Reviewerly/exHarmony huggingface dataset:
Discrimination of β-thalassemia and iron deficiency anemia through extreme learning machine and regularized extreme learning machine based decision support system

The symptoms of Iron Deficiency Anemia (IDA) and β-thalassemia (β-TT) disease are similar and the distinction between them is time consuming and costly. There are several indices used to differentiate IDA from β-thalassemia disease. Complete Blood Count (CBC) is a rapid, inexpensive and accessible test for the diagnosis of anemia and is used as a primary test. However, since CBC cannot fully distinguish between IDA and β-thalassemia, more advanced testing is required. These tests are not available in small centers and are performed on higher-cost devices. Moreover, it is important to differentiate between anemia and β-thalassemia medically for two reasons (IDA). First, if a patient with β-Thalassemia is diagnosed with IDA, the patient is given unnecessary iron supplementation as a result of the treatment, which is recommended by the doctor. Secondly, when the patient with β-thalassemia is diagnosed with IDA, children will have β-thalassemia patients in marriages. A decision support system to distinguish between β-Thalassemia and IDA has been developed. Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Extreme Learning Machine and Regularized Extreme Learning Machine classification algorithms were used in the proposed system. Classification performance was evaluated with Accuracy, sensitivity, f-measure, Specificty parameters using Hemoglobin, RBC, HCT, MCV, MCH, MCHC and RDW parameters obtained from 342 patients. 96.30% accuracy for female, 94.37% for male, and 95.59% in co-evaluation of male and female patients were obtained.
Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.
CoDa

Tangibles can model abstract structures. One educational subject where this can be utilized is instruction on data visualization inter- pretation. Data physicalizations, tangible representations of data, offer graspable handles for the users to manipulate data visualiza- tions directly so that they can better understand what information they hold. However, investigations on the applicability of interac- tive data physicalizations in educational settings are still sparse. In this paper, we explore how students reason with an interactive tangible scatterplot through a collaborative data interpretation tool, CoDa. We report the design, development, and the user experiences in an exploratory study where 11 students, in groups of 2 to 4, completed a data analysis task with CoDa. The qualitative results show insights in the process of data interpretation, how interaction with the tangibles influenced these data interpretations, how the system aided collaboration and, overall user experience. We believe the results and implications offer a step towards nurturing future educational applications on interactive data physicalizations.
Hy-Bridge: A Hybrid Blockchain for Privacy-Preserving and Trustful Energy Transactions in Internet-of-Things Platforms

In the current centralized IoT ecosystems, all financial transactions are routed through IoT platform providers. The security and privacy issues are inevitable with an untrusted or compromised IoT platform provider. To address these issues, we propose Hy-Bridge, a hybrid blockchain-based billing and charging framework. In Hy-Bridge, the IoT platform provider plays no proxy role, and IoT users can securely and efficiently share a credit with other users. The trustful end-to-end functionality of blockchain helps us to provide accountability and reliability features in IoT transactions. Furthermore, with the blockchain-distributed consensus, we provide a credit-sharing feature for IoT users in the energy and utility market. To provide this feature, we introduce a local block framework for service management in the credit-sharing group. To preserve the IoT users' privacy and avoid any information leakage to the main blockchain, an interconnection position, called bridge, is introduced to isolate IoT users' peer-to-peer transactions and link the main blockchain to its subnetwork blockchain(s) in a hybrid model. To this end, a k-anonymity protection is performed on the bridge. To evaluate the performance of the introduced hybrid blockchain-based billing and charging, we simulated the energy use case scenario using Hy-Bridge. Our simulation results show that Hy-Bridge could protect user privacy with an acceptable level of information loss and CPU and memory usage.
Combining Multiple one-class Classifiers for Anomaly based Face Spoofing Attack Detection

One-class spoofing detection approaches have been an effective alternative to the two-class learners in the face presentation attack detection particularly in unseen attack scenarios. We propose an ensemble based anomaly detection approach applicable to one-class classifiers. A new score normalisation method is proposed to normalise the output of individual outlier detectors before fusion. To comply with the accuracy and diversity objectives for the component classifiers, three different strategies are utilised to build a pool of anomaly experts. To boost the performance, we also make use of the client-specific information both in the design of individual experts as well as in setting a distinct threshold for each client. We carry out extensive experiments on three face anti-spoofing datasets and show that the proposed ensemble approaches are comparable superior to the techniques based on the two-class formulation or class-independent settings.
Wildfire Fighting by Unmanned Aerial System Exploiting Its Time-Varying Mass

