Reviewerly/exHarmony huggingface dataset:
A novel dimensionality reduction method: Similarity order preserving discriminant analysis

A novel dimensionality reduction method, similarity order preserving discriminant analysis, is proposed to process high-dimensional data in this communication. Different from the collaborative representation or sparse representation based dimensionality reduction method, we use the pairwise similarity and within-class similarity order to define the more robust global and local structure of the sample. Also, in order to improve the discrimination of the sample in the projection space, we use a parameter to scale the between-class similarity scatter and within-class similarity order scatter. Using the graph embedding framework and the idea of maximum margin criterion, the optimal subspace projection matrix is obtained by eigenvalue decomposition. Experimental results on four public datasets demonstrate that the proposed method has encouraging performance compared to some state-of-the-art dimensionality reduction methods.
Blockchain in the Amazon: creating public value and promoting sustainability

Blockchain is a recent certification technology that has strong potential to transform many business models and to have a social impact by providing solutions, including governance and sustainability. This paper presents a case study on a pioneering blockchain application on an online platform to negotiate forest credits issued for environmental services. The context is the management of the Green Treasure Program in the State of Amapá in the Amazon region of Brazil, a complex and interesting case for research. The purpose of this study is to identify how blockchain can contribute to the negotiation of environmental investments between the public and private sectors. For this purpose, we conducted qualitative research, a case study, using the lens of the theory of public value and an interpretative perspective. We seek to understand the connections between public value theory and the blockchain application.
Vehicle Detection Using Deep Learning Technique in Tunnel Road Environments

This paper proposes a real-time detection method for a car driving ahead in real time on a tunnel road. Unlike the general road environment, the tunnel environment is irregular and has significantly lower illumination, including tunnel lighting and light reflected from driving vehicles. The environmental restrictions are large owing to pollution by vehicle exhaust gas. In the proposed method, a real-time detection method is used for vehicles in tunnel images learned in advance using deep learning techniques. To detect the vehicle region in the tunnel environment, brightness smoothing and noise removal processes are carried out. The vehicle region is learned after generating a learning image using the ground-truth method. The YOLO v2 model, with an optimal performance compared to the performances of deep learning algorithms, is applied. The training parameters are refined through experiments. The vehicle detection rate is approximately 87%, while the detection accuracy is approximately 94% for the proposed method applied to various tunnel road environments.
Hyperfuzzing for SoC security validation

Automated validation of security properties in modern systems-on-chip (SoC) designs is challenging due to three reasons: (i) specification of security in the presence of adversarial behavior, (ii) co-validation of hardware (HW) and firmware (FW) as security bugs may span the HW/FW interface, and (iii) scaling verification to the analysis of large systems-on-chip designs.
Optimising Environmental Educational Narrative Videogames

In May 2019, A Night in the Forum videogame was published in the Sony PlayStation store, for PS VR. It was the final result of the REVEAL EU project, whose goal was to identify strategies, features, and tools to develop educational titles in a more efficient, cost-effective, and valuable way, exploiting their educational, cognitive, and engaging potential for schools and cultural tourism. Environmental Narrative videogames was chosen as a genre suited for this purposes. This article presents how known issues in the production of videogames have been faced, the solutions identified, and results obtained.
Quality Assessment for Omnidirectional Video: A Spatio-Temporal Distortion Modeling Approach

Omnidirectional video, also known as 360-degree video, has become increasingly popular nowadays due to its ability to provide immersive and interactive visual experiences. However, the ultra high resolution and the spherical observation space brought by the large spherical viewing range make omnidirectional video distinctly different from traditional 2D video. To date, the video quality assessment (VQA) for omnidirectional video is still an open issue. The existing VQA metrics for omnidirectional video only consider the spatial characteristics of distortions, but the temporal change of spatial distortions can also considerably influence human visual perception. In this paper, we propose a spatiotemporal modeling approach to evaluate the quality of the omnidirectional video. Firstly, we construct a spatioral quality assessment unit to evaluate the average distortion in temporal dimension at the eye fixation level, based upon which the smoothed distortion value is recursively calculated and consolidated by the characteristics of temporal variations. Then, we give a detailed solution of how to to integrate the three existing spatial VQA metrics into our approach. Besides, the cross-format omnidirectional video distortion measurement is also investigated. Finally, the spatiotemporal distortion of the whole video sequence is obtained by pooling. Based on the modeling approach, a full reference objective quality assessment metric for omnidirectional video is derived, namely OV-PSNR. The experimental results show that our proposed OV-PSNR greatly improves the prediction performance of the existing VQA metrics for omnidirectional video.
Investigation of learning abilities on linguistic features in sequence-to-sequence text-to-speech synthesis

