Reviewerly/exHarmony huggingface dataset:
DecluttAR: An Interactive Visual Clutter Dimming System to Help Focus on Work

Over time, the desks of office workers often get cluttered with documents, items, and other objects that are unrelated to the work being done. These objects become visual noise. Visual noise tends to reduce the worker’s concentration on their work. Therefore, it is desirable to have an uncluttered environment to work. We propose DecluttAR, a system that controls the visibility of clutter objects by using a video see-through head-mounted display and an RGB-D camera. Our system first generates a 3D model of the empty desk, and then automatically generates a 3D model of each object that is newly placed on the desk. Then, the user can interactively control each object’s visibility which is superimposed on the corresponding real object. Four types of object appearance adjustments are implemented: normal, grayscale, outline, and transparency. To investigate changes in the ability to concentrate in different appearances, a user study was conducted in which a simple arithmetic task was performed in four appearances. The results show that the number of answers in the arithmetic task for the transparent display increased compared to the normal display in the first half of the task. This result indicates that the participants are able to enter a state of concentration on the task more quickly by making the object invisible. In addition, the questionnaire-based subjective evaluation revealed that the transparent display had a lower workload and a higher user evaluation followed by the outline display.
CC-Glasses: Color Communication Support for People with Color Vision Deficiency Using Augmented Reality and Deep Learning

People who suffer from color vision deficiency (CVD) can face difficulties when communicating with others by failing to identify target objects referred by their color names. While most existing studies on CVD compensation have focused on the issue of color contrast loss. Although there are approaches can provide clues of color name to users, these techniques either require training, or cannot protect users’ privacy, i.e., the fact of having CVD. In this paper, based on augmented reality (AR) and deep learning technologies, we propose a novel system to provide supporting information to users affected by CVD for color communication assistance. The state-of-the-art deep neural network (DNN) model for referring segmentation (RS) is adopted to generate supporting information, and AR glasses are utilized for information presentation. To improve the performance of the proposed system further, a new dataset is constructed based on a novel concept called Color–Object Noun Pair. The results of evaluation experiments show that the new dataset can enhance the performance of the adopted DNN model, and the proposed system can help users affected by CVD successfully identify target objects by their color names.
A privacy‐preserving model for blockchain‐based data sharing in the industrial internet

Abstract Information technology is an important direction for the development of traditional industries. With the in‐depth study of the industrial internet and blockchain technology in recent years, data‐sharing based on blockchain has become an important means to achieve industrial internet interconnection and has played an important role in achieving production efficiency and product quality. However, the transparency of blockchain can raise privacy issues, and competitors may engage in malicious competition due to the exposure of blockchain transactions. Therefore, based on the general blockchain system, this article unifies the scale of value of data sharing by introducing a token exchange mechanism as a basis for privacy‐preserving mechanisms. Based on smart contracts, a token mixer anonymizes shared participants. By introducing a ring signature scheme with smart contracts, we provide group‐oriented authentication for anonymous identities participating in data sharing and ensure data flow within a controlled range. The abovementioned mechanism provides a feasible solution for the privacy‐preserving requirement of data sharing among industrial entities and could encourage industrial entities to participate in data sharing, supporting the goals of interconnection in the industrial internet.
Effects of the Number of Bodies on Ownership of Multiple Bodies

Virtually reality can induce body ownership (Sense of being one’s own body) of a not innate body or multiple bodies. However, the relationship between the number of bodies and body ownership has not been clarified. In a study that investigated body ownership for one, two, and four virtual bodies, the greater the number of bodies, the closer the body ownership tended to approach the strength of ownership for a single body. Therefore, in the present study, we investigated whether increasing the number of bodies strengthens body ownership for multiple bodies. In the experiment, multiple virtual bodies that moved in synchronization with the participant’s movement were lined up in a single file line, and the participant observed the multiple virtual bodies through a head-mounted display from the position of the hindmost virtual body. We measured body ownership and drifts in self-location. Our results showed that contrary to expectations, the greater the number of bodies, the weaker body ownership.
Gaze Depth Estimation for In-vehicle AR Displays

