Reviewerly/exHarmony huggingface dataset:
Integrally Cooperative Spatio-Temporal Feature Representation of Motion Joints for Action Recognition

In contemporary research on human action recognition, most methods separately consider the movement features of each joint. However, they ignore that human action is a result of integrally cooperative movement of each joint. Regarding the problem, this paper proposes an action feature representation, called Motion Collaborative Spatio-Temporal Vector (MCSTV) and Motion Spatio-Temporal Map (MSTM). MCSTV comprehensively considers the integral and cooperative between the motion joints. MCSTV weighted accumulates limbs’ motion vector to form a new vector to account for the movement features of human action. To describe the action more comprehensively and accurately, we extract key motion energy by key information extraction based on inter-frame energy fluctuation, project the energy to three orthogonal axes and stitch them in temporal series to construct the MSTM. To combine the advantages of MSTM and MCSTV, we propose Multi-Target Subspace Learning (MTSL). MTSL projects MSTM and MCSTV into a common subspace and makes them complement each other. The results on MSR-Action3D and UTD-MHAD show that our method has higher recognition accuracy than most existing human action recognition algorithms.
Towards an intuitive human-robot interaction based on hand gesture recognition and proximity sensors

In this paper, we present a multimodal sensor interface that is capable of recognizing hand gestures for human-robot interaction. The proposed system is composed of an array of proximity and gesture sensors, which have been mounted on a 3D printed bracelet. The gesture sensors are employed for data collection from four hand gesture movements (up, down, left and right) performed by the human at a predefined distance from the sensorised bracelet. The hand gesture movements are classified using Artificial Neural Networks. The proposed approach is validated with experiments in offline and real-time modes performed systematically. First, in offline mode, the accuracy for recognition of the four hand gesture movements achieved a mean of 97.86%. Second, the trained model was used for classification in real-time and achieved a mean recognition accuracy of 97.7%. The output from the recognised hand gesture in real-time mode was used to control the movement of a Universal Robot (UR3) arm in the CoppeliaSim simulation environment. Overall, the results from the experiments show that using multimodal sensors, together with computational intelligence methods, have the potential for the development of intuitive and safe human-robot interaction.
Video Classification Using 3D Convolutional Neural Network

Since video includes both temporal and spatial features, it has become a fascinating classification problem. Each frame within a video holds important information called spatial information, as does the context of that frame relative to the frames before it in time called temporal information. Several methods have been invented for video classification, but each one is suffering from its own drawback. One of such method is called convolutional neural networks (CNN) model. It is a category of deep learning neural network model that can turn directly on the underdone inputs. However, such models are recently limited to handling two-dimensional inputs only. This chapter implements a three-dimensional convolutional neural networks (CNN) model for video classification to analyse the classification accuracy gained using the 3D CNN model. The 3D convolutional networks are preferred for video classification since they inherently apply convolutions in the 3D space.
Applications of artificial intelligence in, early detection of cancer, clinical diagnosis and personalized medicine

Applications of artificial intelligence in, early detection of cancer, clinical diagnosis and personalized medicine
How does the informed trading impact Bitcoin returns and volatility?

This study employed an augmented AR-GJRGARCH model that incorporates an intraday-based buy-sell order size imbalance measure to explore how informed trading behaviour/activity impacted Bitcoin returns and volatility from January 2014 to February 2019. Our results show that the informed trading behaviour dominated by sell-order significantly led to decreased concurrent Bitcoin returns for alternative sample periods. However, the informed trading behaviour dominated by buy-order related positively to Bitcoin returns only for the full sample period. Moreover, the informed trading activity helped to reduce Bitcoin’s volatility, which is consistent with expectations based on dispersion of beliefs models. Finally, we uncovered a positive (inverted) asymmetric volatility effect for both the full and the rising sample periods, indicating the presence of the ‘fear of missing out’ psychological effect.
Algorithm of error-free information embedding into the DCT domain of digital images based on the QIM method using adaptive masking of distortions

This paper proposes a new algorithm for hiding data in the frequency domain of digital images using the discrete cosine transform. This algorithm uses quantization index modulation (QIM) as the basic embedding operation. Frequency embedding is characterized by the problem of error appearance in a secret message arising directly at the embedding stage. The embedded message is distorted due to the loss of information when restoring integer pixel values from the frequency domain. This problem is significant if the integrity of the transmitted information is critical. For example, an insignificant distortion of an encrypted message leads to the impossibility of decrypting and, consequently, to the loss of all encrypted information. The proposed algorithm solves this problem via an iterative embedding procedure, which corrects the arising errors. Another distinctive feature of this algorithm is the adaptive correction of distortions of the frequency coefficients’ histogram. For this purpose, the embedding procedure adapts to the features of the image and masks the arising distortions. Computing experiments and comparison with the state-of-the-art demonstrate the effectiveness of our algorithm.
IndRNN based long-term temporal recognition in the spatial and frequency domain

