Reviewerly/exHarmony huggingface dataset:
NFCSense: Data-Defined Rich-ID Motion Sensing for Fluent Tangible Interaction Using a Commodity NFC Reader

This paper presents NFCSense, a data-defined rich-ID motion sensing technique for fluent tangible interaction design by using commodity near-field communication (NFC) tags and a single NFC tag reader. An NFC reader can reliably recognize the presence of an NFC tag at a high read rate (∼ 300 reads/s) with low latency, but such high-speed reading has rarely been exploited because the reader may not effectively resolve collisions of multiple tags. Therefore, its human–computer interface applications have been typically limited to a discrete, hands-on interaction style using one tag at a time. In this work, we realized fluent, hands-off, and multi-tag tangible interactions by leveraging gravity and anti-collision physical constraints, which support effortless user input and maximize throughput. Furthermore, our system provides hot-swappable interactivity that enables smooth transitions throughout extended use. Based on the design parameters explored through a series of studies, we present a design space with proof-of-concept implementations in various applications.
Accurate Instance-Based Segmentation for Boundary Detection in Robot Grasping Application

With the rapid development of cameras and deep learning technologies, computer vision tasks such as object detection, object segmentation and object tracking are being widely applied in many fields of life. For robot grasping tasks, object segmentation aims to classify and localize objects, which helps robots to be able to pick objects accurately. The state-of-the-art instance segmentation network framework, Mask Region-Convolution Neural Network (Mask R-CNN), does not always perform an excellent accurate segmentation at the edge or border of objects. The approach using 3D camera, however, is able to extract the entire (foreground) objects easily but can be difficult or require a large amount of computation effort to classify it. We propose a novel approach, in which we combine Mask R-CNN with 3D algorithms by adding a 3D process branch for instance segmentation. Both outcomes of two branches are contemporaneously used to classify the pixels at the edge objects by dealing with the spatial relationship between edge region and mask region. We analyze the effectiveness of the method by testing with harsh cases of object positions, for example, objects are closed, overlapped or obscured by each other to focus on edge and border segmentation. Our proposed method is about 4 to 7% higher and more stable in IoU (intersection of union). This leads to a reach of 46% of mAP (mean Average Precision), which is a higher accuracy than its counterpart. The feasibility experiment shows that our method could be a remarkable promoting for the research of the grasping robot.
End-to-End Spoken Language Understanding Using Transformer Networks and Self-Supervised Pre-Trained Features

Transformer networks and self-supervised pre-training have consistently delivered state-of-art results in the field of natural language processing (NLP); however, their merits in the field of spoken language understanding (SLU) still need further investigation. In this paper we introduce a modular End-to-End (E2E) SLU transformer network based architecture which allows the use of self-supervised pre- trained acoustic features, pre-trained model initialization and multi-task training. Several SLU experiments for predicting intent and entity labels/values using the ATIS dataset are performed. These experiments investigate the interaction of pre-trained model initialization and multi-task training with either traditional filterbank or self-supervised pre-trained acoustic features. Results show not only that self-supervised pre-trained acoustic features outperform filterbank features in almost all the experiments, but also that when these features are used in combination with multi-task training, they almost eliminate the necessity of pre-trained model initialization.
Dispersed dummy selection approach for location‐based services to preempt user‐profiling

Summary Location‐based services (LBS) provide specific personalized services based on the location information provided by the user. Disclosing the users' private data is a challenging issue prevailing in LBS. Users become victims of online user‐profile disclosure risks when they continuously utilize the services and attempt to disclose private data. The proposed dispersed dummy selection‐based approaches such as dispersed dummy selection including actual location (DDSIA) and dispersed dummy selection excluding actual location (DDSEA) avert user‐profiling issue. The dummy locations are chosen based on the auxiliary information of the location, user, and physical dispersion. The auxiliary information about the location is fetched from the local fog servers, and auxiliary information about the user is stored and retrieved from the cloud storage. An anonymous circular area is considered to ensure the dummy locations are dispersed, and the user‐location correlations get dissipated. The detailed analysis of the proposed approaches is explored with the real‐world map data, and its effectiveness is verified with simulations using Matlab. In addition, the qualitative analysis tests the resistance of the dummies against human observations. The results obtained substantiate that the proposed approaches are efficient in terms of dispersion degree and resistance to human observations that avert user profiling.
Experimental realization of Hamiltonian tomography by quantum quenches

