Reviewerly/exHarmony huggingface dataset:
Optimized Faster-RCNN in Real-time Facial Expression Classification

Abstract In order to make convolutional neural network adapt to the mobile terminals which lack of hardware resources in facial expressions recognition. We modified the recent algorithm of real-time CNNs on facial expression. Firstly, we used Gaussian distribution to reduce the irrelevant data. Secondly, we used random forest to reduce the time complexity. We implemented the model and algorithm on raspberry pi. As a result, we reduce the amout of data by about 40% and the time complexity to logarithmic level. Thus, our system can run smoothly on mobile terminals with lack of hardware resources. We validate the accuracy of our system on raspberry pi which has the ability to detect faces and classify the emotion. The accuracy in facial expressions recognition remain stable as the original algorithm. In all, compared with the traditional algorithm, our optimize algorithm improve the number of frames remarkably without reducing the accuracy.
Lattice-Based Improvements for Voice Triggering Using Graph Neural Networks

Voice-triggered smart assistants often rely on detection of a trigger-phrase before they start listening for the user request. Mitigation of false triggers is an important aspect of building a privacy-centric non-intrusive smart assistant. In this paper, we address the task of false trigger mitigation (FTM) using a novel approach based on analyzing automatic speech recognition (ASR) lattices using graph neural networks (GNN). The proposed approach uses the fact that decoding lattice of a falsely triggered audio exhibits uncertainties in terms of many alternative paths and unexpected words on the lattice arcs as compared to the lattice of a correctly triggered audio. A pure trigger-phrase detector model doesn't fully utilize the intent of the user speech whereas by using the complete decoding lattice of user audio, we can effectively mitigate speech not intended for the smart assistant. We deploy two variants of GNNs in this paper based on 1) graph convolution layers and 2) self-attention mechanism respectively. Our experiments demonstrate that GNNs are highly accurate in FTM task by mitigating ~87% of false triggers at 99% true positive rate (TPR). Furthermore, the proposed models are fast to train and efficient in parameter requirements.
Extracting Unit Embeddings Using Sequence-To-Sequence Acoustic Models for Unit Selection Speech Synthesis

This paper presents a method of using the intermediate representations between linguistic and acoustic features in a Tacotron model to derive the cost functions for unit selection speech synthesis. By extracting the outputs of the Tacotron encoder, each phone-sized candidate unit in the corpus is represented by a fixed-length unit vector. Similarly, each target unit to be synthesized is also converted into a unit vector of the same dimension by encoding the input phone sequence. The normalized Euclidean distances between these two vectors are utilized to fulfill unit pre-selection and to calculate the target cost for unit selection. Then, another DNN which predicts the unit vector of each phone from its preceding ones is constructed to derive the concatenation cost function. Experimental results demonstrate that the unit vectors extracted from Tacotron contain both duration and acoustic information of phone units. Comparing with our previous work, which learned unit vectors using a DNN and only acoustic features, the method proposed in this paper further improves the naturalness of unit selection speech synthesis in our experiments.
GraphTTS: Graph-to-Sequence Modelling in Neural Text-to-Speech

This paper leverages the graph-to-sequence method in neural text-to-speech (GraphTTS), which maps the graph embedding of the input sequence to spectrograms. The graphical inputs consist of node and edge representations constructed from input texts. The encoding of these graphical inputs incorporates syntax information by a GNN encoder module. Besides, applying the encoder of GraphTTS as a graph auxiliary encoder (GAE) can analyse prosody information from the semantic structure of texts. This can remove the manual selection of reference audios process and makes prosody modelling an end-to-end procedure. Experimental analysis shows that GraphTTS outperforms the state-of-the-art sequence-to-sequence models by 0.24 in Mean Opinion Score (MOS). GAE can adjust the pause, ventilation and tones of synthesised audios automatically. This experimental conclusion may give some inspiration to researchers working on improving speech synthesis prosody.
A Hybrid Fireworks Algorithm to Navigation and Mapping