This paper targets the SHL recognition challenge, which focuses on the location-independent and user-independent activity recognition using smartphone sensors. To address this long-range temporal problem with periodic nature, we propose a new approach (team IndRNN), an Independently Recurrent Neural Network (IndRNN) based long-term temporal activity recognition with spatial and frequency domain features. The data is first segmented into one second sliding windows, then temporal and frequency domain features are extracted as short-term temporal features. A deep IndRNN model is used to predict the unknown test dataset location. Under the predicted location, a deep IndRNN model is further used to classify the 8 activities with best performed features. Finally, transfer learning and model fusion are used to improve the result under the user-independence case. The proposed method achieves 86.94% accuracy on the validation set at the predicted location.
Quasi-Globally Optimal and Near/True Real-Time Vanishing Point Estimation in Manhattan World

Image lines projected from parallel 3D lines intersect at a common point called the vanishing point (VP). Manhattan world holds for the scenes with three orthogonal VPs. In Manhattan world, given several lines in a calibrated image, we aim to cluster them by three unknown-but-sought VPs. The VP estimation can be reformulated as computing the rotation between the Manhattan frame and camera frame. To estimate three degrees of freedom (DOF) of this rotation, state-of-the-art methods are based on either data sampling or parameter search. However, they fail to guarantee high accuracy and efficiency simultaneously. In contrast, we propose a set of approaches that hybridize these two strategies. We first constrain two or one DOF of the rotation by two or one sampled image line. Then we search for the remaining one or two DOF based on branch and bound. Our sampling accelerates our search by reducing the search space and simplifying the bound computation. Our search achieves quasi-global optimality. Specifically, it guarantees to retrieve the maximum number of inliers on the condition that two or one DOF is constrained. Our hybridization of two-line sampling and one-DOF search can estimate VPs in real time. Our hybridization of one-line sampling and two-DOF search can estimate VPs in near real time. Experiments on both synthetic and real-world datasets demonstrated that our approaches outperform state-of-the-art methods in terms of accuracy and/or efficiency.
Magnet

Over the past decade, Apache Spark has become a popular compute engine for large scale data processing. Similar to other compute engines based on the MapReduce compute paradigm, the shuffle operation, namely the all-to-all transfer of the intermediate data, plays an important role in Spark. At LinkedIn, with the rapid growth of the data size and scale of the Spark deployment, the shuffle operation is becoming a bottleneck of further scaling the infrastructure. This has led to overall job slowness and even failures for long running jobs. This not only impacts developer productivity for addressing such slowness and failures, but also results in high operational cost of infrastructure. In this work, we describe the main bottlenecks impacting shuffle scalability. We propose Magnet, a novel shuffle mechanism that can scale to handle petabytes of daily shuffled data and clusters with thousands of nodes. Magnet is designed to work with both on-prem and cloud-based cluster deployments. It addresses a key shuffle scalability bottleneck by merging fragmented intermediate shuffle data into large blocks. Magnet provides further improvements by co-locating merged blocks with the reduce tasks. Our benchmarks show that Magnet significantly improves shuffle performance independent of the underlying hardware. Magnet reduces the end-to-end runtime of Linkedln's production Spark jobs by nearly 30%. Furthermore, Magnet improves user productivity by removing the shuffle related tuning burden from users.
Wasserstein Loss With Alternative Reinforcement Learning for Severity-Aware Semantic Segmentation

Semantic segmentation is important for many real-world systems, e.g., autonomous vehicles, which predict the class of each pixel. Recently, deep networks achieved significant progress w.r.t. the mean Intersection-over Union (mIoU) with the cross-entropy loss. However, the cross entropy loss can essentially ignore the difference of severity for an autonomous car with different wrong prediction mistakes. For example, predicting the car to the road is much more servery than recognize it as the bus. Targeting for this difficulty, we develop a Wasserstein training framework to explore the inter-class correlation by defining its ground metric as misclassification severity. The ground metric of Wasserstein distance can be pre-defined following the experience on a specific task. From the optimization perspective, we further propose to set the ground metric as an increasing function of the pre-defined ground metric. Furthermore, an adaptively learning scheme of the ground matrix is proposed to utilize the high-fidelity CARLA simulator. Specifically, we follow a reinforcement alternative learning scheme. The experiments on both CamVid and Cityscapes datasets evidenced the effectiveness of our Wasserstein loss. The SegNet, ENet, FCN and Deeplab networks can be adapted following a plug in manner. We achieve significant improves on the predefined important classes, and much longer continuous play time in our simulator.
Creating Autonomous Vehicle Systems, Second Edition

