Reviewerly/exHarmony huggingface dataset:
DCIV: Decentralized cross-chain data integrity verification with blockchain

In recent years, blockchain holds promise to impact a wide range of application areas, but it still suffers from technical challenges such as security and scalability. The increase in the number of transactions puts blockchains under data storage pressure. The emergence of cross-chain technologies connects different blockchains and relieves the data storage pressure. However, the existing research generally focuses on the technical realization of cross chain, lacking in-depth research on consistency issues like data integrity verification of cross-chain interaction. In this paper, we propose a decentralized cross-chain data integrity verification scheme (DCIV) from the point of view of governing the chain by chain. We adopt supervision chain to audit the integrity of data in cross-chain interaction. We preprocess the off-chain original data in the form of Merkle tree. Before cross-chain interaction, we process the data with KZG polynomial commitment. During the auditing period, the supervision chain generates a challenge and verifies the integrity of cross-chain data. In particular, we add audit digests into the structure of transactions in blockchain to reduce the storage burden during auditing. Theoretical and experimental analyses demonstrate that DCIV can verify the integrity of data in cross-chain interaction, achieving secure and accurate cross-chain data sharing.
A 3D Visual Security (3DVS) score to measure the visual security level of selectively encrypted 3D objects

Today, 3D objects are becoming more commonly used across many domains. However, it is necessary to secure them during their transmission over networks or when archiving them in the cloud. Encryption is a smart solution to protect 3D objects while remaining format compliant. While there exist many methods proposed for 3D quality evaluation, very few have been developed to evaluate the visual security level of encrypted 3D objects. In this paper, we propose an efficient metric, called 3D Visual Security (3DVS) score, to evaluate the visual security level of selectively encrypted 3D objects. First we present a new dataset composed of selectively encrypted 3D objects that have all been evaluated by more than 50 observers in terms of visual security. Secondly, we propose a model to determine the security parameters according to a desired security level. Finally, we detail our proposed 3DVS score which is based on full reference 3D metrics and serves to measure the visual security level of selectively encrypted 3D objects.
On the subjectivity of emotions in software projects: How reliable are pre-labeled data sets for sentiment analysis?

Social aspects of software projects become increasingly important for research and practice. Different approaches analyze the sentiment of a development team, ranging from simply asking the team to so-called sentiment analysis on text-based communication. These sentiment analysis tools are trained using pre-labeled data sets from different sources, including GitHub and Stack Overflow. In this paper, we investigate if the labels of the statements in the data sets coincide with the perception of potential members of a software project team. Based on an international survey, we compare the median perception of 94 participants with the pre-labeled data sets as well as every single participant’s agreement with the predefined labels. Our results point to three remarkable findings: (1) Although the median values coincide with the predefined labels of the data sets in 62.5% of the cases, we observe a huge difference between the single participant’s ratings and the labels; (2) there is not a single participant who totally agrees with the predefined labels; and (3) the data set whose labels are based on guidelines performs better than the ad hoc labeled data set.
Real-Time Object Detection using an Ultra-High-Resolution Camera on Embedded Systems

Unnamed Aerial Vehicle (UAV) - based remote sensing is a promising technology that is being applied for inspecting live scenes from high altitudes (e.g., for surveillance and recognizing emergencies). The evolution of hardware and software technologies in the last few years has generated additional interest in embedded systems research and its implementation in energy-independent UAVs for remote sensing. Alongside, ultra-high-resolution optical sensors are mandatory for acquiring high-resolution images which are necessary for accurate object detection from a distance (e.g., 1,000 meters). The processing of ultra-high-resolution images (e.g., 4K or 8K) is beyond the typical resolutions which are used for object detection (e.g., &#x003C; 2K) emerging a necessity for special treatment in order to succeed a fast object detection. We propose a three-step approach deployed on a Docker runtime environment in an Nvidia Jetson AGX Xavier board. To support fast object detection, the captured images are split into K parts processed in parallel in separate containers running the YOLOv5 object detection algorithm. A final detection is constructed based on each one of the K detections. The experimental results are a good support to our claims of efficiency: the method can achieve close to real-time object detection for ultra-high (i.e., 8K) resolution images (i.e., in less than 1 second per frame).
Enhancing Classification of Elderly Fall Detection System using Tuned RBF-SVM

