Reviewerly/exHarmony huggingface dataset:
Simultaneous Learning from Human Pose and Object Cues for Real-Time Activity Recognition

Real-time human activity recognition plays an essential role in real-world human-centered robotics applications, such as assisted living and human-robot collaboration. Although previous methods based on skeletal data to encode human poses showed promising results on real-time activity recognition, they lacked the capability to consider the context provided by objects within the scene and in use by the humans, which can provide a further discriminant between human activity categories. In this paper, we propose a novel approach to real-time human activity recognition, through simultaneously learning from observations of both human poses and objects involved in the human activity. We formulate human activity recognition as a joint optimization problem under a unified mathematical framework, which uses a regression-like loss function to integrate human pose and object cues and defines structured sparsity-inducing norms to identify discriminative body joints and object attributes. To evaluate our method, we perform extensive experiments on two benchmark datasets and a physical robot in a home assistance setting. Experimental results have shown that our method outperforms previous methods and obtains real-time performance for human activity recognition with a processing speed of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> Hz.
Exploring Highly Efficient Compact Neural Networks For Image Classification

Group convolution works well with many lightweight convolutional neural networks (CNNs) that can effectively reduce the number of parameters and computational cost. However, feature maps of different groups cannot communicate, which restricts their representation capability. To address this issue, in this work, we propose a novel convolution operation named Hierarchical Group Convolution (HGC) for creating computationally efficient neural networks. Different from standard group convolution which blocks the inter-group information exchange and induces the severe performance degradation, HGC hierarchically fuses the feature maps from each group and leverages the inter-group information effectively. Taking advantage of the proposed operation, we introduce an efficient compact network named HGCNet. Extensive experimental results on image classification task demonstrate that HGCNet obtain significant reduction of computational cost and the number of parameters, while achieving comparable performance over the prior CNN architectures designed for mobile devices.
The Competence Awareness Window: Knowing what I can and cannot do

Systems interacting with each other autonomously becomes more and more a reality. System integration is the subject of several fields within Computer Science and Systems Engineering [2]. However, proper systems integration alone is only a temporary solution. With constantly changing environments, systems also need the ability to constantly improve their collaboration and cooperation. Furthermore, they are prone to new systems joining and existing systems leaving at a moment’s notice, bringing about another changing condition to the environment.
Online Planning for Quadrotor Teams in 3-D Workspaces via Reachability Analysis On Invariant Geometric Trees

We consider the kinodynamic multi-robot planning problem in cluttered 3-D workspaces. Reachability analysis on position invariant geometric trees is leveraged to find kino- dynamically feasible trajectories for the multi-robot team from potentially non-stationary initial states. The key contribution of our approach is that a collision-free geometric solution guarantees a kinodynamically feasible, safe solution without additional refinement. Simulation results with up-to 40 robots and hardware results with 5 robots suggest the viability of the proposed approach for online planning and replanning for large teams of aerial robots in cluttered 3-D workspaces.
Hierarchical Component-attention Based Speaker Turn Embedding for Emotion Recognition

Traditional discrete-time Speech Emotion Recognition (SER) modelling techniques typically assume that an entire speaker chunk or turn is indicative of its corresponding label. An alternative approach is to assume emotional saliency varies over the course of a speaker turn and use modelling techniques capable of identifying and utilising the most emotionally salient segments, such as those with higher emotional intensity. This strategy has the potential to improve the accuracy of SER systems. Towards this goal, we developed a novel hierarchical recurrent neural network model that produces turn level embeddings for SER. Specifically, we apply two levels of attention to learn to identify salient emotional words in a turn as well as the more informative frames within these words. In a set of experiments on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database, we demonstrate that component-attention is more effective within our hierarchical framework than both standard soft-attention and conventional local-attention. Our best network, a hierarchical component-attention network with an attention scope of seven, achieved an Unweighted Average Recall (UAR) of 65.0 % and a Weighted Average Recall (WAR) of 66.1 %, outperforming other baseline attention approaches on the IEMOCAP database.
WinDConv: A Fused Datapath CNN Accelerator for Power-Efficient Edge Devices

