Reviewerly/exHarmony huggingface dataset:
SilentMask: Mask-type Silent Speech Interface with Measurement of Mouth Movement

Silent Speech Interaction (SSI) is a non-speech interaction used as an input method for speech recognition devices such as smartphones and as a support tool for people with speech difficulties. Conventional SSI methods using lip reading, electromyography(EMG), ultrasonic echo, and electrostatic positioning in the palate have been proposed, but there have been issues such as not being able to use one hand and being easily noticeable.
Unintentional modulation evaluation in time domain and frequency domain

With the development of wireless communication technology, the electromagnetic environment has become more and more complex. Conventional signal identification methods are difficult to accurately identify illegal devices. However, electromagnetic signals have an unavoidable device-specific characteristic unintentionally generated by a transmitter, appearing in the form of an UnIntentional Modulation (UIM), namely Radio Frequency Fingerprint (RFF). RFFs can be used to uniquely identify an emitter to match a received signal with its source. In this paper, the authors propose a novel RFF scheme to separate UIM part from the original signals from the time and frequency domain, and then utilize non-Gaussian measuring tools to extract a set of dimension-reduced secondary features. Additionally, Singular Value Reconstruction (SVR) is developed to extract UIM in the frequency spectrum. In time domain, a curve-fitting residual method is proposed to extract the UIM on the estimated instantaneous phase based on Maximum Likelihood Estimator (MLE). Various aspects of the proposed method are evaluated, including identification accuracy under various Signal-to-Noise Ratio (SNR) conditions, energy relationships between the UIM and the whole signal, and sensitivity to training set size. Compared with other methods, experimental results based on real-world signals prove that the proposed method has remarkable performance and high practicability.
Design of 3D Microgestures for Commands in Virtual Reality or Augmented Reality

Virtual and augmented reality (VR, AR) systems present 3D images that users can interact with using controllers or gestures. The design of the user input process is crucial and determines the interactive efficiency, comfort, and adoption. Gesture-based input provides a device-free interaction that may improve safety and creativity compared to using a hand controller while allowing the hands to perform other tasks. Microgestures with small finger and hand motions may have an advantage over the larger forearm and upper arm gestures by reducing distraction, reducing fatigue, and increasing privacy during the interaction. The design of microgestures should consider user experience, ergonomic principles, and interface design to optimize productivity and comfort while minimizing errors. Forty VR/AR or smart device users evaluated a set of 33 microgestures, designed by ergonomists, and linked them to 20 common AR/VR commands based on usability, comfort, and preference. Based primarily on preference, a set of microgestures linked to specific commands is proposed for VR or AR systems. The proposed microgesture set will likely minimize fatigue and optimize usability. Furthermore, the methodology presented for selecting microgestures and assigning them to commands can be applied to the design of other gesture sets.
Robust Vehicle Classification Based on Deep Features Learning

This paper aims to introduce a scientific Semi-Supervised Fuzzy C-Mean (SSFCM) clustering approach for passenger cars classification based on the feature learning technique.The proposed method is able to classify passenger vehicles in the micro, small, middle, upper middle, large and luxury classes.The performance of the algorithm is analyzed and compared with an unsupervised fuzzy C-means (FCM) clustering algorithm and Swiss expert classification dataset.Experiment results demonstrate that the classification of SSFCM algorithm has better correlation with expert classification than traditional unsupervised algorithm.These results exhibit that SSFCM can reduce the sensitivity of FCM to the initial cluster centroids with the help of labeled instances.Furthermore, SSFCM results in improved classification performance by using the resampling technique to deal with the multi-class imbalanced problem and eliminate the irrelevant and redundant features.
Navigating the ethics of remote research data collection

COVID-19 has accelerated broad trends already in place toward remote research data collection and monitoring. This move implicates novel ethical and regulatory challenges which have not yet received due attention. Existing work is preliminary and does not seek to identify or grapple with the issues in a rigorous and sophisticated way. Here, we provide a framework for identifying and addressing challenges that we believe can help the research community realize the benefits of remote technologies while preserving ethical ideals and public trust. We organize issues into several distinct categories and provide points to consider in a table that can help facilitate ethical design and review of research studies using remote health instruments.
Adversarial Reinforced Instruction Attacker for Robust Vision-Language Navigation

