Reviewerly/exHarmony huggingface dataset:
Enhancement of perceived body ownership in virtual reality-based teleoperation may backfire in the execution of high-risk tasks

Guided by previous research on the role of embodiment in virtual environments, this study aimed to investigate the potential effects of using human-like (compared to robotic) virtual hands on work performances in the context of virtual reality (VR)-based teleoperation of high-risk machinery. A 2 × 2 mixed factorial design experiment (N = 74), with the virtual hand representation as a within-subjects factor (robotic vs. human-like virtual hands) and the risk of danger as a between-subjects factor (low vs. high), was conducted to examine the effects of virtual hand representations (i.e., human-likeness) on perceived body ownership (i.e., embodiment), risk perception, intention to work using the teleoperator, and work performance (i.e., the number of successful task completions). In addition, the moderating effects of the risk of danger on the relationship between perceived body ownership and risk perception were explored. Results showed that the enhancement of perceived body ownership in VR-based teleoperation, induced by the use of human-like hands, increased the risk perception and degraded workers' task performances in the execution of high-risk tasks. Further implications of the findings were discussed.
Homotopy based optimal configuration space reduction for anytime robotic motion planning

Anytime sampling-based motion planning algorithms are widely used in practical applications due to limited real-time computing resources. The algorithm quickly finds feasible paths and incrementally improves them to the optimal ones. However, anytime sampling-based algorithms bring a paradox in convergence speed since finding a better path helps prune useless candidates but also introduces unrecognized useless candidates by sampling. Based on the words of homotopy classes, we propose a Homotopy class Informed Preprocessor (HIP) to break the paradox by providing extra information. By comparing the words of path candidates, HIP can reveal wasteful edges of the sampling-based graph before finding a better path. The experimental results obtained in many test scenarios show that HIP improves the convergence speed of anytime sampling-based algorithms.
Towards More Explainability: Concept Knowledge Mining Network for Event Recognition

Event recognition of untrimmed video is a challenging task due to the big gap between low level visual features and event semantics. Beyond feature learning via deep neural networks, some recent works focus on analyzing event videos using concept-based representation. However, these methods simply aggregate the concept representation vectors of frames or segments, which inevitably introduces information loss on video-level concept knowledge. Moreover, the diversified relation between different concept domains (e.g., scene, object and action) has not been fully explored. To address the above issues, we propose a concept knowledge mining network (CKMN) for event recognition. CKMN is composed of an intra-domain concept knowledge mining subnetwork (IaCKM) and an inter-domain concept knowledge mining subnetwork~(IrCKM). IaCKM aims to obtain a complete concept representation by mining the existing pattern of each concept at different time granularities with dilated temporal pyramid convolution and temporal self-attention, while IrCKM explores the interaction between different types of concepts with co-attention style learning. We evaluate our method on FCVID and ActivityNet datasets. Experimental results show the effectiveness and better interpretability of our model on event analytics. Code is available at https://github.com/qzhb/CKMN.
Investigating Visual Analysis of Differentially Private Data

Differential Privacy is an emerging privacy model with increasing popularity in many domains. It functions by adding carefully calibrated noise to data that blurs information about individuals while preserving overall statistics about the population. Theoretically, it is possible to produce robust privacy-preserving visualizations by plotting differentially private data. However, noise-induced data perturbations can alter visual patterns and impact the utility of a private visualization. We still know little about the challenges and opportunities for visual data exploration and analysis using private visualizations. As a first step towards filling this gap, we conducted a crowdsourced experiment, measuring participants' performance under three levels of privacy (high, low, non-private) for combinations of eight analysis tasks and four visualization types (bar chart, pie chart, line chart, scatter plot). Our findings show that for participants' accuracy for summary tasks (e.g., find clusters in data) was higher that value tasks (e.g., retrieve a certain value). We also found that under DP, pie chart and line chart offer similar or better accuracy than bar chart. In this work, we contribute the results of our empirical study, investigating the task-based effectiveness of basic private visualizations, a dichotomous model for defining and measuring user success in performing visual analysis tasks under DP, and a set of distribution metrics for tuning the injection to improve the utility of private visualizations.
Named Entity Recognition for Spoken Finnish

In this paper we present a Bidirectional LSTM neural network with a Conditional Random Field layer on top, which utilizes word, character and morph embeddings in order to perform named entity recognition on various Finnish datasets. To overcome the lack of annotated training corpora that arises when dealing with low-resource languages like Finnish, we tried a knowledge transfer technique to transfer tags from Estonian dataset. On the human annotated in-domain Digitoday dataset, out system achieved F1 score of 84.73. On the out-of-domain Wikipedia set we got F1 score of 67.66. In order to see how well the system performs on speech data, we used two datasets containing automatic speech recognition outputs. Since we do not have true labels for those datasets, we used a rule-based system to annotate them and used those annotations as reference labels. On the first dataset which contains Finnish parliament sessions we obtained F1 score of 42.09 and on the second one which contains talks from Yle Pressiklubi we obtained F1 score of 74.54.
VideoTRM: Pre-training for Video Captioning Challenge 2020

