Reviewerly/exHarmony huggingface dataset:
Automatic Detection Method of High Altitude Falling Object Based on Interframe Difference Method

In order to effectively detect the high-altitude falling behavior in the surveillance video, an automatic detection method of high-altitude falling based on the improved frame difference method(IFD) is proposed. Firstly, the video data are processing for debounce, and the optimal number of interval frames is determined with the proposed method. Then, the motion prospect is obtained by using the optimal number of interval frames. Finally, a fall judgment criterion is proposed to determine whether the motion prospect is a high-altitude fall or not. The experimental results show that the proposed method can process surveillance video efficiently compared with frame difference method and three frame method. In this paper, the problem of low efficiency of other methods in detecting high-altitude falling objects is solved with IFD.
Development of flexible Text Input Device Based on Image Processing for Each Level of Disability Person

This research aims to develop a flexible input text device for physically disable person using image processing. In propose method we use a camera to detect the disable person arm and hand movements via image processing technics, in order to identify these movements with exact disable person intention on what he/she wants to convey. When the intention is detected, the disabled person can then input his/her intention or thought data through the device. The input method can be changed according to the user's disability level, and is expected to have a positive impact for the rehabilitation of the user.
Recommended Method based on Noise Reduction Self Encoder

The user and item features extracted by the traditional recommendation system are relatively simple, and at the same time, the mining of the internal relationship between users and products is not sufficient, resulting in unsatisfactory recommendation effect. Based on the research of the existing recommendation system, aiming at the problem of insufficient user and item information extraction, this paper realizes the screening and classification of items and users through algorithms, and performs shallow semantic information and deep semantic information on the information of users and items. Divide and process them separately to achieve the purpose of fully extracting shallow semantic features and deep semantic features, and recommend different items to different users according to users' ratings and interests. A recommendation method based on deep noise reduction autoencoder proposed in this paper firstly needs to divide the information of users and items into shallow semantic information and deep semantic information, and process them separately. The autoencoders are trained separately to realize the recommendation of different items for users with different interests. Through the above methods, this paper mainly improves the recommendation accuracy of the current recommendation system and reduces the adverse effects of matrix sparsity.
Exploratory Research on Blockchain-based Lost and Found Platform

The comprehensive popularity of the Internet has gradually made it possible for blockchain technology to be applied to different industry sectors. The use of blockchain technology in lost and found platforms is conducive to the decentralised management of the platform. Specific smart contracts achieve open and transparent information interaction between the owner, the finder and the lost and found, solving problems such as information leakage and malicious tampering in lost and found platforms. Therefore, this paper revamps and designs the architecture of the traditional lost and found platform, improves the platform's operation mechanism, makes the operation process of lost and found more relevant to the needs of real users, and provides comprehensive and secure social services for users.
Metallic Dataset Creation based on FR-IQA Model for Industrial Application

As our initial investigation in Image Quality Assessment (IQA) research, the repository of image datasets for industrial applications is less than expected. There are only two primary industrial image datasets: NEU-Dataset and GC-10 DET Metallic Dataset. Both of the datasets specifically work on defect detection and image classification problem. To be precise, no image distortion was provided on the mentioned dataset. As a result, this paper aims to provide an IQA dataset image for industrial applications, especially metallic surfaces. We designed an experiment to build an industrial IQA dataset containing the real-world case of the data acquisition distortion problem, i.e., camera distortion and pre-processing image application. We made our experiment scenario based on our research assumption about the optimum distance of the data acquisition process. Thus, there are ten distortion types, and 2016 image distortions were derived from 144 reference images. To evaluate our distortion creation, we implement two FR-IQA models, Peak Signal-to-Noise Ratio (PNSR) and Structural Similarity Index Measure (SSIM). In addition, to correlate both FR-IQA models, we used Spearman Rank-Order Correlation Coefficient (SRCC) and Pearson Linear Correlation Coefficient (PLCC).
Research on Recommendation Algorithm Based on Comment Text

