Reviewerly/exHarmony huggingface dataset:
Configuration Space Decomposition for Learning-based Collision Checking in High-DOF Robots

Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space $\mathcal{C}$ as one popular solution. Recently, machine learning methods have been introduced into sampling-based motion planning methods, which train a classifier to distinguish collision free subspace from in-collision subspace in $\mathcal{C}$. In this paper, we propose a novel configuration space decomposition method and show two nice properties resulted from this decomposition. Using these two properties, we build a composite classifier that works compatibly with previous machine learning methods by using them as the elementary classifiers. Experimental results are presented, showing that our composite classifier outperforms state-of-the-art single-classifier methods by a large margin. A real application of motion planning in a multi-robot system in plant phenotyping using three UR5 robotic arms is also presented.
Practical Verification of Neural Network Enabled State Estimation System for Robotics

We study for the first time the verification problem on learning-enabled state estimation systems for robotics, which use Bayes filter for localisation, and use deep neural network to process sensory input into observations for the Bayes filter. Specifically, we are interested in a robustness property of the systems: given a certain ability to an adversary for it to attack the neural network without being noticed, whether or not the state estimation system is able to function with only minor loss of localisation precision? For verification purposes, we reduce the state estimation systems to a novel class of labelled transition systems with payoffs and partial order relations, and formally express the robustness property as a constrained optimisation objective. Based on this, practical verification algorithms are developed. As a major case study, we work with a real-world dynamic tracking system that uses a Kalman filter (a special case of the Bayes filter) to localise and track a ground vehicle. Its perception system, based on convolutional neural networks, processes a high-resolution Wide Area Motion Imagery (WAMI) data stream. Experimental results show that our algorithms can not only verify the robustness of the WAMI tracking system but also provide useful counterexamples.
E-ComSupResNet: Enhanced Face Super-Resolution Through Compact Network

Practical systems such as in surveillance applications capture Low-Resolution (LR) face images due to the wider angle of imaging or longer stand-off distance to the camera. However, face recognition applications demand high resolution face images for effective feature extraction and comparison. To meet the requirement of face recognition system despite the low resolution face captured, a number of works have been proposed to super-resolve the LR face images. Here, we propose a compact and computationally efficient Convolutional Neural Network (CNN) to increase the spatial resolution of an LR face image to obtain a High-Resolution (HR) face image with an upscaling factor of up to ×8 by improving our previous approach. The newly proposed approach, which we refer to as E-ComSupResNet, is an enhanced architecture of our preliminary approach ComSupResNet with multiple enhancements. Contrary to other earlier works, the proposed architecture with a compact network focuses on extracting both low-frequency and high-frequency features along with a reconstruction module. With the introduction of Residual Block (ResBlock), a novel upscaling network is provided, to super-resolve the feature maps for the factor of ×2, a Global Residual Learning (GRL) and two newly customized loss functions imposing similarity in the new network architecture we obtain superior SR images compared to our earlier network and competing state-of-art approaches. The newly proposed architecture further has approx. 1.5 M number of parameters as compared to similar earlier work which has more than 57 M parameters. We further attempt to make the approach generalizable by learning the network in a cross-database setting and training the network on CASIA WebFace dataset while evaluating it both on CelebA and LFW datasets. Through empirical evaluations, we demonstrate the gain in high fidelity reconstruction in terms of Structural Similarity Index Metric (SSIM) and Peak-Signal-to-Noise Ratio (PSNR) measures despite the compactness of the model. As another new contribution, we conduct a series of experiments to measure the applicability of obtained SR images using the proposed network through conducting face recognition experiments using a commercial and open source Face Recognition System (FRS) with a clear gain in verification performance over the other existing techniques.
Real-Time Social Media Analytics with Deep Transformer Language Models: A Big Data Approach

Utilisation of transfer learning with deep language models is regarded as one of the most important developments in deep learning. Their application on real-time high-velocity and volume user-generated data has been elusive due to the unprecedented size and complexity of the models which result in substantial computational overhead. Recent iterations of these architectures have produced significantly distilled models with state-of-the-art performance and reduced resource requirement. We utilize deep transformer language models on user-generated data alongside a robust text normalization pipeline to address what is considered as the Achilles heel of deep learning on user-generated text data, namely data normalization.In this paper, we propose a framework for the ingestion, analysis and storage of real-time data streams. A case study in sentiment analysis and offensive/hateful language detection is used to evaluate the framework. We demonstrate inference on a large Twitter dataset using CPU and GPU clusters, highlighting the viability of the fine-tuned distilled language model for high volume data. Fine-tuned model significantly outperforms previous state-of-the-art on several benchmark datasets, providing a powerful model that can be utilized for a variety of downstream tasks. To our knowledge, this is the only study demonstrating powerful transformer language models for realtime social media stream analytics in a distributed setting.
Optimizing Merkle Proof Size for Blockchain Transactions

