OpenRaiser/Intern-Atlas huggingface dataset:
On the Effect of Mutual Coupling in One-Bit Spatial Sigma-Delta Massive MIMO Systems
Venue: arXiv cs.IT 2020
Year: 2020

The one-bit spatial Sigma-Delta concept has recently been proposed as an approach for achieving low distortion and low power consumption for massive multi-input multi-output systems. The approach exploits users located in known angular sectors or spatial oversampling to shape the quantization noise away from desired directions of arrival. While reducing the antenna spacing alleviates the adverse impact of quantization noise, it can potentially deteriorate the performance of the massive array due to excessive mutual coupling. In this paper, we analyze the impact of mutual coupling on the uplink spectral efficiency of a spatial one-bit Sigma-Delta massive MIMO architecture, and compare the resulting performance degradation to standard one-bit quantization as well as the ideal case with infinite precision. Our simulations show that the one-bit Sigma-Delta array is particularly advantageous in space-constrained scenarios, can still provide significant gains even in the presence of mutual coupling when the antennas are closely spaced.
Synaptic Learning with Augmented Spikes
Venue: arXiv cs.NE 2020
Year: 2020

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements on efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this paper, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insight into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness and applicability to practical tasks such as acoustic and visual pattern recognition. The remarkable results highlight the effectiveness and potential merits of our methods. Importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them including neuromorphic computing.
Pretraining Federated Text Models for Next Word Prediction
Venue: arXiv cs.LG 2020
Year: 2020

Federated learning is a decentralized approach for training models on distributed devices, by summarizing local changes and sending aggregate parameters from local models to the cloud rather than the data itself. In this research we employ the idea of transfer learning to federated training for next word prediction (NWP) and conduct a number of experiments demonstrating enhancements to current baselines for which federated NWP models have been successful. Specifically, we compare federated training baselines from randomly initialized models to various combinations of pretraining approaches including pretrained word embeddings and whole model pretraining followed by federated fine tuning for NWP on a dataset of Stack Overflow posts. We realize lift in performance using pretrained embeddings without exacerbating the number of required training rounds or memory footprint. We also observe notable differences using centrally pretrained networks, especially depending on the datasets used. Our research offers effective, yet inexpensive, improvements to federated NWP and paves the way for more rigorous experimentation of transfer learning techniques for federated learning.
Decoding of Grasp Motions from EEG Signals Based on a Novel Data Augmentation Strategy
Venue: arXiv cs.HC 2020
Year: 2020

Electroencephalogram (EEG) based brain-computer interface (BCI) systems are useful tools for clinical purposes like neural prostheses. In this study, we collected EEG signals related to grasp motions. Five healthy subjects participated in this experiment. They executed and imagined five sustained-grasp actions. We proposed a novel data augmentation method that increases the amount of training data using labels obtained from electromyogram (EMG) signals analysis. For implementation, we recorded EEG and EMG simultaneously. The data augmentation over the original EEG data concluded higher classification accuracy than other competitors. As a result, we obtained the average classification accuracy of 52.49% for motor execution (ME) and 40.36% for motor imagery (MI). These are 9.30% and 6.19% higher, respectively than the result of the comparable methods. Moreover, the proposed method could minimize the need for the calibration session, which reduces the practicality of most BCIs. This result is encouraging, and the proposed method could potentially be used in future applications such as a BCI-driven robot control for handling various daily use objects.
Multi-Party Timed Commitments
Venue: arXiv cs.CR 2020
Year: 2020

The problem of obtaining secret commitments from multiple parties and revealing them after a certain time is useful for sealed-bid auctions, games, and other applications. Existing solutions, dating back to Rivest, Shamir and Wagner, either do not scale or rely on synchrony for the commitment phase and trust of $t/n$ parties. We formalize the problem of implementing such commitments with a probabilistic delay and without the aforementioned assumptions as Multi-Party Timed Commitments (MPTC) and present a solution -- the Time-Capsule protocol. Like previous approaches, Time Capsule forms a puzzle whose solution reveals the committed values. But unlike previous solutions, no party has an advantage in solving the puzzle, and individual commitments cannot be revealed before the entire set is committed. A particular application of MPTC realizes an advancement in the study of decentralized systems. The state of the art in decentralized systems is manifested in blockchain systems that utilize Proof of Work to achieve censorship resistance. However, they are still vulnerable to frontrunning, an issue that is plaguing operational systems. By adapting Time Capsule, we allow it to be used for Proof of Work, preventing frontrunning by system operators and tuning the puzzle difficulty using the blockchain mechanism.
A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices
Venue: arXiv cs.CR 2020
Year: 2020

