presents a scoping review of the literature on virtual reality-based training systems. The methodology consisted of four steps, which pose research questions, document search, paper selection, and data extraction. From a total of 350 peer-reviewed database articles, such as SpringerLink, IEEEXplore, MDPI, Scopus, and ACM, 44 were eventually chosen, mostly using the virtual reality haptic glasses and controls from Oculus Rift and HTC VIVE. It was concluded that, among the advantages of using this digital tool in the industry, is the commitment, speed, measurability, preservation of the integrity of the workers, customization, and cost reduction. Even though several research gaps were found, virtual reality is presented as a present and future alternative for the efficient training of human resources in the industrial field.
“I Am Here to Assist You Today”: The Role of Entity, Interactivity and Experiential Perceptions in Chatbot Persuasion

Online users are increasingly exposed to chatbots as one form of AI-enabled media technologies, employed for persuasive purposes, e.g., making product/service recommendations.However, the persuasive potential of chatbots has not yet been fully explored.Using an online experiment (N = 242), we investigate the extent to which communicating with a stand-alone chatbot influences affective and behavioral responses compared to interactive Web sites.Several underlying mechanisms are studied, showing that enjoyment is the key mechanism explaining the positive effect of chatbots (vs.Web sites) on recommendation adherence and attitudes.Contrary to expectations, perceived anthropomorphism seems not to be particularly relevant in this comparison.The communication between online users and organizations is increasingly shifting toward interactions with technology driven by artificial intelligence (AI; Sundar, 2020).Among the most prevalent instances of technology with which users are confronted are AI-based chatbots.Defined as "software that accepts natural language as input and generates natural language as output, engaging in a conversation" (Griol et al., 2013, p. 706), chatbots can be found on social media (e.g., Facebook, Twitter) and messaging apps (e.g., Skype, Facebook Messenger); they can be an alternative to (branded) Web sites (e.g., A.s.r., 2019).These stand-alone chatbots are often used for making product or service recommendations (e.g., shopping, financial/health-related
Multi-criteria of PV solar site selection problem using GIS-intuitionistic fuzzy based approach in Erzurum province/Turkey

Abstract Renewable energy sources have been placed as the key to facilitating to provide source of electricity generation. Solar power is one of the most preferable one among renewable energy sources due to the easy to generate in suitable environment. However, there are concerns with the location of solar power plants installation which causes low efficiency and ineffective use. Hence, determining the location for the usage of solar power sources is critical to mitigating those concerns. In addition, Turkey has been focused on investments on sustainable renewable energy sources and there are few studies which work on cities to reveal potential sources. In this study, GIS and intuitionistic fuzzy set based multi-criteria decision-making method is proposed for determining the most suitable areas for solar energy power plant potential site selection in Erzurum province, Turkey. Firstly, a solar energy power plant potential site selection map is made using a GIS program along with considering ecological risks and ecological criteria. Secondly, 20 districts of Erzurum are investigated in terms of 10 criteria (slope, aspect, solar irradiation, land use, wind speed, air temperature, air pressure, air humidity, land surface temperature and transmission line) using intuitionistic fuzzy sets. In these parameters the first time we looked the land use surface. The land use surface is affected the efficiency of the solar power plant. Finally, the comparisons of two methods are done to check consistency of results obtained. The results indicate that both approaches achieve same areas as the most suitable locations for solar power plants installations for Erzurum province in Turkey. The aim of this work is first to investigate possible locations for solar power plant installation using a mapping method, GIS, and then, Intuitionistic Fuzzy is applied to the problem to obtain optimum areas for solar energy. Also, more accurate results are provided comparing results of two methods, GIS and Intuitionistic Fuzzy. The results expose that 25,065.3 km 2 for solar power plant suitable for solar power plan installation.
SoftSeg: Advantages of soft versus binary training for image segmentation

Most image segmentation algorithms are trained on binary masks formulated as a classification task per pixel. However, in applications such as medical imaging, this “black-and-white” approach is too constraining because the contrast between two tissues is often ill-defined, i.e., the voxels located on objects’ edges contain a mixture of tissues (a partial volume effect). Consequently, assigning a single “hard” label can result in a detrimental approximation. Instead, a soft prediction containing non-binary values would overcome that limitation. In this study, we introduce SoftSeg, a deep learning training approach that takes advantage of soft ground truth labels, and is not bound to binary predictions. SoftSeg aims at solving a regression instead of a classification problem. This is achieved by using (i) no binarization after preprocessing and data augmentation, (ii) a normalized ReLU final activation layer (instead of sigmoid), and (iii) a regression loss function (instead of the traditional Dice loss). We assess the impact of these three features on three open-source MRI segmentation datasets from the spinal cord gray matter, the multiple sclerosis brain lesion, and the multimodal brain tumor segmentation challenges. Across multiple random dataset splittings, SoftSeg outperformed the conventional approach, leading to an increase in Dice score of 2.0% on the gray matter dataset (p=0.001), 3.3% for the brain lesions, and 6.5% for the brain tumors. SoftSeg produces consistent soft predictions at tissues’ interfaces and shows an increased sensitivity for small objects (e.g., multiple sclerosis lesions). The richness of soft labels could represent the inter-expert variability, the partial volume effect, and complement the model uncertainty estimation, which is typically unclear with binary predictions. The developed training pipeline can easily be incorporated into most of the existing deep learning architectures. SoftSeg is implemented in the freely-available deep learning toolbox ivadomed (https://ivadomed.org).
The Modern Mathematics of Deep Learning