The Pre-training for Video Captioning Challenge 2020 mainly focuses on developing video captioning systems by pre-training on the newly released large-scale Auto-captions on GIF dataset and further transferring the pre-trained model to MSR-VTT benchmark. As a part of the submission to this challenge, we propose a Transformer based framework named VideoTRM, which consists of four modules: a textual encoder for encoding the linguistic relationship among words in the input sentence, a visual encoder for capturing the temporal dynamics in the input video, a cross-modal encoder for modeling the interactions between the two modalities (i.e., textual and visual) and a decoder for sentence generation conditioned on the input video and words generated previously. Additionally, we extend the decoder in our VideoTRM with mesh-like connections and gate fusion mechanism in multi-head attention during fine-tuning to take advantage of multi-level visual features and bypass less informative attention results, respectively. In the evaluation on test server, our VideoTRM achieves superior performances and ranks the second place on the leadboard finally.
Histopathological Stain Transfer Using Style Transfer Network with Adversarial Loss

Deep learning models that are trained on histopathological images obtained from a single lab and/or scanner give poor inference performance on images obtained from another scanner/lab with a different staining protocol. In recent years, there has been a good amount of research done for image stain normalization to address this issue. In this work, we present a novel approach for the stain normalization problem using fast neural style transfer coupled with adversarial loss. We also propose a novel stain transfer generator network based on High-Resolution Network (HRNet) which requires less training time and gives good generalization with few paired training images of reference stain and test stain. This approach has been tested on Whole Slide Images (WSIs) obtained from 8 different labs, where images from one lab were treated as a reference stain. A deep learning model was trained on this stain and the rest of the images were transferred to it using the corresponding stain transfer generator network. Experimentation suggests that this approach is able to successfully perform stain normalization with good visual quality and provides better inference performance compared to not applying stain normalization.
Ground subspaces of topological phases of matter as error correcting codes

Topological quantum computing is believed to be inherently fault-tolerant. One mathematical justification would be to prove that ground subspaces or ground state manifolds of topological phases of matter behave as error correction codes with macroscopic distance. While this is widely assumed and used as a definition of topological phases of matter in the physics literature, besides the doubled abelian anyon models in Kitaev's seminal paper, no non-abelian models are proven to be so mathematically until recently. Cui et al. extended the theorem from doubled abelian anyon models to all Kitaev models based on any finite group (Cui et al., 0000). Those proofs are very explicit using detailed knowledge of the Hamiltonians, so it seems to be hard to further extend the proof to cover other models such as the Levin-Wen. We pursue a totally different approach based on topological quantum field theories (TQFTs), and prove that a lattice implementation of the disk axiom and annulus axiom in TQFTs as essentially the equivalence of TQO1 and TQO2 conditions in Bravyi et al. (2010) and Bravyi et al. (2011). We confirm the error correcting properties of ground subspaces for topological lattice Hamiltonian schemas of the Levin-Wen model and Dijkgraaf-Witten TQFTs by providing a lattice version of the disk axiom and annulus of the underlying TQFTs. The error correcting property of ground subspaces is also shared by gapped fracton models such as the Haah codes (Haah and Jeongwan, 2011; Haah, 2013). We propose to characterize topological phases of matter via error correcting properties, and refer to gapped fracton models as lax-topological.
Vision Meets Wireless Positioning: Effective Person Re-identification with Recurrent Context Propagation

Existing person re-identification methods rely on the visual sensor to capture the pedestrians. The image or video data from visual sensor inevitably suffers the occlusion and dramatic variations of pedestrian postures, which degrades the re-identification performance and further limits its application to the open environment. On the other hand, for most people, one of the most important carry-on items is the mobile phone, which can be sensed by WiFi and cellular networks in the form of a wireless positioning signal. Such signal is robust to the pedestrian occlusion and visual appearance change, but suffers some positioning error. In this work, we approach person re-identification with the sensing data from both vision and wireless positioning. To take advantage of such cross-modality cues, we propose a novel recurrent context propagation module that enables information to propagate between visual data and wireless positioning data and finally improves the matching accuracy. To evaluate our approach, we contribute a new Wireless Positioning Person Re-identification (WP-ReID) dataset. Extensive experiments are conducted and demonstrate the effectiveness of the proposed algorithm. Code will be released at https://github.com/yolomax/WP-ReID.
Introducing the NEMO-Lowlands iconic gesture dataset, collected through a gameful human–robot interaction