In our previous study, we proposed a method for judging whether the user is gazing at a semi-transparent virtual object or real objects behind it in augmented reality environments. This paper shows that the accuracy of our method can be improved by selecting the optimal thresholds for the fixation detection. Fourteen participants experienced a virtual reality environment in which there were a transparent subway map and buildings behind it in the distance of 2 m and 15 m away from each participant, respectively. As a result, the accuracy of our method has achieved 88.3 % and improved by 13.8 percentage points from the previous 74.5 %.
Cast Shadow and Color Change Improve Social Impression of Robotic Massage with Virtual Out-of-Body Experience

Massage therapy has been shown to have numerous benefits, including improvements in physical and mental health. In this study, we examined how integrating virtual reality with a robotic massage system could enhance users' social perception of the robot. We implemented a virtual out-of-body experience in the robotic massage system, allowing users to view their own bodies as avatars and observe the robot's approach. Our hypothesis was that adding visual information, such as a color change or cast shadow motion, could improve the social impression of the robot. The results of our human-subject study demonstrated that the robot was perceived as more competent and warmer when the approach of the robotic arm was accompanied by a gradual change in color, while the robot was evaluated as more comfortable when the approach was accompanied by a cast shadow motion. These findings suggest that the combination of a robotic massage system with virtual reality, such as a virtual out-of-body experience, could improve users' social perception of the robotic system, resulting in a feeling of safety, warmth, and comfort during the massage.
Generation of realistic facial animation of a CG avatar speaking a moraic language

We propose a new method for generating real-time realistic facial animation using face mesh data corresponding to the fifty-six C+V (Consonant and Vowel) type morae that form the basis of Japanese speech. This method produces facial expressions by weighted addition of fifty-three face meshes based on the mapping of voice streaming to registered morae in real-time. Both photogrammetric models and existing off-the-shelf head models can be used as face meshes. Natural facial expressions of speech can be synthesized from a modeling to live animation in less than two hours. The results of a user study of our method showed that the facial expression during Japanese speech was more natural than popular real-time methods to generate facial animation, English-base Oculus Lipsync and volume intensity based facial animations.
Mechanism and Model for Data Evaluation of the System of Automated Monitoring of Atmospheric Air Pollution in Industrially Developed Regions

The mechanism of environmental monitoring, a model and a system for evaluating data obtained using an automated environmental monitoring system for atmospheric air pollution, which describes the evolution of a variable measured over the same sampling period as a non-linear function of past evolutions are considered. The object of research is the system of automated monitoring of atmospheric air pollution in industrialized regions. The subject of the study is the mechanisms for conducting environmental monitoring and models for evaluating automated monitoring data.
A Novel Small Target Detection Strategy: Location Feature Extraction in the Case of Self-Knowledge Distillation

Small target detection has always been a hot and difficult point in the field of target detection. The existing detection network has a good effect on conventional targets but a poor effect on small target detection. The main challenge is that small targets have few pixels and are widely distributed in the image, so it is difficult to extract effective features, especially in the deeper neural network. A novel plug-in to extract location features of the small target in the deep network was proposed. Because the deep network has a larger receptive field and richer global information, it is easier to establish global spatial context mapping. The plug-in named location feature extraction establishes the spatial context mapping in the deep network to obtain the global information of scattered small targets in the deep feature map. Additionally, the attention mechanism can be used to strengthen attention to the spatial information. The comprehensive effect of the above two can be utilized to realize location feature extraction in the deep network. In order to improve the generalization of the network, a new self-distillation algorithm was designed for pre-training that could work under self-supervision. The experiment was conducted on the public datasets (Pascal VOC and Printed Circuit Board Defect dataset) and the self-made dedicated small target detection dataset, respectively. According to the diagnosis of the false-positive error distribution, the location error was significantly reduced, which proved the effectiveness of the plug-in proposed for location feature extraction. The mAP results can prove that the detection effect of the network applying the location feature extraction strategy is much better than the original network.
Path integral approach to universal dynamics of reservoir computers

In this work, we give a characterization of the reservoir computer (RC) by the network structure, especially the probability distribution of random coupling constants. First, based on the path integral method, we clarify the universal behavior of the random network dynamics in the thermodynamic limit, which depends only on the asymptotic behavior of the second cumulant generating functions of the network coupling constants. This result enables us to classify the random networks into several universality classes, according to the distribution function of coupling constants chosen for the networks. Interestingly, it is revealed that such a classification has a close relationship with the distribution of eigenvalues of the random coupling matrix. We also comment on the relation between our theory and some practical choices of random connectivity in the RC. Subsequently, we investigate the relationship between the RC's computational power and the network parameters for several universality classes. We perform several numerical simulations to evaluate the phase diagrams of the steady reservoir states, common-signal-induced synchronization, and the computational power in the chaotic time series inference tasks. As a result, we clarify the close relationship between these quantities, especially a remarkable computational performance near the phase transitions, which is realized even near a nonchaotic transition boundary. These results may provide us with a new perspective on the designing principle for the RC.
Online Review System Using Relational Triple Extraction with Novel Data Augmentation Methods

