Reviewerly/exHarmony huggingface dataset:
Robust Face Mask Detection using Deep Learning on IoT Devices

The outbreak of COVID-19 has become a threat to the entire world. Wearing a face mask is an effective method to prevent from droplet spreading of COVID-19 in public areas. Face mask detection is an emerging technique that can be utilized on IoT devices to monitor and remind people to wear face mask. This paper focuses on face mask detection using deep learning on IoT devices. Specifically, four convolutional neural networks, namely, MobileNet V2, Inception V3, VGG 16, and ResNet 50 are implemented for face mask detection and tested by running inference on mobile GPU powered IoT devices including NVIDIA Jetson TX2 and NVIDIA Jetson Nano. Performance comparison and analysis are carried out using various sizes of training data, and the experimental results demonstrate that these deep learning models are robust to various size and quality of the available training data.
FGCAN: Filter-based Gated Contextual Attention Network for event detection

Contextual information is widely used in natural language processing and is important for event detection. How to make full use of contextual information is a challenging problem. Traditional event detection methods mainly use sentence-level information to identify event triggers and classify them into specific types. Event trigger is defined as the word or phrase that most clearly expresses an event occurrence. However, the information used for detecting events is usually spread across multiple sentences, and sentence-level information is often insufficient to resolve ambiguities for some types of events. In this paper, we propose a novel Filter-based Gated Contextual Attention Network model called FGCAN, which is augmented with hierarchical contextualized representations to utilize both sentence-level and document-level information. In document level, we construct a gated contextual attention layer to extract document-level information by considering the relatedness between the current and other sentences and dynamically incorporate it into words. In this way, we can get cross-sentence clues without designing complex inference rules. In sentence level, we feed sentences into the classifier to get global information of them, and devise a rule-based filter algorithm to rectify the prediction of each word based on the probability ranking of the sentence labels, which is highly interpretable. These two mechanisms focusing on different scopes of contextual information can complement each other. The experimental results on the widely used ACE 2005 and KBP 2015 datasets show that our approach outperforms the state-of-the-art methods and the two components are effective in using contextual information.
Quantifying the impact of boundary constraint handling methods on differential evolution

Constraint handling is one of the most influential aspects of applying metaheuristics to real-world applications, which can hamper the search progress if treated improperly. In this work, we focus on a particular case - the box constraints, for which many boundary constraint handling methods (BCHMs) have been proposed. We call for the necessity of studying the impact of BCHMs on metaheuristics' performance and behavior, which receives seemingly little attention in the field. We target quantifying such impacts through systematic benchmarking by investigating 28 major variants of Differential Evolution (DE) taken from the modular DE framework (by combining different mutation and crossover operators) and 13 commonly applied BCHMs, resulting in 28 X 13 = 364 algorithm instances after pairing DE variants with BCHMs. After executing the algorithm instances on the well-known BBOB/COCO problem set, we analyze the best-reached objective function value (performance-wise) and the percentage of repaired solutions (behavioral) using statistical ranking methods for each combination of mutation, crossover, and BBOB function group. Our results clearly show that the choice of BCHMs substantially affects the empirical performance as well as the number of generated infeasible solutions, which allows us to provide general guidelines for selecting an appropriate BCHM for a given scenario.
Spline-Extrapolation of Video Traffic of IoT-Devices Based on Various Cubic Splines

The solution to the problem of predicting the characteristics of real-time video traffic of IoT network objects is considered. The classification of traffic of IoT network objects depending on the type of IoT/5G network services is proposed. For the considered video traffic in real time, the spline-extrapolation method based on the cubic Hermite spline was used. Comparison of the results of predicting the characteristics of video traffic using the spline-extrapolation method based on various cubic spline functions (cubic spline, cubic B-spline and cubic Hermite spline) is carried out. It is established that the obtained results of short-term forecasting of video traffic will provide necessary forecast accuracy and the possibility of using it for various IoT applications in order to prevent network congestion, especially in conditions of maximum network load.
Preferential Bayesian optimisation with skew gaussian processes

Preferential Bayesian optimisation (PBO) deals with optimisation problems where the objective function can only be accessed via preference judgments, such as "this is better than that" between two candidate solutions (like in A/B tests). The state-of-the-art approach to PBO uses a Gaussian process to model the preference function and a Bernoulli likelihood to model the observed pair-wise comparisons. Laplace's method is then employed to compute posterior inferences and, in particular, to build an appropriate acquisition function. In this paper, we prove that the true posterior distribution of the preference function is a Skew Gaussian Process (SkewGP), with highly skewed pairwise marginals and, thus, show that Laplace's method usually provides a very poor approximation. We then derive an efficient method to compute the exact SkewGP posterior and use it as surrogate model for PBO employing standard acquisition functions (Upper-Credible-Bound, etc.). We illustrate the benefits of our exact PBO-SkewGP in a variety of experiments, by showing that it consistently outperforms PBO based on Laplace's approximation both in terms of convergence speed and computational time. We also show that our framework can be extended to deal with mixed preferential-categorical BO, where binary judgments (valid or non-valid) together with preference judgments are available.
Listening Like a Postdigital Human: The Politics and Knowledge of Noise