Currently, Android malware detection is mostly performed on server side against the increasing number of malware. Powerful computing resource provides more exhaustive protection for app markets than maintaining detection by a single user. However, apart from the applications provided by the official market, apps from unofficial markets and third-party resources are always causing serious security threats to end-users. Meanwhile, it is a time-consuming task if the app is downloaded first and then uploaded to the server side for detection, because the network transmission has a lot of overhead. In addition, the uploading process also suffers from the security threats of attackers. Consequently, a last line of defense on mobile devices is necessary and much-needed. In this paper, we propose an effective Android malware detection system, MobiTive, leveraging customized deep neural networks to provide a real-time and responsive detection environment on mobile devices. MobiTive is a preinstalled solution rather than an app scanning and monitoring engine using after installation, which is more practical and secure. Original deep learning models cannot be directly deployed and executed on mobile devices due to various performance limitations, such as computation power, memory size, and energy. Therefore, we evaluate and investigate the following key points:(1) the performance of different feature extraction methods based on source code or binary code;(2) the performance of different feature type selections for deep learning on mobile devices;(3) the detection accuracy of different deep neural networks on mobile devices;(4) the real-time detection performance and accuracy on different mobile devices;(5) the potential based on the evolution trend of mobile devices' specifications; and finally we further propose a practical solution (MobiTive) to detect Android malware on mobile devices.
Prior choice affects ability of Bayesian neural networks to identify unknowns
Venue: arXiv cs.LG 2020
Year: 2020

Deep Bayesian neural networks (BNNs) are a powerful tool, though computationally demanding, to perform parameter estimation while jointly estimating uncertainty around predictions. BNNs are typically implemented using arbitrary normal-distributed prior distributions on the model parameters. Here, we explore the effects of different prior distributions on classification tasks in BNNs and evaluate the evidence supporting the predictions based on posterior probabilities approximated by Markov Chain Monte Carlo sampling and by computing Bayes factors. We show that the choice of priors has a substantial impact on the ability of the model to confidently assign data to the correct class (true positive rates). Prior choice also affects significantly the ability of a BNN to identify out-of-distribution instances as unknown (false positive rates). When comparing our results against neural networks (NN) with Monte Carlo dropout we found that BNNs generally outperform NNs. Finally, in our tests we did not find a single best choice as prior distribution. Instead, each dataset yielded the best results under a different prior, indicating that testing alternative options can improve the performance of BNNs.
Facial Electromyography-based Adaptive Virtual Reality Gaming for Cognitive Training
Venue: arXiv cs.HC 2020
Year: 2020

Cognitive training has shown promising results for delivering improvements in human cognition related to attention, problem solving, reading comprehension and information retrieval. However, two frequently cited problems in cognitive training literature are a lack of user engagement with the training programme, and a failure of developed skills to generalise to daily life. This paper introduces a new cognitive training (CT) paradigm designed to address these two limitations by combining the benefits of gamification, virtual reality (VR), and affective adaptation in the development of an engaging, ecologically valid, CT task. Additionally, it incorporates facial electromyography (EMG) as a means of determining user affect while engaged in the CT task. This information is then utilised to dynamically adjust the game's difficulty in real-time as users play, with the aim of leading them into a state of flow. Affect recognition rates of 64.1% and 76.2%, for valence and arousal respectively, were achieved by classifying a DWT-Haar approximation of the input signal using kNN. The affect-aware VR cognitive training intervention was then evaluated with a control group of older adults. The results obtained substantiate the notion that adaptation techniques can lead to greater feelings of competence and a more appropriate challenge of the user's skills.
Intelligent Tutoring Systems for Generation Z's Addiction
Venue: arXiv cs.HC 2020
Year: 2020