Diverse applications of Deep convolution neural networks (CNNs), such as image classification, semantic segmentation, video recognition, etc., in smart systems require high-throughput acceleration for real-time performance. Such CNNs when realized on edge devices of the Internet of Things, a power/energy-efficient compute platform is required, which can meet the limited power/energy budget of the devices. In this regard, an end-to-end power-optimized acceleration for the compute-intensive CNNs is proposed in this work. The proposed architecture, termed WinDConv, introduces a scheme to support both regular and energy-efficient Winograd convolutions on the same architecture through a fused datapath. Furthermore, using a thoroughly investigated data sparsity enhancement, the data reuse scheme, and a suitable memory hierarchy for power efficiency, the proposed architecture is able to exhibit a practical average power efficiency of at least 12.35 tera operations per second per Watt, which is at least 2× higher than the generic z-first storage baseline architecture with over 3× higher energy efficiency. The proposed architecture also demonstrates the applicability of the proposed schemes in commonly occurring variants of the convolution operation.
Concept drift detection with False Positive rate for multi-label classification in IoT data stream

Machine learning, as a significant component of the Industrial Internet of Things (IIoT), has been widely applied in many fields. The continuously generated data from the various sensors are collected and stored, this is also known as a data stream. However, the non-stationary phenomenon in data stream, concept drift, is important to be detected immediately for the operation of the IoT system. Therefore, the detection method for concept drift is needed to alert the requirement to maintain or replace some components in advance, so as to avoid or mitigate the risk of malfunction of the IoT system. The majority of existing literature focuses on concept drift detection on binary classification. To fill this gap, here we propose an algorithm to detect multi-class. Moreover to improve the performance of detection, we also introduce an algorithm which integrates the existing error rate which is widely used with our newly proposed False Positive rate. The new method is called Drift Detection Method with False Positive rate for multi-label classification (DDM-FP-M). The DDM-FP-M firstly defines the false positive rate calculation method in multi-label classification, then integrates it with Drift Detection method with False positive rate (DDM-FP). The performance of the proposed method is evaluated through Intel Lab data and is found to outperform the Drift Detection method(DDM) over 50% cases.
Deep Learning Based Cross-Spectral Disparity Estimation For Stereo Imaging

Recently, cross-spectral stereo-camera setups found their way from special applications to mass market, especially in smartphones, automotive systems, or drones. In the following, a novel concept is introduced to bring stereo cameras and cross-spectral disparity estimation together. So far, either monomodal stereo algorithms exist that are not suitable for cross-spectral image registration, or structural template matching is applied that achieves a low quality. To overcome these limitations, a technique is proposed to synthesize arbitrary spectral components from widely available color stereo databases, and to retrain mono-modal deep learning methods. In this contribution, the estimation of spectral bands based on random processes is shown together with noise models, which also allow for a robust registration of narrowband components. The theoretical examination is completed by an extensive evaluation, including a self-manufactured cross-spectral camera setup. In comparison to state-of-the-art techniques, the end-point error is on average reduced by a factor of seven.
Semantically Supervised Maximal Correlation For Cross-Modal Retrieval

With the rapid growth of multimedia data, the cross-modal retrieval problem has attracted a lot of interest in both research and industry in recent years. However, the inconsistency of data distribution from different modalities makes such task challenging. In this paper, we propose Semantically Supervised Maximal Correlation (S2MC) method for cross-modal retrieval by incorporating semantic label information into the traditional maximal correlation framework. Combining with maximal correlation based method for extracting unsupervised pairing information, our method effectively exploits supervised semantic information on both common feature space and label space. Extensive experiments show that our method outperforms other current state-of-the-art methods on cross-modal retrieval tasks on three widely used datasets.
Evaluating the Scalability of Non-Preferred Hand Mode Switching in Augmented Reality

Mode switching allows applications to support a wide range of operations (e.g. selection, manipulation, and navigation) using a limited input space. While the performance of different mode switching techniques has been extensively examined for pen- and touch-based interfaces, investigating mode switching in augmented reality (AR) is still relatively new. Prior work found that using non-preferred hand is an efficient mode switching technique in AR. However, it is unclear how the technique performs when increasing the number of modes, which is more indicative of real-world applications. Therefore, we examined the scalability of non-preferred hand mode switching in AR with two, four, six, and eight modes. We found that as the number of modes increase, performance plateaus after the four-mode condition. We also found that counting gestures have varying effects on mode switching performance in AR. Our findings suggest that modeling mode switching performance in AR is more complex than simply counting the number of available modes. Our work lays a foundation for understanding the costs associated with scaling interaction techniques in AR.
Hand Gesture Interaction with a Low-Resolution Infrared Image Sensor on an Inner Wrist

