Reviewerly/exHarmony huggingface dataset:
The influence of different environments on reinforcement learning

The environment of the reinforcement learning is important to training learning. A good environment can speed up training, reduce costs, and better test new RL algorithms. Arcade Learning Environment, MinGrid and MinAtar are analyzed and summarize the characteristics of the three platforms and their respective advantages. Three reinforcement learning algorithms PPO, DQN and AC were tested in different environments. The conclusion is that, under certain constraints, simplifying the representation of the environment can speed up the learning of the agent and it can still show the characteristics of different algorithms very well. However, at the same time, inappropriate simplification may also lead to instability in training. This work investigates and demonstrates the importance of environments for new algorithm testing, as well as the strengths and weaknesses of existing environments.
Monocular Gaze Estimation Network based on Mixed Attention

As an indicator of human attention, gaze is useful to diagnose mental state and predict human intention. Binocular fixation difference is the main standard to evaluate attention, which needs to be obtained by calculating the fixation points of both eyes. In recent years, appearance-based gaze estimation technology has been used in human attention diagnosis. However, most of the existing appearance-based gaze estimation methods only predict the gaze convergence point of both eyes. In this paper, we propose a Monocular Gaze Estimation Network based on Mixed Attention (MGE-Net). Through Convolutional Neural Networks(CNNs), we utilize the monocular feature, face feature, and monocular position information to predict the gaze point of each eye. Finally, the superior performance of MGE-Net is proved by experiments on the MPIIFaceGaze and GazeCapture dataset.
Human fall detection based on improved particle swarm optimization algorithm and neural network

As the global population continues to age, fall detection has become a common concern in the field of public safety. Fast and accurate detection of falling behaviors in surveillance videos and timely sending out help signals can effectively reduce the injuries caused by falls in the elderly. This paper proposes a hybrid algorithm based on an improved particle swarm optimization algorithm and a neural network for real-time fall detection in indoor environments. Human keypoints in video frames are first extracted using the alphapose model, and then the human keypoints are classified in real-time using an improved particle swarm optimization neural network model. Experimental results show that this method can effectively detect falling behaviors in indoor scenes.
Solar position acquisition method by structural similarity characteristics of skylight polarized pattern images

Obtaining solar position from the skylight polarized information is one of the key issues in the research of the polarization navigation. Through the statistical observation of the skylight polarized pattern images information, a method is proposed to obtain the solar position by utilizing characteristics of the skylight polarized pattern based on the structural similarity. Based on the characteristics of the skylight polarized pattern, the structural similarity function of the skylight polarized pattern is established for solving solar position. The bat optimization algorithm is utilized to optimally estimate the structural similarity function. And the theoretical skylight polarized pattern that best matches the measured skylight polarized pattern can be obtained. Then, the corresponding solar position can be calculated. The experimental results show that the average errors of azimuth and zenith angle estimation are 0.55° and 0.66° without interference, respectively. In the interference experiment, when the cloud area is less than 65%, the errors of azimuth and zenith angle are within 1.5°. It is proved by experimental results that the proposed method can obtain solar position in the measured skylight polarized pattern. And it has a certain anti-interference ability to the sky occlusion caused by clouds.
ADNet: A neural network model for adversarial example detection based on steganalysis and attention mechanism

The vulnerability of deep learning to adversarial attacks has brought many security risks to its development. However, the currently proposed adversarial attack detection methods are ineffective in defending large-size images. Studies have shown that detection methods based on steganalysis have apparent advantages in defense of large-scale adversarial examples. Still, their detection results are very dependent on the quality of manually extracted features. This paper proposes an end-to-end convolutional neural network (CNN) model based on steganalysis for adversarial examples detection. We found that adversarial attacks tend to embed perturbations in high-texture regions. To focus more on vulnerable areas during feature learning, we use the original image as information to learn the attention distribution, which improves the model's accuracy. And an adversarial loss function is proposed to fine-tune the model so that the normal images and adversarial examples are more separated in the spatial distribution, reducing the probability of misclassifying normal images. Experiments show that this model has a high defense effect against the current state-of-the-art attack methods.
Overlapped Pedestrian Detection Based on YOLOv5 in Crowded Scenes

