Reviewerly/exHarmony huggingface dataset:
Recognizing Injured Faces via SCIFI Loss

Deaths and injuries are common in road accidents, violence, and natural disaster. In accidents and natural disasters scenarios, one of the tasks of responders is to retrieve the identity of the victims to reunite families or ensure proper identification of deceased persons. Apart from this, the identification of unidentified dead bodies due to violence and accidents is crucial for the police investigation. In the absence of identification cards, different forensic techniques such as DNA profiling and dental profiling may be used for identification. In this research, we present face recognition as a fast and viable approach for recognizing individuals with injuries. Face, which can be captured easily, is one of the most commonly used and widely accepted biometric modalities. However, face recognition is challenging in the presence of injuries as facial injuries change the appearance and geometric properties of the face due to swelling, bruises, blood clots, and accidental cuts. These changes introduce large intra-class variations among the same subject and small inter-class separability among different subjects. To address the challenge, we propose a novel Subclass Injured Face Identification (SCIFI) loss which is used in learning feature representation agnostic to injury variations. Additionally, an extended Injured Face (IF-V2) database of 150 subjects is presented to evaluate the performance of face recognition models. Multiple experiments and comparisons are performed to showcase the efficacy of the proposed SCIFI loss based face recognition.
A Method for Optimizing Deep Learning Object Detection in Edge Computing

Recently, edge computing has received considerable attention as a promising solution to provide deep learning-based video analysis services in real-time. However, due to the limited computation capability of the data processing units (such as CPUs, GPUs, and specialized accelerators) embedded in the edge devices, the question of how to use the limited resources of the edge devices is one of the most pressing issues affecting deep learning-based video analysis service efficiency. In this paper, we introduce a practical approach to optimize deep learning object detection at the edge devices embedding CPUs and GPUs. The proposed approach adopts TVM, an automated end-to-end deep learning compiler that automatically optimizes deep learning workloads with respect to hardware-specific characteristics. In addition, task-level pipeline parallelism is applied to maximize resource utilization of the CPUs and GPUs so as to improve overall object detection performance. Through experiment results, we show that the proposed approach achieves performance improvement for detecting objects on multiple video streams in terms of frame per second.
The study of skeleton description reduction in the human fall-detection task

Accurate and reliable real-time fall detection is a key aspect of any intelligent elderly people care system. A lot of modern RGB-D cameras can provide a skeleton description of a human figure as a compact pose presentation. This makes it possible to use this description for further analysis without access to real video and, thus, to increase the privacy of the whole system. The skeleton description reduction based on the anthropometrical characteristics of a human body is proposed. The experimental study on the TST Fall Detection dataset v2 by the Leave-One-Person-Out method shows that the proposed skeleton description reduction technique provides better recognition quality and increases the overall performance of a Fall-Detection System.
A Model Transformation Approach for Detecting Distancing Violations in Weighted Graphs

This work presents the design of an Internet of Things (IoT) edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places.A wireless sensor network based on Bluetooth Low Energy is introduced as the infrastructure of the proposed design.A hybrid model transformation strategy for generating a graph database to represent groups of people is presented as a core middleware layer of the detecting system's proposed architectural design.A Neo4j graph database is used as a target implementation generated from the proposed transformational system to store all captured real-time IoT data about the distances between individuals in an indoor area and answer user predefined queries, expressed using Neo4j Cypher, to provide insights from the stored data for decision support.As proof of concept, a discrete-time simulation model was adopted for the design of a COVID-19 physical distancing measures case study to evaluate the introduced system architecture.Twenty-one weighted graphs were generated randomly and the degrees of violation of distancing measures were inspected.The experimental results demonstrate the capability of the proposed system design to detect violations of COVID-19 physical distancing measures within an enclosed area.
Cognition, Care, and Usability: Applying Cognitive Concepts to User Experience Design in Health and Medical Contexts

Meeting the needs of users requires an understanding of the contexts where they interact with materials. This entry presents an approach for integrating script theory into usability to develop medical materials individuals can use in the settings where they receive or perform healthcare activities. The entry introduces technical communication professionals to script theory and presents mechanisms for using script theory to research patient expectations of and presents usable materials for health and medical contexts.
A deep learning based system for mathematical expression detection and recognition in document images

