Reviewerly/exHarmony huggingface dataset:
Accent classification in human speech biometrics for native and non-native english speakers

Accent classification provides a biometric path to high resolution speech recognition. This preliminary study explores various methods of human accent recognition through classification of locale. Classical, ensemble, timeseries and deep learning techniques are all explored and compared. A set of diphthong vowel sounds are recorded from participants from the United Kingdom and Mexico, and then formed into a large static dataset of statistical descriptions by way of their Mel-frequency Cepstral Coefficients (MFCC) at a sample window length of 0.02 seconds. Using both flat and timeseries data, various machine learning models are trained and compared to the scientific standard Hidden Markov Model (HMM). Results through 10 fold cross validation show that a vote of average probabilities between a Random Forest and Long Short-term Memory Neural Network result in a classification accuracy of 94.74%, outperforming the speech classification standard Hidden Markov Model by a 5% increase in accuracy.
Super resolution-assisted deep aerial vehicle detection

Vehicle detection in aerial imagery has become tremendously a challenging task due to the low resolution characteristics of the aerial images. Super-Resolution; a technique which recovers high-resolution image from a single low-resolution image can be an effective approach to resolve this shortcoming. Hence, our prime focus is to design a framework for detecting vehicles in super resolved aerial images. Our proposed system can be represented as a combination of two deep sub-networks. The first sub-network aims to use a Generative Adversarial Network (GAN) for getting super resolved images. A GAN consists of two networks: a generator network and a discriminator network. It ensures recovery of photo-realistic images from down-sampled images. The second sub-network consists of a deep neural network (DNN)-based object detector for detecting vehicles in super resolved images. In our architecture, the Single Shot Multi Box Detector (SSD) is used for vehicle detection. The SSD generates fixed-size bounding boxes with predicting scores for different object class instances in those boxes. It also employs a non-maximum suppression step to produce final detections. In our algorithm, our deep SSD detector is trained with the predicted super resolved images and its performance is then compared with an SSD detector that is trained only on the low-resolution images. Finally, we compare the performance of our proposed pre-trained SSD detector on super-resolved images with an SSD that is trained only on the original high resolution images.
Basic Daily Activity Recognition with a Data Glove

Many people in the world are affected by the Alzheimer disease leading to the dysfunctionality of the hand. In one side, this symptom is not the most important of this disease and not much attention is given to this one. In the other side, the literrature provides two main solutions such as computer vision and data glove allowing to recognize hand gestures for virtual reality or robotic applications. From this finding and need, we decided to developed our own data glove prototype allowing to monitor the evolution of the dysfunctionality of the hand by recognizing objects in basic daily activities. Our approach is simple, cheap (~220$) and efficient (~100% of correct predictions) considering that we are abstracting all the theory about the gesture recognition. Also, we can access directly and easily to the raw data. Finally, the proposed prototype is described in a way that researchers can reproduce it.
Design Archaeology: Generating Design Knowledge from Real-World Artifact Design

When formulating prescriptive design knowledge in design science research (DSR), we usually reflect on our vision of created artifacts, relevant design decisions, and what we have learned throughout the design process. Seldom do we attempt to extract prescriptive knowledge from existing and widely acknowledged artifacts in the manner of ex-post facto or in situ. But what can we learn from decades of designing digital artifacts that have fundamentally revamped work processes across industries, allowed for the emergence of new business models, and even spurred entirely new industries? This essay is inspired by the way archaeologists make sense of the past and represent the resulting knowledge. We propose a novel approach to the analysis of digital artifacts based on the archaeological approaches to context reconstruction and artifact analysis. We explain how a design archaeologist can shift among the perspectives of designers, users, and the generated artifact to make inferences about the artifact (i.e., design artifact), how it has been designed (i.e., design process), the context in which it has been designed (i.e., the design context), and the situations in which it has been used (i.e., the use contexts).
Machine Learning Approaches to Choose Heroes in Dota 2

The winning in the multiplayer online game Dota 2 for teams is a sum of many factors. One of the most significant of them is the right choice of heroes for the team. It is possible to predict a match result based on the chosen heroes for both teams. This paper considers different approaches to predicting results of a match using machine learning methods to solve the classification problem. The experimental comparison of predictive classification models was done, including the optimization of their hyperparameters. It showed that the best classification models are linear regression, linear support vector machine, as well as neural network with Softplus and Sigmoid activation functions. The fastest of them is the linear regression model, so it is best suited for practical implementation.
Reservoir Transfer on Analog Neuromorphic Hardware

