Miracle-dz/UniFAR-FTUs huggingface dataset:
Cortical Regions Encoding Hardness Perception Modulated by Visual Information Identified by Functional Magnetic Resonance Imaging With Multivoxel Pattern Analysis

Recent studies have revealed that hardness perception is determined by visual information along with the haptic input. This study investigated the cortical regions involved in hardness perception modulated by visual information using functional magnetic resonance imaging (fMRI) and multivoxel pattern analysis (MVPA). Twenty-two healthy participants were enrolled. They were required to place their left and right hands at the front and back, respectively, of a mirror attached to a platform placed above them while lying in a magnetic resonance scanner. In conditions SFT, MED, and HRD, one of three polyurethane foam pads of varying hardness (soft, medium, and hard, respectively) was presented to the left hand in a given trial, while only the medium pad was presented to the right hand in all trials. MED was defined as the control condition, because the visual and haptic information was congruent. During the scan, the participants were required to push the pad with the both hands while observing the reflection of the left hand and estimate the hardness of the pad perceived by the right (hidden) hand based on magnitude estimation. Behavioral results showed that the perceived hardness was significantly biased toward softer or harder in >73% of the trials in conditions SFT and HRD; we designated these trials as visually modulated (SFTvm and HRDvm, respectively). The accuracy map was calculated individually for each of the pair-wise comparisons of (SFTvm vs. MED), (HRDvm vs. MED), and (SFTvm vs. HRDvm) by a searchlight MVPA, and the cortical regions encoding the perceived hardness with visual modulation were identified by conjunction of the three accuracy maps in group analysis. The cluster was observed in the right sensory motor cortex, left anterior intraparietal sulcus (aIPS), bilateral parietal operculum (PO), and occipito-temporal cortex (OTC). Together with previous findings on such cortical regions, we conclude that the visual information of finger movements processed in the OTC may be integrated with haptic input in the left aIPS, and the subjective hardness perceived by the right hand with visual modulation may be processed in the cortical network between the left PO and aIPS.
Indoor Position Method of Industrial Robot Based on Wifi Fingerprint Position Technology

With the development of AI and industrial technology, indoor positioning has gradually attracted wide attention. General solution in the industrial positioning field has not yet be found with current positioning technology. The problem of industrial intelligent positioning technology is worth studying. Due to the extensive deployment of Wi-Fi, this paper will apply the improved intelligent fingerprint positioning technology to the field of robot positioning. At present, most fingerprint positioning methods need to collect a large amount of data, and field investigation requires a lot of time and manpower, which complicates the positioning method. This paper puts forward the idea of synchronization while collecting and locating. By collecting wifi-list and processed the information into fingerprint information, the fingerprint database is updated regularly in the meanwhile to eliminate fingerprint noise and reduce the dependence of location on environment. The improved Gauss filtering method is used to test the collected fingerprint data to increase the reliability of fingerprint data. An effective matching method TNN is proposed which reduce redundant information in fingerprint database, improve the speed and accuracy of location by adjusting fingerprint information. After testing, the accuracy of the fingerprint positioning method proposed in this study reaches 2 meters, and the average accuracy of fingerprint data in the accuracy range is 98.5%, and what's more, it is easy to deploy and implement.
Nonlinear Output Feedback Control of Flexible Rope Crane Systems With State Constraints

When the suspension rope of the crane system is relatively long and its mass cannot be simply ignored compared with that of the payload, or when the crane rope is underwater, it will exhibit flexible characteristics, that is, the bending deformation will occur during swing. Undoubtedly, unreasonable bending deformation of the crane rope will further excite larger-amplitude payload swing, which will seriously influence the efficiency and safety. To this end, for flexible rope crane systems, this paper carries out in-depth analysis and puts forward an effective control method. Specifically, by introducing the concept of lumped mass method and virtual spring, a nonlinear dynamic model is established. Furthermore, in order to solve the problem that velocity signals are not measurable, a nonlinear control strategy, which does not need velocity signals for feedback, is proposed in this paper. Finally, rigorous theoretical analysis and a series of experimental results verify the effectiveness of the proposed control strategy.
Incentivized Social-Aware Proactive Device Caching With User Preference Prediction

