Reviewerly/exHarmony huggingface dataset:
Frame-Level Event Detection in Athletics Videos with Pose-Based Convolutional Sequence Networks

In this paper we address the problem of automatic event detection in athlete motion for automated performance analysis in athletics. We specifically consider the detection of stride-, jump- and landing related events from monocular recordings in long and triple jump. Existing work on event detection in sports often uses manually designed features on body and pose configurations of the athlete to infer the occurrence of events. We present a two-step approach, where temporal 2D pose sequences extracted from the videos form the basis for learning an event detection model. We formulate the detection of discrete events as a sequence translation task and propose a convolutional sequence network that can accurately predict the timing of event occurrences. Our best performing architecture achieves a precision/recall of 92.3%/89.0% in detecting start and end of ground contact during the run-up and jump of an athlete at a temporal precision of +/- 1 frame at 200Hz. The results show that 2D pose sequences are a suitable motion representation for learning event detection in a sequence-to-sequence framework.
HealChain: A Decentralized Data Management System for Mobile Healthcare Using Consortium Blockchain

Nowadays, massive healthcare data is collected for analysis and diagnosis from various applications and devices in mobile healthcare. To manage these data, existing work proposed centralized data management while this causes unexpected security threats, such as single point of failure and typical DDoS attack. For secure data management, we leverage blockchain technology and present a decentralized data management system based on consortium blockchain, called by HealChain. Specifically, network-wide healthcare data is processed by consortium blockchain nodes (CBNs) from collection, verification and recording in a strictly distributed manner. To realize HealChain, a hierarchical architecture with three layers is elaborately designed to facilitate network functionalities. After that, an operation procedure is put forward for ensuring considerate interactions between users and CBNs. Moreover, we formulate an optimization problem for a CBN to maximize the economic benefit in the mining procedure by optimizing the computing power. Genetic algorithm is utilized to solve the problem. Security analysis and numerical results demonstrate that HealChain is effective and efficient to achieve secure data management for mobile healthcare.
A Blockchain Based Proposal for Protecting Healthcare Systems through Formal Methods

Blockchain technology is one of the most important and disruptive technologies in the world. Multiple industries are adopting the blockchain technology to innovate the way they work. One of the industries that are looking to adopt the blockchain is the healthcare industry. In fact, the protection of the private information stored in hospital database is a critical issue. In this paper we propose a method aimed to protect information exchanged in hospital networks, with particular regard to magnetic resonance images. As required from blockchain technology, each host network must validate the transiting data network: we exploit formal equivalence checking to perform this validation, by modeling magnetic resonance images in terms of automata by exploiting radiomic features.
Quantitative nuclear histomorphometric features are predictive of Oncotype DX risk categories in ductal carcinoma in situ: preliminary findings

Abstract Background Oncotype DX (ODx) is a 12-gene assay assessing the recurrence risk (high, intermediate, and low) of ductal carcinoma in situ (pre-invasive breast cancer), which guides clinicians regarding prescription of radiotherapy. However, ODx is expensive, time-consuming, and tissue-destructive. In addition, the actual prognostic meaning for the intermediate ODx risk category remains unclear. Methods In this work, we evaluated the ability of quantitative nuclear histomorphometric features extracted from hematoxylin and eosin-stained slide images of 62 ductal carcinoma in situ (DCIS) patients to distinguish between the corresponding ODx risk categories. The prognostic value of the identified image signature was further evaluated on an independent validation set of 30 DCIS patients in its ability to distinguish those DCIS patients who progressed to invasive carcinoma versus those who did not. Following nuclear segmentation and feature extraction, feature ranking strategies were employed to identify the most discriminating features between individual ODx risk categories. The selected features were then combined with machine learning classifiers to establish models to predict ODx risk categories. The model performance was evaluated using the average area under the receiver operating characteristic curve (AUC) using cross validation. In addition, an unsupervised clustering approach was also implemented to evaluate the ability of nuclear histomorphometric features to discriminate between the ODx risk categories. Results Features relating to spatial distribution, orientation disorder, and texture of nuclei were identified as most discriminating between the high ODx and the intermediate, low ODx risk categories. Additionally, the AUC of the most discriminating set of features for the different classification tasks was as follows: (1) high vs low ODx (0.68), (2) high vs. intermediate ODx (0.67), (3) intermediate vs. low ODx (0.57), (4) high and intermediate vs. low ODx (0.63), (5) high vs. low and intermediate ODx (0.66). Additionally, the unsupervised clustering resulted in intermediate ODx risk category patients being co-clustered with low ODx patients compared to high ODx. Conclusion Our results appear to suggest that nuclear histomorphometric features can distinguish high from low and intermediate ODx risk category patients. Additionally, our findings suggest that histomorphometric features for intermediate ODx were more similar to low ODx compared to high ODx risk category.
Ontology Alignment using Stable Matching