Merkle trees play a crucial role in blockchain networks in organizing network state. They allow proving a particular value of an entry in the state to a node that maintains only the root of the Merkle trees, a hash-based signature computed over the data in a hierarchical manner. Verification of particular state entries is crucial in reaching a consensus on the execution of a block where state information is required in the processing of its transactions. For instance, a payment transaction should be based on the balance of the involved accounts. The proof length affects the network communication and is typically logarithmic in the state size. In this paper, we take advantage of typical transaction characteristics for better organizing Merkle trees to improve blockchain network performance. We focus on the common transaction processing where Merkle proofs are jointly provided for multiple accounts. We provide lower bounds for the communication cost and describe algorithms based on traffic patterns can significantly reduce them. We rely on real blockchain data to show the savings allowed by our approach.
Hyper-Noise Interference Privacy Protection Framework for Intelligent Medical Data-Centric Networks

Convolutional neural network (CNN) has been widely used in the medical domain, but how to protect privacy is still a challenge. In addition, traditional differential privacy protection methods are very sensitive to noise; hyper-scale noise has a serious effect on the validity of the network. In this article, therefore, a novel framework named improved private aggregation of teacher ensembles (IPATE) is proposed. IPATE aims to protect the privacy of trained models and medical data by adding hyperscale Laplace noise to the extended voting difference of many CNN models. An experimental example including 12 leads data of 148 myocardial infarction patients and 52 normal subjects was used in the study. Experimental results show that with the increase of noise and under the condition of limited data, IPATE can still achieve about 90 percent accuracy, and it is not sensitive to noise.
Sentiment Analysis and Topic Modelling Using the LDA Method related to the Flood Disaster in Jakarta on Twitter

The widespread use of social media makes people tend to offer various information and opinions via Twitter. One of them is related to the flood disaster that occurred in Jakarta. This study aims to analyze the sentiment shown by the public when floods occur using a lexicon-based approach. Besides, this research also applies the topic modeling approached using the Latent Dirichlet Allocation (LDA) method to identify the topics discussed during the flood disaster. The results show that most opinions show negative sentiment with the topics discussed include information about the flooded areas, the impact of the flood disaster, conditions during the disaster, and feedback from the public to related parties of flood disaster management. The originality of this research lies in the use of the LDA method in modeling topics and analyzing sentiments related to the Jakarta flood disaster on social media.
Anomaly detection frameworks for outlier and pattern anomaly of time series in wireless sensor networks

As a major form of data, the collection and analysis of time series have been widely used in many fields. In practice, wireless sensor network is a popular mechanism for data collection and it is suitable for time series. However, due to the diversity deployment area for a wireless sensor network, the external environmental factors pose a great challenge on the quality of data collected by a sensor node. Besides, the accuracy and reliability of collected data are also affected by internal factors of a sensor node and the performance of the whole wireless sensor network. Common internal factors are resource limitation such as battery capacity, computing ability, and memory size. Moreover, a wireless sensor network deployed for production use is very likely to suffer from malicious attacks from both inside and outside. Therefore, anomaly is inevitable for time series collected by a wireless sensor network. In the paper, we make an extensive review about two types of anomaly detection methods in time series for wireless sensor networks: outlier detection and pattern anomaly detection. For outlier detection, six categories of detection methods are elaborated. For pattern anomaly detection, two main ideas are discussed. Finally, we present the main drawbacks of existing approaches to outlier detection and pattern anomaly detection for time series. The possible future research directions are also summarized.
DeComplex: Task planning from complex natural instructions by a collocating robot