In order to offload network traffic, we design a device caching strategy by jointly considering a popularity model, social influence and incentive design in this paper. Firstly, we propose a prediction model by virtue of users’ social network information to evaluate users’ encounter probability. Moreover, users’ content preference is predicted using users’ context information. Based on these predicted values, a content placement algorithm is described provided that the users will fully cooperate to optimize system performance. Thereafter, a more practical scenario where users are selfish and unwilling to devote their resources is considered. A Stackelberg game is established between the mobile network operator (MNO) and users by providing an incentive to encourage cooperation. Device caching strategy and incentive price design are determined by analyzing the Stackelberg game and finding the Stackelberg equilibrium point. We verify the effectiveness of our prediction models utilizing real data sets. Simulation results show that the cache hit ratio can be considerably improved by exploiting social and context information. Incentive design and profit analysis are also thoroughly investigated.
Microbiome-Gut-Brain Axis as a Biomolecular Communication Network for the Internet of Bio-NanoThings

This article presents fundamental challenges in the development of a self-sustainable and biocompatible network infrastructure to interconnect the next-generation electrical and biological wearable and implantable devices, i.e., the Internet of Bio-NanoThings. The direct contact of IoBNT devices with the human body, where the cells naturally communicate and organize into networks, suggests the possibility to exploit these biological communications for the device-to-device interconnection. The aim of this work is to investigate minimally invasive, heterogeneous, and externally accessible electrical/molecular communication channels to transmit information between these devices through the Microbiome-Gut-Brain Axis (MGBA), composed of the gut microbial community, the gut tissues, the enteric nervous system. A framework to develop a network infrastructure on top of the biological processes underlying the MGBA, and the intercommunications among its components is proposed. To implement this framework, the following challenges need to be tackled. First, physical channel models should be developed to quantitatively characterize electrical and molecular communications through the MGBA. Second, novel technological solutions in information modulation, coding and routing should be developed. Third, to support these efforts with experimental data, a first-of-a-kind implantable MGBA network probe device composed of a hub connected to an ensemble of electrical and molecular stimulation and sensing modules should be designed and engineered, together with an innovative gut-on-a-chip in-vitro model system. The discussion in this paper establishes the basis for a completely novel transdisciplinary networking domain at the core of the next-generation biomedical systems for pervasive, perpetual, and remote healthcare.
Does Training on Broad Band Tactile Stimulation Promote the Generalization of Learning?

Given the clear role of sensory feedback in successful motor control, there is a growing interest in integrating substitutionary tactile feedback into robotic limb devices. To enhance the utility of such feedback, here we investigate how to best improve the limited generalization of tactile learning across body parts and stimulus properties. Specifically, we sought to understand how perceptual learning with different types of tactile stimuli may give rise to different patterns of learning generalization. To address this, we utilized vibro-tactile effectors to present patterns of stimulation in a match-to-sample paradigm. One group of participants trained on narrow-band stimulation consisting of simple sinusoidal vibrations, and the other on broad-band stimulation generated from music. We hypothesized that training on broad-band tactile stimulation would promote greater generalization of learning outcomes. We found training with broad-band stimuli generalized to underlying stimulus features of frequency discrimination but showed weaker generalization to un-trained digits. This study provides a first step towards devising perceptual learning paradigms that will generalize broadly to the untrained perceptual contexts.
C-Map: Hyper-Resolution Adaptive Preprocessing System for CSI Amplitude-Based Fingerprint Localization

With the increasing popularity of location-based services, channel state information (CSI) has received widespread attention in positioning due to the fine-grained information it provides. However, the raw amplitudes of CSI are especially sensitive to noise and easily effected by the frequency-selective fading. As the expanding of test area, positioning is disturbed by random noise and insufficient resolution of features severely. In this paper, we propose Cluster-Mapping (C-Map), an adaptive pre-processing system for CSI amplitude-based fingerprint localization. This system models positioning as a classification problem, an adaptive processing mechanism is constructed according to the characteristics of CSI amplitude. C-Map mainly contains two parts: dynamic denoising and feature enhancement. In the dynamic denoising, effective features are screened out by clustering iteratively, without specifying parameters according to the experimental environment. In the feature enhancement, polynomial fitting with regularization is used to reduce jagged fluctuations, then the trend of variation over 30 sub-carriers are described by combining derivation with nonlinear kernel function. Extensive experiments are conducted in typical environments to verify the superior performance of C-Map for the preprocessing of CSI amplitude. Compared with the combination of mean and multidimensional scaling (MDS), the average positioning error of C-Map is reduced 28.1% in comprehensive indoor environment, and 19% in garage. Furthermore, compared with the combination of DBSCAN and principal component analysis (PCA), the average positioning error of C-Map is reduced 33.1% in comprehensive indoor environment, and 28% in garage.
Learning Navigation via R-VIN on Road Graphs