This letter presents an approach for accurately dropping a relatively large amount of fire retardant, water or some other extinguishing agent onto a wildfire from an autonomous unmanned aerial vehicle (UAV), in close proximity to the epicenter of the fire. The proposed approach involves a risky maneuver outside of the safe flight envelope of the UAV. This maneuver exploits the expected weight reduction resulting from the release of the payload, enabling the UAV to recover without impacting the terrain. The UAV is tilted to high pitch angles, at which the thrust may be pointed almost horizontally. The vehicle can therefore achieve higher horizontal speeds than would be allowed by conventional motion planners. This high speed allows the UAV to significantly reduce the time spent close to the fire. As a result, the overall high heat exposure is reduced, and the payload can be dropped closer to the target, minimizing its dispersion. A constrained optimal control problem (OCP) is solved taking into account environmental parameters such as wind and terrain gradients, as well as various payload releasing mechanisms. The proposed approach was verified in simulations and in real experiments. Emphasis was put on the real time recalculation of the solution, which will enable future adaptation into a model predictive controller (MPC) scheme.
Region and Decision Tree-Based Segmentations for Multi-Objects Detection and Classification in Outdoor Scenes

Accurate segmentation and detection of all mixed and occluded objects in the complex indoor/outdoor scenes become a vital topic of computer vision. These above-mentioned scenarios are the significant part of important vision applications such as autonomous driving, traffic monitoring, security surveillance, humane body parts detection, objects tracking and scene recognition. It is still difficult to accurately detect all the objects in the image due to illumination changes, occlusion and different directions. Meanwhile, segmenting the image in parts helps to detect the multi objects accurately. In this paper, we designed a system having improved techniques for the accurate segmentation and detection of multi objects. Firstly, we have combined the results of two methods for accurate segmentation of multiple objects, (i) Decision trees for labeling every neighboring pixel and assigning a separate color to all object present in complex images and (ii) Region-based segmentation for significant detection of multiple regions and drawing boundaries of all objects. Secondly, detection is performed by searching the objects with previously assigned colors. Finally, we have performed labeling with class name to all objects present in the images. We have performed our experimental over two benchmarked datasets as Instance Saliency images and MSRC. Our experimental work has shown improved results with respect to other state of the art algorithms.
Differential Flatness Based Path Planning With Direct Collocation on Hybrid Modes for a Quadrotor With a Cable-Suspended Payload

Generating agile maneuvers for a quadrotor with a cable-suspended load is a challenging problem. State-of-the-art approaches often need significant computation time and complex parameter tuning. We use a coordinate-free geometric formulation and exploit a differential flatness based hybrid model of a quadrotor with a cable-suspended payload. We perform direct collocation on the differentially-flat hybrid system, and use complementarity constraints to avoid specifying hybrid mode sequences. The non-differentiable obstacle avoidance constraints are reformulated using dual variables, resulting in smooth constraints. We show that our approach has lower computational time than the state-of-the art and guarantees feasibility of the trajectory with respect to both the system dynamics and input constraints without the need to tune lots of parameters. We validate our approach on a variety of tasks in both simulations and experiments, including navigation through waypoints and obstacle avoidance.
Dual-CNN: A Convolutional language decoder for paragraph image captioning

The task of paragraph image captioning aims to generate a coherent paragraph describing a given image. However, due to their limited ability to capture long-term dependency, recurrent neural network or long-short term memory based decoders could hardly generate satisfactory textual descriptions with a long paragraph. In addition, the training inefficiency in the sequential decoders is significantly observed. Motivated by the advantage of convolutional neural network (i.e., CNN), in this paper, we propose a Dual-CNN decoder with long-term memory ability and parallel computation, which can produce a semantically coherent paragraph for an image. Our Dual-CNN model is evaluated on the Stanford image-paragraph dataset. Extensive experiments demonstrate that our Dual-CNN achieves comparable results compared with state-of-the-art models. Furthermore, the diversity and coherence of generated paragraphs are analyzed to show the superiority of our approach.
Multirepresentation, Multiheuristic A* search‐based motion planning for a free‐floating underwater vehicle‐manipulator system in unknown environment