We describe the new field of the mathematical analysis of deep learning. This field emerged around a list of research questions that were not answered within the classical framework of learning theory. These questions concern: the outstanding generalization power of overparametrized neural networks, the role of depth in deep architectures, the apparent absence of the curse of dimensionality, the surprisingly successful optimization performance despite the non-convexity of the problem, understanding what features are learned, why deep architectures perform exceptionally well in physical problems, and which fine aspects of an architecture affect the behavior of a learning task in which way. We present an overview of modern approaches that yield partial answers to these questions. For selected approaches, we describe the main ideas in more detail.
Fast and Robust Multi-Person 3D Pose Estimation and Tracking From Multiple Views

This paper addresses the problem of reconstructing 3D poses of multiple people from a few calibrated camera views. The main challenge of this problem is to find the cross-view correspondences among noisy and incomplete 2D pose predictions. Most previous methods address this challenge by directly reasoning in 3D using a pictorial structure model, which is inefficient due to the huge state space. We propose a fast and robust approach to solve this problem. Our key idea is to use a multi-way matching algorithm to cluster the detected 2D poses in all views. Each resulting cluster encodes 2D poses of the same person across different views and consistent correspondences across the keypoints, from which the 3D pose of each person can be effectively inferred. The proposed convex optimization based multi-way matching algorithm is efficient and robust against missing and false detections, without knowing the number of people in the scene. Moreover, we propose to combine geometric and appearance cues for cross-view matching. Finally, an efficient tracking method is proposed to track the detected 3D poses across the multi-view video. The proposed approach achieves the state-of-the-art performance on the Campus and Shelf datasets, while being efficient for real-time applications.
Being an Avatar “for Real”: A Survey on Virtual Embodiment in Augmented Reality

Virtual self-avatars have been increasingly used in Augmented Reality (AR) where one can see virtual content embedded into physical space. However, little is known about the perception of self-avatars in such a context. The possibility that their embodiment could be achieved in a similar way as in Virtual Reality opens the door to numerous applications in education, communication, entertainment, or the medical field. This article aims to review the literature covering the embodiment of virtual self-avatars in AR. Our goal is (i) to guide readers through the different options and challenges linked to the implementation of AR embodiment systems, (ii) to provide a better understanding of AR embodiment perception by classifying the existing knowledge, and (iii) to offer insight on future research topics and trends for AR and avatar research. To do so, we introduce a taxonomy of virtual embodiment experiences by defining a “body avatarization” continuum. The presented knowledge suggests that the sense of embodiment evolves in the same way in AR as in other settings, but this possibility has yet to be fully investigated. We suggest that, whilst it is yet to be well understood, the embodiment of avatars has a promising future in AR and conclude by discussing possible directions for research.
Differentially private secure multi-party computation for federated learning in financial applications

Federated Learning enables a population of clients, working with a trusted server, to collaboratively learn a shared machine learning model while keeping each client's data within its own local systems. This reduces the risk of exposing sensitive data, but it is still possible to reverse engineer information about a client's private data set from communicated model parameters. Most federated learning systems therefore use differential privacy to introduce noise to the parameters. This adds uncertainty to any attempt to reveal private client data, but also reduces the accuracy of the shared model, limiting the useful scale of privacy-preserving noise. A system can further reduce the coordinating server's ability to recover private client information, without additional accuracy loss, by also including secure multiparty computation. An approach combining both techniques is especially relevant to financial firms as it allows new possibilities for collaborative learning without exposing sensitive client data. This could produce more accurate models for important tasks like optimal trade execution, credit origination, or fraud detection. The key contributions of this paper are: We present a privacy-preserving federated learning protocol to a non-specialist audience, demonstrate it using logistic regression on a real-world credit card fraud data set, and evaluate it using an open-source simulation platform which we have adapted for the development of federated learning systems.
Weakly Supervised Contrastive Learning

Unsupervised visual representation learning has gained much attention from the computer vision community because of the recent achievement of contrastive learning. Most of the existing contrastive learning frameworks adopt the instance discrimination as the pretext task, which treating every single instance as a different class. However, such method will inevitably cause class collision problems, which hurts the quality of the learned representation. Motivated by this observation, we introduced a weakly supervised contrastive learning framework (WCL) to tackle this issue. Specifically, our proposed framework is based on two projection heads, one of which will perform the regular instance discrimination task. The other head will use a graph-based method to explore similar samples and generate a weak label, then perform a supervised contrastive learning task based on the weak label to pull the similar images closer. We further introduced a K-Nearest Neighbor based multi-crop strategy to expand the number of positive samples. Extensive experimental results demonstrate WCL improves the quality of self-supervised representations across different datasets. Notably, we get a new state-of-the-art result for semi-supervised learning. With only 1% and 10% labeled examples, WCL achieves 65% and 72% ImageNet Top-1 Accuracy using ResNet50, which is even higher than SimCLRv2 with ResNet101.
Multi-Scale Ship Detection Algorithm Based on a Lightweight Neural Network for Spaceborne SAR Images