The parameter estimation of the Hamiltonian of an unknown quantum system is essential in quantum information processing. However, the general methods based on quantum state tomography and process tomography are resource demanding and challenging in large quantum systems. On the other hand, if the interaction form of a system is known, one can adopt the quantum quench protocol to reconstruct the generic many-body Hamiltonian with local interactions, requiring significantly lower complexity and measurement cost compared to the general tomography technique. Here we report an experimental demonstration of the quench method to determine the scalar coupling constants in a three-spin chain. In the experiments, we demonstrate that how the quality of the estimation is affected by the duration of the quench and the number of input states. Our results show that the quantum quench protocol is a promising strategy to implement Hamiltonian estimation for many-body systems, especially when the cost of measurement is crucial in practical cases.
Planar object tracking benchmark in the wild

• The first benchmark to evaluate planar object trackers in the natural environment. • Evaluate 22 representative algorithms from four groups. • Analyze the results in detail and provide discussions of future work. Planar object tracking is an important problem in vision-based robotic systems. Several benchmarks have been constructed to evaluate the tracking algorithms. However, these benchmarks are built in constrained laboratory environments and there is a lack of video sequences captured in the wild to investigate the effectiveness of trackers in practical applications. In this paper, we present a carefully designed planar object tracking benchmark containing 280 videos of 40 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change , rotation , perspective distortion , motion blur , occlusion , out-of-view , and unconstrained . In addition, we design a semi-manual approach to annotate the ground truth with high quality. Moreover, 22 representative algorithms are evaluated on the benchmark using two evaluation metrics. Detailed analysis of the evaluation results is also presented to provide guidance on designing algorithms working in real-world scenarios. We expect that the proposed benchmark would benefit future studies on planar object tracking.
Incentivizing Data Donations and the Adoption of COVID-19 Contact-Tracing Apps: A Randomized Controlled Online Experiment on the German Corona-Warn-App

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Matching Transportation Ontologies with Word2Vec and Alignment Extraction Algorithm

The development of intelligent transportation systems (ITSs) faces the challenge of integrating data from multiple unrelated sources. As one of the core technologies of knowledge integration in ITS, an ontology typically provides a normative definition of transportation domain that can be used as a reference for information integration. However, due to the subjectivity of domain experts, a concept may be expressed in multiple ways, yielding the ontology heterogeneity problem. Ontology matching (OM) is an effective method of addressing it, which is of help to further realize the mutual communication between the ontology-based ITSs. In this work, we first propose to use Word2Vec to model the entities in vector space and calculate their similarity values. Then, a stable marriage-based alignment extraction algorithm is presented to determine high-quality alignment. In the experiment, the performance of the proposal is tested by using the benchmark track of OAEI and real transportation ontologies. The experimental results show that our approach is able to obtain higher quality alignment results than OAEI’s participants and other state-of-the-art ontology matching techniques.
A Study on the Size of Tangible Organ-shaped Controllers for Exploring Medical Data in VR

Virtual reality (VR) is, by nature, excellent in showing spatial relationships, e.g. for viewing medical 3D data. In this work, we propose a VR system to view and manipulate medical 3D images of livers in combination with 3D printed liver models as controllers. We investigate whether users benefit from a controller in the shape of a liver and if the size matters by using three different sizes (50 %, 75 %, 100 %). In a user study with 14 surgeons, we focused on presence, workload and qualitative feedback such as preference. While neither size differences nor the VIVE tracker as control resulted in significant differences, most surgeons preferred the 75 % model. Qualitative results indicate that high similarity of physical and virtual objects regarding shape and a focus on good manageability of the physical object is more important than providing an exact replica in size.
Reversible Data Hiding in Jpeg Images for Privacy Protection

Recently, how to protect privacy in JPEG images has become an important issue in social networks. Besides encryption, reversible visual transformation has been studied for content and privacy protection. In this paper, an improved algorithm is proposed to conceal privacy information in JPEG images. By adopting face detection, the regions to be protected can be identified so that the DC and some AC coefficients in them are modified. The changes that have been made are reversibly saved in the other AC coefficients, and the block selection information is hidden into the whole image also by reversible data hiding. As a secret key is employed to control the process of transformation, the protected content can hardly be obtained without knowing the key. We conduct the proposed algorithm on a set of test images, and compare the performance with several existing algorithms in terms of information leakage, file size increment, and image quality. The experimental results show that improved image quality and less information leakage can be achieved with the proposed reversible transformation algorithm.
Multi-Step Spoken Language Understanding System Based on Adversarial Learning