Abstract A key challenge in autonomous mobile manipulation is the ability to determine, in real time, how to safely execute complex tasks when placed in unknown or changing world. Addressing this issue for Intervention Autonomous Underwater Vehicles (I‐AUVs), operating in potentially unstructured environment is becoming essential. Our research focuses on using motion planning to increase the I‐AUVs autonomy, and on addressing three major challenges: (a) producing consistent deterministic trajectories, (b) addressing the high dimensionality of the system and its impact on the real‐time response, and (c) coordinating the motion between the floating vehicle and the arm. The latter challenge is of high importance to achieve the accuracy required for manipulation, especially considering the floating nature of the AUV and the control challenges that come with it. In this study, for the first time, we demonstrate experimental results performing manipulation in unknown environment. The Multirepresentation, Multiheuristic A* (MR‐MHA*) search‐based planner, previously tested only in simulation and in a known a priori environment, is now extended to control Girona500 I‐AUV performing a Valve‐Turning intervention in a water tank. To this aim, the AUV was upgraded with an in‐house‐developed laser scanner to gather three‐dimensional (3D) point clouds for building, in real time, an occupancy grid map (octomap) of the environment. The MR‐MHA* motion planner used this octomap to plan, in real time, collision‐free trajectories. To achieve the accuracy required to complete the task, a vision‐based navigation method was employed. In addition, to reinforce the safety, accounting for the localization uncertainty, a cost function was introduced to keep minimum clearance in the planning. Moreover a visual‐servoing method had to be implemented to complete the last step of the manipulation with the desired accuracy. Lastly, we further analyzed the approach performance from both loose‐coupling and clearance perspectives. Our results show the success and efficiency of the approach to meet the desired behavior, as well as the ability to adapt to unknown environments.
Head Motion Tracking Through in-Ear Wearables

Head tracking is a fundamental component in visual attention detection, which, in turn, can improve the state of the art of hearing aid devices. A multitude of wearable devices for the ear (so called earables) exist. Current devices lack a magnetometer which, as we will show, represents a big challenge when one tries to use them for accurate head tracking.
On the Transferability of Spectral Graph Filters

This paper focuses on spectral filters on graphs, namely filters defined as elementwise multiplication in the frequency domain of a graph. In many graph signal processing settings, it is important to transfer a filter from one graph to another. One example is in graph convolutional neural networks (ConvNets), where the dataset consists of signals defined on many different graphs, and the learned filters should generalize to signals on new graphs, not present in the training set. A necessary condition for transferability (the ability to transfer filters) is stability. Namely, given a graph filter, if we add a small perturbation to the graph, then the filter on the perturbed graph is a small perturbation of the original filter. It is a common misconception that spectral filters are not stable, and this paper aims at debunking this mistake. We introduce a space of filters, called the Cayley smoothness space, that contains the filters of state-of-the-art spectral filtering methods, and whose filters can approximate any generic spectral filter. For filters in this space, the perturbation in the filter is bounded by a constant times the perturbation in the graph, and filters in the Cayley smoothness space are thus termed linearly stable. By combining stability with the known property of equivariance, we prove that graph spectral filters are transferable.
Novel Multi-Channel Fractional-Order Radial Harmonic Fourier Moments for Color Image Analysis

The classical radial harmonic Fourier moments (RHFMs) and the quaternion radial harmonic Fourier moments (QRHFMs) are gray-scale and color image descriptors.The radial harmonic functions with integer orders are not able to extract fine features from the input images.In this paper, the authors derived novel fractional-order radial harmonic functions in polar coordinates.The obtained functions are used to defined novel multi-channel fractional-order radial harmonic moments (FrMRHFMs) for color image description and analysis.The invariants to geometric transformations for these new moments are derived.A theoretical comparison between FrMRHFMs and QRHFMs is performed from the aspects of kernel function and the spectrum analysis.Numerical simulation is carried out to test these new moments in terms of image reconstruction capabilities, invariance to the similarity transformations, color image recognition and the CPU computational times.The obtained theoretical and numerical results clearly show that the proposed FrMRHFMs is superior to the QRHFMs and the existing fractional-order orthogonal moments.
Generation of a time-frequency grid state with integrated biphoton frequency combs

