Reviewerly/exHarmony huggingface dataset:
Deep Learning for Localization of Mixed Image Tampering Techniques

With technological advances leading to an increase in mechanisms for image tampering, fraud detection methods must continue to be upgraded to match their sophistication. One problem with current methods is that they require prior knowledge of the method of forgery in order to determine which features to extract from the image to localize the region of interest. When a machine learning algorithm is used to learn different types of tampering from a large set of various image types, with a large enough database we can easily classify which images are tampered. However, we still are left with the question of which features to train on, and how to localize the manipulation. In this work, deep learning for object detection is adapted to tampering detection to solve these two problems, while fusing features from multiple classic techniques for improved accuracy. A Multi-stream version of the Faster RCNN network will be employed with the second stream having an input of the element-wise sum of the Error Level Anyalysis (ELA) and Block Artifact Grid (BAG) error maps to provide even higher accuracy than a single stream alone.
Adaptive Auto-Scaling of Delay-Sensitive Serverless Services with Reinforcement Learning

Serverless services such as image recognition and natural language processing have strict response-time constraints. The incoming workloads and resource requirements of a newly deployed serverless service are always unpredictable due to the lack of available historical tracing data. Therefore, making effective auto-scaling decisions for these services is challenging. Open source serverless platforms often work in a best-effort manner, which cannot guarantee the response delay. Moreover, existing studies usually adopt threshold-based methods by configuring additional resource, which cannot well balance the trade-off between the quality of service and resource efficiency. To address the above issues, we propose an adaptive auto-scaling approach for delay-sensitive serverless services with reinforcement learning. First, we characterize the service's resource profile by exploring the performance improvement of different resource allocations with the reinforcement learning method. Then, we propose an adaptive auto-scaling method combining both horizontal and vertical scaling strategies based on the characterized profile to dynamically adjust the resource allocation. Finally, we select three typical services to validate our approach by comparing with two existing state-of-the-art auto-scaling methods. The experimental results show that our approach can accurately characterize services' resource profile, and effectively ensure the response delay constraints while achieving about 10.50% reduction of cost on average.
Time-Optimal Nonlinear Model Predictive Control for Radar-based Automated Parking

Motion planning and control are crucial components for automated vehicles. Especially in parking scenarios, high precision is required due to the small distances to obstacles. Common approaches utilize ultrasonic or camera sensors. This paper presents a radar-based system architecture for automated parking, which can operate more robustly under difficult weather conditions. Moreover, this work develops a unified planning and control framework based on nonlinear model predictive control. Real vehicles often exhibit dynamic behavior that the commonly used kinematic bicycle model does not represent. Therefore, the paper at hand proposes an extended model, which rests upon a systematic analysis of the test vehicle's dynamics for the low-velocity range. Experiments in simulation and on real-world data show the efficiency of the approach and the importance of the extended vehicle dynamics modeling for closed-loop control.
Anonym-Recognizer

With the widespread application of big data technology, we are exposed to more and more video monitoring. To prevent serious social problems caused by face data leakage, face anonymization has become an important kind of method to protect face privacy. The face anonymization mentioned in this paper refers to the anonymization generation of the visual appearance in face images. Existing face anonymization methods mainly focus on removing identity information. However, in the scenario of face recognition technology that needs to protect privacy, existing face anonymization technology makes anonymized faces that can no longer be used for face recognition, limiting the application scope of face anonymization. Therefore, when using face anonymization, it is equally important to ensure that the anonymized face images can still be used for downstream tasks such as face recognition. To this end, we propose Anonym-Recognizer, a relationship-preserving face anonymization and recognition method. Our method uses relationship cyphertext which can be any binary identity number representing the identity of the image owner and designs a generative adversarial network to perform face anonymization and relationship cyphertexts embedding. In our framework, we first use Visual Anonymizer to manipulate the visual appearance of the input image, then use Cyphertext Embedder to get the anonymized image with the identity information embedded. With the help of Anonym Recognizer, the face recognition system can extract the relationship cyphertexts from the anonymized image as the credentials to match the identity information. The proposed Anonym-Recognizer provides a new perspective for the recognition and application of anonymized face images. Experiments on the Megaface dataset show that our method can encourage a 100% recognition accuracy on anonymized faces while finishing the task of face anonymization with high qualitative and quantitative quality.
On the Impact of Spatial Rendering on Point Cloud Subjective Visual Quality Assessment