Neural sequence-to-sequence text-to-speech synthesis (TTS) can produce high-quality speech directly from text or simple linguistic features such as phonemes. Unlike traditional pipeline TTS, the neural sequence-to-sequence TTS does not require manually annotated and complicated linguistic features such as part-of-speech tags and syntactic structures for system training. However, it must be carefully designed and well optimized so that it can implicitly extract useful linguistic features from the input features. In this paper we investigate under what conditions the neural sequence-to-sequence TTS can work well in Japanese and English along with comparisons with deep neural network (DNN) based pipeline TTS systems. Unlike past comparative studies, the pipeline systems also use neural autoregressive (AR) probabilistic modeling and a neural vocoder in the same way as the sequence-to-sequence systems do for a fair and deep analysis in this paper. We investigated systems from three aspects: a) model architecture, b) model parameter size, and c) language. For the model architecture aspect, we adopt modified Tacotron systems that we previously proposed and their variants using an encoder from Tacotron or Tacotron2. For the model parameter size aspect, we investigate two model parameter sizes. For the language aspect, we conduct listening tests in both Japanese and English to see if our findings can be generalized across languages. Our experiments on Japanese demonstrated that the Tacotron TTS systems with increased parameter size and input of phonemes and accentual type labels outperformed the DNN-based pipeline systems using the complicated linguistic features and that its encoder could learn to compensate for a lack of rich linguistic features. Our experiments on English demonstrated that, when using a suitable encoder, the Tacotron TTS system with characters as input can disambiguate pronunciations and produce natural speech as good as those of the systems using phonemes. However, we also found that the encoder could not learn English stressed syllables from characters perfectly and hence resulted in flatter fundamental frequency. In summary, these experimental results suggest that a) a neural sequence-to-sequence TTS system should have a sufficient number of model parameters to produce high quality speech, b) it should also use a powerful encoder when it takes characters as inputs, and c) the encoder still has a room for improvement and needs to have an improved architecture to learn supra-segmental features more appropriately.
Open-World Class Discovery with Kernel Networks

We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples. There are two critical challenges to addressing this paradigm: (a) transferring knowledge from old to new classes, and (b) incorporating knowledge learned from new classes back to the original model. We propose Class Discovery Kernel Network with Expansion (CD-KNet-Exp), a deep learning framework, which utilizes the Hilbert Schmidt Independence Criterion to bridge supervised and unsupervised information together in a systematic way, such that the learned knowledge from old classes is distilled appropriately for discovering new classes. Compared to competing methods, CD-KNet-Exp shows superior performance on three publicly available benchmark datasets and a challenging real-world radio frequency fingerprinting dataset.
D2D: Keypoint Extraction with Describe to Detect Approach

In this paper, we present a novel approach that exploits the information within the descriptor space to propose keypoint locations. Detect then describe, or jointly detect and describe are two typical strategies for extracting local features. In contrast, we propose an approach that inverts this process by first describing and then detecting the keypoint locations. Describe-to-Detect (D2D) leverages successful descriptor models without the need for any additional training. Our method selects keypoints as salient locations with high information content which are defined by the descriptors rather than some independent operators. We perform experiments on multiple benchmarks including image matching, camera localisation, and 3D reconstruction. The results indicate that our method improves the matching performance of various descriptors and that it generalises across methods and tasks.
Multi-Model-Based Local Path Planning Methodology for Autonomous Driving: An Integrated Framework

Autonomous driving systems (ADSs) need to be able to respond quickly to changes in the dynamic traffic scenario. However, regardless of the changes occurring in traffic scenes, the current local path planning frameworks of ADSs are based on the fixed frequency re-planning path (i.e., running their planning algorithms repeatedly). This planning method makes it difficult to provide a reasonable traveling path, agility, and comfort for driverless vehicles in changing traffic scenarios. Therefore, this article performs an in-depth analysis of the problems of traditional planning frameworks which use a fixed frequency to replan the path and proposes a novel path planning framework that is universal based on multiple-models. The proposed framework divides the planning process into several layers, each of which has different functions. With this framework, the ADS can adaptively adjust the planning process according to the changes in traffic scenes and then provide different path planning algorithms to ensure its safety and flexibility in the process of driving. Moreover, the problems caused by the traditional planning framework can be solved. This framework has been applied to the autonomous vehicle “Pioneer”, which won first place in the 2019 China Intelligent Vehicle Future Challenge (IVFC). The effectiveness and rationality of the integrated framework of local path planning proposed in this article were verified by a large number of tests in real-world traffic scenarios.
Detection of Backdoors in Trained Classifiers Without Access to the Training Set