Analog, unclocked, spiking neuromorphic microchips open new perspectives for implantable or wearable biosensors and biocontrollers, due to their low energy consumption and heat dissipation.However, the challenges from a computational point of view are formidable.Here we outline our solutions to realize the reservoir computing paradigm on such hardware and address the combined problems of low bit resolution, device mismatch, approximate neuron models, and timescale mismatch.The main contribution is a computational scheme, called Reservoir Transfer, which enables us to transfer the dynamical properties of a well-performing neural network which has been optimized on a digital computer, onto neuromorphic hardware that displays the abovementioned problematic properties.Here we present a case study of implementing an ECG heartbeat abnormality detector to showcase the proposed method.
A behaviorally inspired fusion approach for computational audiovisual saliency modeling

Human attention is highly influenced by multi-modal combinations of perceived sensory information and especially audiovisual information. Although systematic behavioral experiments have provided evidence that human attention is multi-modal, most bottom-up computational attention models, namely saliency models for fixation prediction, focus on visual information, largely ignoring auditory input. In this work, we aim to bridge the gap between findings from neuroscience concerning audiovisual attention and the computational attention modeling, by creating a 2-D bottom-up audiovisual saliency model. We experiment with various fusion schemes for integrating state-of-the-art auditory and visual saliency models in a single audiovisual attention/saliency model based on behavioral findings, that we validate in two experimental levels: (1) using results from behavioral experiments aiming to reproduce the results in a mostly qualitative manner and to ensure that our modeling is in line with behavioral findings, and (2) using 6 different databases with audiovisual human eye-tracking data. For this last purpose, we have also collected eye-tracking data for two databases: ETMD, a movie database that contains highly edited videos (movie clips), and SumMe, a database that contains unstructured and unedited user videos. Experimental results indicate that our proposed audiovisual fusion schemes in most cases improve performance compared to visual-only models, without any prior knowledge of the video/audio content. Also, they can be generalized and applied to any auditory saliency model and any visual spatio-temporal saliency model.
A Meta-Learning Recommendation System for Characterizing Unsupervised Problems: On Using Quality Indices to Describe Data Conformations

The clustering of a new unsupervised problem usually requires knowing both if the samples may be separable in different groups and the number of these groups. This information, which has a great impact on the results obtained, is generally unknown beforehand. A wide explored research line in the literature proposes to use some metrics, known as quality indices, to determine the number of clusters in a dataset. However, they may lead to variable results depending on the metric chosen. This research analyzes the usage of a novel meta-learning system for determining the number of clusters in unsupervised data, called Meta-Learning Recommendation System for Cluster Cardinality Estimation (MLRS-CCE). It is based on the idea of using quality metrics not as a solution to the problem, but as a means to characterize the inner structure of each dataset and employing this information to detect when unsupervised data is not uniform and suggest additional information about the number of clusters in the data. In order to achieve such goals a large collection of both real-world and synthetic datasets, in which the number of clusters is known a priori, are used to build the system and check its performance. The meta-learning system was successfully tested on such data, showing that it is accurate enough, both separating uniform data from non-uniform one and predicting cluster cardinality when it is compared to the results given by individual quality indices.
A combined digital image correlation/particle image velocimetry study of water-backed impact

Several aeronautical and naval structures are routinely exposed to impulsive loading conditions that elicit complex fluid-structure interactions. An archetypal problem that has attracted considerable attention is the impact on a compliant plate, fixed on a water surface. Despite significant progress in mathematical modeling of the impact, several technical questions remain open due to the lack of a simultaneous experimental characterization of the structural response and fluid flow. Here, we seek to fill this gap of knowledge through the integration of digital image correlation (DIC) and particle image velocimetry (PIV). We employ DIC to measure the apparent in-plane displacement of the plate, from which we reconstruct its out-of-plane deflection. On the other hand, PIV is utilized to measure the velocity field of the water, from which we infer the pressure field in the fluid and the hydrodynamic loading on the plate. We examine a number of aggregated measures, including the mid-span deflection, strain energy stored in bending and stretching of the plate, modal contribution factors, hydrodynamic loading, and added mass coefficient. Just as our approach constitutes a significant methodological step forward in the study of unsteady fluid-structure interactions, our experimental results contribute new evidence for an improved understanding of water-backed impact.
On the Discretized Gaussian Modulation (DGM)- Based Continuous Variable-QKD

To overcome the low reconciliation efficiency problem of Gaussian modulation (GM)-based-continuous variable (CV)-quantum key distribution (QKD), in this paper, we propose to use discretized GM (DGM)-based-CV-QKD. The proposed CV-QKD scheme has complexity and reconciliation efficiency similar to that of discrete modulation (DM)-based-CV-QKD and at the same time solves for the problem of the nonexistence of strict security proofs for the DM-CV-QKD under the collective attacks. We demonstrate that the 32-points-based DGM CV-QKD can closely approach the theoretical SKR-limit in medium and high channel losses regimes. On the other hand, the 64-points-based DGM CV-QKD scheme closely approaches the SKR-limit for all channel losses.
Real-time and multimodal brain slice-to-volume registration using CNN