This chapter explores how the postdigital era has or might change listening practices. Locating the productive possibilities of the postdigital human in the ways it opens up definitions of humans, machines, and the relations between the two to struggle and contestation, this chapter extends these debates to the sonic register. It begins with a brief review of postdigital literature that emphasizes the old and the new characteristics of such an era, before turning to Marx’s conception of machinery to draw out how the machine has been seen as the objectification of human intelligence. Next it considers the aesthetic and political questions about postdigital human senses, examines key works in sound studies, and briefly revisits the history of the production and reception of music in the twentieth century. This allows not only for situating the argument within the context of colonialism and anti-colonialism but also for exploring the importance of noise in rebellions and the shifting definitions of noise that took place in response to such rebellions. In some ways, digital music production eliminates noise by turning analogue signals into ones and zeros. In other ways, however, it introduces noise into the most “human” of all instruments: the voice. Here it becomes literally impossible to tell where the human ends and the digital begins.
Automatic classification method of thyroid pathological images using multiple magnification factors

Thyroid cancer is the most common form of endocrine malignancy, and the informative pathological images are critical for thyroid cancer risk stratification, prognosis and treatment guidance. Deep learning methods have achieved promising results on pathology image classification benchmarks. However, due to the complexity of thyroid carcinoma pathological images and the lack of labeled data, there are few researches study on the auto-classification of thyroid cancer. Inspired by the diagnostic process of pathologists, this paper proposes an active classification method for papillary thyroid carcinoma (PTC) pathological images classification to divide thyroid pathological images into PTC and normal thyroid pathological images. We employ the attention mechanism to combine pathological images with different magnification factors, which imitates the diagnosis procedures of thyroid cancer under the microscope. At the same time, we utilize the uncertainty and representative information provided by the convolutional neural network to determine the most valuable samples for annotation that can reduce the labeling cost. Besides, a pathological image dataset named VIP-TCHis is conducted from thyroid tissues of 55 real cases. The experimental results show that our method can obtain good performance of PTC recognition on the VIP-TCHis dataset.
Using Visual Guides to Reduce Virtual Reality Sickness in First-Person Shooter Games: Correlation Analysis

Background The virtual reality (VR) content market is rapidly growing due to an increased supply of VR devices such as head-mounted displays (HMDs), whereas VR sickness (reported to occur while experiencing VR) remains an unsolved problem. The most widely used method of reducing VR sickness is the use of a rest frame that stabilizes the user's viewpoint by providing fixed visual stimuli in VR content (including video). However, the earth-fixed grid and natural independent visual background that are widely used as rest frames cannot maintain VR fidelity, as they reduce the immersion and the presence of the user. A visual guide is a visual element (eg, a crosshair of first-person shooter [FPS]) that induces a user's gaze movement within the VR content while maintaining VR fidelity, whereas there are no studies on the correlation of visual guide with VR sickness. Objective This study aimed to analyze the correlation between VR sickness and crosshair, which is widely used as a visual guide in FPS games. Methods Eight experimental scenarios were designed and evaluated, including having the visual guide on/off, the game controller on/off, and varying the size and position of the visual guide to determine the effect of visual guide on VR sickness. Results The results showed that VR sickness significantly decreased when visual guide was applied in an FPS game. In addition, VR sickness was lower when the visual guide was adjusted to 30% of the aspect ratio and positioned in the head-tracking direction. Conclusions The experimental results of this study indicate that the visual guide can achieve VR sickness reduction while maintaining user presence and immersion in the virtual environment. In other words, the use of a visual guide is expected to solve the existing limitation of distributing various types of content due to VR sickness.
Tandem Deep Learning Side-Channel Attack on FPGA Implementation of AES