Detection and recognition of mathematical expressions in document images are two key steps for the development of a mathematical expression retrieval system. So far, many researches have proposed for the recognition of expressions. However, few systems have integrated the detection and recognition of expressions in document images. This paper presents a deep learning based system for mathematical expression detection and recognition. Firstly, mathematical expressions have been detected on document images using the You Only Look Once (YOLO) v3 network. Then, detected expressions have been recognized in an end-to-end way using the advanced neural network that is the Watch, Attend and Parse (WAP). The proposed system has been tested on the Marmot public dataset. The obtained accuracies of the detection of isolated and inline expressions are 93% and 73%, respectively. Meanwhile, accuracies of the recognition for isolated and detected expressions are 51.77% and 45.50%, respectively. The results have shown the promising application of our preliminaiy research.
Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups

Causal relations among many statistical variables have been assessed using a Linear non-Gaussian Acyclic Model (LiNGAM). Using access to large amounts of health checkup data from Osaka prefecture obtained during the six fiscal years of years 2012–2017, we applied the DirectLiNGAM algorithm as a trial to extract causal relations among health indices for age groups and genders. Results show that LiNGAM yields interesting and reasonable results, suggesting causal relations and correlation among the statistical indices used for these analyses.
Constrained Online Cut-Paste for Object Detection

Well-annotated training samples show necessity in achieving high performance of object detection, but collection of massive samples is extremely laborious and costly. Recently, cut-paste based methods show the potential to augment the training samples by cutting the foreground instances and pasting them on some background regions. However, existing cut-paste based methods hardly guarantee the quality of synthetic images due to lack of mechanism to ensure rationality of the pasted instances (e.g., context, geometry and diversity), limiting the effectiveness of data augmentation. To overcome above issues, this paper proposes a novel Constrained Online Cut-Paste (COCP) method, making an attempt to effectively and efficiently augment training data for improving performance of object detection. Specifically, our COCP generates synthetic images by switching instances of same class from various image pairs in each training mini-batch, ensuring context coherence between the cut instances and the pasted backgrounds. Furthermore, two constraints based on geometric consistency and sample diversity are developed to eliminate counterproductive and meaningless switched instances those suffer from significant geometric discrepancy or lack variations, further improving quality of the synthetic images. The experiments are conducted on both MS COCO and PASCAL VOC datasets using various state-of-the-art detectors (e.g., Faster R-CNN, RetinaNet, FCOS and Mask R-CNN). The results show that our proposed COCP can be well generalized to various datasets and detectors with clear performance gains, while performing favorably against its counterparts.
COTS Drone Detection using Video Streaming Characteristics

Cheap commercial off-the-shelf (COTS) drones have become widely available for consumers in recent years. Unfortunately, they also provide low-cost capabilities for attackers. Therefore, effective methods to detect the presence of non-cooperating rogue drones within a restricted area are highly required. Approaches based on detection of control traffic have been proposed but were not yet shown to work against other benign traffic, such as that generated by wireless security cameras. In this work, we propose a novel drone detection framework based on a Random Forest classification model. In essence, the framework leverages specific patterns in video traffic transmitted by drones. The patterns consist of repetitive synchronization packets (denoted as pivots) which we use as features in the proposed machine learning classifier. We show that our framework can achieve up to 99% detection accuracy over an encrypted WiFi channel using only 20 packets originated from the drone. Our system is able to identify drone transmissions even among very similar WiFi transmission (such as a security camera video stream) and in a noisy scenario with background traffic.
Permutations avoiding a pattern of length three under Mallows distributions

Abstract We consider permutations avoiding a pattern of length three under the family of Mallows distributions. In particular, for any pattern , we obtain rather precise results on the asymptotic probability as n → ∞ that a permutation under the Mallows distribution with parameter q ∈ (0, 1) avoids the pattern. By a duality between the parameters q and , we also obtain rather precise results on the above probability for q &gt; 1 and any pattern .
Prediction of tourist flow based on multi‐source traffic data in scenic spot