An elderly FDS or Fall-related incident Detection System can reduce the severity of late treatment after a falling incident by shortening the period between the inevitable fall and the proper medical care. A failure to comply the need of urgent medical attention to the falling victims can lead to painful death. But first, a machine must be trained to automatically and accurately detect a falling incident. The Support Vector Machine (SVM) is generally acknowledged as one of the best tree-based machine learning algorithms for classification and regression data analysis. This tree-based Deep Learning model has been used in fields, together with bioinformatics, face recognition, and image recognition, and urgent cases such as FDS. This paper defines a work of FDS using SVM. We used 16,261 instances and 33 attributes of fall simulation from eHomeSeniors dataset, in which the dataset employs the infrared thermal sensor. This dataset stated more than 15 classes of fall, to achieve best result in capturing falling incidents. We also set the gamma value from the default 0.01, to 0.9, with no normalization or standardization needed in the process. The training data and testing data are also split into the scale of 50:50, 60:40, 70:30, 80:20, and 90:10. Consequently, we were able to reached 84.62% accuracy and 50.32 seconds learning runtime at data split of 90:10.
Strategies for Improving the Quality of Music Teaching in Primary and Secondary Schools in the Context of Artificial Intelligence and Evaluation

With the advancement of society, we have entered the era of “artificial intelligence,” with a plethora of sophisticated electronic devices infiltrating every aspect of our daily lives. People may work and study more conveniently with these sophisticated devices, dramatically increasing efficiency and saving time. When it comes to music instruction, there's no shortage of smart technologies to be found. This includes everything from advanced electronic musical instruments to sophisticated instructional software and mobile music apps. It is becoming increasingly difficult for youngsters to accept the traditional music instruction mode as they are exposed to and enjoy high-tech advancements. Many parents put their children into music training institutions to improve their children's after-school lives. When the emergence of new intelligent music teaching is a good solution to this problem, for this reason, this study explores the strategy and evaluation of music teaching quality improvement in primary and secondary schools based on the background of artificial intelligence. Therefore, those students who have just started to receive music education no longer feel monotonous and boring, and the indulgence mode of teaching makes students enjoy the fun and sense of achievement brought by music more when learning music, which can make more children enter the concentration state faster in the music initiation stage.
On Why the System Makes the Corner Case: AI-based Holistic Anomaly Detection for Autonomous Driving

One big challenge regarding the development of highly automated driving (HAD) functions is validation and, in particular, providing proof of the desired functionality in any given scenario. Especially, corner cases, representing atypical, rare scenarios such as unexpected object movements are of high interest and thus must be detected to treat them with special attention. First, this paper presents a taxonomy for corner cases (CC) with focus on HAD. Specifically, so-called systemic corner cases (SCC) are introduced. Next, a feasibility study is presented on how these SCCs can be detected using different Machine Learning (ML) approaches for anomaly detection. We propose to use a hybrid ensemble of a One-Class Support Vector Machine (OCSVM) and a Clustering-Based Local Outlier Factor (CBLOF) incorporating domain knowledge to account for the nature of corner cases in timely correlated scenarios. The underlying data are unlabeled multivariate time series of HAD-system internal variables. Our experiments on both, synthetically generated and representative real-world CC, show that the hybrid ensemble can detect a variety of real corner cases, which allows for promising validation support of HAD functions.
Point-Voxel Fusion for Multimodal 3D Detection

Many LiDAR-based methods have achieved encouraging results on 3D detection tasks, but detection of small objects such as pedestrians remains challenging. On the contrary, it is easy to detect small dimensional objects from images of cameras. The existing point cloud and image feature fusion methods are dominated by the point cloud, and due to the sparseness of the point cloud, some information of the image is lost. We propose a new fusion method named PVFusion to try to fuse more image features. We first divide each point into a separate perspective voxel and project the voxel onto the image feature maps. Then the semantic feature of the perspective voxel is fused with the geometric feature of the point. A 3D object detection model is designed using PVFusion. During training we employ the ground truth paste (GT-Paste) data augmentation and solve the occlusion problem caused by newly added object.
Revisiting PatchMatch Multi-View Stereo for Urban 3D Reconstruction

In this paper, a complete pipeline for image-based 3D reconstruction of urban scenarios is proposed, based on PatchMatch Multi-View Stereo (MVS). Input images are firstly fed into an off-the-shelf visual SLAM system to extract camera poses and sparse keypoints, which are used to initialize PatchMatch optimization. Then, pixelwise depths and normals are iteratively computed in a multi-scale framework with a novel depth-normal consistency loss term and a global refinement algorithm to balance the inherently local nature of PatchMatch. Finally, a large-scale point cloud is generated by back-projecting multi-view consistent estimates in 3D. The proposed approach is carefully evaluated against both classical MVS algorithms and monocular depth networks on the KITTI dataset, showing state of the art performances.
Deep Sensor Fusion with Pyramid Fusion Networks for 3D Semantic Segmentation