Pedestrian detection in a crowded environment is challenging for vehicle intelligent driving systems. At present, pedestrian detection algorithms have achieved great performance in detecting well-separated figures. However, pedestrians are generally overlapped in crowded scenes, resulting in slow detection speed, low detection accuracy, and poor robustness in pedestrian detection technology. In this paper, the YOLOv5 algorithm is used for pedestrian detection. In the aspect of data pretreatment, Mosaic data enhancement, unified image size, adaptive anchor frame calculation, and other processing are carried out for data.YOLOv5 can detect targets at multiple scales, and CIOU_Loss and DIOU_nms are applied to the YOLOv5 algorithm. It can improve the recognition ability of the occlusion target and has a good detection effect on the detection of the occlusion pedestrian target through the training network of amplified data set. The verification experiment shows that the pedestrian detection model based on YOLOv5 has great detection accuracy and recall rate in detecting covered pedestrians.
An image caption generation method based on improved attention mechanism

Image captioning is a research hotspot in the field of image understanding. It spans the fields of computer vision and natural language processing. Most of the image captioning researches use deep learning technology, especially using Convolutional Neural Networks(CNN) as encoder-decoder structure for feature extraction. The existing captioning model is not of high quality, the important parts of the image are not sufficiently extracted features and the model is too complex. To address these problems, this paper introduces a new generation method based on attention mechanism. First, an efficient attention module is introduced to improve the feature extraction effect based on traditional attention mechanism. It reduces the model complexity and better extracts the important part of image features. The method uses convolutional features extracted from a pre-trained CNN model which integrate the features with the attention mechanism. Second, we also select the GRU language model as the decoder to construct descriptive sentences. Finally, experiments on the MSCOCO dataset and Flickr30k dataset show that the model proposed in this paper effectively improves the quality of descriptive sentences.
Shadow Removal Based on 2Cycles-GAN

Shadow removal and restoration of the image content in the shadowed regions by using shadow removal has become more and more popular in computer vision. But almost all current shadow-removal approaches use shadow-free images for training. Recently, an innovative approach that trains samples without this requirement due to this method crops patches with and without shadows from shadow images. However, it is insufficient to directly learn the essential relationships between shadow and shadow-free domains using adversarial learning and cycle-consistency constraints. Moreover, constructing many of these unpaired patches is still time-consuming and laborious. In our paper, we propose a new method named 2Cycles-G2R-ShadowNet. A shadow mask is used in our framework. We use the mask to guide the shadow generation to reformulate cycle-consistency constraints. To weakly-supervised shadow removal, we train shadow images and corresponding masks to leverage shadow generation. In our 2Cycles-G2R-ShadowNet, three subnetworks are used for shadow generation, shadow removal, and image post-processing, and we jointly train and test them end-to-end. Our method can optimize the performance by simultaneously learning to produce shadow masks and remove shadows. Extensive experiments on the ISTD dataset show that 2Cycles-G2R-ShadowNet achieves competitive performances and outperforms the current state of arts and patch-based shadow-removal method.
Single image super-resolution via deep learning

Single image Super-resolution (SISR) is a computer vision (CV) problem that aims to acquire a high-resolution (HR) image from a distorted low-resolution (LR) image, making it a valuable technology that could be utilized in various fields such as photography, medical imaging, satellite imaging, etc. As a result of the advancement of computing hardware and richer computational power, deep learning-based image super-resolution models have emerged at an unprecedented rate. This paper reviews SISR and its recent development. Three widely used deep architectures: convolutional neural network (CNN), generative adversarial network (GAN), and transformer are explained. Next, six different deep learning-based models that summarize research on SISR are analyzed. Finally, this review concludes with applications of SR, current challenges SISR models encountered, and potential future research directions.
Personality Recognition Method based on Facial Appearance

Inspired by psychological findings that facial appearance can be viewed as the key clue of evaluating individual’s personality, we propose an automatic personality recognition method via portrait images in this paper. A face-personality dataset named as CelebAMask-HQ-Personality is constructed in a crowd-sourced manner. This dataset consists of facial images, facial geometric features and personality annotations. The facial images are refined from CelebAMask-HQ by removing obscured ones. We utilize facial landmark information to generate geometric features under the guidance of psychological discoveries. The personality annotations are obtained by volunteers in a right amount via Ten-Item Personality Inventory (TIPI). Then, the correlations between facial appearance and personality are explored using some representative classifiers such as multi-layer perceptron, random forests, support vector machines, and k-nearest neighbors, etc. Experimental results show that it is feasible to infer the personality of individuals by their facial appearance.
Dual Relation-Aware Synergistic Attention Network for Image-Text Matching