Language instruction plays an essential role in the natural language grounded navigation tasks. However, navigators trained with limited human-annotated instructions may have difficulties in accurately capturing key information from the complicated instruction at different timesteps, leading to poor navigation performance. In this paper, we exploit to train a more robust navigator which is capable of dynamically extracting crucial factors from the long instruction, by using an adversarial attacking paradigm. Specifically, we propose a Dynamic Reinforced Instruction Attacker (DR-Attacker), which learns to mislead the navigator to move to the wrong target by destroying the most instructive information in instructions at different timesteps. By formulating the perturbation generation as a Markov Decision Process, DR-Attacker is optimized by the reinforcement learning algorithm to generate perturbed instructions sequentially during the navigation, according to a learnable attack score. Then, the perturbed instructions, which serve as hard samples, are used for improving the robustness of the navigator with an effective adversarial training strategy and an auxiliary self-supervised reasoning task. Experimental results on both Vision-and-Language Navigation (VLN) and Navigation from Dialog History (NDH) tasks show the superiority of our proposed method over state-of-the-art methods. Moreover, the visualization analysis shows the effectiveness of the proposed DR-Attacker, which can successfully attack crucial information in the instructions at different timesteps. Code is available at https://github.com/expectorlin/DR-Attacker.
Quantum Software Models: The Density Matrix for Classical and Quantum Software Systems Design

Linear Software Models enable rigorous linear algebraic procedures for modular design of classical software systems. These procedures apply a spectral approach to matrix representations - e.g. the Laplacian - of the software system. Recent intensive research efforts towards quantum computers have increased expectations that quantum computing could in due time materialize as a practical alternative to classical computing. It is reasonable to inquire about quantum software desirable features and prepare in advance modular design procedures for quantum software systems. However, it does not make sense to have two totally separate procedures for modular design, one for classical software systems and another for quantum software systems. This paper claims that there should be just a single unified and rigorous design procedure for both classical and quantum software systems. Our common design procedure starting point for both classical and quantum software systems is Von Neumann's quantum notion of Density Operator and its Density Matrix representation. This paper formulates and demonstrates modular design in terms of projection operators obtained from a design Density Matrix and shows their equivalence to the Linear Software Models results of the Laplacian matrix spectrum for the classical case. The application in practice of the design procedure for both classical and quantum software is illustrated by case studies.
Quantum Circuit Learning with Parameterized Boson Sampling

A quantum circuit learning approach is studied to carry out the fast-fitting of Gaussian functions.First, a parameterized structure is designed for quantum circuits based on the boson sampling model.And then, the training procedure of exploiting gradient-based optimizations is presented to iteratively update the gradient of the loss function concerning circuit parameters.For efficiency, two kinds of circuit loss, the kernel maximum mean discrepancy and the mean absolute error, are used in the training procedure, which are both competent to achieve quantum circuit learning well.It is significant that the two circuit losses assist in reducing the variance to 2.54 × 10 -6 and 6.91 × 10 -6 , respectively.Finally, a kind of quantum circuit fixed structure is developed with the boson sampling model that can decrease the model complexity as the circuit depth d grows.Sets of experiments have been conducted to evaluate the proposed quantum circuit learning scheme, and demonstrate that our parameterized approach is efficient and promising, and it is worth looking forward to solving practical application problems with quantum computers since valid quantum circuits for Gaussian function fast-fitting can be designed indeed.
Evaluating Threat Modeling Tools: Microsoft TMT versus OWASP Threat Dragon

Threat modeling is an invaluable part of secure software development. However the use of threat modeling tools has not been well documented, even though they are an important asset. In this paper we examine and compare the two prominent threat modeling tools OWASP Threat Dragon and Microsoft Threat Modeling Tool. We outline core concepts related to threat modeling, namely threat identification approaches, data flow diagrams and the importance of risk analysis.
Detecting anomalies in spacecraft telemetry using evolutionary thresholding and LSTMs

Detecting anomalies in telemetry data captured on-board satellites is a pivotal step towards their safe operation. The data-driven algorithms for this task are often heavily parameterized, and the incorrect hyperparameters can deteriorate their performance. We tackle this issue and introduce a genetic algorithm for evolving a dynamic thresholding approach that follows a long short-term memory network in an unsupervised anomaly detection system. Our experiments show that the genetic algorithm improves the abilities of a detector operating on multi-channel satellite telemetry.
Automatic first-arrival picking method via intelligent Markov optimal decision processes

