Reviewerly/exHarmony huggingface dataset:
Research on Continuous Dynamic Gesture Recognition of Chinese Sign Language Based on Multi-Mode Fusion

To solve the problem of the low recognition rate of continuous dynamic gestures in Chinese sign language, a non-invasive end-to-end continuous dynamic gesture recognition system combining Inertial Measurement Unit (IMU) signal and surface electromyography (sEMG) signal is constructed. After the preprocessing and fusion of the IMU signal and sEMG signal, the fusion gesture features which include acceleration, angular velocity, attitude quaternion, and sEMG information are extracted. A network model based on the Bi-directional Long-Short Term Memory (BiLSTM) network and Connectionist Temporal Classification (CTC) as loss function is then constructed, which avoids the adverse effects of inaccurate pre-segmentation of gesture sequences on continuous dynamic gesture recognition and realizes the end-to-end continuous dynamic gesture recognition. Finally, the Chinese Sign Language Database (CSLD), containing 10 kinds of Chinese sign language and 20000 gesture samples, is established. The training and testing of the database indicate that the average recognition rate reaches 98.66%, confirming the superiority of this method.
Did COVID-19 Pandemic Influence the Ether Cryptocurrency Market? Perception and Analysis

In recent days, DeFi tokens have gained popularity as an investment option in the pandemic period and has gained a significant amount of investment. Cryptocurrency trading is a type of DeFi that has gained a lot of attention in the global market. The value of such currencies is increasing on a daily basis and peaked during the pandemic. One of these significant cryptocurrencies is the ether cryptocurrency, which ranks second only to the bitcoin cryptocurrency in terms of the value of a single coin. The ARIMA model will be used to forecast the price of ether. In this paper, an hourly forecast and a short term period forecast are performed. The forecast clearly shows that the ARIMA model performed better on log transformed data than on the original data. It’s also evident that COVID-19 pandemic has also aided the growth of ethereum when compared to previous year.
A Lightweight Neural Network for Vehicle Object Detection

In order to meet the needs of real-time monitoring on mobile devices, the message have observed the amazing achievements of VGG16 in the 2014 Imagenet image classification competition and the application advantages of convolutional neural network in various fields such as image classification, image segmentation and target detection. At the same time, the demand for neural network computing speed and robustness is greatly improved. However, most neural networks have a large number of parameters. In this project, the NanoDet model is trained in the Vehicles-Openimages data set, and finally deployed to the mobile terminal to complete the identification of vehicle target detection. The significance of this project is to deploy the small model with high precision and speed to the mobile terminal equipment, so that the smart phone can carry out real-time vehicle target detection. Experiments have proved that the operation speed and accuracy of the lightweight model in the mobile terminal are relatively ideal. Under the premise of high accuracy, the network size is compressed, and the test is carried out in the equipment with poor performance such as the mobile terminal. The faster calculation speed also completes the goal of real-time detection.
<i>Implicit Hammer</i>: Cross-Privilege-Boundary Rowhammer Through Implicit Accesses

Rowhammer is a hardware vulnerability in DRAM memory, where repeated access to hammer rows can induce bit flips in neighboring <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">victim rows</i> . Rowhammer attacks have enabled privilege escalation, sandbox escape, cryptographic key disclosures, etc. A key requirement of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all</i> existing rowhammer attacks is that an attacker must have access to at least part of an exploitable hammer row. We term such rowhammer attacks as Explicit Hammer. Recently, several proposals leverage the spatial proximity between the accessed hammer rows and the location of the victim rows for a defense against rowhammer. These all aim to deny the attacker's permission to access hammer rows near sensitive data, thus defeating explicit hammer-based attacks. In this paper, we question the core assumption underlying these defenses. We present Implicit Hammer, a confused-deputy attack that causes accesses to hammer rows that the attacker is not allowed to access. It is a paradigm shift in rowhammer attacks since it crosses privilege boundary to stealthily rowhammer an inaccessible row by implicit DRAM accesses. Such accesses are achieved by abusing inherent features of modern hardware and/or software. We propose a generic model to rigorously formalize the necessary conditions to initiate implicit hammer and explicit hammer, respectively. Compared to explicit hammer, implicit hammer can defeat the advanced software-only defenses, stealthy in hiding itself and hard to be mitigated. To demonstrate the practicality of implicit hammer, we have created two implicit hammer's instances, called PThammer and SyscallHammer.
Intelligent Information Network Security Management Strategy for Service Innovation of Manufacturing Enterprises Under Blockchain