The online review system is a fundamental application system. However, the existing system not only can not automatically check the matching of comments and score ratings but also can not give a fair reference score according to the comments, In this work, we utilize a relational triple extraction method to solve the two problems for the first time. In addition, considering that the existing online review systems are generally characterized by a lack of high-quality labeled data, we present five novel data augmentation techniques for boosting performance specifically on relational triple extraction tasks. The five data augmentation techniques demonstrate particularly strong results for both datasets of the review system and the public datasets of relational triple extraction.
A Digital Identity System for Real World Assets using Blockchain

The rapid development of computing, database, communication, and networking technologies has led to the invention of Digital Twin (DT). A Digital Twin is a representation of a real-world object. The configuration, monitoring, diagnostics, and prognostics of a physical component can all be done with a DT after it has been placed in service. We advise a blockchain-primarily based totally improvement process for DT to ensure security and reliable traceability and immutability of transactions, protocols and data lineage. Smart contracts are used in our suggested method to control and record actions taken by those involved in the construction of DTs. We're planning to use digital twins in the suggested system for expensive items like gold and platinum.
Unscented and Complex Unscented Kalman Filtering for Parameter Estimation of a Single and Multiple Sinusoids in the Area of Power and Communication Signals

The problem of estimating the sinusoidal parameters in white Gaussian noise is a fundamental well studied research topic.
Multi-view Graph Learning with Fuzzy Linear Discriminant Analysis

The multi-view clustering method based on graph learning has been extensively studied because of its good clustering effect. However, most of the graph learning methods are based on the original data features, which often contain noise and outliers, and using them directly may lead to learning suboptimal graph. To address the above problems, we propose a multi-view graph learning method based on linear discriminant analysis. On the one hand, the manifold structure obtained using the global graph guides the learning of the discriminative projections for each view, and on the other hand, the common graph is learned using the projection data of each view. Then, an efficient solution is proposed by dividing the problem into two sub-problems. Finally, experiments are conducted on some public multi-view datasets to verify the effectiveness of the method.
Multi-granularity Feature Fusion Algorithm for Short Chinese Texts Based on Hierarchical Attention Networks

Chinese short texts comprises a small number of words and many ambiguities, making it challenging to extract semantic information. The mainstream approach of extracting semantic characteristics from Chinese short texts is to combine character and word granularity, although this method suffers from partial loss of semantic features extraction. To address this issue, this study provides a multi-granularity feature fusion technique that combines character, word, pinyin, and radical granularity. Meanwhile, in order to solve the problem of misspelled words in short Chinese texts, we introduce Hierarchical Attention Networks in the model to assign more attention weights to the correct words. The studies show that our model(MGCHA) can successfully improve the performance of semantic matching for short Chinese texts on the LCQMC and BQ datasets.
Unsupervised Feature Selection Based on 3-D Feature Decision Graph for High-dimensional Data

Feature selection is aimed at reducing the dimensionality of data sets and obtaining a feature subset with better performance for the target learner. Unsupervised feature selection is more challenging because of the lack of label information. In this paper, the idea of decision graph is applied to unsupervised feature selection. Specifically, a 3-D decision graph model of features is proposed to reveal the characteristics of each feature and the relationship between them. Then, a movable hyperplane is constructed to select specified number of features from the original feature space to form the final feature subset. Experiments in comparison with both traditional and novel algorithms on benchmark data sets reveal that the proposed method is able to select feature subset with both of lower redundancy and higher performance.
DTTrack: Target Tracking Algorithm Combining DaSiamRPN Tracker and Transformer Tracker