The current recommendation systems based on comment text information generally have the problems of insufficient comment information extraction and low recommendation accuracy. Therefore, this paper proposes a recommendation model integrating global and local features of comment text. Firstly, Convolutional Neural Network(CNN) model is designed to extract global features, secondly, Gating Recurrent Unit (GRU) is designed to extract global features, and finally, Latent Factor Model(LFM) is combined to model users and commodities. Score prediction is carried out by embedding the expression of users and commodities. Experimental results show that the proposed algorithm has good recommendation performance.
CPABE Scheme with Real-time Changes of User Permissions

The problem of attribute revocation exists in attribute encryption schemes, and with attribute revocation, user permissions will change accordingly. Aiming at the problem of user permissions and shared data verification caused by various attribute changes. This paper proposes a CPABE scheme with real-time user permissions. This scheme controls the user attribute capability by setting independent parameters for each attribute and changes the user authority at a time when the user attribute changes. When setting the attribute private key, set the identity key for the user to prevent the user from retaining the previous decryption authority after the user attribute is changed. In addition, combined with the blockchain technology, the user's operations will be recorded and permanently stored on the blockchain, and the hash values of the plaintext and ciphertext of the data in the same period are also uploaded to the blockchain for data integrity verification. Finally, according to the security analysis and scheme comparison, it is shown that this scheme not only realizes the real-time change of user permissions but also has certain advantages in improving computing efficiency and reducing communication overhead.
Efficient Algorithms for the Ensemble Computation Problem

Ensemble Computation (EC) is an optimization problem to find the least operations sequence to calculate all of the given multiple monomials. EC can be applied to simplify the logic circuits, and to the optimization of the compiler. In this paper, we suggest efficient algorithms for EC. By the computational experiments, we compared an algorithm based on Beam Search (BS) with an algorithm derived from the compression algorithm Re-Pair. Experimental results show that the BS based algorithm produces better solutions although it is slower than the Re-Pair based algorithm.
Fast and Accurate Object Detection Based on Fusion of YOLOv2 and R-CNN Predicted Result for Autonomous Driving

How to improve the safety of autonomous driving matters. Object detection could solve some problems at the perception level of autonomous driving. However, current models of object detection face the problem of low detection precision. In this paper, we propose a combined model, the fusion of You Only Look Once version 2(YOLOv2) and Regions With Convolutional Neural Networks(R-CNN) predicted results. Specifically, Regions With Convolutional Neural Networks(R-CNN) is used to predict positional parameters of a bounding box and You Only Look Once version 2(YOLOv2) is used to predict the class name and probability of a bounding box. The obtained results show that the combined model possesses higher precision than other compared models especially You Only Look Once version 2(YOLOv2) or Regions With Convolutional Neural Networks(R-CNN) individually.
Research on Complex Scene Recognition Based on Semantic Segmentation

Image semantic segmentation plays an important role in scene understanding, which is a hot topic in the field of computer vision. There are two main methods to improve the semantic segmentation accuracy of complex scenes in the existing models. One is to consider the spatial relationship between pixels, but it will produce a high amount of calculation. The other is to expand the receptive field, but the context representation is still not clear enough. Aiming at the semantic segmentation problem in complex scenes, a convolution neural network segmentation model with attention mechanism is proposed to detect the recognition technology of traffic scenes. Context Semantic Encoding(CSE) module is introduced to capture the global context information and highlight the category information associated with the scene. Multi-scale feature extraction is realized to increase the weight of the foreground target feature. The distribution law between autonomous learning data of generative confrontation network can solve the problem of ignoring the spatial relationship between pixels. The network is implemented based on PyTorch framework. The experimental results on the Cityscapes dataset show that the mloU reaches 75.7 %.
A Multi-Class Nuclei Segmentation Model using Attention Modules

Accurate and precise segmentation of nuclei is one of the main challenges in clinical pathology due to the multiplex structure of nuclei which includes shape, appearance, and tortuosity pattern. In this study, we proposed a nuclei segmentation model by introducing attention modules within U-Net blocks to automate the digital pathology workflow. We extracted segmented patches from digital pathology images to train our model. We used channel and spatial attention modules to extract and identify the exhaustive relationship within nuclei regions and learn the micro-structure (complex) attributes of the nuclei from the different body organs. Traditional models use simple convolutional layers with kernels for extracting features information but we used a drop-out block within each layer to randomly discard the local block area in the learning phase and enhance the learning performance of the digital pathology image segmentation tasks. We evaluated the performance of our model on the MoNuSAC dataset. Experimental results of our model have shown superior performance compared to state-of-the-art (SoTA) models.
3D Object Detection for Point Cloud in Virtual Driving Environment