Abstract Picking the first arrival is an important step in seismic processing. The large volume of the seismic data calls for automatic and objective picking. In this paper, we formulate first-arrival picking as an intelligent Markov decision process in the multi-dimensional feature attribute space. By designing a reasonable model, the global optimization is carried out in the reward function space to obtain the path with the largest cumulative reward value, to achieve the purpose of automatically picking up the first arrival. The state-value function contains a distance-related discount factor γ, which enables the Markov decision process to pick up the first-arrival continuity to consider the lateral continuity of the seismic data and avoid the bad trace information in the seismic data. On this basis, the method of this paper further introduces the optimized model that is a fuzzy clustering-based multi-dimensional attribute reward function and structure-based Gaussian stochastic policy, thereby reducing the difficulty of model design, and making the seismic data pick up more accurately and automatically. Testing this approach in the field seismic data reveals its properties and shows it can automatically pick up more reasonable first arrivals and has a certain quality control ability, especially the first-arrival energy is weak (the signal-to-noise ratio is low) or there are adjacent complex waveforms in the shallow layer.
Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation

Hand segmentation is a crucial task in first-person vision. Since first-person images exhibit strong bias in appearance among different environments, adapting a pre-trained segmentation model to a new domain is required in hand segmentation. Here, we focus on appearance gaps for hand regions and backgrounds separately. We propose (i) foreground-aware image stylization and (ii) consensus pseudo-labeling for domain adaptation of hand segmentation. We stylize source images independently for the foreground and background using target images as style. To resolve the domain shift that the stylization has not addressed, we apply careful pseudo-labeling by taking a consensus between the models trained on the source and stylized source images. We validated our method on domain adaptation of hand segmentation from real and simulation images. Our method achieved state-of-the-art performance in both settings. We also demonstrated promising results in challenging multi-target domain adaptation and domain generalization settings.
DeepFake-o-meter: An Open Platform for DeepFake Detection

In recent years, the advent of deep learning-based techniques and the significant reduction in the cost of computation resulted in the feasibility of creating realistic videos of human faces, commonly known as DeepFakes. The availability of open-source tools to create DeepFakes poses as a threat to the trustworthiness of the online media. In this work, we develop an open-source online platform, known as DeepFake-o-meter, that integrates state-of-the-art DeepFake detection methods and provide a convenient interface for the users. We describe the design and function of DeepFake-o-meter in this work.
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules

The problem of grounding VQA tasks has seen an increased attention in the research community recently, with most attempts usually focusing on solving this task by using pretrained object detectors. However, pre-trained object detectors require bounding box annotations for detecting relevant objects in the vocabulary, which may not always be feasible for real-life large-scale applications. In this paper, we focus on a more relaxed setting: the grounding of relevant visual entities in a weakly supervised manner by training on the VQA task alone. To address this problem, we propose a visual capsule module with a query-based selection mechanism of capsule features, that allows the model to focus on relevant regions based on the textual cues about visual information in the question. We show that integrating the proposed capsule module in existing VQA systems significantly improves their performance on the weakly supervised grounding task. Overall, we demonstrate the effectiveness of our approach on two state-of-the-art VQA systems, stacked NMN and MAC, on the CLEVR-Answers benchmark, our new evaluation set based on CLEVR scenes with groundtruth bounding boxes for objects that are relevant for the correct answer, as well as on GQA, a real world VQA dataset with compositional questions. We show that the systems with the proposed capsule module consistently outperform the respective baseline systems in terms of answer grounding, while achieving comparable performance on VQA task. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Model Explainability using SHAP Values for LightGBM Predictions

The paper aims at demonstrating the cutting-edge tool for machine learning models explainability leveraging LightGBM modelling. The proposed methodology improves inference performance, training time and significantly reduces the "fitting-to-noise" problem for complex datasets, and additionally, grants more insight into model predictions. Discussed methodology is versatile and finds applications in processing big data sets from different fields of science, ranging from econometry to medical or SHM applications, in which networks of MEMS sensors are used.
CFLedger: Preventing chargeback fraud with blockchain

Chargeback fraud is a fast-growing threat to e-commerce, bringing huge losses to online sellers, but there is still a lack of effective technical counter measures. To combat chargeback fraud, this paper proposes a consortium blockchain based chargeback fraud data sharing mechanism — CFLedger, which includes multi-source reporting, anti-tamper accounting, and post-supervision. This paper also analyzes the efficiency, effectiveness, and practicality of CFLedger, proving that it is an effective and feasible mechanism to prevent chargeback fraud.
Design of PID Controller Based on Echo State Network With Time-Varying Reservoir Parameter