Most of the existing spoken language understanding systems can perform only semantic frame parsing based on a single-round user query. They cannot take users’ feedback to up-date/add/remove slot values through multiround interactions with users. In this paper, we introduce a novel multi-step spoken language understanding system based on adversarial learning that can leverage the multiround user’s feedback to update slot values. We perform two experiments on the benchmark ATIS dataset and demonstrate that the new system can improve parsing performance by at least 2.5% in terms of F1, with only one round of feedback. The improvement becomes even larger when the number of feedback rounds increases. Furthermore, we also compare the new system with state-of-the-art dialogue state tracking systems and demonstrate that the new interactive system can perform better on multiround spoken language understanding tasks in terms of slot- and sentence-level accuracy.
Feynman-type representation of the scattering matrix on the line via a discrete-time quantum walk

Abstract The aim of this article is to relate the discrete quantum walk on <?CDATA $\mathbb{Z}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="double-struck">Z</mml:mi> </mml:math> with the continuous Schrödinger operator on <?CDATA $\mathbb{R}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="double-struck">R</mml:mi> </mml:math> in the scattering problem. Each point of <?CDATA $\mathbb{Z}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="double-struck">Z</mml:mi> </mml:math> is associated with a barrier of the potential, and the coin operator of the quantum walk is determined by the transfer matrix between bases of WKB solutions on the classically allowed regions of both sides of the barrier. This correspondence enables us to represent each entry of the scattering matrix of the Schrödinger operator as a countable sum of probability amplitudes associated with the paths of the quantum walker. In particular, the barrier-top scattering corresponds to the Hadamard walk in the semiclassical limit.
IFSM: An Iterative Feature Selection Mechanism for Few-Shot Image Classification

Nowadays many deep learning algorithms have been employed to solve different types of problems in the area of computer vision; however, most of them require a great amount of training data and tend to struggle in few-shot learning tasks. On the other hand, those methods designed for few-shot learning usually face the difficulty that once one or more samples show a relatively large bias, the predicted result may be much less reliable due to the fact that the sample will cause a large shift of class-level features during few-shot learning. To solve this problem, this paper presents a novel and Iterative Feature Selection Mechanism (IFSM) for few-shot image classification, which can be applied to lots of metric-based few-shot learners. IFSM learns to construct a more feasible class-level feature which is less affected by samples with relatively large biases, using an iterative approach. The proposed mechanism is tested on three previous state-of-the-art few-shot learning methods, and the experimental results show that the proposed mechanism considerably improves (by 1% to 2%) the image classification accuracies of both methods on the miniImageNet, tieredImageNet or CUB benchmarks in 5-way 5-shot tasks. This approves the effectiveness and generality of the proposed mechanism.
Weighted SRGAN and Reconstruction Loss Analysis for Accurate Image Super Resolution

Abstract Super resolution (SR) image generation is to generate a high-resolution image from a given low-resolution image, which can be used in numerous vision tasks, such as small object detection and specific image processing. Generative adversarial network for super resolution (SRGAN) is the mainstream framework in SR task, which utilizes two reconstruction losses, including MSE loss and VGG loss. However, the influence of these losses on model learning is not examined. In this paper, the importance of MSE and VGG losses is analyzed. The loss function of traditional SRGAN is improved, and the weights of MSE and VGG losses are set manually. The weights range from 0 to 1, and the sampling interval is 0.1. Furthermore, a learnable parameter to dynamically adjust the two weights is proposed. Experiments on the datasets, including Set5, Set14, BAS100, and Urban100, show that our method is capable of generating much better images than SRGAN, with higher values of PSNR and SSIM. It is found that the MSE loss makes a greater contribution to learning the discriminative model and the VGG loss plays a supplementary role. Our WSRGAN can apply to most SRGAN-based methods to improve their accuracy.
Decision Support System Two Dimensional Cattle Weight Estimation using Fuzzy Rule Based System

Cattle weight estimation technology is needed in the present era. Cattle scales are very expensive and not all cattle farmers can afford this. When weighing cattle, farmer must bring their cattle to the cattle market or a place provided by the government for cattle weighing. This becomes impractical, so technology is needed to make it easier for farmers. This research proposes decision support system for farmer estimating of cow weight using the Fuzzy Rule Based System. The method used in this research is first to capture cows from a certain distance, then through image processing, the length of the cow's body and chest circumference are measured. On the other hand, the cow is weighed, and the cow's body length and chest circumference are measured directly. The next process is to build a decision support system based on the data that has been obtained. Fuzzy used in this research is Mamdani by using fuzzification defuzzification centroid, bisector, mean of maxima (mom), and som. The membership function used gaussian with five membership function for input (2 input) and output. The test results obtained using fuzzification defuzzification centroid and mom are 81.24%. While when implement bisector 80.945 and using mom produced accuracy 73.36%.
A Two-Stage Neural Network-Based Cold Start Item Recommender