As the number of robots in our daily surroundings like home, office, restaurants, factory floors, etc. are increasing rapidly, the development of natural human-robot interaction mechanism becomes more vital as it dictates the usability and acceptability of the robots. One of the valued features of such a cohabitant robot is that it performs tasks that are instructed in natural language. However, it is not trivial to execute the human intended tasks as natural language expressions can have large linguistic variations. Existing works assume either single task instruction is given to the robot at a time or there are multiple independent tasks in an instruction. However, complex task instructions composed of multiple inter-dependent tasks are not handled efficiently in the literature. There can be ordering dependency among the tasks, i.e., the tasks have to be executed in a certain order or there can be execution dependency, i.e., input parameter or execution of a task depends on the outcome of another task. Understanding such dependencies in a complex instruction is not trivial if an unconstrained natural language is allowed. In this work, we propose a method to find the intended order of execution of multiple inter-dependent tasks given in natural language instruction. Based on our experiment, we show that our system is very accurate in generating a viable execution plan from a complex instruction.
Breaches Detection in Zebra Cross Traffic Light Using Haar Cascade Classifier

Traffic violations are common in the area zebra crossing at the location of traffic lights, violations are generally caused by the negligence of motorists who do not comply with existing regulations. As a result, there were many traffic accidents that could have been avoided. Starting from this problem, a simulation model tool is needed that is able to provide direct appeals to drivers when a violation occurs. Based on this, the purpose of this research is to create a simulation model to prevent accidents caused by motorists who are negligent of the rules by identifying vehicles that stop crossing the line boundaries. zebracross or drivers waiting for an out of place traffic light using imagery HaarCascade Classifiers which is programmed using Python and OpenCV to help process digital images. The results of this study that the Haar Cascade Classifier algorithm can characterize motorbikes well, with the best average accuracy value of 91.5% at a resolution of 720p. Based on the results obtained shows that the algorithm haar cascade classifier can detect violations that exist at traffic lights. Hence this indicates the rate of traffic violations can reduce by utilizing the algorithm haar cascade classifier.
Text data-augmentation using Text Similarity with Manhattan Siamese long short-term memory for Thai language

Abstract In this paper, we address the issue of using small text datasets for learning of neural networks. We explore the method that is used with image and sound datasets to augment data for increasing the performance of models. We then leverage this data augmentation technique to expand the training set of textual data. A great challenge in our dataset is that the amount of data is insufficient for training models. For this reason, we propose a method for augmenting text data specifically for Thai language which is based on Text Similarity and using the model to determine the semantic relationship between two sentences. The experimental results indicated that our proposed method is able to improve the performance of text classification.
Two-Qutrit Entangled f-Coherent States

Nonlinear coherent states or f -coherent states are one of the important class of quantum states of light attached to the f -deformed oscillators. They have been introduced in a pioneering work by Manko et al. and have been realized physically as the stationary states of the centre of mass motion of a trapped ion by de Matos Filho et al. To gain insight into the effectiveness of these states in quantum information theory, we introduce qutrit-like states as a result of two-mode superposition coherent states based on two f -coherent states ∣α, f〉 with a specific selection of the nonlinearity function f(n). The control role of the involved parameters, especially the nonlinearity function on the entanglement and statistical properties of the system state is investigated. The generalized I-concurrence measure has been applied to quantify the entanglement of these states and the condition in which quantum entanglement can be enhanced and maximized has been identified. We show that depending on the chosen nonlinearity function, one can observe different nonclassical effects. These trends are also compared with those obtained for the qubit-like superposition of f -coherent states.
Automated Test Case Generation from Requirements: A Systematic Literature Review

Software testing is an important and cost intensive activity in software development. The major contribution in cost is due to test case generations. Requirement-based testing is an approach in which test cases are derivative from requirements without considering the implementation’s internal structure. Requirement-based testing includes functional and nonfunctional requirements. The objective of this study is to explore the approaches that generate test cases from requirements. A systematic literature review based on two research questions and extensive quality assessment criteria includes studies. The study identifies 30 primary studies from 410 studies spanned from 2000 to 2018. The review’s finding shows that 53% of journal papers, 42% of conference papers, and 5% of book chapters’ address requirements-based testing. Most of the studies use UML, activity, and use case diagrams for test case generation from requirements. One of the significant lessons learned is that most software testing errors are traced back to errors in natural language requirements. A substantial amount of work focuses on UML diagrams for test case generations, which cannot capture all the system’s developed attributes. Furthermore, there is a lack of UML-based models that can generate test cases from natural language requirements by refining them in context. Coverage criteria indicate how efficiently the testing has been performed 12.37% of studies use requirements coverage, 20% of studies cover path coverage, and 17% study basic coverage.
Reactive Receding Horizon Planning and Control for Quadrotors with Limited On-Board Sensing