Encoding quantum information in continuous variables is intrinsically faulty. Nevertheless, redundant qubits can be used for error correction, as proposed by Gottesman et al. [Phys. Rev. A 64 012310 (2001)]. We show how to experimentally implement this encoding using time-frequency continuous degrees of freedom of photon pairs produced by spontaneous parametric down conversion. We illustrate our results using an integrated AlGaAs photon-pair source. We show how single-qubit gates can be implemented and finally propose a theoretical scheme for correcting errors in circuit-like and measurement-based architectures.
A Survey on Chaos Based Image Encryption Techniques

Image encryption is an important area in recent days. It is widely known as an effective method for secure transmission. The objective of any image encryption algorithm is to obtain the top quality of a noisy image in order to keep information secret. In this chapter, different image encryption techniques based on the chaos theory which provides good security are reviewed.
Training Language Models for Long-Span Cross-Sentence Evaluation

While recurrent neural networks can motivate cross-sentence language modeling and its application to automatic speech recognition (ASR), corresponding modifications of the training method for that end are rarely discussed. In fact, even more generally, the impact of training sequence construction strategy in language modeling for different evaluation conditions is typically ignored. In this work, we revisit this basic but fundamental question. We train language models based on long short-term memory recurrent neural networks and Transformers using various types of training sequences and study their robustness with respect to different evaluation modes. Our experiments on 300h Switchboard and Quaero English datasets show that models trained with back-propagation over sequences consisting of concatenation of multiple sentences with state carry-over across sequences effectively outperform those trained with the sentence-level training, both in terms of perplexity and word error rates for cross-utterance ASR.
A novel data augmentation scheme for pedestrian detection with attribute preserving GAN

Recently pedestrian detection has progressed significantly. However, detecting pedestrians of small scale or in heavy occlusions is still notoriously difficult. Besides, the generalization ability of pre-trained detectors across different datasets remains to be improved. Both of these issues can be attributed to insufficient training data coverage. To cope with this, we present an efficient data augmentation scheme by transferring pedestrians from other datasets into the target scene with a novel Attribute Preserving Generative Adversarial Networks (APGAN). The proposed methodology consists of two steps: pedestrian embedding and style transfer. The former step can simulate pedestrian images of various scale and occlusion, in any pose or background, thus greatly promoting the data variation. The latter step aims to make the generated samples more realistic while guarantee the data coverage. To achieve this goal, we propose APGAN, which pursues both good visual quality and attribute preserving after style transfer. With the proposed method, we can make effective sample augmentations to improve the generalization ability of the trained detectors and enhance its robustness to scale change and occlusions. Extensive experiment results validate the effectiveness and advantages of our method.
Spike Sequence Learning in a Photonic Spiking Neural Network Consisting of VCSELs-SA With Supervised Training

We propose a fully-connected photonic spiking neural network (SNN) consisting of excitable vertical-cavity surface-emitting lasers with an embedded saturable absorber (VCSELs-SA) to implement spike sequence learning by a supervised training. The photonic spike-timing-dependent plasticity (STDP) is incorporated into a classical remote supervised method (ReSuMe) algorithm to implement supervised training of a photonic SNN for the first time. The computation model of the photonic SNN is derived based on the Yamada model. To optimize the learning process, we further propose a novel measure, the so-called spike sequence distance, to quantitatively evaluate the effects of controllable parameters. The numerical results show that, the photonic SNN successfully reproduces a desirable output spike sequence in response to a spatiotemporal input spike pattern by means of the iteration algorithm to update synaptic weights continuously. These results contribute one step forward toward the device-algorithm co-design and optimization of the all-VCSELs-based energy-efficient photonic SNN.
Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization

Data visualisation is a key tool in data mining for understanding big datasets. Many visualisation methods have been proposed, including the well-regarded state-of-the-art method t-Distributed Stochastic Neighbour Embedding. However, the most powerful visualisation methods have a significant limitation: the manner in which they create their visualisation from the original features of the dataset is completely opaque. Many domains require an understanding of the data in terms of the original features; there is hence a need for powerful visualisation methods which use understandable models. In this work, we propose a genetic programming approach named GPtSNE for evolving interpretable mappings from a dataset to highquality visualisations. A multi-objective approach is designed that produces a variety of visualisations in a single run which give different trade-offs between visual quality and model complexity. Testing against baseline methods on a variety of datasets shows the clear potential of GP-tSNE to allow deeper insight into data than that provided by existing visualisation methods. We further highlight the benefits of a multi-objective approach through an in-depth analysis of a candidate front, which shows how multiple models can