Nowadays, recommendation systems have been successfully adopted in variant online services such as e-commerce, news, and social media. The recommenders provide users a convenient and efficient way to find their exciting items and increase service providers’ revenue. However, it is found that many recommenders suffered from the cold start (CS) problem where only a small number of ratings are available for some new items. To conquer the difficulties, this research proposes a two-stage neural network-based CS item recommendation system. The proposed system includes two major components, which are the denoising autoencoder (DAE)-based CS item rating (DACR) generator and the neural network-based collaborative filtering (NNCF) predictor. In the DACR generator, a textual description of an item is used as auxiliary content information to represent the item. Then, the DAE is applied to extract the content features from high-dimensional textual vectors. With the compact content features, a CS item’s rating can be efficiently derived based on the ratings of similar non-CS items. Second, the NNCF predictor is developed to predict the ratings in the sparse user–item matrix. In the predictor, both spare binary user and item vectors are projected to dense latent vectors in the embedding layer. Next, latent vectors are fed into multilayer perceptron (MLP) layers for user–item matrix learning. Finally, appropriate item suggestions can be accurately obtained. The extensive experiments show that the DAE can significantly reduce the computational time for item similarity evaluations while keeping the original features’ characteristics. Besides, the experiments show that the proposed NNCF predictor outperforms several popular recommendation algorithms. We also demonstrate that the proposed CS item recommender can achieve up to 8% MAE improvement compared to adding no CS item rating.
TwinkleBands: Wearable Technology Supporting Movement Imitation and Coordination in Fast-Paced Fitness Training

Fast-paced fitness training (e.g. aerobics, Bodycombat, step workouts) is one of the most popular activities at fitness clubs worldwide because it is beneficial for physical health and it can enhance participants’ motivation and engagement of fitness training. Yet, their fast pace and required coordination make it difficult for some participants, especially for beginners and those with coordination problems, to follow the class. Here we present the design, implementation and qualitative user evaluation of TwinkleBands, a piece of wearable technology that provides real-time support with trainees’ movement and coordination learning by providing discriminative visual cues on the extremities. We show that TwinkleBands helps movement imitation and coordination in several ways. Based on this, we discuss key design takeaways for future technology design to support movement teaching and learning in fast-paced activities.
Real-time virtual UR5 robot imitation of human motion based on 3D camera

Human robot interaction is playing significant roles in many industries, in which humans are allowed to corporate with robots or control robots to complete some tasks. Achieving realtime control of robots by demonstrating human arm motion is one of the effective methods to improve the quality of human robot interaction. In this paper, a new method to accomplish human skeleton key points extraction is proposed and a system for controlling a virtual UR5 robot (which is modeling in Unity 3D) is developed to imitate human motions. A sequential architecture composed of convolutional networks is used to identify the 2D coordinates; the depth information from Kinect v2 sensor is used to obtain the actual 3D coordinates and the result of the proposed method is used for joint angles calculation. The appropriate mapping relationships between human arm and UR5 robots are built by transforming the arm angel to UR5's degrees of freedom. The performance of this method is measured by the degree of real-time simulation of human hand trajectory. Further, the comparison between the method in this paper and other method is implemented. The experimental results indicate that the developed system and proposed method are reliable and efficient to accomplish human motion imitation.
PCLoss: Fashion Landmark Estimation with Position Constraint Loss

Fashion landmark estimation aims at locating functional key points of clothes, which has wide potential applications in electronic commerce. However, due to the occlusion and weak outline information, landmark estimation occurs outliers and duplicate detection problems. To alleviate these issues, we propose Position Constraint Loss (PCLoss) to constrain error landmark locations by utilizing the position relationship of landmarks. Specifically, PCLoss adds a regularization term for each landmark to regularize their relative positions, and it can be easily applied to both regression and heatmap based methods without extra computation during inference. Unlike existing approaches that propagate landmark information between feature layers by specific network structures, PCLoss introduces position relations of landmarks in the label space without modifying the network structure. In addition, we leverage the skeleton-like relation of clothing to further strengthen position constraints between landmarks. Extensive experimental results on DeepFashion, FLD and FashionAI demonstrate that our methods can effectively increase the performance of mainstream frameworks by a large margin. We also explore the effectiveness of PCLoss on human pose estimation task, and the experimental results on COCO 2017 prove the generality of our methods on other key point estimation tasks.