Brain slice-to-volume registration is an essential problem in numerous clinical applications; it is aimed at determining the rigid transformation parameters for locating intraoperative 2D slices in the preoperative 3D volume. This task has several challenges: (1) it has to be carried out in real-time; (2) it should not require manual intervention; (3) it requires multimodal operation; (4) it should not require landmark; and (5) it should not require initialization. However, available methods are incapable of overcoming these obstacles simultaneously. In this study, a discrete strategy is introduced, and the slice-to-volume registration is transformed into a global multilabel classification problem. An automatic system using a convolutional neural network (CNN) is applied to implement multilabel classification. The network is trained by slices extracted from the volume and applied in a multibranch manner. Moreover, silhouettes are utilized to unify the multimodal images into the same metric. Centralization and spindle transformation are applied to reduce the dimension of the parameter space, resulting in fewer training samples and less computational complexity. This system exhibits the advantages of not requiring initialization, landmark, or manual intervention; moreover, it can satisfy the real-time and multimodal requirements simultaneously. Experiments on five datasets demonstrate that our system can achieve highly accurate registration and strong robustness; moreover, it is observed to be superior to the state-of-the-art methods by an average of approximately 4.5 mm in terms of the mean target registration error.
A Blockchain Future for Secure Clinical Data Sharing

In the digital healthcare era, it is utmost important to harness medical information scattered across healthcare institutions to support in-depth data analysis. However, the boundaries of cyberinfrastructure of healthcare providers place obstacles on data sharing. In this position paper, we firstly identify the challenges of medical data sharing and management. Then we introduce the background and give a brief survey on the state-of-the-art. Finally, we conclude the paper by discussing a few possible research directions to cope with the challenges in current medical information sharing.
MISFP-Growth: Hadoop-Based Frequent Pattern Mining with Multiple Item Support

In practice, single item support cannot comprehensively address the complexity of items in large datasets. In this study, we propose a big data analytics framework (named Multiple Item Support Frequent Patterns, MISFP-growth algorithm) that uses Hadoop-based parallel computing to achieve high-efficiency mining of itemsets with multiple item supports (MIS). The proposed architecture consists of two phases. First, in the counting support phase, a Hadoop MapReduce architecture is employed to determine the support for each item. Next, in the analytics phase, sub-transaction blocks are generated according to MIS and the MISFP-growth algorithm identifies the frequency of patterns. To facilitate decision makers in setting MIS, we also propose the concept of classification of item (COI), which classifies items of higher homogeneity into the same class, by which the items inherit class support as their item support. Three experiments were implemented to validate the proposed Hadoop-based MISFP-growth algorithm. The experimental results show approximately 38% reduction in the execution time on parallel architectures. The proposed MISFP-growth algorithm can be implemented on the distributed computing framework. Furthermore, according to the experimental results, the enhanced performance of the proposed algorithm indicates that it could have big data analytics applications.
Assessing the Robustness of Conversational Agents using Paraphrases

Assessing a conversational agent's understanding capabilities is critical, as poor user interactions could seal the agent's fate at the very beginning of its lifecycle with users abandoning the system.In this paper we explore the use of paraphrases as a testing tool for conversational agents.Paraphrases, which are different ways of expressing the same intent, are generated based on known working input by performing lexical substitutions.As the expected outcome for this newly generated data is known, we can use it to assess the agent's robustness to language variation and detect potential understanding weaknesses.As demonstrated by a case study, we obtain encouraging results as it appears that this approach can help anticipate potential understanding shortcomings and that these shortcomings can be addressed by the generated paraphrases.
An Optimized Image-Based Visual Servo Control for Fixed-Wing Unmanned Aerial Vehicle Target Tracking With Fixed Camera

This paper presents an optimized image-based visual servo (IBVS) control scheme for tracking a ground target by using a fixed-wing unmanned aerial vehicle (UAV) with a monocular camera fixed on it.Unlike the widely used rotor UAVs, the fixed-wing UAV has much more dynamical constraints such as it cannot move omni-direction and its minimum speed is limited by the stalling speed.This makes the target tracking problem more challenging.The proposed scheme leverages the image Jacobian matrix to build a connection between the velocity of the feature point and that of the UAV.Afterward, considering the camera is fixed on the body of the UAV, an ''ideal camera'' model is proposed to compensate the shifts of the feature point caused by the changes of the UAV's attitude.Then, an optimized control law without solving the pseudo-inverse of the image Jacobian matrix is proposed with the aid of the least square method from the target center in the image coordinate system.This control law takes the velocity of the feature point as inputs and the yaw angular velocity of the UAV as outputs.The stability of the proposed law is analyzed with the Lyapunov method, showing that the UAV will circle around the ground target asymptotically.Finally, the proposed scheme is evaluated by a hardware-in-the-loop (HIL) simulation based on the Gazebo simulator and the off-the-shelf autopilot hardware.
A comparison between UCB and UCB-Tuned as selection policies in GGP