Ontology matching is a solution to the problem of semantic heterogeneity in the integration and sharing of information. It consists in establishing mappings between entities which semantically belong to different ontologies. Most ontology mapping approaches use elementary matching techniques (eg, string-based methods, linguistic methods, etc.). These techniques map the elements by analyzing the entities in isolation and ignoring their relationships with other entities (father/son, brother, etc.). Bypassing the latter aspects, the determination of the semantics of an entity is often difficult. Hence, the structural information of an ontology plays an important role in ontologies mapping. In this work, we study methods of structural alignment. We adopt two alignment methods that are based on the structure. The first, called Method of Similarity of Inheritance (MSI), applies the initial similarity method based on the concepts and integrates the inheritance relation (father/son) into the calculation of similarity. The second, called Method of Sibling Similarity (MSS), involves sibling relationships to enrich the similarity score between two concepts. Moreover, we enhance the mappings selection strategy by integrating the stable marriage algorithm as an optimal matching strategy. This algorithm improves the ontology alignment quality through efficient optimization techniques and creates a faster and more robust alignment method.
RFTouchPads

This paper presents RFTouchPads, a system of batteryless and wireless modular hardware designs of two-dimensional (2D) touch sensor pads based on the ultra-high frequency (UHF) radio-frequency identification (RFID) technology. In this system, multiple RFID IC chips are connected to an antenna in parallel. Each chip connects only one of its endpoints to the antenna; hence, the module normally turns off when it gets insufficient energy to operate. When a finger touches the circuit trace attached to another endpoint of the chip, the finger functions as part of the antenna that turns the connected chip on, while the finger touch location is determined according to the chip's ID. Based on this principle, we propose two hardware designs, namely, StickerPad and TilePad. StickerPad is a flexible 3×3 touch-sensing pad suitable for applications on curved surfaces such as the human body. TilePad is a modular 3×3 touch-sensing pad that supports the modular area expansion by tiling and provides a more flexible deployment because its antenna is folded. Our implementation allows 2D touch inputs to be reliability detected 2 m away from a remote antenna of an RFID reader. The proposed batteryless, wireless, and modular hardware design enables fine-grained and less-constrained 2D touch inputs in various ubiquitous computing applications.
Convergence Rate of Incremental Gradient and Incremental Newton Methods

The incremental gradient method is a prominent algorithm for minimizing a finite sum of smooth convex functions, used in many contexts including large-scale data processing applications and distributed optimization over networks. It is a first-order method that processes the functions one at a time based on their gradient information. The incremental Newton method, on the other hand, is a second-order variant which exploits additionally the curvature information of the underlying functions and can therefore be faster. In this paper, we focus on the case when the objective function is strongly convex and present fast convergence results for the incremental gradient and incremental Newton methods under the constant and diminishing stepsizes. For a decaying stepsize rule $\alpha_k = \Theta(1/k^s)$ with $s \in (0,1]$, we show that the distance of the IG iterates to the optimal solution converges at rate ${\cal O}(1/k^{s})$ (which translates into ${\cal O}(1/k^{2s})$ rate in the suboptimality of the objective value). For $s>1/2$, this improves the previous ${\cal O}(1/\sqrt{k})$ results in distances obtained for the case when functions are non-smooth. We show that to achieve the fastest ${\cal O}(1/k)$ rate, incremental gradient needs a stepsize that requires tuning to the strong convexity parameter whereas the incremental Newton method does not. The results are based on viewing the incremental gradient method as a gradient descent method with gradient errors, devising efficient upper bounds for the gradient error to derive inequalities that relate distances of the consecutive iterates to the optimal solution and finally applying Chung's lemmas from the stochastic approximation literature to these inequalities to determine their asymptotic behavior. In addition, we construct examples to show tightness of our rate results.
Investigating the Effect of Distractor Interactivity for Redirected Walking in Virtual Reality