Abstract This paper describes a novel dataset of iconic gestures, together with a publicly available robot-based elicitation method to record these gestures, which consists of playing a game of charades with a humanoid robot. The game was deployed at a science museum (NEMO) and a large popular music festival (Lowlands) in the Netherlands. This resulted in recordings of 428 participants, both adults and children, performing 3715 silent iconic gestures for 35 different objects in a naturalistic setting. Our dataset adds to existing collections of iconic gesture recordings in two important ways. First, participants were free to choose how they represented the broad concepts using gestures, and they were asked to perform a second attempt if the robot did not recognize their gesture the first time. This provides insight into potential repair strategies that might be used. Second, by making the interactive game available we enable other researchers to collect additional recordings, for different concepts, and in diverse cultures or contexts. This can be done in a consistent manner because a robot is used as a confederate in the elicitation procedure, which ensures that every data collection session plays out in the same way. The current dataset can be used for research into human gesturing behavior, and as input for the gesture recognition and production capabilities of robots and virtual agents.
Hearing like Seeing

Many cognitive researches have shown that human may 'see voices' or 'hear faces', and such ability can be potentially associated by machine vision and intelligence. However, this research is still under early stage. In this paper, we present a novel adversarial deep semantic matching network for efficient voice-face interactions and associations, which can well learn the correspondence between voices and faces for various cross-modal matching and retrieval tasks. Within the proposed framework, we exploit a simple and efficient adversarial learning architecture to learn the cross-modal embeddings between faces and voices, which consists of two subnetworks, respectively, for generator and discriminator. The former subnetwork is designed to adaptively discriminate the high-level semantical features between voices and faces, in which the triplet loss and multi-modal center loss are in tandem utilized to explicitly regularize the correspondences among them. The latter subnetwork is further leveraged to maximally bridge the semantic gap between the representations of voice and face data, featuring on maintaining the semantic consistency. Through the joint exploitation of the above, the proposed framework can well push representations of voice-face data from the same person closer while pulling those representations of different person away. Extensive experiments empirically show that the proposed approach involves fewer parameters and calculations, adapts various cross-modal matching tasks for voice-face data and brings substantial improvements over the state-of-the-art methods.
E-IOTA: an efficient and fast metamorphism for IOTA

IOTA opened recently a new line of research in distributed ledgers area by targeting algorithms that ensure a high throughput for the transactions generated in IoT systems. Transactions are continuously appended to an acyclic structure called Tangle. Each new transaction selects and approves, as parents, two existing transactions who have not been approved yet (called Tips). Recent research indicates that, IOTA suffers from a Fairness Problem, where a part of submitted transactions might not be approved by new coming transactions. They are called Left-Behind transactions. Recently, G-IOTA has been proposed: by using the Left-Behind Protection mechanism, G-IOTA can fix the fairness problem. G-IOTA, however, needs additional computation resources for left-behind protection mechanism, which is not suitable for an energy-aware IoT system. Furthermore, both IOTA and G-IOTA suffer from a potential security defect. This side effect reduces the Unpredictability when transactions are appended to the tangle. Leaking the unpredictability, an adversary can easily induce the tendency of transactions appending to the tangle. This approach is called Prediction Attack. In this paper, we propose E-IOTA to solve the fairness problem and at the same time, guarantee the unpredictability of the tangle. Interestingly, our real scenario simulation shows that E-IOTA offers better performance efficiency compared to G-IOTA and even original IOTA. Simultaneously, E-IOTA retains the core data structure of IOTA, which makes E-IOTA be compatible with current IOTA implementation. Our solution can therefore be a perfect alternative solution for current IOTA.
Multilayer Long Short-Term Memory (LSTM) Neural Networks in Time Series Analysis

Traditional methods that deal with time series analysis problems are based on statistical models. The latest methods that solve time series analysis problems are based on the use of a special architecture of recurrent neural networks known as Long Short-Term Memory Networks. This paper presents solutions that address the prediction of product sales on a monthly basis.
Joint Attention Mechanisms for Monocular Depth Estimation With Multi-Scale Convolutions and Adaptive Weight Adjustment