Abstract At present, China's tourism industry has entered an era of rapid development. It is of great significance to predict the precise and real‐time tourist flow of the popular tourist areas with dense crowds, providing decision support for rational evacuation of tourist flow, activation of emergency plans, and prevention of security accidents. This article presents a long short‐term memory (LSTM) neural network model based on the global attention mechanism. The model first uses two LSTM layers to calculate attention weights using multi‐source data at different time steps to improve the model’s learning ability. Then the model predicts the tourist flow of the scenic spots in the next time period based on the weights and outputs previously obtained. At the final stage, the pre‐trained parameters of the network are used to initialize the model. To verify the validity of the model, we compared it with the LSTM model, back propagation neural network model, and autoregressive integrated moving average model based on the data of Beijing’s South Luogu Lane scenic spot. It turned out that the results of our solution were more in line with the true value, which proves that it is feasible for real‐time prediction of tourist flow in popular scenic spots.
BUFFing signature schemes beyond unforgeability and the case of post-quantum signatures

Modern digital signature schemes can provide more guarantees than the standard notion of (strong) unforgeability, such as offering security even in the presence of maliciously generated keys, or requiring to know a message to produce a signature for it. The use of signature schemes that lack these properties has previously enabled attacks on real-world protocols. In this work we revisit several of these notions beyond unforgeability, establish relations among them, provide the first formal definition of non re-signability, and a transformation that can provide these properties for a given signature scheme in a provable and efficient way.Our results are not only relevant for established schemes: for example, the ongoing NIST PQC competition towards standardizing post-quantum signature schemes has six finalists in its third round. We perform an in-depth analysis of the candidates with respect to their security properties beyond unforgeability. We show that many of them do not yet offer these stronger guarantees, which implies that the security guarantees of these post-quantum schemes are not strictly stronger than, but instead incomparable to, classical signature schemes. We show how applying our transformation would efficiently solve this, paving the way for the standardized schemes to provide these additional guarantees and thereby making them harder to misuse.
Multiclass Classification of Brain Cancer with Machine Learning Algorithms

Brain cancer is one the most important disease to be treated all around the world. Classification of brain cancer using machine learning techniques has been widely studied by researchers. Microarray gene expression data are commonly used medical data to get observable results in this manner. In this study, multiclass classification of brain cancer is aimed by using different machine learning approaches. Some preprocessing methods were applied to get improved results. According to the result, feature selection has greatly affected the overall performance of each method in terms of overall accuracy and per class accuracy. Experimental results show that Multilayer Perceptron (MP) method has higher accuracy rate compared with other machine learning methods.
Wasmachine: Bring the Edge up to Speed with A WebAssembly OS

WebAssembly is gaining wide adoption in browser-centric applications thanks to its high portability and security. More recently, researchers start exploring the potential of WebAssembly in non-web environments, particularly in IoT and Edge computing. A key challenge of WebAssembly is performance gap with native code: applications compiled to WebAssembly run slower by an average of 45% as reported by previous benchmark. The main causes of the slowdown are two-folded. Firstly, conventional WebAssembly runtimes translate WebAssembly instructions to native machine code using just-in-time compilers, which do not apply complex code optimization. Secondly, system calls from WebAssembly applications have to be proxy-ed by runtimes in order to reach operating systems, which incurs a significant performance overhead. To address these issues, we present Wasmachine, an OS aiming to efficiently and securely execute WebAssembly applications in IoT and edge devices with constrained resources. Wasmachine achieves efficient execution by compiling WebAssembly ahead of time to native binary and executing it in kernel mode for zero-cost system calls. Even when executing in kernel space, Wasmachine stays secure by exploiting various language features of WebAssembly to deliver software-based fault isolation. We implement the Was-machine prototype in the memory-safe programming language Rust and conduct a performance evaluation. Our results show that WebAssembly applications running in Wasmachine are up to 21% faster than their native counterparts in Linux.
Channel Estimation Method Based on Transformer in High Dynamic Environment

As the relative movement speeds of the communication parties increase, the Doppler frequency offset gradually increases, and the speed of channel state information(CSI) change also increases, which limits the performance of traditional channel estimation algorithms. To solve the above problems, we propose a channel estimation structure based on Transformer. Convolutional Neural Network (CNN) is used to extract the feature vectors of channel response and Transformer is used for channel estimation. Utilize Transformer's deep learning capabilities to better track channel variation characteristics in highly dynamic environments. By simulation, we get results that compared with traditional channel estimation methods, the performance of the proposed channel estimation method is improved significantly under high dynamic environment.
Generalized and Efficient Skill Assessment from IMU Data with Applications in Gymnastics and Medical Training