In recent years, computer technology and artificial intelligence have developed rapidly, and research in the field of mobile robots has continued to deepen with development of artificial intelligence. Path planning is an essential content of mobile robot navigation of computing a collision-free path between a starting point and a goal. It is necessary for mobile robots to move and maneuver in different kinds of environment with objects and obstacles. The main goal of path planning is to find the optimal path between the starting point and the target position in the minimal possible time. A new firework algorithm (FWA) integrated with a graph theory, Dijkstra's algorithm developed for autonomous robot navigation, is proposed in this chapter. The firework algorithm is improved by a local search procedure that a LIDAR-based local navigator algorithm is implemented for local navigation and obstacle avoidance. The grid map is utilized for real-time intelligent robot mapping and navigation. In this chapter, both simulation and comparison studies of an autonomous robot navigation demonstrate that the proposed model is capable of planning more reasonable and shorter, collision-free paths in non-stationary and unstructured environments compared with other approaches.
MST3 Cryptosystem Based on the Automorphism Group of the Hermitian Function Field

The article describes a new implementation of MST3 cryptosystems based on the automorphism group of the field of the Hermitian function. The main difference in the presented implementation is the presence of two-stage recovery of parts of the message from the encrypted text. The presented implementation of a cryptosystem has a greater reliability. The complexity of the cryptanalysis and the size of the message for encryption in the square root is greater than that of the MST3 cryptosystem on the Suzuki group.
Gated Multi-Layer Convolutional Feature Extraction Network for Robust Pedestrian Detection

Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, it remains a challenging problem how to robustly detect pedestrians of varied sizes and with occlusions. In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions. The proposed gated feature extraction framework consists of squeeze units, gate units and concatenation layers which perform feature dimension squeezing, feature manipulation and features combination from multiple CNN layers, respectively. We proposed two different gate models that can manipulate the regional feature maps in a channel-wise selection manner and a spatial-wise selection manner, respectively. Experiments on the challenging CityPersons dataset demonstrate the effectiveness of the proposed method, especially on detecting small-size and occluded pedestrians.
Deriving Compact Feature Representations Via Annealed Contraction

It is common practice to use pretrained image recognition models to compute feature representations for visual data. The size of the feature representations can have a noticeable impact on the complexity of the models that use these representations, and by extension on their deployablity and scalability. Therefore it would be beneficial to have compact visual representations that carry as much information as their high-dimensional counterparts. To this end we propose a technique that shrinks a layer by an iterative process in which neurons are removed from the and network is fine tuned. Using this technique we are able to remove 99% of the neurons from the penultimate layer of AlexNet and VGG16, while suffering less than 5% drop in accuracy on CIFAR10, Caltech101 and Caltech256. We also show that our method can reduce the size of AlexNet by 95% while only suffering a 4% reduction in accuracy on Caltech101.
Multi scale mirror connection based encoder decoder network for text localization

Encoder decoder models with multi-scale feature concatenations have become ubiquitous for various natural scene segmentation tasks. In the current approach, a similar model with an improved mirror connection from encoders to decoder has been proposed. Three different types of mirror connections, namely, linear, parametric and convolutional, have been demonstrated in the proposed work. We have also implemented the use of internal skips to facilitate better gradient propagation within the encoder-decoder architecture. The proposed model also consists of an ensemble module that combines outputs from models with different kernel sizes, such as, 3 × 3, 5 × 5 and 7 × 7 to combine multi-scale features for efficient detections. The model was tested on the ICDAR 2003, SVT, ICDAR 2015 and the Total-Text dataset where it proved to be superior to other state of the art encoder-decoder architectures for pixel level classification.
Cognitive Distraction to Improve Cybersickness in Virtual Reality Environment

Immersion into a virtual environment (VE) often results in adverse symptoms including nausea, dizziness, and disorientation. These symptoms are an indicator of cybersickness, which is a condition similar to motion sickness experienced in VEs. In this paper, we hypothesized that administered cognitive distraction can accelerate the rate of habituation to a VE. This acceleration, therefore, can lower severity of cybersickness in fewer amount of immersions. To evaluate the impact of cognitive distraction on reducing the effects of cybersickness, we designed a VE and carried out a human subject study with control and experimental groups created through stratified random sampling. Subjects were immersed in our VE on four separate sessions, and our experimental group received cognitive distraction throughout the immersions. Cybersickness was measured using the Simulator Sickness Questionnaire (SSQ) and Presence Questionnaire (PQ). Upon comparing the average SSQ subgroups nausea, oculomotor, and disorientation scores reported by participants for each immersion session, we observed that our experimental group exhibited decrease in cybersickness to a greater extent than that of our control group. We completed t-tests for each of these comparisons, to find that these results are statistically insignificant. We plan to continue with this work by incorporating up to 30 total participants to clarify these findings.
Context and Uncertainty Modeling for Online Speaker Change Detection