This book is one of the first technical overviews of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences designing autonomous ve
Permissioned Blockchain-Based Security for SDN in IoT Cloud Networks

The advancement in cloud networks has enabled connectivity of both traditional networked elements and new devices from all walks of life, thereby forming the Internet of Things (IoT). In an IoT setting, improving and scaling network components as well as reducing cost is essential to sustain exponential growth. In this domain, software-defined networking (SDN) is revolutionizing the network infrastructure with a new paradigm. SDN splits the control/routing logic from the data transfer/forwarding. This splitting causes many issues in SDN, such as vulnerabilities of DDoS attacks. Many solutions (including blockchain based) have been proposed to overcome these problems. In this work, we offer a blockchain-based solution that is provided in redundant SDN (load-balanced) to service millions of IoT devices. Blockchain is considered as tamper-proof and impossible to corrupt due to the replication of the ledger and consensus for verification and addition to the ledger. Therefore, it is a perfect fit for SDN in IoT Networks. Blockchain technology provides everyone with a working proof of decentralized trust. The experimental results show gain and efficiency with respect to the accuracy, update process, and bandwidth utilization.
A Futuristic Blockchain based Vehicular Network Architecture and Trust Management System

In a complex network of smart vehicles, some issues arise related to security, privacy, selfishness of nodes and node failures. We have proposed an architecture of vehicular network in a smart city based on blockchain. Some scenarios and design principles are also provided. Contrary to prior architectures of vehicular networks, our proposed model provides robustness, scalability, adaptability, trust management as well as privacy and security. It eliminates the issue of selfish nodes and malicious nodes. Unlike other vehicular architectures, it also takes into account the passengers' medical facilities and fault tolerance. In case of any failure of sensors or nodes, system will effectively tackle it. Both big data storage and fast computation are not possible on vehicles end. This can be handled by moving these processes to static nodes and data center. Malicious behaviour of nodes is handled using trust values and incentives mechanism in order to motivate nodes to work effectively while assigning penalty for selfish nodes.
A new resolution enhancement method for sandstone thin-section images using perceptual GAN

The thin section is a sliver cut of rock sample and photographed by a microscope to help researchers investigate the mineral, structure, and geology details of a rock sample. For the purpose of photographing using the microsctope, only a small portion of the thin section can be visualized. The results, however, can hardly satisfy the requirement of a high resolution and larger scale visualization. The thin section is then trapped into a dilemma of contradiction between resolution and field of view. To solve this problem, we propose the Generative Adversarial Networks(GAN) based architectures representing the perceptual SR in this paper. Four perceptual super-resolution methods and two pixel-wise super-resolution methods are trained and tested for comparison with the dataset of sandstone thin-section. The perceptual GAN based super-resolution method is demonstrated to compensate for this limitation by enhancing the resolution of a large field of view image, which will provide a higher resolution detail recovery and larger field of view simultaneously. Based on the digital experiment analysis and comparison, thin-section perceptual loss with Residual-in-Residual Dense Block (RRDB) and Relativistic average GANs (RaGAN) shows a more realistic texture and evident edge for minerals and pores than other methods. With the new perceptual GAN based image enhancement method, the petroleum geologist can significantly improve the accuracy and reliability of all their thin section based research quality.
Study and implementation on the application of blockchain in electronic evidence generation