Immersive imaging modalities have been receiving growing attention over the last years. In this context, point clouds demonstrated to be a competitive data representation format, mainly due to its compatibility with most acquisition devices such as LiDAR scans and depth cameras. On the other hand, the large associated data volume is a challenge to storage and transmission, resulting in the need for efficient point cloud compression methods. Multiple subjective studies have been conducted to assess the performance of such compression methods, mostly limiting the analysis to flat monitors or virtual and augmented reality headsets. In this paper, we investigate the impact of a novel eye-sensing light field display on several aspects of quality of experience, as well as on the subjective perception of compression artifacts. The two visualization devices have been observed to create distinct user experiences which lead to noticeable differences in subjective opinion. The advantages and disadvantages of each visualization strategy are underlined, based on rigorous statistical analysis. The subjectively annotated dataset is also released in order to foster future research.
VRstalls: A Dataset on the QoE of Frame Stalls in 6-DOF VR

A dataset has been created from an experiment regarding the subjective Quality of Experience (QoE) in relation to stalling video feeds in interactive 6-Degrees-of-freedom Virtual Reality (6-DOF VR). The main purpose of collecting this data is to provide insight into the QoE of stalls that may occur in interactive remote-rendered 6-DOF VR. The data contains information regarding user ratings coupled with various stall lengths and for both frozen or reprojected images. Additionally, user motion and pixel changes (in the form of Mean Square Errors) in successive images in the vicinity of the stall are also stored. In summary, 29 users participated in testing, each rating 16 scenes, amounting to a total of 464 rated scenes.
Design of Automated Exam Proctoring for User Authentication Through Face Augmentation and Verification

The uses of online exam continue to increase during covid-19 pandemic. The challenge of online examination is proctoring method to detect cheating possibility to maintain integrity in its implementation. Various design of proctoring methods had been proposed from remote proctoring using video surveillance until automatic proctoring using machine learning approaches. One of important things in proctoring is user authentication during exam session. Recently, face recognition algorithm using image are widely used in automatic proctoring method instead of video which use high load data for storage, transmission and processing. Despite the progress made to detect identity of examinee, these models very dependent on data and required iterative training process to get a robust model whereas there are many and various examinee facial image like pose, lighting, etc. This problem makes the training process require large resources and take a long time. In this paper, we proposed automatic proctoring design for examinee verification that more flexible and efficient through face verification with key-face augmentation instead of face recognition. This proctoring design verifies the examinee's image with variations of key-face augmentation so that it does not require repeated training processes. As a result, the design of the proposed method is presented in this paper.
Simulation of Manipulator Path Planning Based on Improved RRT* Algorithm

The problem of obstacle avoidance path planning of 6-axis manipulator is studied. After analyzing the disadvantages of large randomness and low algorithm efficiency of RRT*, an improved RRT* algorithm is proposed, which can quickly search the shortest expected path that can effectively avoid obstacles. Firstly, the path pruning process of RRT* is improved to improve the degree of path optimization, and then the global sampling space is reduced, so that the random tree can quickly expand to the target point. The algorithm is verified on the MATLAB simulation platform. The results show that the average time of path search and the number of expansion nodes of the improved RRT* algorithm are greatly reduced, the path length is also better than the traditional RRT*, and can be well applied to the path planning of manipulator.
SA-Model: Multi-feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model

Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing, ancient literature research, etc. However, the existing research on sentiment analysis is relatively small and does not effectively solve the problems such as the weak feature extraction ability of poetry text, which leads to the low performance of the model on sentiment analysis for Chinese classical poetry. In this research, we offer the SA-Model, a poetry sentiment analysis model. SA-Model firstly extracts text vector information and fuses it through BERT-wwm-ext and ERNIE models to enrich text vector information; Secondly, it incorporates numerous encoders to extract text features at multiple levels, thereby enriching text feature information, improving text semantics accuracy, and enhancing the model's learning and generalization capabilities; finally, multi-feature fusion poetry sentiment analysis model is constructed. The feasibility and accuracy of the model are validated through the ancient poetry sentiment corpus. Compared with other baseline models, the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.
The Effects of Network Latency on Counter-strike: Global Offensive Players