The image and text matching task plays an essential role in bringing the semantic gulf between language and vision. It still remains challenging since previous methods lack a detailed comprehension of the contextual interplays reflected in diverse visual relationships between objects. In this work, we present one novel dual relation-aware synergistic attention (DRSA) network to produce visual representations that incorporate crucial semantic concepts and salient objects of image scenes. First, we construct each image as two subgraphs and perform the multi-type inter-object interactions utilizing the sentence-guided graph attention mechanism. Specially, two types of visual relations are exploited: Implicit Relations extracting the latent dynamics between objects and Explicit Relations capturing the semantic dependencies and relative geometric positions. Second, a synergistic fusion module is designed to adaptively merge the implicit, explicit, and all-mixed relation features based on the sentence contexts, which works like multi-head attention. Third, adversarial learning is conducted to reinforce the interaction of implicit and explicit relation encoding modules to explore more effective multi-view associations between images and sentences. Experiments show that DRSA outperforms existing state-of-the-art approaches on two widely used datasets (i.e., MSCOCO, Flickr30K), proving the efficacy of our proposed elaborate matching technique.
Traceable Attribute-Based Encryption on OBDD Access Structure from Lattice

In order to solve the problem of key abuse in attribute-based encryption, a traceable ciphertext-policy attribute-based encryption scheme is proposed based on the learning with error over ring and the ordered binary decision diagram structure. Firstly, the specific information of the user is embedded in each private key to realize the traceability of the key. Secondly, the access of illegal users and malicious users is avoided by maintaining the white list. In addition, the scheme adopts the access structure of ordered binary decision diagram, and increases the positive and negative values of attributes on the basis of supporting the operation of “AND”, “OR” and “Threshold”. Finally, through analysis, the scheme meets the security of anti-collusion attack and chosen-plaintext attack security, reduces the storage and computing overhead, and is more practical than other schemes.
Parallelizing Graph Neural Networks via Matrix Compaction for Edge-Conditioned Networks

Graph neural networks (GNNs) are a powerful approach for machine learning on graph datasets. Such datasets often consist of millions of modestly-sized graphs, making them well-suited for data-parallel training. However, existing methods show poor scaling due to load imbalances and kernel overheads. We propose an optimized 2D scatter-gather based represen-tation of GNNs that is amenable to distributed, data-parallel training without changing the underlying mathematics of the GNN. By padding graph data to a fixed size on each process, we can simplify data ingestion, make use of efficient compute kernels, equally distribute computation load, and reduce overheads. We benchmark edge-conditioned GNNs with the PCQM4M-LSC and OGB-PPA datasets. Our implementation shows better runtime performance than the state-of-the-art, with a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$12\times$</tex> strong-scaling speedup on 16 GPUs and an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$89.4\times\ \text{weak}$</tex> -scaling speedup on 100 GPUs.
SGX-Bundler: speeding up enclave transitions for IO-intensive applications

Process-based confidential computing enclaves such as Intel SGX can be used to protect the confidentiality and integrity of workloads, without the overhead of virtualisation. However, they introduce a notable performance overhead, especially when it comes to transitions in and out of the enclave context. Such overhead makes the use of enclaves impractical for running IO-intensive applications, such as network packet processing or biological sequence analysis. We build on earlier approaches to improve the IO performance of work-loads in Intel SGX enclaves and propose the SGX-Bundler library, which helps reduce the cost of both individual single enclave transitions well as of the total number of enclave transitions in trusted applications running in Intel SGX enclaves. We describe the implementation of the SGX-Bundler library, evaluate its performance and demonstrate its practicality using the case study of Open vSwitch, a widely used software switch implementation.
Edge-Eye: Rectifying Millimeter-level Edge Deviation in Manufacturing using Camera-enabled IoT Edge Device