We propose a hand gesture interaction method using a low-resolution infrared image sensor on an inner wrist. We attach the sensor to the strap of a wrist-worn device, on the palmar side, and apply machine-learning techniques to recognize the gestures made by the opposite hand. As the sensor is placed on the inner wrist, the user can naturally control its direction to reduce privacy invasion. Our method can recognize four types of hand gestures: static hand poses, dynamic hand gestures, finger motion, and the relative hand position. We developed a prototype that does not invade surrounding people's privacy using an 8 x 8 low-resolution infrared image sensor. Then we conducted experiments to validate our prototype, and our results imply that the low-resolution sensor has sufficient capabilities for recognizing a rich array of hand gestures. In this paper, we introduce an implementation of a mapping application that can be controlled by our specified hand gestures, including gestures that use both hands.
A Light-weight Method to Foster the (Grad)CAM Interpretability and Explainability of Classification Networks

We consider a light-weight method which allows to improve the explainability of localized classification networks. The method considers (Grad)CAM maps during the training process by modification of the training loss and does not require additional structural elements. It is demonstrated that the (Grad)CAM interpretability, as measured by several indicators, can be improved in this way. Since the method shall be applicable on embedded systems and on standard deeper architectures, it essentially takes advantage of second order derivatives during the training and does not require additional model layers.
Going Deeper With Neural Networks Without Skip Connections

We propose the training of very deep neural networks (DNNs) without shortcut connections known as PlainNets. Training such networks is a notoriously hard problem due to: (1) the relatively popular challenge of vanishing and exploding activations, and (2) the less studied ‘near singularity’ problem. We argue that if the aforementioned problems are tackled together, the training of deeper PlainNets becomes easier. Subsequently, we propose the training of very deep PlainNets by leveraging Leaky Rectified Linear Units (LReLUs), parameter constraint and strategic parameter initialization. Our approach is simple and allows to successfully train very deep PlainNets having up to 100 layers without employing shortcut connections. To validate this approach, we validate on five challenging datasets; namely, MNIST, CIFAR-10, CIFAR100, SVHN and ImageNet datasets. We report the best results known on the ImageNet dataset using a PlainNet with top-1 and top-5 error rates of 24.1% and 7.3%, respectively.
Evolving Fuzzy-Probabilistic Neural Network and Its Online Learning

In paper rapid learning algorithm for evolving fuzzy-probabilistic neural-network (EFPNN) in which architecture and characteristics of activation functions are configured is proposed. The proposed approach is based on methods of lazy learning, the concept "Neurons in data points", selflearning based on "Winner takes all", classic supervised learning and evolving systems, allowing to change the network architecture simultaneously with its parameter tuning. The peculiarity of EFPNN is ability of classification task solving in case of fuzziness - overlapping classes. In doing so, in classification process probability of membership of every image to particular class and fuzzy membership levels of this data are estimated. Proposed EFPNN is designed to solve fuzzy classification tasks in general problem of Data Stream Mining.
Behaviour recognition of planning agents using Behaviour Trees

Research on AI is more and more focusing towards explainable technology that accounts for the outcomes of programs and products. One important aspect in this direction is the ability to recognize the behaviour patterns of our application in order to make sensible and informed decisions. In this work, we aim to uncover the behaviour of a planning agent by means of Behaviour Trees (BTs), a flexible and controllable mathematical model of plan execution used to describe flows between tasks in a modular fashion. We analyze the behaviour of a planning agent when solving a simple logistics problem by comparing the agent’s plan with the plans resulting from various BTs, each representing a different behaviour. We propose different distance metrics as a similarity measurement between plans and we evaluate their accuracy at identifying similar behaviours.
Image Dehazing With Contextualized Attentive U-NET

Haze, which occurs due to the accumulation of fine dust or smoke particles in the atmosphere, degrades outdoor imaging, resulting in reduced attractiveness of outdoor photography and the effectiveness of vision-based systems. In this paper, we present an end-to-end convolutional neural network for image dehazing. Our proposed U-Net based architecture employs Squeeze-and-Excitation (SE) blocks at the skip connections to enforce channel-wise attention and parallelized dilated convolution blocks at the bottleneck to capture both local and global context, resulting in a richer representation of the image features. Experimental results demonstrate the effectiveness of the proposed method in achieving state-of-the-art performance on the benchmark SOTS dataset.
Segmentation to Label: Automatic Coronary Artery Labeling from Mask Parcellation

Automatic and accurate coronary artery labeling technique from CCTA can greatly reduce clinician’s manual efforts and benefit large-scale data analysis. Current line of research falls into two general categories: knowledge-based methods and learning-based techniques. However, no matter in which fashion it is developed, the formation of problem finally attributes to tree-structured centerline classification and requires hand-crafted features. Here, instead we present a new concise, effective and flexible framework for automatic coronary artery labeling by modeling the task as coronary artery parsing task. An intact pipeline is proposed and two paralleled sub-modules are further designed to consume volumetric image and unordered point cloud correspondingly. Finally, a self-contained loss is proposed to supervise labeling process. At experiment section, we conduct comprehensive experiments on collected 526 CCTA scans and exhibit stable and promising results.
Understanding Privacy-Preserving Techniques in Digital Cryptocurrencies