Since the beginning of the new century, all manufacturing enterprises have been seeking ways to transform and upgrade the systems to maintain their competitive advantages. In the process of transformation and upgrading, service innovation has become more vital. In the service innovation mode, customers and suppliers are mainly involved in the manufacture of products. In the process of communication, these suppliers and customers need to rely on public key infrastructure (PKI) for data encryption and identity authentication operations, thereby achieving reliable information exchange and identity authentication in an untrusted environment. Therefore, ensuring the security of network communication for various types of users is crucial. The purpose of this paper is to solve the problem that results when a tampered certificate is difficult to trace due to the single point of failure and vulnerability of the current settlement and authentication system. In this scenario, the user's identity information is leaked in the certificate application. Resolving this problem is necessary to ensure the security of the information network.
In-Vehicle Seat Occupancy Detection Using Ultra-Wideband Radar Sensors

In this paper, we propose a method for identifying the seats in which people are sitting using ultra-wideband radar sensor data. First, we install two radar sensors in locations suitable for monitoring people inside the vehicle and then collect reflected radar signals. Based on the acquired radar sensor data, we design a convolutional neural network (CNN)-based classifier for seat occupancy detection. As input data for training the CNN, the radar signals accumulated over time acquired from two radar sensors are synthesized in parallel and used. As a result, our proposed method can determine the seats occupied by people with more than 93% accuracy.
MAP-Gen: An Automated 3D-Box Annotation Flow with Multimodal Attention Point Generator

Manually annotating 3D point clouds is laborious and costly, limiting the training data preparation for deep learning in real-world object detection. While a few previous studies tried to automatically generate 3D bounding boxes from weak labels such as 2D boxes, the quality is sub-optimal compared to human annotators. This work proposes a novel autolabeler, called multimodal attention point generator (MAP-Gen), that generates high-quality 3D labels from weak 2D boxes. It leverages dense image information to tackle the sparsity issue of 3D point clouds, thus improving label quality. For each 2D pixel, MAP-Gen predicts its corresponding 3D coordinates by referencing context points based on their 2D semantic or geometric relationships. The generated 3D points densify the original sparse point clouds, followed by an encoder to regress 3D bounding boxes. Using MAP-Gen, object detection networks that are weakly supervised by 2D boxes can achieve 94 ~ 99% performance of those fully supervised by 3D annotations. It is hopeful this newly proposed MAP-Gen autolabeling flow can shed new light on utilizing multimodal information for enriching sparse point clouds.
Structural Triangulation: A Closed-Form Solution to Constrained 3D Human Pose Estimation

We propose Structural Triangulation, a closed-form solution for optimal 3D human pose considering multi-view 2D pose estimations, calibrated camera parameters, and bone lengths. To start with, we focus on embedding structural constraints of human body in the process of 2D-to-3D inference using triangulation. Assume bone lengths are known in prior, then the inference process is formulated as a constrained optimization problem. By proper approximation, the closed-form solution to this problem is achieved. Further, we generalize our method with Step Constraint Algorithm to help converge when large error occurs in 2D estimations. In experiment, public datasets (Human3.6M and Total Capture) and synthesized data are used for evaluation. Our method achieves state-of-the-art results on Human3.6M Dataset when bone lengths are known and competitive results when they are not. The generality and efficiency of our method are also demonstrated.
Learning Deep Contrastive Network for Facial Age Estimation

Age estimation from a single facial image is an attractive and challenging research topic in the computer vision community. Most of previous works conventionally estimate absolute age from the input face images. However, telling someone's precise age at a glance without any reference information is essentially difficult even for humans. In this paper, we propose a novel Deep Contrastive Network (DCN) for age estimation, which can mine the variation information of samples. DCN model is trained end-to-end to learn the age distances between input unseen face and several reference images by contrasting deep feature maps. In the test phase, the input image is compared with a set of selected references to determine how many years younger or older than each of baselines which is more conform with human cognitive processes. We also propose a Cyclic Iterative Approximation Algorithm (CIAA) for post age voting which can further improve the accuracy. Therefore, the age estimation problem is cast as a metric learning task in our DCN model. By jointly learned with the cost sensitive loss and KL divergence loss, our DCN is easy to train and has very stable convergence. Extensive experiments show that the proposed approach significantly outperforms other state-of-the-art age estimation methods on MORPH II and FG-NET datasets.
A Network Vulnerability Assessment Method Using General Attack Tree

Vulnerability assessment is an important strategy to enhance the security of network/system assets. In order to realize the accuracy and usability, a feasible framework of vulnerability assessment with quantized elements is required. Therefore, a general network attack tree model is proposed. The attack path can be traced by following the most possible leaf node and branch node of the tree. Due to the consideration of visualization, computability and simulating hacker's thought, a fuzzy mathematics approach is brought into the model for the quantization of the elements in each attack step. Using this framework, the vulnerability can be located conveniently, and the attack path and behavior can be dug out.
Find Small Objects in UAV Images by Feature Mining and Attention