In this paper, we present a comparative analysis of two selection policies in the General Game Playing (GGP) context: Upper Confidence Bound (UCB) and Upper Confidence Bound Tuned (UCB-Tuned). The aim of the analysis is to identify which policy has the best performance in terms of victories in the GGP domain, a measure used in most of literature with other policies. In order to carry out the comparison, two agents were programmed using the GGP-base framework and the Monte Carlo Tree Search (MCTS) method. The games Breakthrough, Knightthrough and Connect Four were used as experimental scenarios, not compared previously to the best of our knowledge. The results show that UCB-Tuned is better when less than 100 simulations are used in MCTS; however, when 1000 simulations are used, both policies have similar performance.
Making the A Priori Procedure Work for Differences Between Means

Previous researchers have proposed the a priori procedure, whereby the researcher specifies, prior to data collection, how closely she wishes the sample means to approach corresponding population means, and the degree of confidence of meeting the specification. However, an important limitation of previous research is that researchers sometimes are interested in differences between means, rather than in the means themselves. To address this limitation, we propose additional equations that expand the a priori procedure to handle differences between means, both in matched and in independent samples. Finally, implications are discussed.
Model poisoning attacks against distributed machine learning systems

Future military coalition operations will increasingly rely on machine learning (ML) methods to improve situational awareness. The coalition context presents unique challenges for ML: the tactical environment creates significant computing and communications limitations while also having to deal with an adversarial presence. Further, coalition operations must operate in a distributed manner, while coping with the constraints posed by the operational environment. Envisioned ML deployments in military assets must be resilient to these challenges. Here, we focus on the susceptibility of ML models to be poisoned (during training) or fooled (after training) by adversarial inputs. We review recent work on distributed adversarial ML, and present new results from our own investigations into model poisoning attacks on distributed learning systems without a central parameter aggregation node.
Mining significant fuzzy association rules with differential evolution algorithm

This article presents a new differential evolution (DE) algorithm for mining optimized statistically significant fuzzy association rules that are abundant in number and high in rule interestingness measure (RIM) values, with strict control over the risk of spurious rules. The risk control over spurious rules, as the most distinctive feature of the proposed DE compared with existing evolutionary algorithms (EAs) for association rule mining (ARM), is realized via two new statistically sound significance tests on the rules. The two tests, in the experimentwise and generationwise adjustment approach, can respectively limit the familywise error rate (the probability that any spurious rules occur in the ARM result) and percentage of spurious rules upon the user specified level. Experiments on variously sized data show that the proposed DE can keep the risk of spurious rules well below the user specified level, which is beyond the ability of existing EA-based ARM. The new method also carries forward the advantages of EA-based ARM and distinctive merits of DE in optimizing the rules: it can obtain several times as many rules and as high RIM values as conventional non-evolutionary ARM, and even more informative rules and better RIM values than genetic-algorithm-based ARM. Case studies on hotel room price determinants and wildfire risk factors demonstrate the practical usefulness of the proposed DE.
FloatSD: A New Weight Representation and Associated Update Method for Efficient Convolutional Neural Network Training

In this paper, we propose a floating-point signed digit (FloatSD) format for convolutional neural network (CNN) weight representation and its update method during CNN training. The number of non-zero digits in a weight can be as few as two during the forward and backward passes of the CNN training, reducing the convolution multiplication to addition of two shifted multiplicands (partial products). Furthermore, the mantissa field and the exponent field of neuron activations and gradients during training are also quantized, leading to floating-point numbers represented by eight bits. We tested the FloatSD method using three popular CNN applications, namely, MNIST, CIFAR-10, and ImageNet. These three CNNs were trained from scratch using the conventional 32-bit floating-point arithmetic and the FloatSD weight representation, 8-bit floating-point numbers for activations and gradients, and half-precision 16-bit floating-point accumulation. We obtained FloatSD accuracy results very close to or even better than those trained in 32-bit floating-point arithmetic. Finally, the proposed method not only significantly reduces the computational complexity for CNN training but also achieves memory capacity and bandwidth saving of about three quarters, demonstrating its effectiveness in the low-complexity implementation of CNN training.