The paper presents a receding horizon planning strategy for a quadrotor-type mav to navigate through an unknown cluttered environment at high speed. Utilizing a lightweight on-board short-range sensor that generates point-clouds within a narrow Field of View (FOV), the reported approach generates safe and dynamically feasible trajectories within the fov of the sensor, which the mav uses to navigate without relying on any global planner or prior information about the environment. The effectiveness of this planner-controller combination is demonstrated in both indoor and outdoor tests featuring speeds of up to of 3.5 m/s. With minor adjustments, the local motion planner can be utilized for interception and tracking of a moving target; evidence to this effect are provided in the form of numerical (Gazebo) simulations. Given the absence of any global information about the robot's workspace, the extent to which the local planner can provide convergence guarantees is limited; when complemented by a global planner and/or target tracker, the reported lower-level, sensor-driven reactive motion control strategy completes the autonomous mav navigation stack, enabling navigation in dynamic, uncertain, and partially-known environments with guaranteed convergence to any static or dynamic target.
A Multi-node Collaborative Storage Strategy via Clustering in Blockchain Network

Blockchain is essentially a distributed ledger shared by all nodes in the system. All nodes in blockchain are equal, and each node holds all transactions and blocks in the network. As the network continues to expand, the data rises linearly. Participates are about to face the problem of storage limitation. Blockchain is hard to scale.This paper introduces ICIStrategy, a multi-node collaborative storage strategy based on intra-cluster integrity. In ICIStrategy, we divide all participates into several clusters. Each cluster requires holding all data of the network, whereas a node within the cluster does not need to maintain data integrity. It aims to solve the storage pressure by reducing the amount data that each participate need to store and reduce communication overhead by collaboratively storing and verifying blocks through in-cluster nodes. Moreover, the ICIStrategy could greatly save the overhead of bootstrapping. We show the mode of operation in our strategy. We further analysis the performance of ICIStrategy and conduct simulation experiments. The results of several comparative experiments show that our strategy just needs 25% of storage space needed by Rapidchain, which indeed solve the problem of storage limitation and improve the blockchain performance.
Occlusion-robust method for RGB-D 6-DOF object tracking with particle swarm optimization

Visual trackers can be used to determine the trajectories of rigid 3D objects with 6-DOF. This functionality is required for many types of applications, such as augmented reality or robotics, and its efficiency is commonly related to the accuracy, robustness and execution time of these trackers. Several works contributed to the improvement of these techniques, among which stand out optimization-based, learning-based, and hybrid methods, that is, that use both cooperatively. However, despite the variety of 3D tracking object techniques, in general they have difficulty to perform tracking efficiently in challenging environments as those with occlusion or quick rotation and translation movements of the object. In this context, we propose improvements in the 6-DOF tracking of arbitrary 3D objects that uses particle swarm optimization and try to overcome those limitations. In this type of tracker, a top-down approach is used in which, for each video frame, a pose hypothesis set is created and optimized in order to find the pose as close as possible to the tracked object real pose. To perform this task, a fitness function uses 3D scene information obtained from RGB-D sensors to evaluate each hypothesis. The proposed approach tries to solve problems that in practice reduce the efficiency of this optimization, and are associated with the dynamism of the search space, the definition of the limits of the search subspace at each iteration, the exclusion of global optimum of search subspace, the premature convergence to local optima, the different types of object occlusion, the definition of a fitness function independent of the tracked object class and environment characteristics, and the execution time for processing all steps of the particle swarm optimization. Therefore, in this scenario we present a tracker algorithm with a novel fitness function based on the harmonic mean of 3D coordinate, color and normal vector distances. We also introduce a new approach for computing the boundaries of solutions subspace at runtime, observing the inertia of the target object. We present a robust method for filtering the visible model points. Finally, in order to improve execution time, we employ a dynamic region of interest selection in the scene point cloud and implement the particle swarm optimization algorithm completely in GPU. Experiments have shown that such changes led to improvements in accuracy, robustness and execution time. When compared to a state-of-the-art learning-based technique, our tracker was, on average, 19.3% and 16.3% more accurate with respect to translation and rotation errors, respectively, and presented 43.2% less tracking fails. Our tracker was also 5–17 times faster than an existing particle swarm optimization-based technique.