With the increasing popularity of Unmanned Aerial Vehicles (UAVs), the accuracy of detecting small objects in large-view images is also expected to increase. However, accurate small object detection is still a challenging problem. Currently, Image Pyramid Network, Feature Pyramid Network (FPN), rich training strategies and data augmentation are widely used to address this problem. To accurately detect small objects, the most important thing is to mine for more feature information. We propose Widened Residual Block (WRB) to break through the bottleneck of residual information gain to extract more feature information. The second is to emphasize or suppress features to prevent small objects from being overwhelmed by a broad background. We introduce an attention mechanism into PANet and propose Enhanced Attention PANet (EA-PANet), which consists of two parts: Context Attention Module (COAM) and Attention Enhancement Module (AEM). COAM outputs attention heatmaps with context, and AEM fuses features from the channel attention module (CAM) and COAM to avoid distraction from a vast background. In addition, we design a lightweight Decoupled Attention Head (DA-head) to dynamically compute important regions for specific tasks and achieve reliable predictions. Experiments show that our method outperforms state-of-the-art (SOTA) detectors. The source code for this work is available at https://github.com/liuxiaolei111/FindSmallObjects.
New Directions in Quantum Music: Concepts for a Quantum Keyboard and the Sound of the Ising Model

We explore ideas for generating sounds and eventually music using quantum devices in the NISQ era using quantum circuits. In particular, we first consider a concept for a Qeyboard, that is, a quantum keyboard, where the real-time behaviour of expectation values using a time-evolving quantum circuit can be associated to sound features like intensity, frequency and tone. Then, we examine how these properties can be extracted from physical quantum systems, taking the Ising model as an example. This can be realized by measuring physical quantities of the quantum states of the system, e.g. the energies and the magnetization obtained via variational quantum simulation techniques.
How are diverse end-user human-centric issues discussed on GitHub?

Many software systems fail to meet the needs of the diverse end-users in society and are prone to pose problems, such as accessibility and usability issues. Some of these problems (partially) stem from the failure to consider the characteristics, limitations, and abilities of diverse end-users during software development. We refer to this class of problems as human-centric issues. Despite their importance, there is a limited understanding of the types of human-centric issues encountered by developers. In-depth knowledge of these human-centric issues is needed to design software systems that better meet their diverse end-users' needs. This paper aims to provide insights for the software development and research communities on which human-centric issues are a topic of discussion for developers on GitHub. We conducted an empirical study by extracting and manually analysing 1,691 issue comments from 12 diverse projects, ranging from small to large-scale projects, including projects designed for challenged end-users, e.g., visually impaired and dyslexic users. Our analysis shows that eight categories of human-centric issues are discussed by developers. These include Inclusiveness, Privacy & Security, Compatibility, Location & Language, Preference, Satisfaction, Emotional Aspects, and Accessibility. Guided by our findings, we highlight some implications and possible future paths to further understand and incorporate human-centric issues in software development to be able to design software that meets the needs of diverse end users in society.
MULTI-Stream Graph Convolutional Networks with Efficient spatial-temporal Attention for Skeleton-based Action Recognition

In skeleton-based action recognition, graph convolutional networks (GCN) based methods have achieved remarkable performance by building skeleton coordinates into spatial-temporal graphs and explored the relationship between body joints. ST-GCN [19] proposed by Yan et al is regarded as a heuristic method, which firstly introduced GCN to skeleton-based action recognition. However, it applied graph convolution on joints of each frame equally. Less contribution joints caused interference in generating intermediate feature maps. We designed a spatial-temporal attention module to capture significant feature in spatial and temporal dimension simultaneously. Moreover, we adopted inverted bottleneck temporal convolutional networks to decrease computational amount and learned more feature with residual construction. Besides useful message in joints, bones and their movement also contain learnable information for analyzing action categories. We input data to a multi-stream framework. Finally, we demonstrated the efficiency of our proposed MSEA-GCN on NTU RGB+D datasets.
Deepfake Detection in Videos and Picture: Analysis of Deep Learning Models and Dataset

Deepfake detection is the concept of distinguishing a computer manipulated graphic from a real recorded graphic. The technology used for this purpose is deep learning. It is a sub branch of artificial intelligence. With technology becoming more readily available, deepfakes are also increasing in use in recent years. It becomes evident that a system is needed that detects deepfakes and prevents its use in suspicious activities. Development of a deepfake detection technology becomes evident to avoid the use of deepfakes in such activities. For this purpose, many tech giants have assimilated huge datasets which consist of videos that were made using deepfakes already available. To detect a deepfake, one requires an equally capable or even better algorithm and detection technique. Generative Adversarial Nets, GANs, is one such technique that might be able to rival other deepfake techniques. This paper will discuss various methods to apply to detect deep fakes along with the process, libraries used, dataset liabilities and limitations, analysis and efficiency. Since Deep Learning technology is evolving each day with new innovations, this paper provides a comparative study about methods that have already been tested and their limitations with respective models and how to possibly make them more efficient.
EEG-based Evaluation on Intuitive Gesture Interaction in Virtual Environment