Abstract Side-channel attacks have become a realistic threat to implementations of cryptographic algorithms, especially with the help of deep-learning techniques. The majority of recently demonstrated deep-learning side-channel attacks use a single neural network classifier to extract the secret from implementations of cryptographic algorithms. The potential benefits of combining multiple classifiers using the ensemble learning method have not been fully explored in the side-channel attack’s context. In this paper, we propose a tandem approach for the attack in which multiple models are trained on different attack points but are used in parallel to recover the key. Such an approach allows us to considerably reduce (33.5% on average) the number of traces required to recover the key from an FPGA implementation of AES by power analysis. We also show that not all combinations of classifiers improve the attack efficiency.
Recursive maximum likelihood estimation with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1769" altimg="si5.svg"><mml:mi>t</mml:mi></mml:math>-distribution noise model

In this paper, a recursive t-distribution noise model based maximum likelihood estimation algorithm for discrete-time dynamic state estimation is proposed. The proposed estimator is robust to outliers because the "thick tail" of the t-distribution reduces the effect of large errors in the likelihood function. A computationally efficient recursive algorithm is derived using the influence function. As the t-distribution reduces to the Gaussian distribution when its degree of freedom tends to infinity, the proposed estimator reduces to the Kalman filter. The mean squared error is used to evaluate the performance of the proposed estimator. Compared with the Kalman filter, the proposed estimator is more robust to outliers in the process and measurement noise. Simulations show that for the particle filter to give a better mean squared error, its computational time is two orders of magnitude slower than the proposed estimator.
Tunable U-Net: Controlling Image-to-Image Outputs Using a Tunable Scalar Value

Image-to-image conversion tasks are more accurate and sophisticated than ever thanks to advances in deep learning.However, since typical deep learning models are trained to perform only one task, multiple trained models are required to perform each task even if they are related to each other.For example, the popular image-to-image convolutional neural network, U-Net, is normally trained for a single task.Based on U-Net, this study proposes a model that outputs variable results using only one trained model.The proposed method produces a continuously changing output by setting an external parameter.We confirm the robustness of our proposed model by evaluating it on binarization and background blurring.According to these evaluations, we confirmed that the proposed model can generate well-predicted outputs by using un-trained tuning parameters as well as the outputs by using trained tuning parameters.Furthermore, the proposed model can generate extrapolated outputs outside the learning range.
Architecture information communication in two OSS projects: The why, who, when, and what

Architecture information is vital for Open Source Software (OSS) development, and mailing list is one of the widely used channels for developers to share and communicate architecture information. This work investigates the nature of architecture information communication (i.e., why, who, when, and what) by OSS developers via developer mailing lists. We employed a multiple case study approach to extract and analyze the architecture information communication from the developer mailing lists of two OSS projects, ArgoUML and Hibernate, during their development life-cycle of over 18 years. Our main findings are: (a) architecture negotiation and interpretation are the two main reasons (i.e., why) of architecture communication; (b) the amount of architecture information communicated in developer mailing lists decreases after the first stable release (i.e., when); (c) architecture communications centered around a few core developers (i.e., who); (d) and the most frequently communicated architecture elements (i.e., what) are Architecture Rationale and Architecture Model. There are a few similarities of architecture communication between the two OSS projects. Such similarities point to how OSS developers naturally gravitate towards the four aspects of architecture communication in OSS development.
Progressive conditional GAN-based augmentation for 3D object recognition

We consider the 3D object recognition problem from the perspective of the lack of labelled data. In this paper, we propose a novel progressive conditional generative adversarial network (PC-GAN) for 3D object recognition by conditioning the input with progressive learning strategies. PC-GAN is a powerful adversarial model whose generator automatically produces realistic 3D objects with annotations, and the discriminator distinguishes them from the training distribution and recognizes their categories. We train the discriminative classifier simultaneously with the generator to predict the class label by embedding a SoftMax classifier. Progressive learning uses input samples from lower to higher resolutions to increase the generator performance gradually and produce informative objects for a certain class of objects. The key idea of adopting progressing learning is to mitigate overshoots issues of the discriminator and increase variations in the generated objects by learning progressively. This strategy helps the generator to produce more realistic synthetic objects and improve the active classification performance of the discriminator. Our proposed PC-GAN is trained for object classification in a supervised manner and the performance is evaluated on two public datasets. Experimental results demonstrate that our adversarial PC-GAN outperforms the existing volumetric discriminative classifiers in term of classification accuracy.
Securing Wireless Communication Using RF Fingerprinting

Recently, RF fingerprinting has become an arousing and emerging technology in identifying multiple wireless devices. The method is also believed to have a strong impact on its applications in the wireless security system. Security has always been a critical issue for wireless devices including in the application of Wireless Local Area Network (WLAN). For instance, Media Access Control (MAC) spoofing which is a malicious technique of changing a factory-assigned MAC address of a Network Interface Card (NIC) installed in a device. Due to this issue, this study suggests on making use of a network device's unique RF fingerprint obtained from its raw baseband IQ samples to identify the transmitting radio. For WLAN, as RF fingerprinting is a physical layer security implementation, WLAN physical layer protocol data unit (PPDU) which contains L-LTF in preamble is extracted. Particularly, the RF fingerprinting process includes deep learning of convolutional neural network (CNN) as a classifier. The neural network is used to train a model by tuning and test-validation test before finalizing it as a final model for classification method for a security system.
Pixel-Wise Failure Prediction For Semantic Video Segmentation