Speaker change detection is often addressed as a key component in speaker diarization systems. In this work we focus on online speaker change detection as a standalone task which is required for online closed captioning of broadcast television. Contrary to related works, we do not operate on frame-level features such as MFCC. Instead, we leverage state-of-the-art speaker recognition-based technology by modeling sequences of pretrained speaker embeddings (x-vectors) using a deep neural network. We explicitly address two types of uncertainties. The first one is uncertainty in embedding point estimate which is due to short and varying segment duration. The second type is uncertainty in which context segments are relevant to representing the speaker talking right before the hypothesized speaker change. We also show the robustness of affinity matrix-representation for speaker change detection. Our methods provide very significant accuracy improvements compared to several baselines including a recently published end-to-end system.
More precise construction of static single assignment programs using reaching definitions

The Static Single Assignment (SSA) form is an intermediate representation used for the analysis and optimization of programs in modern compilers. The ϕ-function placement is the most computationally expensive part of converting any program into its SSA form. The most widely-used ϕ-function placement algorithms are based on computing dominance frontiers (DF). However, this kind of algorithms works under the limiting assumption that all variables are defined at the beginning of the program, which is not the case for local variables. In this paper, we introduce an innovative ϕ-placement algorithm based on computing reaching definitions (RD), which generates a precise number of ϕ-functions. We provided theorems and proofs showing the correctness and the theoretical computational complexity of our algorithms. We implemented our approach and a well-known DF-based algorithm in the Clang/LLVM compiler framework, and performed experiments on a number of benchmarks. The results show that the limiting assumption of the DF-based algorithm when compared with the more accurate results of our RD-based approach leads to generating up to 87% (69% on average) superfluous ϕ-functions on all benchmarks, and thus brings about a significant precision loss. Moreover, even though our approach computes more information to generate precise results, it is able to analyze up to 92.96% procedures (65.63% on average) of all benchmarks with execution time within twice the execution time of the reference DF-based approach.
Multi-Eye Guidance Method for UAVs Path Following

Improving upon L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> guidance laws, this paper introduces a novel solution to the issue of overshoot and sudden acceleration changes in unmanned aerial vehicles (UAVs) during in complex missions (e.g. collision avoidance maneuvers) and/or when it is in tight turns. Nonlinear multi-eye guidance law tracks multiple reference points at once instead of single point tracking, introduced in L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> guidance laws. Multi-eye tracking requires the aircraft follow multiple reference points, each at difference distances from the aircraft, which will allow UAVs to anticipate turns sooner with the farther reference points, while being kept stable with the shorter reference points. This method eliminates the need to use switching distance at corners when using to prevent overshoots. The results of this paper show that multi-eye guidance is a robust method giving UAVs capabilities to perform complex missions.
Known unknowns: Indeterminacy in authentication in IoT

The Internet of Things (IoT), comprising a plethora of heterogeneous devices, is an enabling technology that can improve the quality of our daily lives, for instance by measuring parameters from the environment (e.g., humidity, temperature, weather, energy consumption, traffic, and others) or our bodies (e.g., health data). However, as with any technology, IoT has introduced a number of security and privacy challenges. Indeed, IoT devices create, process, transfer and store data, which are often sensitive, and which must be protected from unauthorized access. Similarly, the infrastructure that links with IoT, as well as the IoT devices themselves, is an asset that needs to be protected. The focus of this work is examining authentication in IoT. In particular, in this work we conducted a state-of-the-art review of the access control models that have been proposed, including both traditional access control models and emerging models that have recently been proposed and are tailored for IoT. We identified that the existing models cannot cope with indeterminacy, an inherent characteristic of IoT, which hinders authentication decisions. In this context, we studied the two known components of indeterminacy, i.e., uncertainty and ambiguity, and proposed a new model that handles indeterminacy in authentication in IoT environments.
Full Attention-Based Bi-GRU Neural Network for News Text Classification