Human activity recognition is progressing from automatically determining what a person is doing and when, to additionally analyzing the quality of these activities—typically referred to as skill assessment. In this chapter, we propose a new framework for skill assessment that generalizes across application domains and can be deployed for near-real-time applications. It is based on the notion of repeatability of activities defining skill. The analysis is based on two subsequent classification steps that analyze (1) movements or activities and (2) their qualities, that is, the actual skills of a human performing them. The first classifier is trained in either a supervised or unsupervised manner and provides confidence scores, which are then used for assessing skills. We evaluate the proposed method in two scenarios: gymnastics and surgical skill training of medical students. We demonstrate both the overall effectiveness and efficiency of the generalized assessment method, especially compared to previous work.
A Content-based Image Retrieval Scheme Using Compressible Encrypted Images

In this paper, we propose a novel content based-image retrieval scheme using compressible encrypted images, called encryption-then-compression (EtC) images. The proposed scheme allows us not only to directly retrieve images from visually protected images, but also to make the sensitive management of secret keys unnecessary. In addition, encrypted images can be compressed by using JPEG compression. Weighted SIMPLE image descriptors, which are generated from global descriptors of localized regions, are extended, and then the extended descriptors are applied to EtC images. In an experiment, the proposed scheme is demonstrated to have almost the same accuracy as conventional retrieval methods with plain images.
A unified system for multilingual speech recognition and language identification

In this paper, a multilingual automatic speech recognition (ASR) and language identification (LID) system is designed. In contrast to conventional multilingual ASR systems, this paper takes advantage of the complementarity of the ASR and LID modules. First, the LID module contributes to the language adaptive training of the multilingual acoustic model. Then, the ASR decoding information acts as the confidence metric to balance the LID results. To simulate complex multilingual speech recognition situations, two types of LID strategies are conducted. For a multilingual speech recognition task in which only one language is contained in the speech stream, the language information can be directly determined based on utterance-level judgment. Under this condition, a segment-level statistical component and a two-stage update strategy are designed to assist in the utterance-level language classification. In another multilingual speech recognition task, where the speech stream contains multiple languages simultaneously, the Viterbi language state retrieval method based on neural network (NN) classification is used to perform dynamic detection of the language state. In both cases, the ASR decoding information is used to adjust the language classification results. Without prior knowledge of language identity information, the enhanced LID module achieves an accuracy of 99.3% for utterance-level language judgment and 92.4% for dynamic language detection, and the multilingual ASR system also provides performance comparable to that of monolingual ASR systems.
A Vision Measurement Method for the Gear Shaft Radial Runout With Line Structured Light

The gear shaft needs to measure its radial runout for straightening before finishing, while it is difficult to measure the gear shaft radial runout without rotating the gear shaft by the existing measurement technology, which results the measurement inefficient.Present work puts forward a method of measuring a gear shaft radial runout with line structured light vision.This method can measure the radial runout value and direction online for straightening the bending of the gear shaft.The center position of the gear shaft cross section is obtained by the ellipse geometric fitting on the laser plane to eliminate the influence of projection distortion.The measurement method is tested by a specialized experiment, and the experimental results show that measurements of the method meet the requirements of straightening the gear shaft.The method can realize online measurement of the gear shaft radial runout just by a single image without rotating the gear shaft, so it features fast measurement compared with the existing measurement technology.
Automatic Classification of Network Traffic Data based on Deep Learning in ONOS Platform

Machine learning has been deployed in networks for automatically analyzing network data, proactively monitoring network dynamics, and predicting network resource availability. This becomes one of key technologies for efficient and autonomous network management in particular for software defined networks (SDN) environments. Especially, deep learning has brought recent breakthrough in machine learning algorithm as it can extract features based on artificial neural networks from data. In this paper, we study the deployment of deep neural network (DNN) for network traffic data classification, where DNN is deployed to automatically classify real network traffic data collected from ONOS (Open Network Operating System) platform. From the experiment results with simple network topologies, we conclude that DNN can be a potential approach to effective network packet classification. Moreover, it is confirmed that a deployment of DNN for a real network traffic data classification should consider not only the data packets that are intended to be delivered but also data packets required to maintain networks, as the classification performance of DNN significantly depends on the network traffic data.