Due to the mismatch in size between a Virtual Environment and the physical space available, the use of alternative locomotion techniques becomes necessary. In small spaces, Redirected Walking methods provide limited benefits and approaches such as the use of distractors can provide an alternative. Distractors are virtual elements or characters that attempt to catch the attention of the user while the system subtly steers them away from physical boundaries. In this research we explicitly focused on understanding how different levels of interactivity affect user performance and behaviour. We developed three types of continuous redirecting distractors, with varying levels of interaction possibilities, called Looking, Touching, and Interacting. We compared them in a user study to a discrete reorientation technique, called Stop and Reset, in a task requiring users to traverse a 30 m path. While discrete reorientation is faster, continuous redirection through distractors was significantly less noticeable. Results suggest that more complex interaction is preferred and able to better captivate user attention for longer.
Continuous Integration Theater

Background: Continuous Integration (CI) systems are now the bedrock of several software development practices. Several tools such as TravisCI, CircleCI, and Hudson, that implement CI practices, are commonly adopted by software engineers. However, the way that software engineers use these tools could lead to what we call “Continuous Integration Theater”, a situation in which software engineers do not employ these tools effectively, leading to unhealthy CI practices. Aims: The goal of this paper is to make sense of how commonplace are these unhealthy continuous integration practices being employed in practice. Method: By inspecting 1,270 open-source projects that use TravisCI, the most used CI service, we quantitatively studied how common is to use CI (1) with infrequent commits, (2) in a software project with poor test coverage, (3) with builds that stay broken for long periods, and (4) with builds that take too long to run. Results: We observed that 748 (~60%) projects face infrequent commits, which essentially makes the merging process harder. Moreover, we were able to find code coverage information for 51 projects. The average code coverage was 78%, although Ruby projects have a higher code coverage than Java projects (86% and 63%, respectively). However, some projects with very small coverage (~4%) were found. Still, we observed that 85% of the studied projects have at least one broken build that take more than four days to be fixed. Interestingly, very small projects (up to 1,000 lines of code) are the ones that take the longest to fix broken builds. Finally, we noted that, for the majority of the studied projects, the build is executed under the 10 minutes rule of thumb. Conclusions: Our results are important to an increasing community of software engineers that employ CI practices on daily basis but may not be aware of bad practices that are eventually employed.
Three-dimensional shape measurement technique for large-scale objects based on line structured light combined with industrial robot

Three-dimensional(3-D) shape measurement for large-scale objects based on line structured light combined with industrial robot has been increasingly important because of its attributes of high-flexibility and high-robustness. To acquire the shape of large-scale objects, the line structured light obtained 3-D lines are stitched together by using robot hand-eye calibration results. However, in the line structured light technique, the traditional light plane calibration is complex and the result 3-D measurement accuracy is relatively low. Besides, because the light plane equation of the line structured light is arbitrary, the traditional standard ball hand-eye calibration method cannot be directly used. In this paper, a light plane calibration method is proposed by using binocular cameras, which simplifies the calibration process and improves the 3-D measurement accuracy. Furthermore, a modified space circle fitting method is proposed for the standard ball hand-eye calibration, which can be flexibly combined with the line structured light for arbitrary light plane. Based on the above improvement of the light plane calibration and the robot hand-eye calibration, the proposed measurement technique can accurately measure 3-D shape for large-scale objects. Theoretical analysis and experiments are provided to evaluate its performance.
Detecting human abnormal behaviour through a video generated model

Detecting human abnormal activities is the process of observing rare events that deviate from normality. In this study, an automated camera-based system that is able to detect irregular human behaviour is proposed. PoseNet and OpenPose, which are pre-trained pose estimation models are used to detect the person in the frame and extract the body keypoints. Such data is used to train two types of AutoEncoders based on LSTM and CNN units in a semi-supervised approach where the goal is to learn a general representation of the normal behaviour. Evaluated on a challenging realistic video dataset, the results show that both types of models were able to correctly distinguish between normal and abnormal data sequences, with an average F-score of 0.93. The results also show that the proposed method outperformed similar work done on the same dataset. Furthermore, it was also determined that pose estimated data compares very well with sensor data. This shows that pose estimated data can be informative enough to understand and classify human actions.
Driving Anomaly Detection with Conditional Generative Adversarial Network using Physiological and CAN-Bus Data

New developments in advanced driver assistance systems (ADAS) can help drivers deal with risky driving maneuvers, preventing potential hazard scenarios. A key challenge in these systems is to determine when to intervene. While there are situations where the needs for intervention or feedback is clear (e.g., lane departure), it is often difficult to determine scenarios that deviate from normal driving conditions. These scenarios can appear due to errors by the drivers, presence of pedestrian or bicycles, or maneuvers from other vehicles. We formulate this problem as a driving anomaly detection, where the goal is to automatically identify cases that require intervention. Towards addressing this challenging but important goal, we propose a multimodal system that considers (1) physiological signals from the driver, and (2) vehicle information obtained from the controller area network (CAN) bus sensor. The system relies on conditional generative adversarial networks (GAN) where the models are constrained by the signals previously observed. The difference of the scores in the discriminator between the predicted and actual signals is used as a metric for detecting driving anomalies. We collected and annotated a novel dataset for driving anomaly detection tasks, which is used to validate our proposed models. We present the analysis of the results, and perceptual evaluations which demonstrate the discriminative power of this unsupervised approach for detecting driving anomalies.
Target Detection Based on Improved Mask Rcnn in Service Robot