In this article, a new design method based on the echo state network with time-varying reservoir parameter (TVRP-ESN) is proposed to optimize the proportional-integral-derivative (PID) controller parameters for a class of discrete-time systems with time delay. The TVRP-ESN can quickly obtain the PID controller parameters to meet the control performance of the system. According to the network learning and approximation ability of TVRP-ESN, the output weights and the reservoir parameters of TVRP-ESN can be synchronously updated, and then the TVRP-ESN can improve the convergence speed of determining the PID controller parameters. In order to update the output weights and the reservoir parameters of TVRP-ESN, the partial derivative of the system output error is used. Three simulation examples are used to show the effectiveness of the proposed method.
Cross-Dataset Point Cloud Recognition Using Deep-Shallow Domain Adaptation Network

In this work, we propose a new two-view domain adaptation network named Deep-Shallow Domain Adaptation Network (DSDAN) for 3D point cloud recognition. Different from the traditional 2D image recognition task, the valuable texture information is often absent in point cloud data, making point cloud recognition a challenging task, especially in the cross-dataset scenario where the training and testing data exhibit a considerable distribution mismatch. In our DSDAN method, we tackle the challenging cross-dataset 3D point cloud recognition task from two aspects. On one hand, we propose a two-view learning framework, such that we can effectively leverage multiple feature representations to improve the recognition performance. To this end, we propose a simple and efficient Bag-of-Points feature method, as a complementary view to the deep representation. Moreover, we also propose a cross view consistency loss to boost the two-view learning framework. On the other hand, we further propose a two-level adaptation strategy to effectively address the domain distribution mismatch issue. Specifically, we apply a feature-level distribution alignment module for each view, and also propose an instance-level adaptation approach to select highly confident pseudo-labeled target samples for adapting the model to the target domain, based on which a co-training scheme is used to integrate the learning and adaptation process on the two views. Extensive experiments on the benchmark dataset show that our newly proposed DSDAN method outperforms the existing state-of-the-art methods for the cross-dataset point cloud recognition task.
Robust phase unwrapping algorithm for noisy and segmented phase measurements

This paper proposes a robust phase unwrapping algorithm (RPUA) for phase unwrapping in the presence of noise and segmented phase. The RPUA method presents a new model of phase derivatives combined with error-correction iterations to achieve an anti-noise effect. Moreover, it bridges the phase islands in the spatial domain using numerical carrier frequency and fringe extrapolation thus eliminating height faults to enable solving segmented phase unwrapping. Numerical simulation and comparison with three conventional methods were performed, proving the high robustness and efficiency of the RPUA. Further, three experiments demonstrated that the RPUA can obtain the unwrapped phase under different noise accurately and possesses the capability to process segmented phases, indicating reliable practicality.
Motion planning by learning the solution manifold in trajectory optimization

The objective function used in trajectory optimization is often non-convex and can have an infinite set of local optima. In such cases, there are diverse solutions to perform a given task. Although there are a few methods to find multiple solutions for motion planning, they are limited to generating a finite set of solutions. To address this issue, we present an optimization method that learns an infinite set of solutions in trajectory optimization. In our framework, diverse solutions are obtained by learning latent representations of solutions. Our approach can be interpreted as training a deep generative model of collision-free trajectories for motion planning. The experimental results indicate that the trained model represents an infinite set of homotopic solutions for motion planning problems.
Skills Gaps in the Industry

Many practitioners in the software-intensive embedded industry often face difficulties after beginning their careers due to misalignment of the skills learned at the university with what is required in the workplace. Companies spend crucial resources to train personnel whose academic backgrounds are not only based on “computing disciplines” but also on non-computing ones. Analyzing the gap between the software industry and academia is important for three reasons: (1) for employers, hiring properly trained practitioners allows them to spend less time in training them while incorporating them more efficiently into the workforce; (2) for practitioners, knowing the most important skillset is helpful to increase their chance of employability; and (3) for academia, understanding the necessary skillset is critical to making curriculum changes. To achieve these objectives, we conducted a survey that yielded responses from 659 software professionals working worldwide in different roles. In this study, we only included the responses of 393 embedded software practitioners whose undergraduate degree was completed in Turkey, working in 10 countries. This article sheds light on the most important skills in the embedded software industry by presenting various cross-factor analyses. Understanding the coverage of these skills in the curriculum (mostly in Turkish universities) helps bridge the gaps, which can and should be achieved through more Industry Academia Collaborations (IACs).