With wide deployment of deep neural network (DNN) classifiers, there is great potential for harm from adversarial learning attacks. Recently, a special type of data poisoning (DP) attack, known as a backdoor (or Trojan), was proposed. These attacks do not seek to degrade classification accuracy, but rather to have the classifier learn to classify to a target class t∗ whenever the backdoor pattern is present in a test example originally from a source class s∗ . Launching backdoor attacks does not require knowledge of the classifier or its training process-only the ability to poison the training set with exemplars containing a backdoor pattern (labeled with the target class). Defenses against backdoors can be deployed before/during training, post-training, or at test time. Here, we address post-training detection in DNN image classifiers, seldom considered in existing works, wherein the defender does not have access to the poisoned training set, but only to the trained classifier itself, as well as to clean (unpoisoned) examples from the classification domain. This scenario is of great interest because e.g., a classifier may be the basis of a phone app that will be shared with many users. Detection may thus reveal a widespread attack. We propose a purely unsupervised anomaly detection (AD) defense against imperceptible backdoor attacks that: 1) detects whether the trained DNN has been backdoor-attacked; 2) infers the source and target classes in a detected attack; 3) estimates the backdoor pattern itself. Our AD approach involves learning (via suitable cost function minimization) the minimum size/norm perturbation (putative backdoor) required to induce the classifier to misclassify (most) examples from class s to class t , for all (s,t) pairs. Our hypothesis is that nonattacked pairs require large perturbations, while the attacked pair (s∗, t∗) requires much smaller ones. This is convincingly borne out experimentally. We identify a variety of plausible cost functions and devise a novel, robust hypothesis testing approach to perform detection inference. We test our approach, in comparison with the state-of-the-art methods, for several backdoor patterns, attack settings and mechanisms, and data sets and demonstrate its favorability. Our defense essentially requires setting a single hyperparameter (the detection threshold), which can e.g., be chosen to fix the system's false positive rate.
Mutual-learning sequence-level knowledge distillation for automatic speech recognition

Automatic speech recognition (ASR) is a crucial technology for man-machine interaction. End-to-end models have been studied recently in deep learning for ASR. However, these models are not suitable for the practical application of ASR due to their large model sizes and computation costs. To address this issue, we propose a novel mutual-learning sequence-level knowledge distillation framework enjoying distinct student structures for ASR. Trained mutually and simultaneously, each student learns not only from the pre-trained teacher but also from its distinct peers, which can improve the generalization capability of the whole network, through making up for the insufficiency of each student and bridging the gap between each student and the teacher. Extensive experiments on the TIMIT and large LibriSpeech corpuses show that, compared with the state-of-the-art methods, the proposed method achieves an excellent balance between recognition accuracy and model compression.
Multi-Task Multi-Domain Learning for Digital Staining and Classification of Leukocytes

This paper addresses digital staining and classification of the unstained white blood cell images obtained with a differential contrast microscope. We have data coming from multiple domains that are partially labeled and partially matching across the domains. Using unstained images removes time-consuming staining procedures and could facilitate and automatize comprehensive diagnostics. To this aim, we propose a method that translates unstained images to realistically looking stained images preserving the inter-cellular structures, crucial for the medical experts to perform classification. We achieve better structure preservation by adding auxiliary tasks of segmentation and direct reconstruction. Segmentation enforces that the network learns to generate correct nucleus and cytoplasm shape, while direct reconstruction enforces reliable translation between the matching images across domains. Besides, we build a robust domain agnostic latent space by injecting the target domain label directly to the generator, i.e., bypassing the encoder. It allows the encoder to extract features independently of the target domain and enables an automated domain invariant classification of the white blood cells. We validated our method on a large dataset composed of leukocytes of 24 patients, achieving state-of-the-art performance on both digital staining and classification tasks.
Machine Learning based Solar Power Generation Forecasting with and without MPPT Controller