Bi-manual interaction in virtual reality (VR) is generally used and has unlimited potential in interdisciplinary fields such as health care, education, and industry due to the facts of its conveniences, intuition, and beyond. With the continuous development of these interfaces, passive monitoring of user state can play a key role in extending the immersive VR manual experience and tracking activities for user well-being. To date, research on investigation immersive and intuitive manual experience has mostly conducted in questionnaire. It may have several disadvantages such as differences in understanding and interpretation or hard to convey feelings and emotions. However,when using VR devices, physiological signals, such as electroencephalograms (EEG), are recorded, the interaction of the user in the virtual environment can be adjusted in real time according to the cognitive state of the user. The current VR headset provides a logical, convenient, and non-obtrusive framework for mounting EEG sensors. The study evaluates the feasibility of passively monitoring cognitive work loads via EEG during classical gesture tasks in the interactive VR environment. Data were collected from 15 participants and the EEG features were analyzed with respect to task performance. The results indicate that participants feel more intuitive and relaxed when using Vive wands in the virtual environment but the Leapmotion has the great potential for providing immersive experience.
Learn Spatial! Introducing the MARBLE-App - A Mixed Reality Approach to Enhance Archaeological Higher Education

Emerging modern technologies including Mixed Reality (MR) are gradually becoming an inherent component of higher education. Studies concerning the effectiveness of their use as a didactic tool have been conducted in several fields of research. While the benefits of spatial representation through MR have already been employed in the fields of cultural heritage and museums, hardly any research is dedicated to higher archaeological education. In this paper, we present the first results of the ongoing development of the MARBLE-App of the research programme »Mixed and Augmented Reality in Blended Learning Environments« on the integration of MR into everyday archaeological higher education. The results are presented on the basis of learners' feedback from a focus group study carried out with archaeology students on understanding the spatial complexity of an archaeological structure. They show an increase in the students' impression, their self-reflected learning gains and their engagement towards spatial-related topics in the field of archaeology.That said, we perceive a high potential yet to be explored for the use of this technology in archaeological spatial education.
Graph Neural Networks-Based Multilabel Classification of Citation Network

There is an increasing number of applications where data can be represented as graphs. Besides, it is well-known that artificial intelligence approaches have become a very active and promising research field, mostly due to deep learning technologies. However popular deep learning architectures were designed to treat mostly image and text data. Graph Neural Network is the branch of machine learning which builds neural networks for graph data. In this context, many authors have recently proposed to adapt existing approaches to graphs and networks. In this paper we train three models of Graph Neural Networks on an academic citation network of Computer Science papers, and we explore the advantages of turning the problem into a multilabel classification problem.
End-to-End Hierarchical Approach for Emotion Detection in Short Texts

Textual emotion analysis aims to identify and recognize a set of predefined emotions from the text (e.g., sentences, documents). Vast amounts of textual data are created and distributed daily, for example, in news reports and social media, especially microblogging services. Understanding emotion in short text snippets has become an important area of study due to its proliferation in daily life. This paper proposes a novel hierarchical approach for detecting emotion from short texts (microblogs) and conducts a broad set of experiments. Based on the observation that sentiment classification can get up to 80–90% accuracy, we develop a hierarchical end-to-end emotion classifier in which the first layer is the polarity detector and the second layer is the emotion detector. The proposed hierarchical approach can incorporate state-of-the-art neural models, and we have experimented with a BiLTSM-based self-attention model, a BiGRU model, and a BERT-based model for hierarchical emotion classification. Our approaches are evaluated on four large datasets, demonstrating statistically significant improvements.
An Improved Term Frequency-Inverse Document Frequency Method Solving Multi-Text Label Problem

With the advent of the Internet era, people can express their likes and feelings without leaving home. A large amount of text data has also emerged, such as microblog comments, movie reviews, news reports and other data. Accurate tagging of text is a key step in quickly finding the text data that they are interested in. Since the frequency of words in the text message varies, the more frequency words may not be suitable for the text label, but fewer frequency words are better suited to the content of the text. This work considers how to avoid the interference of common words and extract tags more accurately. A new solution method called Term Frequency-Inverse Document Frequency and K-Nearest Neighbor combined to extract multi-text tags are proposed to solve it. The feasibility and effectiveness of this method are verified by an example - Douban film review.