As generation Z's big data is flooding the Internet through social nets, neural network based data processing is turning an important cornerstone, showing significant potential for fast extraction of data patterns. Online course delivery and associated tutoring are transforming into customizable, on-demand services driven by the learner. Besides automated grading, strong potential exists for the development and deployment of next generation intelligent tutoring software agents. Self-adaptive, online tutoring agents exhibiting "intelligent-like" behavior, being capable "to learn" from the learner, will become the next educational superstars. Over the past decade, computer-based tutoring agents were deployed in a variety of extended reality environments, from patient rehabilitation to psychological trauma healing. Most of these agents are driven by a set of conditional control statements and a large answers/questions pairs dataset. This article provides a brief introduction on Generation Z's addiction to digital information, highlights important efforts for the development of intelligent dialogue systems, and explains the main components and important design decisions for Intelligent Tutoring System.
Interactive Sensor Dashboard for Smart Manufacturing
Venue: arXiv cs.HC 2020
Year: 2020

This paper presents development of a smart sensor dashboard for Industry 4.0 encompassing both 2D and 3D visualization modules. In 2D module, we described physical connections among sensors and visualization modules and rendering data on 2D screen. A user study was presented where participants answered a few questions using four types of graphs. We analyzed eye gaze patterns in screen, number of correct answers and response time for all the four graphs. For 3D module, we developed a VR digital twin for sensor data visualization. A user study was presented evaluating the effect of different feedback scenarios on quantitative and qualitative metrics of interaction in the virtual environment. We compared visual and haptic feedback and a multimodal combination of both visual and haptic feedback for VR environment. We found that haptic feedback significantly improved quantitative metrics of interaction than a no feedback case whereas a multimodal feedback is significantly improved qualitative metrics of the interaction.
Delightful Companions: Supporting Well-Being Through Design Delight
Venue: arXiv cs.HC 2020
Year: 2020

This paper presents three design products referred to as delightful companions that are intended to help people engage in well-being practices. It also introduces the approach utilized to guide the design decisions during their creation. Design delight is the name of this approach, which comprises six experiential qualities that are regarded as antecedents of delight. The objective of this paper is to introduce the approach and the companions and state the two paths that have defined the future steps of this research.
Sparsifying Parity-Check Matrices
Venue: arXiv cs.IT 2020
Year: 2020

Parity check matrices (PCMs) are used to define linear error correcting codes and ensure reliable information transmission over noisy channels. The set of codewords of such a code is the null space of this binary matrix. We consider the problem of minimizing the number of one-entries in parity-check matrices. In the maximum-likelihood (ML) decoding method, the number of ones in PCMs is directly related to the time required to decode messages. We propose a simple matrix row manipulation heuristic which alters the PCM, but not the code itself. We apply simulated annealing and greedy local searches to obtain PCMs with a small number of one entries quickly, i.e. in a couple of minutes or hours when using mainstream hardware. The resulting matrices provide faster ML decoding procedures, especially for large codes.
Do we know the operating principles of our computers better than those of our brain?
Venue: arXiv cs.DC 2020
Year: 2020

The increasing interest in understanding the behavior of the biological neural networks, and the increasing utilization of artificial neural networks in different fields and scales, both require a thorough understanding of how neuromorphic computing works. On the one side, the need to program those artificial neuron-like elements, and, on the other side, the necessity for a large number of such elements to cooperate, communicate and compute during tasks, need to be scrutinized to determine how efficiently conventional computing can assist in implementing such systems. Some electronic components bear a surprising resemblance to some biological structures. However, combining them with components that work using different principles can result in systems with very poor efficacy. The paper discusses how the conventional principles, components and thinking about computing limit mimicking the biological systems. We describe what changes will be necessary in the computing paradigms to get closer to the marvelously efficient operation of biological neural networks.
Robot art, in the eye of the beholder?: Personalization through self-disclosure facilitates visual communication of emotions in representational art
Venue: arXiv cs.HC 2020
Year: 2020