Robust environment perception for autonomous vehicles is a tremendous challenge, which makes a diverse sensor set with e.g. camera, lidar and radar crucial. In the process of understanding the recorded sensor data, 3D semantic segmentation plays an important role. Therefore, this work presents a pyramid-based deep fusion architecture for lidar and camera to improve 3D semantic segmentation of traffic scenes. Individual sensor backbones extract feature maps of camera images and lidar point clouds. A novel Pyramid Fusion Backbone fuses these feature maps at different scales and combines the multimodal features in a feature pyramid to compute valuable multimodal, multi-scale features. The Pyramid Fusion Head aggregates these pyramid features and further refines them in a late fusion step, incorporating the final features of the sensor backbones. The approach is evaluated on two challenging outdoor datasets and different fusion strategies and setups are investigated. It outperforms recent range view based lidar approaches as well as all so far proposed fusion strategies and architectures.
Image Inpainting Using AutoEncoder and Guided Selection of Predicted Pixels

Image inpainting is one of the most important ways to enhance corrupted digital images or pictures with missing pixels. For this purpose, different methods have been proposed. Some methods use the information of neighboring pixels to reconstruct the image. However, recent advances in deep learning have shown that receiving reasonable structural and semantic details from images can solve this problem. In this paper, we propose a network for image inpainting. This network, similar to U-Net, extracts various features from images, leading to better results. We improve the final results by replacing the damaged pixels with the recovered pixels of the output images. Our experimental results show that this method produces high-quality results compare to the traditional methods.
Automated Feature Engineering for Fraud Prediction in Online Credit Loan Services

Internet finance fraud is an increasingly serious social and economic problem. Online loan services are a typical mode of Internet finance. Constructing machine learning models has become a promising solution for Internet financial anti-fraud. In the process of constructing a fraud detection model, feature engineering is the most critical step. The quality of features directly affects the performance of models. It is also one of the most time-consuming and customized steps. The existing Internet finance anti-fraud model is mainly carried out by experts using manually constructed features based on business knowledge. However, this artificial feature construction method is time-consuming and labor-intensive in the ever-changing Internet finance scenarios, and it has the disadvantages of missing complex features. Artificial feature construction methods can no longer meet the increasing demand for anti-fraud. Automated feature engineering is a promising solution for efficiently constructing anti-fraud models. To evaluate fraud risk for online loan services, this paper proposes an automated feature engineering method based on deep feature synthesis and feature selection, which can be easily cooperated with the current mainstream machine learning models. This method reduces the scale of synthesized features by dividing the original data table into small parts. Combined with the designed feature selection strategy, the method picks out important features from a large number of redundant and meaningless synthesized features. Experiments indicate that our method can improve the efficiency of feature construction and select important features for anti-fraud models.
A Generalized Model for Crowd Violence Detection Focusing on Human Contour and Dynamic Features

The research on detecting violent behavior in videos has made good progress, which provides good support for monitoring abnormal videos spread in the network, so as to achieve the effect of purifying the network space environment. A large number of current violence detection models have achieved good performance in experimental environments, but their generalization ability is insufficient. Violent behavior often occurs in a variety of scenarios, automatic detection of violent behavior requires a model with strong generalization. In this paper, a crowd violence behavior detection model with good generalization ability based on human contour and dynamic characteristics was designed. The model generalization ability is improved by focusing on the human features in the video and using the human dynamic features obtained from adjacent frames. In our model, a 3D-CNN framework was used to extract spatial features of the input feature map, and LSTM was used to fuse the temporal feature, we call this model HD-Net. Through multiple contrast experiments, the generalization ability of HD-Net is tested on three datasets: RLVS, Hockey and violent flow. Comparing with other classical violence detection models, the good generalization ability of the model is verified.
Programmable photonic circuits

Abstract We highlight a recent trend in photonics where integrated photonic chips are designed and operated as programmable devices like in the case of integrated electronics. We discuss the potential application of these programmable photonic circuits in signal processing.
Towards a Robust Approach to Analyze Time-Dependent Data in Software Engineering