Electronic evidence relying on emerging technologies such as computer science and network communication plays a crucial role in the process of proving the facts of cases. Meanwhile, some features of electronic evidence, such as technological complexity, mutability, and incomplete provenance, can create difficulties for the judge to examine and authenticate the electronic evidence. However, features of blockchain technology, such as consensus mechanisms, hash checks, asymmetric encryption, timestamps, can help the judge verify integrity and authenticity of electronic evidence. In addition, collection and preservation of electronic evidence with blockchain technology have legal force, which has been recognized by some countries and regions, but ensuring authenticity and integrity of the data on the blockchain is still an urgent problem. In this paper, the application potential of blockchain in electronic evidence generation in the judicial field is explored, and the key role of blockchain-based verification of electronic evidence in case review with an example of an Internet case in China is analyzed as well. To make the process of electronic evidence generation more standardized, procedural and systematic, and in light of judicial independence, the electronic evidence generation system based on consortium blockchain is built. Most importantly, the collection, preservation, and examination of evidence are conducted within the blockchain system. Judicial bodies, as the nodes in the consortium blockchain, can examine the legitimacy of evidence at any time, which can ensure high authoritativeness, efficiency, and credibility of the electronic evidence generation system and strengthen the evidence competence and probative force of the electronic evidence.
Research on human performance evaluation model based on neural network and data mining algorithm

Abstract In order to effectively evaluate personnel performance, a distributed data mining algorithm for spatial networks based on BP neural wireless network is proposed. In the cloud computing environment, an excavator is used to construct multiple input multiple output spatial network data, analyze the data structure, and perform redundant data compression of massive data through time-frequency feature extraction. Combined with the adaptive matching filtering method, the characteristics of the data are matched. The spatial frequency feature extraction method is used to locate the features of the multiple-input multiple-output spatial network data. In order to improve the accuracy of data mining, the BP neural network is used to classify and identify the extracted data features to achieve the optimization of data mining. A wireless sensor network is a wireless network composed of a large number of stationary or moving sensors in a self-organizing and multi-hop manner. It cooperatively senses, collects, processes, and transmits the information of the perceived objects in the geographical area covered by the network and finally puts these The information is sent to the owner of the network. This algorithm improves the accuracy of personnel performance evaluation, simultaneously establishes a hierarchical analysis and quantitative evaluation model for the performance of government managers, and adjusts the results of hierarchical statistical analysis on government administrators as needed. The performance evaluation and optimization of government administrators were introduced. The empirical analysis results show that the method has higher accuracy for government managers’ performance evaluation, higher efficiency of big data processing, and better integration.
Visual Analytics for Decision-Making During Pandemics

We introduce a trans-disciplinary collaboration between researchers, healthcare practitioners, and community health partners in the Southwestern U.S. to enable improved management, response, and recovery to our current pandemic and for future health emergencies. Our Center work enables effective and efficient decision-making through interactive, human-guided analytical environments. We discuss our PanViz 2.0 system, a visual analytics application for supporting pandemic preparedness through a tightly coupled epidemiological model and interactive interface. We discuss our framework, current work, and plans to extend the system with exploration of what-if scenarios, interactive machine learning for model parameter inference, and analysis of mitigation strategies to facilitate decision-making during public health crises.
Trajectory multi-Bernoulli filters for multi-target tracking based on sets of trajectories

This paper presents two multi-Bernoulli filters on sets of trajectories for multiple target tracking. The first filter provides a multi-Bernoulli approximation of the posterior density over the set of alive trajectories at the current time step. The second filter provides a multi-Bernoulli approximation of the posterior density over the set of all trajectories (alive and dead) up to the current time. We also explain the Gaussian implementation of the filters and compare them with other multiple target tracking algorithms in a simulated scenario.
When Are Smells Indicators of Architectural Refactoring Opportunities

Refactoring is a widely adopted practice for improving code comprehension and for removing severe structural problems in a project. When refactorings affect the system architecture, they are called architectural refactorings. Unfortunately, developers usually do not know when and how they should apply refactorings to remove architectural problems. Nevertheless, they might be more susceptible to applying architectural refactoring if they rely on code smells and code refactoring -- two concepts that they usually deal with through their routine programming activities. To investigate if smells can serve as indicators of architectural refactoring opportunities, we conducted a retrospective study over the commit history of 50 software projects. We analyzed 52,667 refactored elements to investigate if they had architectural problems that could have been indicated by automatically-detected smells. We considered purely structural refactorings to identify elements that were likely to have architectural problems. We found that the proportion of refactored elements without smells is much lower than those refactored with smells. By analyzing the latter, we concluded that smells can be used as indicators of architectural refactoring opportunities when the affected source code is deteriorated, i.e., the code hosting two or more smells. For example, when God Class or Complex Class appear together with other smells, they are indicators of architectural refactoring opportunities. In general, smells that often co-occurred with other smells (67.53%) are indicators of architectural refactoring opportunities in most cases (88.53% of refactored elements). Our study also enables us to derive a catalog with patterns of smells that indicate refactoring opportunities to remove specific types of architectural problems. These patterns can guide developers and make them more susceptible to apply architectural refactorings.