This paper proposes a novel approach for text classification by using attention mechanism. In recent works, several models based on deep learning with traditional attention mechanism mainly learn the weights of steps in the entire text. However, the information of each step is filtered by the encoder, and the same information has different effects on different steps. This paper proposes a full attention-based bidirectional GRU (Bi-GRU) neural network, which is called FABG. FABG uses a Bi-GRU to learn the semantic information of text, and uses full attention mechanism to learn the weights of previous and current outputs of the Bi-GRU at each step, which enables the representation of each step to obtain the important information and ignore the irrelevant information. Finally, through a pooling layer, we get the representation of the text. Thereby FABG can learn more information, which enhances the effect of text classification. Experiments on the English news dataset agnews and the Chinese news dataset chnews show that FABG achieve better performance than the baselines.
Forecasting Multi-Dimensional Processes Over Graphs

The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework. However, problems in the representation and the processing arise when each time series carries a vector of quantities rather than a scalar one. To tackle this issue, we devise a new framework and propose new methodologies based on the graph vector autoregressive model. More explicitly, we leverage product graphs to model the high-dimensional graph data and develop multidimensional graph-based vector autoregressive models to forecast future trends with a number of parameters that is independent of the number of time series and a linear computational complexity. Numerical results demonstrating the prediction of moving point clouds corroborate our findings.
Hierarchical Attention-Based Astronaut Gesture Recognition: A Dataset and CNN Model

In the space station, visual gesture recognition is an important component for gesture based human-robot interaction to control the robot, which assists astronauts to complete simple, repetitive and cooperative work.However, public gesture datasets are captured in the daily environment and existing approaches trained on these datasets achieve imprecise performance for astronaut gesture recognition.In this paper, we introduce a new astronaut gesture dataset (DSSL-Astronaut gesture dataset) and a novel hierarchical attention single-shot detector network (HA-SSD) for astronaut gesture recognition.Specifically, this dataset consists of the real and augmented images.The real images captured in the simulated space station are closed to the real images in the space station environment.Meanwhile, we utilize the Mask-RCNN model to segment the foreground image including an astronaut from real data and capture the background images of the simulated space station at different views and illuminations.Then we combine them to synthesize the augmented images.A novel HA-SSD model consists of a lightweight backbone named MobileNet and a hierarchical attention mechanism.The MobileNet is used as the feature extractor, which contains several depth-wise separable convolutions to make a trade-off between latency and accuracy.Meanwhile, the hierarchical channel-wise attention module is adopted to exploit the fine semantic information to enrich the features for improving the performance of gesture recognition.We conduct experiments to demonstrate that our dataset is suitable for the space station and our approach is able to effectively localize and recognize the gestures with strong generality.
Anatomy on Malware Distribution Networks

Malware distribution networks are a huge network that involves in malware distribution. We do not much realize the seriousness of the network in daily life. Until now, the works to analyze the network have been studied, but they are still limited because many researchers focused on detection, not investigating the internal structures of malware distribution networks. In this circumstance, the recent works tried to analyze the malware distribution networks in terms of social network analysis based on graph theories. They analyzed the malware distribution networks with nodes used in malware distribution such as malicious URLs, FQDN, malware and IPs, generated during drive-by downloads, or appeared outbound contacts. However, this approach is still lack in understandings malware distribution networks. In this study, we realized that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$degree$ </tex-math></inline-formula> (or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$closeness$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$betweenness$ </tex-math></inline-formula> , or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$eigenvector$ </tex-math></inline-formula> ) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$centrality~measures$ </tex-math></inline-formula> are beneficial in finding central nodes engaging in malware distribution. This central information is by far valuable in understanding the properties of malicious network infrastructure. For instance, from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$degree~centrality~measures$ </tex-math></inline-formula> , we realized that malware distribution networks show high in-degree, while benign networks present high out-degree. This result offers artifacts that classify malicious networks from benign networks. After all, this study provides fundamental information to help distinguish heterogeneous networks useful in future research.