Players of first-person shooter (FPS) games, such as Counter-strike: Global Offensive (CS: GO), seek low latencies in order to play well and have fun. Even network latencies as small as 10 milliseconds may decrease accuracy, score, and Quality of Experience (QoE), degredations that may be exacerbated for some weapons. This paper presents results from 40+ person user study that measures the impact of network latencies on players for the FPS game CS: GO. We setup a testbed where participants played 20+ rounds of CS: GO with controlled amounts of network latency with either a mid-range, rapid fire, high-precision weapon (an AK-47 assault rifle) or a close-range, slow fire, lower-precision weapon (a Nova shotgun). Analysis of the results shows even network latencies under 100 milliseconds degrade player performance (accuracy and score), avatar movements, and QoE, with the impact on player performance more pronounced for the assault rifle compared to the shotgun.
Facial Expression Recognition Based on Genetic Programming Learning CCA Fusion

The quality of the features directly affect the overestimation of image classification. However, it is difficult to distinguish between two categories only by a single feature when there is no obvious difference between them. One of the ideas to solve this problem is multi-feature fusion. However, the existing fusion methods have the problems of complex models, fixed models and the need for relevant knowledge in the field. Genetic programming (GP) is a feature fusion method with flexible representation. It can automatically learn other excellent fusion methods without operating how the computer fuses. According to this property, this paper presents a new LCGP method, which can automatically learn existing fusion methods. In the proposed method, the excellent fusion model will be refined into a mathematical form of the function operator. In fact, this is a three-tiered GP tree that integrates different features and fusion methods into the same tree through operators. The proposed method can automatically learn and evolve different mathematical fusion models, validate them on two small sample datasets, CK+ and JAFFE, and compare them with several state-of-the-art methods. The results show that although the training examples are limited, the performance of this method is better than or similar to that of the related methods.
Hierarchical Feature Fusion and Multi-scale Cost Aggregation for Stereo Matching

To further improve the accuracy of disparity estimation in ill-posed regions and weak texture regions, in this paper we propose HFMANet: which is a stereo matching method based on hierarchical feature fusion and multi-scale cost aggregation. Specifically, we first propose a hierarchical feature fusion module, which innovatively fuses low-level features and high-level features to obtain rich semantic information while retaining the edge information of the image. Secondly, we propose a multi-scale cost aggregation module to extract rich global context information. At the same time, the layer-by-layer fusion optimization helps increase the receptive field to capture more structural information, reduce the dependence on local information, and help the disparity estimation of ill-posed regions and weak-textured regions. Comprehensive experiments are conducted on the SceneFlow and KITTI datasets, and achieve competitive results, which proves the effectiveness of the proposed method.
A Trusted BID Document Upchain Method Based on Blockchain Infrastructure and Facility

With the continuous maturity of technical theory, the enhanced support of communication network and the industrialization of sensing terminal and data acquisition equipment, the Internet of Things (IoT) has been deployed and applied on a large scale in many fields such as manufacturing, agriculture, transportation, medical treatment and so on. In order to uniquely identify items, many different identification coding schemes have been designed and proposed. However, most of them have been difficult to satisfy the requirements of security, storage, and efficiency. Blockchain can provide a decentralized storage method to release the impact of security. Therefore, this paper proposed a trust blockchain-based Identifier (BID) uplink method. Frist, BID code scheme is given to realize unique identification. Second, BID document is designed to store recognizable digital object data on blockchain. Third, BID document resolution method is proposed to obtain BID document from blockchain. Finally, based on the above elements, the BID upchain method is implemented to manage identification on blockchain infrastructure and facility.
Using Improved YOLOX for Underwater Object Recognition

Underwater object recognition has limited sample data acquired and most of the images are of low resolution and poor quality resulting in less data that can be used for training. In this paper, we propose the underwater object recognition algorithm AX-YOLO based on YOLOX, which mainly improves the data enhancement mode in the data processing stage and the optimizer in network training according to the characteristics of under-water objects to test underwater object recognition in the case of open data sets underwater. The experimental results show that AX-YOLO model has the highest mAP of 91.05% and FPS of 71. The algorithm has a short training time and high accuracy, which supports underwater object recognition for marine operations development.
AR Mobile Video Calling System Based on WebRTC API

During the COVID-19 pandemic, the demand for video calling and video conferencing is at peak and continues to grow. Integrating Augmented Reality (AR) with a regular video call can deliver a more interactive, immersive, and collaborative communication experience. This paper presents an AR video calling system that uses WebRTC protocol for establishing real-time video communication and uses AR SDK such as Google AR Core for adding AR features over the lively streaming video feed. To evaluate the performance and feasibility of the proposed AR video calling system, we implemented it on an Android mobile device and measured the relevant performance data, such as frame rate, CPU usage and network usage. The experimental results suggest that the proposed system is practicable and stable.
Galois Fields for Digital Image and Signal Processing: Evidence for the Importance of Field Specificity