Monocular depth estimation is a fundamental problem for various vision applications, and is therefore gaining increasing attention in the field of computer vision.Though a great improvement has been made thanks to the rapid progress of deep convolutional neural networks, depth estimation of the object at finer details remains an unsatisfactory issue, especially in complex scenes that has rich structure information.In this article, we proposed a deep end-to-end learning framework with the combination of multi-scale convolutions and joint attention mechanisms to tackle this challenge.Specifically, we firstly elaborately designed a lightweight up-convolution to generate multi-scale feature maps.Then we introduced an attention-based residual block to aggregate different feature maps in joint channel and spatial dimension, which could enhance the discriminant ability of feature fusion at finer details.Furthermore, we explored an effective adaptive weight adjustment strategy for the loss function to further improve the performance, which adjusts the weight of each loss term during training without additional hyper-parameters.The proposed framework was evaluated using challenging NYU Depth v2 and KITTI datasets.Experimental results demonstrated that the proposed approach is superior to most of the state-of-the-art methods.
MetaTPOT

Automatic machine learning (AutoML) aims to automate the different aspects of the data science process and, by extension, allow non-experts to utilize "off the shelf" machine learning solution. One of the more popular AutoML methods is the Tree-based Pipeline Optimization Tool (TPOT), which uses genetic programming (GP) to efficiently explore the vast space of ML pipelines and produce a working ML solution. However, TPOT's GP process comes with substantial time and computational costs. In this study, we explore TPOT's GP process and propose MetaTPOT, an enhanced variant that uses a meta learning-based approach to predict the performance of TPOT's pipeline candidates. MetaTPOT leverages domain knowledge in the form of pipelines pre-ranking to improve TPOT's speed and performance. Evaluation on 65 classification datasets shows that our approach often improves the outcome of the genetic process while simultaneously substantially reduce its running time and computational cost.
Performance and Cost-aware HPC in Clouds: A Network Interconnection Assessment

The availability of computing resources has significantly changed due to the growing adoption of the cloud computing paradigm. Aiming at potential advantages such as cost savings through the pay-per-use method and resource allocation in a scalable/elastic way, we witnessed consistent efforts to execute high-performance computing (HPC) applications in the cloud. Performance in this environment depends heavily upon two main system components: processing power and network interconnection. If, on the one hand, allocating more powerful hardware theoretically boosts performance, on the other hand, it increases the allocation cost. In this paper, we evaluated how the network interconnection impacts on performance and cost efficiency. Our experiments were carried out using NAS Parallel Benchmarks and Alya HPC application on Microsoft Azure public cloud provider, with three different cloud instances/network interconnections. The results revealed that through the use of the accelerated networking approach, which allows the instance to have a high-performance interconnect without additional charges, the performance of HPC applications can be significantly improved with a better cost efficiency.
Attributed Network Embedding based on Mutual Information Estimation

Attributed network embedding (ANE) attempts to represent a network in short code, while retaining information about node topological structures and node attributes. A node's feature and topological structure information could be divided into different local aspects, while in many cases, not all the information but part of the information contained in several local aspects determine the relations among different nodes. Most of the existing works barely concern and identify the aspect influence from network embedding to our knowledge. We attempt to use local embeddings to represent local aspect information and propose InfomaxANE which encodes both global and local embeddings from the perspective of mutual information. The local aspect embeddings are forced to learn and extract different aspect information from nodes' features and topological structures by using orthogonal constraint. A theoretical analysis is also provided to further confirm its correctness and rationality. Besides, to provide complete and refined information for local encoders, we also optimize feature aggregation in SAGE with different structures: feature similarities are concerned and aggregator is seperated from encoder. InfomaxANE is evaluated on both node clustering and node classification tasks (including both transductive and inductive settings) with several benchmark datasets, the results show the outperformance of InfomaxANE over competitive baselines. We also verify the significance of each module in our proposed InfomaxANE in the additional experiment.
April

The great amount and complex structure of graph data bring a big challenge to graph data management. However, traditional management approaches cannot tackle the challenge. Fortunately, reinforcement learning provides a new approach to solve this problem due to its automation and adaptivity in decision making. Motivated by this, we develop April, an automatic graph data management system, which performs storage structure selection, index selection, and query optimization based on reinforcement learning. The system selects storage structure, indices effectively and automatically, and optimizes the SPARQL queries efficiently. April also offers a friendly interface for users, which allows users to interact with the system in a customized mode. We demonstrate the effectiveness and efficiency of April with two graph data benchmarks.
Towards a Safer Conversation Space: Detection of Toxic Content in Social Media (Student Consortium)

With content on social media turning increasingly toxic, it has attracted intensive research in the Natural Language Processing domain to detect aggression, hate, profanity, insult, cyberbullying and other personal attacks. Unlike most of the work in toxic content detection where the nature of toxicity is determined, we treat the detection of toxic content as a binary classification task. Here, we have explored Support Vector Machine, Boosting and deep neural networks for classification. We have trained the model on twitter datasets. With a goal of better predictive performance, our approach uses a majority voting ensemble to aggregate the predictions of individual classifiers.