Guiding vehicles to their destination is an essential service. Nowadays navigation systems are mainly relying on the traffic conditions of road network, and other influence factors are not taken into account accurately, which is easy to lead to imbalance between the supply and demand of roads, resulting in congestion. In this paper, we introduce an online guiding approach via value iteration network on road graphs, R-VIN for short, which is an end-to-end planning model. In R-VIN, a large-scale real GPS trajectories are mapped via map-matching based road topology, which enables R-VIN to catch the experienced driving knowledge. Then we propose a conversion method from irregular road graphs to regular grid images to formalize the learning model. For a global optimum, ConvLSTM is used to predict the future traffic situation to form "prediction reward" of R-VIN. Combining with "current reward", a double rewarded VIN is used to solve the plan-involved function. Lastly, we train and evaluate R-VIN on planning problem in road networks, showing that R-VIN can achieve segment-based autonomous navigation with high top-k accuracy and less commuting time.
Impromptu Accompaniment of Pop Music using Coupled Latent Variable Model with Binary Regularizer

Symbolic music generation has long been an attractive topic in machine intelligence, which aims to automatically learn musical distribution from musical corpora and then to generate samples from the estimated distribution. As one of the most popular kinds, pop music is usually polyphonic and multi-track which makes difficulties in music generation. Furthermore, different from other tasks in machine intelligence such as image processing, the piano roll representation of music is discrete and binary, thus leading to a non-differentiable problem. In other words, most state-of-the-art models such as neural networks cannot be directly applied to achieve piano-roll-based symbolic music generation. To address these two issues, we propose a coupled latent variable model with binary regularizer. On the one hand, the proposed model employs a coupled mechanism to learn a latent variable that simultaneously captures the internal distribution of each track and the joint distribution of multiple tracks. On the other hand, we propose to reformulate the discrete and binary properties into a convex constraint in an elegant way, thus obtaining a differentiable optimization problem and smoothly cooperating with neural networks. To show the effectiveness of our method, we carry out the experiment in impromptu accompaniment generation which is a popular application of music generation, and both the quantitative evaluation and human evaluation demonstrate promising performance of our model compared with some state-of-the-art models.1
Spinal Stenosis Detection in MRI using Modular Coordinate Convolutional Attention Networks

Spinal stenosis is a condition in which a portion of spinal canal narrows and exerts pressure on nerves that travel through it causing pain and numbness that might require surgery. This narrowing can be caused by pathologies in bony structures (vertebrae) or soft tissue structures (intervertebral discs) that comprise the spine. Radiography, particularly Magnetic Resonance Imaging (MRI) is the modality of choice to evaluate stenosis and intervertebral disc pathology. Radiologists examine axial MRI scans at various levels along the spine to detect stenosis. Further, they evaluate the diameters of spinal canal and bulging in nearby discs which can indicate narrowing and compression on nerves. Hence measuring various diameters in a scan is a crucial step in diagnosis. However, affected regions occupy a very small fraction of the scan and there is virtually no room for error as a deviation of few pixels will also lead to discrepancies in measured and original lengths which makes it a very difficult and laborious task to measure the length of such intricate structures accurately. This paper proposes a novel deep learning based solution to tackle this problem. Proposed method attempts to solve it in two independent modules and makes prediction on the enlarged section of the scan which also makes it easier to measure various lengths. Human radiologists focus on certain parts of the scan rather than attending to the entire scan which largely consists of irrelevant background. Proposed modular approach is designed to mimic this attention mechanism. Both modules are built using coordinate convolutional networks, comparisons with baseline method empirically demonstrate superiority of the proposed approach.
Ensembling 3D CNN Framework for Video Recognition

Video-based behavior recognition is a challenging research topic. The three dimensional convolution neural network (3D CNN) is effectively adopted to capture features from videos directly. 3D CNN is extended by two-dimensional convolution neural network, in which a time dimension is added. 3D CNN is better than two-dimensional convolution network in expressing effective motion information, and it has certain advantages. In order to make better use of the valuable features extracted from the original video information, only stacked RGB frame data sets can be used as the input of network. Ensembling 3D CNN framework for video recognition is proposed in the paper. Firstly, the pre-training model of Sports-1M is initialized firstly, and a 3D convolution neural network based on multi-level feature fusion is constructed. The final high-dimensional feature combination is obtained by fusing multiple convolution features. Then 3D convolutional neural network based on ensemble learning is proposed to increase motion information, enrich motion features and enhance the robustness of single feature representation. Three incomplete training data sets are obtained by Bagging algorithm. To get different networks, three data sets are employed to train three 3D convolution neural networks respectively, and the output of the three networks is integrated. The output features of the three networks are input into the SVM classifier through the Stacking algorithm and the final results are obtained. The integration effects of different ensemble methods are compared. The experimental results show that the method of this work can improve recognition accuracy on UCF-101 data set effectively.
A modular design process for developing humanoid mobile robot VieBot