Target detection is one of the core algorithms in robot applications, and the recognition speed has a significant impact on robot's target capture. In this paper, the scene of long-distance and small targets is used as the test scene, and the purpose is to enhance the speed of detection without reducing the accuracy of detection. Consider that the mask branch and excessive full connection layer in the Mask Rcnn network will take up a lot of network detection time, and the feature map extracted by the convolutional neural network has a high dimension, which will occupy a large amount of computational memory. So, in this paper the Mask Rcnn network is improved: remove the mask branch; introduce Light-Head Rcnn into the Mask Rcnn network, increase R-CNN subnet and RoI warping; adjust the proportion of the Anchor in the RPN network. Finally, the improved model is applied to the tensorflow framework. These methods can save computer memory space and improve detection's speed. In the end, the improved Mask Rcnn network has been verified in a service robot platform with Kinect II. The test results show that compared with Faster Rcnn, the improved Mask Rcnn can have a high accuracy of detection; Compared with the original Mask Rcnn, the improved Mask Rcnn network can greatly improve the speed of the algorithm while ensuring the detection accuracy. The detection time is reduced by more than 2 times, which helps to improve the efficiency of the service robot's target capture task.
Noise sensing via stochastic quantum Zeno

The dynamics of any quantum system is unavoidably influenced by the external environment. Thus, the observation of a quantum system (probe) can allow the measure of the environmental features. Here, to spectrally resolve a noise field coupled to the quantum probe, we employ dissipative manipulations of the probe, leading to so-called Stochastic Quantum Zeno (SQZ) phenomena. A quantum system coupled to a stochastic noise field and subject to a sequence of protective Zeno measurements slowly decays from its initial state with a survival probability that depends both on the measurement frequency and the noise. We present a robust sensing method to reconstruct the unkonwn noise power spectral density by evaluating the survival probability that we obtain when we additionally apply a set of coherent control pulses to the probe. The joint effect of coherent control, protective measurements and noise field on the decay provides us the desired information on the noise field.
Privacy Preserving QoE Modeling using Collaborative Learning

Machine Learning (ML) based Quality of Experience (QoE) models potentially suffer from over-fitting due to limitations including low data volume, and limited participant profiles. This prevents models from becoming generic. Consequently, these trained models may under-perform when tested outside the experimented population. One reason for the limited datasets, which we refer in this paper as small QoE data lakes, is due to the fact that often these datasets potentially contain user sensitive information and are only collected throughout expensive user studies with special user consent. Thus, sharing of datasets amongst researchers is often not allowed. In recent years, privacy preserving machine learning models have become important and so have techniques that enable model training without sharing datasets but instead relying on secure communication protocols. Following this trend, in this paper, we present Round-Robin based Collaborative Machine Learning model training, where the model is trained in a sequential manner amongst the collaborated partner nodes. We benchmark this work using our customized Federated Learning mechanism as well as conventional Centralized and Isolated Learning methods.
WeGAN: Deep Image Hashing With Weighted Generative Adversarial Networks

Image hashing has been widely used in image retrieval tasks. Many existing methods generate hashing codes based on image feature representations. They rarely consider the rich information such as image clustering information contained in the image set as well as uncertain relationships between images and tags simultaneously. In this paper, we develop a Weighted Generative Adversarial Networks (WeGAN) to transfer the clustering information of images to construct the hashing code. WeGAN consists three modules: 1) a hashing learning process for transferring knowledge of the image set to hashing codes of single images; 2) by means of hashing codes, a module to generate image content, tag representation, and their joint information which reflects the correlation between the image and the corresponding tags; 3) a discriminator to distinguish the generated data from the original source, and then formulating three loss functions. Different weights are assigned to these loss functions in order to deal with the uncertainties between images and tags. Through introducing the image set to process the image hashing with different tags, WeGAN can naturally provide the information of clustering results, which is useful for image hashing with multi-tags. The generated hashing code has the ability to dynamically process the uncertain relationships between images and tags. Experiments on three challenging datasets show that WeGAN outperforms the state-of-the-art methods.