It is shown that any digital signal and image processing algorithms can be reduced to multivalued logic operations. The basis for this is signal models, in which each discrete level is associated with an element of the Galois field. Therefore, the development of algorithms and computing systems operating directly in Galois fields is topical. To simplify the calculation of products in Galois fields, in turn, it is convenient to pass from an element of the Galois field to the degree of a primitive element that defines this element. This allows the multiplication operation to be reduced to the addition operation. A specific example of an algorithm and a block diagram of a device that implements it, which allows such a reduction, are presented. This example demonstrates that for solving problems of digital signal and image processing, it is very important to take into account the specifics of specific Galois fields.
Named Entity Recognition for Medical Dialogue Based on BERT and Adversarial Training

In the medical dialogue named entity recognition, in order to solve the problem that the traditional named entity recognition method is difficult to recognize medical corpus, this paper proposes a BERT-based medical dialogue named entity recognition method BERT-BiLSTM-CRF-ADV. First, we use the BERT pre-training model to obtain word vectors with rich semantic information, then send the word vectors to BiLSTM to extract features, and finally input them into CRF for restriction correction and output. During the training process, we use adversarial training to improve the model performance. This paper applies the proposed method to the medical field to perform named entity recognition on medical dialogue materials. Experimental results show that this method achieves 92.82% of the F1 value in medical dialogue materials, which is greater than the baseline model BERT-CRF.
Monomial Codes With Predefined Automorphisms

The automorphism group of a monomial code may include automorphisms induced by affine transforms of the variables used for code construction. Certain monomial codes, like Reed-Muller codes, have a lot of these automorphisms, which makes them suitable for permutation-based decoding, while other, like polar codes, in general, do not have this property. We define the condition that the code automorphism group includes automorphisms induced by certain affine transform subgroups, and describe the restrictions imposed on monomial code construction by this condition. We also consider code construction approach that yields monomial codes with given automorphism subgroup and optimized additional parameters.
Efficient Robustness Verification of the Deep Neural Networks for Smart IoT Devices

Abstract In the Internet of Things, smart devices are expected to correctly capture and process data from environments, regardless of perturbation and adversarial attacks. Therefore, it is important to guarantee the robustness of their intelligent components, e.g. neural networks, to protect the system from environment perturbation and adversarial attacks. In this paper, we propose a formal verification technique for rigorously proving the robustness of neural networks. Our approach leverages a tight liner approximation technique and constraint substitution, by which we transform the robustness verification problem into an efficiently solvable linear programming problem. Unlike existing approaches, our approach can automatically generate adversarial examples when a neural network fails to verify. Besides, it is general and applicable to more complex neural network architectures such as CNN, LeNet and ResNet. We implement the approach in a prototype tool called WiNR and evaluate it on extensive benchmarks, including Fashion MNIST, CIFAR10 and GTSRB. Experimental results show that WiNR can verify neural networks that contain over 10 000 neurons on one input image in a minute with a 6.28% probability of false positive on average.
Corrosion Detection Method of Transmission Line Components in Mining Area Based on Multiscale Enhanced Fusion

Component corrosion is one of the potential safety hazards in transmission lines in mining areas. In order to solve the problem of poor detection accuracy caused by the large proportion of small targets and complex background in the current distant view corrosion inspection task by UAV, we propose a PWR-YOLOV5 detection method for corrosion components based on the YOLOV5 algorithm. Firstly, a new feature fusion network, WA-PANet, is reconstructed on the basis of the path aggregation network (PANet) to make full use of the features at different stages and advance the detection accuracy of small targets in distant view by deepening the process of feature fusion and introducing the skip layer connections and adaptive feature fusion factors. Secondly, the pyramid split attention (PSA) module is introduced into the deep layers of the network to highlight the feature expression of corrosion targets and enhance the ability to detect pixel-level objects. Then, we construct a receptive feature enhancement network (RFENet), which can heighten the feature fusion effect of the WA-PANet and alleviate the problem of the feature expression ability weakening due to the fusion of different receptive field features. Finally, the EIoU Loss is adopted to optimize the loss function and improve the positioning accuracy of the bounding box. The experimental results show that the mAP of the PWR-YOLOV5 algorithm can reach up to 95.37%, which is 5.22% higher than YOLOV5, and the detection speed is 64.9FPS. Compared with the algorithms such as YOLOV4, Faster R-CNN, and YOLOX, the improved algorithm has better overall detection performance for the corrosion components of transmission lines in the mining area.