Socially assistive robots could help to support people's well-being in contexts such as art therapy where human therapists are scarce, by making art such as paintings together with people in a way that is emotionally contingent and creative. However, current art-making robots are typically either contingent, controlled as a tool by a human artist, or creative, programmed to paint independently, possibly because some complex and idiosyncratic concepts related to art, such as emotion and creativity, are not yet well understood. For example, the role of personalized taste in forming beauty evaluations has been studied in empirical aesthetics, but how to generate art that appears to an individual to express certain emotions such as happiness or sadness is less clear. In the current article, a collaborative prototyping/Wizard of Oz approach was used to explore generic robot art-making strategies and personalization of art via an open-ended emotion profile intended to identify tricky concepts. As a result of conducting an exploratory user study, participants indicated some preference for a robot art-making strategy involving "visual metaphors" to balance exogenous and endogenous components, and personalized representational sketches were reported to convey emotion more clearly than generic sketches. The article closes by discussing personalized abstract art as well as suggestions for richer art-making strategies and user models. Thus, the main conceptual advance of the current work lies in suggesting how an interactive robot can visually express emotions through personalized art; the general aim is that this could help to inform next steps in this promising area and facilitate technological acceptance of robots in everyday human environments.
Evaluating Sparse Interpretable Word Embeddings for Biomedical Domain
Venue: arXiv cs.CL 2020
Year: 2020

Word embeddings have found their way into a wide range of natural language processing tasks including those in the biomedical domain. While these vector representations successfully capture semantic and syntactic word relations, hidden patterns and trends in the data, they fail to offer interpretability. Interpretability is a key means to justification which is an integral part when it comes to biomedical applications. We present an inclusive study on interpretability of word embeddings in the medical domain, focusing on the role of sparse methods. Qualitative and quantitative measurements and metrics for interpretability of word vector representations are provided. For the quantitative evaluation, we introduce an extensive categorized dataset that can be used to quantify interpretability based on category theory. Intrinsic and extrinsic evaluation of the studied methods are also presented. As for the latter, we propose datasets which can be utilized for effective extrinsic evaluation of word vectors in the biomedical domain. Based on our experiments, it is seen that sparse word vectors show far more interpretability while preserving the performance of their original vectors in downstream tasks.
Conformally Mapped Polynomial Chaos Expansions for Maxwell's Source Problem with Random Input Data
Venue: arXiv cs.CE 2020
Year: 2020

Generalized Polynomial Chaos (gPC) expansions are well established for forward uncertainty propagation in many application areas. Although the associated computational effort may be reduced in comparison to Monte Carlo techniques, for instance, further convergence acceleration may be important to tackle problems with high parametric sensitivities. In this work, we propose the use of conformal maps to construct a transformed gPC basis, in order to enhance the convergence order. The proposed basis still features orthogonality properties and hence, facilitates the computation of many statistical properties such as sensitivities and moments. The corresponding surrogate models are computed by pseudo-spectral projection using mapped quadrature rules, which leads to an improved cost accuracy ratio. We apply the methodology to Maxwell's source problem with random input data. In particular, numerical results for a parametric finite element model of an optical grating coupler are given.
ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Electrocardiogram (ECG) detection and delineation are key steps for numerous tasks in clinical practice, as ECG is the most performed non-invasive test for assessing cardiac condition. State-of-the-art algorithms employ digital signal processing (DSP), which require laborious rule adaptation to new morphologies. In contrast, deep learning (DL) algorithms, especially for classification, are gaining weight in academic and industrial settings. However, the lack of model explainability and small databases hinder their applicability. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. For this purpose, we adapted and validated the most used neural network architecture for image segmentation, the U-Net, to one-dimensional data. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings, for single- and multi-lead scenarios. To alleviate data scarcity, data regularization techniques such as pre-training with low-quality data labels, performing ECG-based data augmentation and applying strong model regularizers to the architecture were attempted. Other variations in the model's capacity (U-Net's depth and width), alongside the application of state-of-the-art additions, were evaluated. These variations were exhaustively validated in a 5-fold cross-validation manner. The best performing configuration reached precisions of 90.12%, 99.14% and 98.25% and recalls of 98.73%, 99.94% and 99.88% for the P, QRS and T waves, respectively, on par with DSP-based approaches. Despite being a data-hungry technique trained on a small dataset, DL-based approaches demonstrate to be a viable alternative to traditional DSP-based ECG processing techniques.
System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis
Venue: arXiv cs.AI 2020
Year: 2020

This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for system failures. As a result, methods that are effective for forecasting risk and informing maintenance decisions regarding individual components do not readily scale to provide reliable sub-system or system level insights. A novel holistic modeling approach is needed to incorporate available structural and physical knowledge and naturally handle the complexities of actively fielded and maintained assets.