At present, transformer-based target tracking algorithms mainly use transformers to fuse deep convolution features, their tracking accuracy is competitive, however compared with convolutional neural networks, their tracking speed is slow. Due to the long-distance dependence characteristics, it is difficult to obtain rich local information when extracting visual features, the tracking results may become worse, or even the tracking may fail in the later tracking procedures. The partial target tracking algorithm based on the Siamese network has great advantages in extracting local information, however its tracking accuracy cannot fully reach the transformer-based target tracking algorithm. According to the characteristics of the two trackers, combining the response scores and Hamming distance which is used to calculate the similarity, then a target tracking algorithm combining DaSiamRPN and Transformer is proposed. This structure can judge whether the tracking effect of the transformer tracker has deteriorated according to the response score and the Hamming distance between the resulting frame and the initial frame during transformer tracking, in order to replace another tracker in time. The proposed method can reduce the drift and obtain higher accuracy as well. Experiments show that our tracker achieves good results on three datasets. Our method achieved 72.0%, 69.1%, and 67.1% success rates on the GOT-10k, OTB2015, and UAV123 datasets, respectively.
Entangled Representation Learning: A Bidirectional Encoder Decoder Model

Encoder-decoder model encodes input sentences to hidden representations and decodes the representations to the output in unidirectional way. We introduce a bidirectional encoder decoder model that adds a reverse decoder-encoder for the feedback from the output to the input. We implement the bidirectional encoder decoder model based on the transformer. The decoder-encoder attention mechanism enhances the representations via the interaction between encoder and decoder. Experiment results demonstrate the benefit of bidirectional model. And the visualization results of bidirectional representations reveal that our model learn an entangled representation of both encoder and decoder.
Fast and Exact Subgraph Isomorphism Querying: Using Embedding and Searching Techniques

Subgraph isomorphism querying finds if there is any instance of a given pattern within the data graph, and has a wide applications in all fields related to graphs, such as databases, social network analysis, chemistry and biology. It is well-known that this task can be done by brute-force, but the complexity increases exponentially with the graph size and is proven an NP-hard problem. Many works have been done on accelerating subgraph isomorphism with the sacrifice of accuracy, most of which still suffer from the inherent difficulty, especially when the graph size get large. In this paper we propose a novel method, named SubQHS (Subgraph Querying based on Heuristic Search), which can quickly find an existing pattern in the data graph using heuristic. We also propose its variant SubQHS-T, which is guaranteed to terminate in polynomial time to get an approximate result.
A Privacy Preserving Energy Trading Platform Based on Smart Contract

The smart grid has provided a fascinating opportunity to move the energy industry into a new era of reliability, availability and efficiency that contributes to both energy saving and environment protection. Besides, the smart grid has abandoned the single power supply paradigm in traditional power grid, and it can promote information and resource exchange through peer-to-peer transactions. For example, electricity can be traded effectively in real-time, so that all users can be benefited from cost saving. However, the energy trading data may contain sensitive information of the participating parties. If this information is leaked, user privacy might be violated. Moreover, the trading information should be enforced with fine-grained access control. To fulfil these security requirements, we propose a privacy preserving energy trading platform based on smart contract. First, ElGamal encryption is used to protect the privacy of exchanged messages. Second, proxy re-encryption is employed to achieve fine-grained access control, and it is more efficient than attribute based encryption that is widely used in existing solutions. Third, smart contract is used as the arbitrator, and thanks to its attractive characteristics, such as transparency and trustworthy execution, it can replace the trusted third parties in many existing schemes. Security analyses prove that our scheme satisfies all the desirable security requirements, such as correctness, privacy, fine-grained access control, robustness. And performance analyses demonstrate that it is practical for large-scale applications.
Domain Adaptation Based on Multi-Kernel Learning

Abstract: Domain adaptation is used to solve the inconsistency in distributions of training samples (source domain) and test samples (target domain) and improve the accuracies of traditional learning machines. Domain adaptation methods attempt to map the two domains to a latent space where the distributions of them are aligned. The model trained in the source domain then can be effectively generalized to the target domain. However, the linear mapping adopted by the existing distribution matching methods has a limited ability to represent the complex transformation between source domain and target domain. In order to overcome this defect, we put forward Domain Adaptation based on Multi-Kernel learning (DAMK) method, which uses a nonlinear mapping. In order to satisfy the different requirements in the nonlinearity of the feature mapping of different datasets, DAMK uses the weighted sum of multiple mappings and optimizes the weighted coefficients. Because of the difficulty in obtaining nonlinear mapping directly, we adopt multi-kernel function instead of explicit expression to express the nonlinear mapping function. Experiments conducted on object recognition datasets and face recognition datasets show that DAMK is more effective that the existing linear mapping methods.