Asset security and transaction efficiency have always been concerned, so that digital cryptocurrencies based on blockchain are increasingly favored. In the transaction, it is necessary to effectively protect private information such as identity and transaction content through technical means. This survey investigates privacy-preserving techniques commonly used in cryptocurrencies, and comparatively analyzes the advantages and disadvantages of different methods. The work mainly covers three technical dimensions. First, this paper investigates and summarizes privacy issues in cryptocurrencies. Second, we investigate mainstream privacy preserving techniques, such as Mixer, Homomorphic Encryption, and State Channel. Specially, we focus on the development of Zero-Knowledge Proof schemes and analyze current defects. Finally, the future direction of blockchain privacy protection technology is prospected.
Highly collaborative distributed systems: Synthesis and enactment at work

Summary Service choreographies support a distributed composition approach that is based on the specification of the external interaction of the participant services in terms of flows of message exchanges, given from a global perspective. When developing distributed service‐based applications, different services are produced by different teams; at the same time, such choreographies can also interact with third‐party services, hence leading to the reuse of black‐box services. Enforcing a global coordination logic across the many in‐house and third‐party services to correctly realize the specified choreography is a nontrivial endeavor. Automatic support is then desirable. In this article, we present an integrated development and run‐time environment for choreography‐based systems, which covers all the development activities, including specification, code synthesis, automatic deployment, enactment, and monitoring on the Cloud. We focus on providing a practical solution, that is, applicable by the community and considering technological standards used in the industry. We report the results of an experiment that we conducted with a use case in the in‐store marketing and sales domain. Results confirm confidence in the approach and show that the platform can be applied in practical contexts.
With registered reports towards large scale data curation

The scale of manually validated data is currently limited by the effort that small groups of researchers can invest for the curation of such data. Within this paper, we propose the use of registered reports to scale the curation of manually validated data. The idea is inspired by the mechanical turk and replaces monetary payment with authorship of data set publication.
Strategy of Effective Pricing Policy of Biofuel Enterprises

An approach to formation an effective pricing strategy for enterprises of biofuel market is proposed. The method of economically grounded pricing for products is put forward. This approach is based on the Gutenberg model, which centres on the theory of elasticity. This makes it possible to partially smooth over the impact of uncertainty and information limitation, which affects the price in the future. The developed technique makes it possible to solve the classic problem of strategic management-the task of determining the optimal price and the optimum volume of sales, which allows to actualize the strategy of price policy of enterprise for profit maximizing.
Probabilistic Modeling for Frequency Vectors Using a Flexible Shifted-Scaled Dirichlet Distribution Prior

Burstiness and overdispersion phenomena of count vectors pose significant challenges in modeling such data accurately. While the dependency assumption of the multinomial distribution causes its failure to model frequency vectors in several machine learning and data mining applications, researchers found that by extending the multinomial distribution to the Dirichlet Compound multinomial (DCM), both phenomena modeling can be addressed. However, Dirichlet distribution is not the best choice, as a prior, given its negative-correlation and equal-confidence requirements. Thus, we propose to use a flexible generalization of the Dirichlet distribution, namely, the shifted-scaled Dirichlet, as a prior to the multinomial, which grants the model a capability to better fit real data, and we call the new model the Multinomial Shifted-Scaled Dirichlet (MSSD). Given that the likelihood function plays a key role in statistical inference, e.g., in maximum likelihood estimation and Fisher information matrix investigation, we propose to improve the efficiency of computing the MSSD log-likelihood by approximating its function based on Bernoulli polynomials where the log-likelihood function is computed using the proposed mesh algorithm. Moreover, given the sparsity and high-dimensionality nature of count vectors, we propose to improve its computation efficiency by approximating the novel MSSD as a member of the exponential family of distribution, which we call EMSSD. The clustering is based on mixture models, and for learning a model, selection approach is seamlessly integrated with the estimation of the parameters. The merits of the proposed approach are validated via challenging real-world applications such as hate speech detection in Twitter, real-time recognition of criminal action, and anomaly detection in crowded scenes. Results reveal that the proposed clustering frameworks offer a good compromise between other state-of-the-art techniques and outperform other approaches previously used for frequency vectors modeling. Besides, comparing to the MSSD, the approximation EMSSD has reduced the computational complexity in high-dimensional feature spaces.