A R T I C L E I N F O A B S T R A C T Article history: Received: 14 June, 2018 Accepted: 30 July, 2018 Online: 05 August, 2018 This paper introduces a design process for developing a humanoid mobile robot, namely VieBot. Stemming from the design process, it is easy to adjust the design and extent robot behaviors to meet customer needs. Our key solution is to split the design into three modules related to robot behavior, kinematic computation and motion control. Base on the results from testbed module under comprehensive testing, the design is gradually improved to satisfy sophisticated requirement of assisting disabled people.
Understanding Language Dependency on Emotional Speech using Siamese Network

Emotion is made up of three components; physiological arousal, expressive behaviors, and conscious experience. Psychological theories like Social Constructionist theory believe that although emotions are a universal phenomenon, they are deeply influenced by the cultural background of the speaker. Since culture and language have an intimate connection between them, the language used by a particular social group would have a significant effect on emotions expressed in their verbal communication. We aim to study the susceptibility of emotions in different languages, which is expressed in speech. We introduce a novel Deep Convolution Siamese Network (DCSN) for determining the similarity between speech samples. The speech samples were obtained from emotional speech datasets spoken in different languages, namely English, Italian, and German. Two experiments were undertaken for studying the correlation between language and emotional speech. Similarity metric along with scatter plots were obtained, which establish that the DCSN is capable of identifying both languages and emotions in a speech sample. The results hence provide a conclusive proof for the model to be used for the study of different psychological aspects of emotional speech.
Quantitative Trading on Stock Market Based on Deep Reinforcement Learning

With the development of computer science technology and artificial intelligence, quantitative trading attracts more investors due to its efficiency and stable performance. In this paper, we explore the potential of deep reinforcement learning in quantitative trading. A LSTM-based agent is proposed to learn the temporal pattern in data and automatically trades according to the current market condition and the historical data. The input to the agent is the raw financial data and the output of the agent is decision of trading. The goal of the agent is to maximize the ultimate profit. Besides, to reduce the influence of noise in the market and to improve the performance of the agent, we use several technical indicators as an extra input. The proposed system has been back-tested on the stock market. The results demonstrate that our method performs well in most conditions.
An Efficient Method for High-Speed Railway Dropper Fault Detection Based on Depthwise Separable Convolution

The overhead contact system (OCS) is an indispensable part of high-speed railway power supply system. The stable operation of the OCS depends on the stability of the contact between the pantograph and catenary. The dropper is an important guarantee for the stable contact. The three faults of the dropper, such as slack, breakage and disappearance can damage the quality of traction power supply and reduce the safety of the railway operation. Therefore, it is necessary to efficiently detect the dropper fault and guide the maintenance in time. Recently, automatic dropper fault detection methods based on monitor-video have been introduced to improve railway operation safety. However, the existing methods were still not stable enough in complex backgrounds. To improve the accuracy and real-time performance of the dropper fault detection, this paper proposed a method combining depthwise separable convolution with object detection network to detect the dropper fault. The proposed method consists of two stages. First, a dropper progressive location network (DPLN) was adopted to obtain the dropper. The DPLN was mainly composed of a pantograph location network (PLN) and a dropper location network (DLN). Then, the dropper fault recognition network (DFRN) was used to recognize the type of dropper fault. The experimental results demonstrated the accuracy and real-time performance of the proposed method.
Initialization for NMF-based audio source separation using priors on encoding vectors

Nonnegative matrix factorization (NMF) has shown good performances on blind audio source separation (BASS). While the NMF analysis is a non-convex optimization problem when both the basis and encoding matrices need to be estimated simultaneously, the source separation step of the NMF-based BASS with a fixed basis matrix has been considered convex. However, because the basis matrix for the BASS is typically constructed by concatenating the basis matrices trained with individual source signals, the subspace spanned by the basis vectors for one source may overlap with that for other sources. In this paper, we have shown that the resulting encoding vector is not unique when the subspaces spanned by basis vectors for the sources overlap, which implies that the initialization of the encoding vector in the source separation stage is not trivial. Furthermore, we propose a novel method to initialize the encoding vector for the separation step based on the prior model of the encoding vector. Experimental results showed that the proposed method outperformed the uniform random initialization by 1.09 and 2.21dB in the source-to-distortion ratio, and 0.20 and 0.23 in PESQ scores for supervised and semi-supervised cases, respectively.