Background. Several recent software engineering studies use data mined from the version control systems adopted by the different software projects. However, inspecting the data and statistical methods used in those studies reveals several problems with the current approach, mainly related to the dependent nature of the data. Objective. We analyzed time-dependent data in software engineering at commit level, and propose an alternative approach based on time series analysis. Method. We identified statistical tests designed for time series analysis and propose a technique to model time dependent data, similarly to what is done in finance and weather forecasting. We applied our approach to a small set of projects of different sizes, investigating the behaviour of the SQALE Index, in order to highlight the time and interdependency of the different commits. Results. Using these techniques, we analysed and model the data, showing that it is possible to investigate this type of commit data using methods from time series analysis. Conclusion. Based on the promising results, we plan to validate the robustness of the approach by replicating previous works.
A Strategy of Potential Fields and Neural Networks in the Control of an Autonomous Vehicle Within Dangerous Environments

This article focuses on the development of an autonomous navigation system by generating real-time 3D maps of different urban environments with different properties within simulation software. This system used the Pioneer 3-DX vehicle, a LiDAR sensor, GPS, and a gyroscope. For the elaboration of the trajectory, the mathematical tool of artificial potential fields was used, which will generate an attractive field to a dynamic goal identified by the robot and repulsive to the obstacles present in the environment, recognized with great precision thanks to the use of a neural network. The topology neural network 8–16–32 was developed using forward propagation, reverse propagation, and gradient descent algorithms. By combining the tools of potential fields and neural networks, a path was traced through which the robotic system will be able to move freely under an off-center point kinematic control algorithm. Finally, a 3D map of the environment was obtained to provide information on the morphology and most outstanding characteristics of the deployment environment to users who use the system.
Application of artificial intelligence in diagnosis and therapy of prostate cancer

Prostate cancer is one of the most commonly diagnosed malignant diseases and second leading cause of death connected with cancer in men. Diagnosis and therapy of prostate cancer involves interpretation of information from variant sources which can help us to make a final decision like level od PSA (prostate specific antigen), prostatic acid phosphatase (PAP), free to total PSA ratio, genomic biomarkers and biopsy complemented with MRI and Gleans score. In prostate cancer, artificial intelligence has been a really good tool in stratification assessment and also in therapy of prostate cancer using diagnosis through biomarkers, MRI and histopathology analysis. This paper presents the development of an artificial neural network (ANN) for diagnosis of prostate cancer based on 4 input parameters. The dataset consisted of 20% healthy control subjects and 80% diseased subjects. The obtained accuracy of the developed ANN indicates its potential for use as aid in diagnosis and prediction of prostate cancer.
Learning-Based Distortion Correction and Feature Detection for High Precision and Robust Camera Calibration

Camera calibration is a crucial technique which significantly influences the performance of many robotic systems. Robustness and high precision have always been the pursuit of diverse calibration methods. State-of-the-art calibration techniques, however, still suffer from inexact corner detection, radial lens distortion and unstable parameter estimation. Therefore, in this paper, we improve the precision and robustness of calibration by widening these bottlenecks. In particular, effective distortion correction is performed by a learning-based method. Then, accurate sub-pixel feature location is achieved by the combination of robust learning detection, exact refinement and complete post-processing. To obtain stable parameter estimation, an image-level RANSAC-based calibration procedure is proposed. Ultimately, we assemble these methods into a novel and practical calibration framework. Compared with state-of-art methods, experiment results on both real and synthetic datasets under noise, bad lighting and distortion manifest the better robustness and higher precision of the proposed framework.
Politics, Sentiment and Virality: A Large-Scale Multilingual Twitter Analysis in Greece, Spain and United Kingdom

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An Effective Machine Learning Based Algorithm for Inferring User Activities From IoT Device Events

The rapid and ubiquitous deployment of Internet of Things (IoT) in smart homes has created unprecedented opportunities to automatically extract environmental knowledge, awareness, and intelligence. Many existing studies have adopted either machine learning approaches or deterministic approaches to infer IoT device events and/or user activities from network traffic in smart homes. In this paper, we study the problem of inferring user activity patterns from a sequence of device events by first deterministically extracting a small number of representative user activity patterns from the sequence of device events, then applying unsupervised learning to compute an optimal subset of these user activity patterns to infer user activity patterns. Based on extensive experiments with sequences of device events triggered by 2,959 real user activities and up to 30,000 synthetic user activities, we demonstrate that our scheme is resilient to device malfunctions and transient failures/delays, and outperforms the state-of-the-art solution.