We propose a pixel-accurate failure prediction approach for semantic video segmentation. The proposed scheme improves previously proposed failure prediction methods which so far disregarded the temporal information in videos. Our approach consists of two main steps: First, we train an LSTM-based model to detect spatio-temporal patterns that indicate pixel-wise misclassifications in the current video frame. Second, we use sequences of failure predictions to train a denoising autoencoder that both refines the current failure prediction and predicts future misclassifications. Since public data sets for this scenario are limited, we introduce the large-scale densely annotated video driving (DAVID) data set generated using the CARLA simulator. We evaluate our approach on the real-world Cityscapes data set and the simulator-based DAVID data set. Our experimental results show that spatiotemporal failure prediction outperforms single-image failure prediction by up to 8.8%. Refining the prediction using a sequence of previous failure predictions further improves the performance by a significant 15.2% and allows to accurately predict misclassifications for future frames. While we focus our study on driving videos, the proposed approach is general and can be easily used in other scenarios as well.
Deep Nearest Neighbor Website Fingerprinting Attack Technology

By website fingerprinting (WF) technologies, local listeners are enabled to track the specific website visited by users through an investigation of the encrypted traffic between the users and the Tor network entry node. The current triplet fingerprinting (TF) technique proved the possibility of small sample WF attacks. Previous research methods only concentrate on extracting the overall features of website traffic while ignoring the importance of website local fingerprinting characteristics for small sample WF attacks. Thus, in the present paper, a deep nearest neighbor website fingerprinting (DNNF) attack technology is proposed. The deep local fingerprinting features of websites are extracted via the convolutional neural network (CNN), and then the k-nearest neighbor (k-NN) classifier is utilized to classify the prediction. When the website provides only 20 samples, the accuracy can reach 96.2%. We also found that the DNNF method acts well compared to the traditional methods in coping with transfer learning and concept drift problems. In comparison to the TF method, the classification accuracy of the proposed method is improved by 2%–5% and it is only dropped by 3% when classifying the data collected from the same website after two months. These experiments revealed that the DNNF is a more flexible, efficient, and robust website fingerprinting attack technology, and the local fingerprinting features of websites are particularly important for small sample WF attacks.
POI Recommendation Method Using Deep Learning in Location-Based Social Networks

To solve the problems of large data sparsity and lack of negative samples in most point of interest (POI) recommendation methods, a POI recommendation method based on deep learning in location-based social networks is proposed. Firstly, a bidirectional long-short-term memory (Bi-LSTM) attention mechanism is designed to give different weights to different parts of the current sequence according to users’ long-term and short-term preferences. Then, the POI recommendation model is constructed, the sequence state data of the encoder is input into Bi-LSTM-Attention to get the attention representation of the current POI check-in sequence, and the Top- <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>N</mi> </math> recommendation list is generated after the decoder processing. Finally, a negative sampling method is proposed to obtain an effective negative sample set, which is used to improve the calculation of the Bayesian personalized ranking loss function. The proposed method is demonstrated experimentally on Foursquare and Gowalla datasets. The experimental results show that the proposed method has better accuracy, recall, and F1 value than other comparison methods.
Extractive Summarization of Related Bug-fixing Comments in Support of Bug Repair

When developers investigate a new bug report, they search for similar previously fixed bug reports and discussion threads attached to them. These discussion threads convey important information about the behavior of the bug including relevant bug-fixing comments. Often times, these discussion threads become extensively lengthy due to the severity of the reported bug. This adds another layer of complexity, especially if relevant bug-fixing comments intermingle with seemingly unrelated comments. To manually detect these relevant comments among various cross-cutting discussion threads can become a daunting task when dealing with high volume of bug reports. To automate this process, our focus is to initially extract and detect comments in the context of query relevance, the use of positive language, and semantic relevance. Then, we merge these comments in the form of a summary for easy understanding. Specifically, we combine Sentiment Analysis, and the TextRank Model with the baseline Vector Space Model (VSM). Preliminary findings indicate that bug-fixing comments tend to be positive and there exists a semantic relevance with comments from other cross-cutting discussion threads. The results also indicate that our combined approach improves overall ranking performance against the baseline VSM.