The current limited spaceborne hardware resources and the diversity of ship target scales in SAR images have led to the requirement of on-orbit real-time detection of ship targets in spaceborne synthetic aperture radar (SAR) images. In this paper, we propose a lightweight ship detection network based on the YOLOv4-LITE model. In order to facilitate the network migration to the satellite, the method uses MobileNetv2 as the backbone feature extraction network of the model. To solve the problem of ship target scale diversity in SAR images, an improved receptive field block (RFB) structure is introduced, enhancing the feature extraction ability of the network, and improving the accuracy of multi-scale ship target detection. A sliding window block method is designed to detect the whole SAR image, which can solve the problem of image input. Experiments on the SAR ship dataset SSDD show that the detection speed of the improved lightweight network could reach up to 47.16 FPS, with the mean average precision (mAP) of 95.03%, and the model size is only 49.34 M, which demonstrates that the proposed network can accurately and quickly detect ship targets. The proposed network model can provide a reference for constructing a spaceborne real-time lightweight ship detection network, which can balance the detection accuracy and speed of the network.
Trust access authentication in vehicular network based on blockchain

Data sharing and privacy securing present extensive opportunities and challenges in vehicular network. This paper introduces `trust access authentication scheme' as a mechanism to achieve real-time monitoring and promote collaborative sharing for vehicles. Blockchain, which can provide secure authentication and protected privacy, is a crucial technology. However, traditional cloud computing performs poorly in supplying low-latency and fast-response services for moving vehicles. In this situation, edge computing enabled Blockchain network appeals to be a promising method, where moving vehicles can access storage or computing resource and get authenticated from Blockchain edge nodes directly. In this paper, a hierarchical architecture is proposed consist of vehicular network layer, Blockchain edge layer and Block-chain network layer. Through a authentication mechanism adopting digital signature algorithm, it achieves trusted authentication and ensures valid verification. Moreover, a caching scheme based on many-to-many matching is proposed to minimize average delivery delay of vehicles. Simulation results prove that the proposed caching scheme has a better performance than existing schemes based on centralized model or edge caching strategy in terms of hit ratio and average delay.
Evolving Playable Content for Cut the Rope through a Simulation-Based Approach

In order to automatically generate high-quality game levels, one needs to be able to automatically verify that the levels are playable. The simulation-based approach to playability testing uses an artificial agent to play through the level, but building such an agent is not always an easy task and such an agent is not always readily available. We discuss this prob- lem in the context of the physics-based puzzle game Cut the Rope, which features continuous time and state space, mak- ing several approaches such as exhaustive search and reactive agents inefficient. We show that a deliberative Prolog-based agent can be used to suggest all sensible moves at each state, which allows us to restrict the search space so that depth-first search for solutions become viable. This agent is successfully used to test playability in Ropossum, a level generator based on grammatical evolution. The method proposed in this paper is likely to be useful for a large variety of games with similar characteristics.
Physical Presence, Social Presence, and Anxiety in Participants with Social Anxiety Disorder During Virtual Cue Exposure

Although social anxiety disorders (SADs) are increasingly treated by means of virtual cue exposure, the mechanisms leading to sufficient anxiety levels and thus to a success of virtual reality exposure therapy are still poorly understood. Also, most studies with SAD participants fail to evaluate social presence, although it may be a more appropriate indicator for virtual social stress scenarios than physical presence. Hence, for the first time, this study sets out to examine the link between social presence, physical presence, and emotional responses to phobogenic virtual social stimuli. A group of n = 12 participants with SAD and n = 12 healthy controls were exposed to three social tasks in an interactive virtual environment (VE). Self-report measures of physical and social presence as well as state anxiety were used alongside heart rate measures to evaluate the virtual experience. Results show significantly higher anxiety levels—both self-report and physiological—in SAD participants than in controls. Also, socially anxious subjects reported to experience more copresence and mutual attention as well as a higher sense of being in the VE than their healthy peers. In sum, social presence experiences may be more predicative of the anxiety response in SAD individuals than physical presence. Especially attentional processes seem to crucially shape the interplay between presence and affective responses.
Extracting health-related causality from twitter messages using natural language processing

Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily lives. In this paper we evaluate an approach to extracting causalities from tweets using natural language processing (NLP) techniques. Lexico-syntactic patterns based on dependency parser outputs are used for causality extraction. We focused on three health-related topics: “stress”, “insomnia”, and “headache.” A large dataset consisting of 24 million tweets are used. The results show the proposed approach achieved an average precision between 74.59 to 92.27% in comparisons with human annotations. Manual analysis on extracted causalities in tweets reveals interesting findings about expressions on health-related topic posted by Twitter users.