Irradiated Cross-linked Polyethylene Foam (IXPE) has been one of the most commonly used materials in industry. During the production process of IXPE sheets, their edges need keep aligned strictly, otherwise, they could quickly get out of the border of the rolling plate and cause the huge economic loss. In this paper, we propose a camera-enabled approach, called Edge-Eye, to rectify the edge deviation automatically for IXPE production with millimeter-level accuracy. We deploy a commercial camera with mobile edge node in front of the IXPE sheet to continuously detect and rectify the edge deviation. Particularly, to handle the complex production en-vironment when extracting the edge of IXPE sheet, we deploy a pair of reference bars with high-contrast colors to efficiently differ-entiate the sheet edge from the background. Then, we propose a Bi-direction Edge Tracking method to perform the edge detection from both vertical and horizontal aspects. To realize the rectification using mobile edge nodes with limited computing resources, we reduce the cost of computation by extracting the Minimized Region of Interest, i.e., the edge area overlapped with the higher contrast reference bar on both sides. We further design a negative feedback control system with multi-stage feedback regulation mechanism, keeping the edge deviation within millimeter-level. We implemented Edge-Eye on the ARM64 platform and performed evaluation in the practical IXPE production process. The experimental results show that Edge-Eye achieves the average accuracy of 5mm for the edge deviation rectification, with the average latency of 200ms for edge deviation detection. During the process of 20-month real deployment for 36 production lines, 66 manpower per day (90% of the overall manpower) has been saved, and the utilization rate of IXPE material increases from 87% to 94%.
ELITE: Near-Optimal Heuristics for Coflow Scheduling

Reducing Coflow Completion Time (CCT) has a significant impact on data-intensive application performance in datacenter networks. An efficient allocation of network resources allows for accelerating the computations to be performed. In this paper, we propose a new scheduler, named ELITE, to minimize the Weighted Coflow Completion Time (WCCT). Our scheduling algorithm is a 2-approximation of the optimal and the rate allocation can achieve a 4-approximation as long as the scheduling priority is respected. We also present a new rate allocation procedure, named RACO, that shows near-optimal performance when combined with our scheduling algorithms. We also propose a low complexity online scheduler, named LSPRT to attain near-optimal performance in online setting. With extensive simulations, we demonstrate the effectiveness of our algorithms by measuring the performance gain of ELITE and LSPRT over previous solutions in the literature. In particular, ELITE and LSPRT perform about 44 % better than Varys, while Sincronia achieves only 32 % against Varys.
An Open-Source Cloud Testbed for Security Experimentation

The use of container and orchestration technologies, such as Docker and Kubernetes keeps growing every year. For the purpose of security experimentation and reproducibility of security attacks and defenses, an open-source testbed would also be an important step forward. Yet, while several security experimentation testbeds from web application testing to capture-the-flag (CTF) competitions have been proposed, a similar solution for cloud experiments is wanting. To fill this gap, we propose an open-source cloud testbed that, by using Domain Specific Language (DSL) files (e.g. with JSON or YAML syntax), allows defining experimentation scenarios as configuration files. Using DSL files allows to create, share, customize, automatically deploy, and reproduce different scenarios in a user-friendly manner. We describe the design and the corresponding tools and technologies for different implementations.
A Detailed Investigation on the Role of Internet of Things (IOT) for Better Model-Driven Processing System Based on User Defined Constraints

Model-driven Processing (MDP) or Model-driven engineering (MDE) is an interesting practice in recent days to develop virtual prototypes of conventional procedures. Other outcomes reported are high productivity, meeting users' needs and so on. Thus, the research has been conducted to understand the algorithms and tools used in MDP. A primary random sampling method has been carried out to understand the more popular algorithms and tools in this new practice. The random sampling method has collected the number of MDP use in different platforms. As the sampling method is random, thus not specific field was analysed for the ultimate justification. The random sampling therefore upheld the tool and model preferences based on random MDE devices. Findings suggested that Rational Software Architect (RSA) is used mostly for designing the plans. Other software included are Simulink and Sirius which are also in practice; however, due to having advanced feature in RSA, the RSA is used mostly. Apart from this, both Machine Learning (ML) and Convolutional Neural Networks (CNN) are used in MDP belonging to different fields. The overall beneficial and comparative features have been identified in this research.
Deep Learning Based Hybrid Word Representation for Detection of Hate Speech

In this era of very easy access to information, it has become very easier for anyone to share their ideas, opinions and beliefs. Internet being the most used space for all lets every user share their thoughts with just a click. With a lot of people sharing their ideas and opinions, it is obvious that there can be some dissatisfactions. Internet has witnessed some of the greatest social movements in the history. With smartphones available in every hand, people can join the movement easily and express their opinions. Social movements are drives related to a particular community which gets attention from the entire world. Social media mostly gets polarized into two parts, one in support of the movement and another in opposition of the movement. Machine learning algorithms give us the power to do the analysis of people's sentiment in the internet. Using the power of machine learning and its applications, this paper tries to do the fundamental analysis of which sides the twitter users are leaning to subsequently enable recognizing hate speech. With the help of tweets publicly available, the analysis is done using multiple deep learning algorithms.