The renewable resources based power generation is unpredictable since it highly depends on the conditions of climate. In India, after wind power, the second largest renewable based power generation is solar power. Therefore, forecasting for solar power generation is necessary since it depends on solar irradiance and temperature. In this paper, forecasting for solar power generation using machine learning has been done with and without using MPPT controller. The study has been done on Badabenakudi, Orissa, India. Machine learning based forecasting techniques has always been proved best than statistical based forecasting techniques. Different machine learning models have been applied on the data set taken. The result shows that Coarse Tree is the best model for solar power generating forecasting with MPPT controller having RMSE of 1.675 and Rational Quadratic Gaussian Process Regression (RQGPR) is the best model for solar power generation forecasting without MPPT controller having RMSE of 1.628.
Modeling research of satellite-to-ground quantum key distribution constellations

Satellite-based quantum key distribution is the key to develop the intercontinental quantum network. After the demonstrations by Chinese satellite Micius, the prospect of global-scale quantum internet is no more out of reach. However, the research efforts on modeling of constellation of quantum satellite is still insufficient so far. In this paper, we study the algorithm to select quantum key distribution access, the key size re-distribution optimization model and corresponding key size consumption along inter-satellite links. Besides, we present the brief discussion of how the constellation configuration interferes quantum key distribution performance.
A selection framework of sensor combination feature subset for human motion phase segmentation

Motion phase plays an important role in the spatial–temporal parameters of human motion analysis. Multi-sensor fusion technology based on inertial sensors frees the monitoring of the human body phase from space constraints and improves the flexibility of the system. However, human phase segmentation methods usually rely on the determination of the positioning of the sensor and the number of sensors, it is difficult to artificially select the number and position of the sensors, especially when human motion phases are diverse. This paper proposes a selection framework for the sensor combination feature subset for motion phase segmentation, which combines feature selection algorithms with the subsequent classifiers, and determine the optimum combination of the sensor and the feature subset according to the performance of the trained model. Through the constraint and the sensor combination feature subset (SCFS), the filter method can select any number of sensors and control the size of the feature subset; the embedded method can select any number of sensors, but the size of the feature subset is determined by the classifier model. Experimental results show that the proposed framework can effectively select a specified number of sensors without human intervention, and the number of sensors has an impact on the recognition rate of the classifier within 1.5%. In addition, the filter method has good adaptability to a variety of classifiers, and the classifier prediction time can be controlled by setting the subset size of the feature; the embedded method can achieve a better phase segmentation effect than the filter method. For the application of motion phase segmentation, the proposed framework can reliably and quickly identify redundant sensors that provide effective support for reducing the complexity of the wearable sensor system and improving user comfort.
Convex and Compact Superpixels by Edge- Constrained Centroidal Power Diagram

Superpixel segmentation, as a central image processing task, has many applications in computer vision and computer graphics. Boundary alignment and shape compactness are leading indicators to evaluate a superpixel segmentation algorithm. Furthermore, convexity can make superpixels reflect more geometric structures in images and provide a more concise over-segmentation result. In this paper, we consider generating convex and compact superpixels while satisfying the constraints of adhering to the boundary as far as possible. We formulate the new superpixel segmentation into an edge-constrained centroidal power diagram (ECCPD) optimization problem. In the implementation, we optimize the superpixel configurations by repeatedly performing two alternative operations, which include site location updating and weight updating through a weight function defined by image features. Compared with existing superpixel methods, our method can partition an image into fully convex and compact superpixels with better boundary adherence. Extensive experimental results show that our approach outperforms existing superpixel segmentation methods in boundary alignment and compactness for generating convex superpixels.
A Simulation Framework for Developing Autonomous Drone Navigation Systems

Unmanned aerial vehicles are gaining popularity in an ever-increasing range of applications, mainly because they are able to navigate autonomously. In this work, we describe a simulation framework that can help engineering students who are starting out in the field of aerial robotics to acquire the necessary competences and skills for the development of autonomous drone navigation systems. In our framework, drone behavior is defined in a graphical way, by means of very intuitive state machines, whereas low-level control details have been abstracted. We show how the framework can be used to develop a navigation system proposal according to the rules of the “ESII Drone Challenge” student competition. We also show how the proposal can be evaluated in different test scenarios.