In autopilot field, 3D object detection is typically done with a complimentary pair of sensors: RGB cameras and LIDARs, either alone or in tandem. Cameras provide rich information in color and texture, while LIDARs focus on geometric and relative distance information. However, the challenge of 3D object detection lies in the difficulty of effectively fusing the 2D camera images with the 3D LIDAR point cloud. In this paper, we propose a two-stage cross-modal fusion panoramic driving perception network for 3D object detection, drivable area segmentation and lane segmentation tasks in parallel and in real time, based on the Carla autopilot dataset. On the one hand, this detector uses a pre-trained semantic segmentation model to decorate the point cloud and complete the drivable area segmentation and lane line segmentation tasks, and then performs the 3D target detection task on the BEV-encoded point cloud. On the other hand, thanks to the novelty data enhancement algorithms and enhanced training strategies designed in this paper, they significantly improve the robustness of the detector. Our detector outperforms existing mainstream 3D object detectors based on pure LIDAR sensors when it comes to detecting tiny targets like pedestrians.
Condition Number-based Evolving ESN

Echo state network (ESN) is a kind of recurrent neural network without involving gradient problem. However, the reservoir of ESN often contains hundreds of neurons, whose corresponding high-dimensional state matrix may result in ill-conditioned solution problem. To solve it, the condition number-based evolving ESN (CNEESN) is proposed, whose sub-reservoir is generated by condition number analysis and differential evolution algorithm (DE). Firstly, the influence of condition number on output weight matrix is analyzed. Secondly, the randomly generated singular values are optimized by condition number and DE based optimize strategy. Finally, simulation result on a benchmark dataset has shown the superiority of the proposed CNEESN.
Design of Occupational Accident Prevention System for Transportation Vehicles Based on TOF

Abstract This paper proposes a system concept to prevent work accidents caused by vehicles transporting goods within a particular space. Furthermore, the system uses image processing methods to avoid these accidents. This is meant to allow the system to calculate motion vectors for all objects in the vehicle's path, so such a design can predict possible collisions. In addition to this, additional person detection has a higher priority, enabling a two-stage warning. This way, collisions endangering people can be warned earlier than collisions with other obstacles.
End-to-End Task-oriented Dialogue System Using Knowledge Filter and Attention Memory Pointer

The end-to-end neural model provides a more robust solution to generate responses than the traditional pipe-line method in the task-oriented dialogue system. However, it is challenging to incorporate the proper knowledge into the gen-erated response, especially when there are substantially related knowledge tuples. This paper proposes a knowledge filter and an attention memory pointer to improve the task-oriented dia-logue model. Specifically, the model uses the knowledge filter to obtain the knowledge tuples most relevant to the keywords of dialog context and builds the knowledge vector. Besides, the task-oriented dialogue model usually needs to copy objects from the correct knowledge tuples to form the question's an-swer. We define an attention memory pointer to help the model choose the correct knowledge tuples. Finally, we conduct ex-periments on the In-Car Assistant dataset. The experimental results show that our model can generate more accurate re-sponses than baseline models in automatic and human evaluations.
A SAR Ship Target Detection Method Based on the Analysis of Region Stability Characteristics

Traditional SAR ship target detection method could not work well in non-uniform background clutter conditions, and it has low detection accuracy, slow speed, and many false alarms. To solve these problems, this paper proposes a SAR ship target detection method based on the analysis of region stability characteristics. The method uses gravitational field enhancement technology to enhance the contrast between sea background and ship targets, and suppress the interference of speckle noise. To improve detection speed and reduce calculation, a frequency tuning saliency mechanism is introduced. Finally, maximum stable extreme value detection strategy is adopted to reduce false alarms. Experimental results verify the effectiveness of the proposed method.
Research on Image Registration of Different Wavebands Based on SIFT Algorithm

With the continuous development of space industry, the types of remote sensing data obtained are increasing. Image registration is widely studied as a key step in image applications. However, the research of existing image registration methods focuses on the same band and the same data source, and there are relatively few studies on different data sources for different bands and different angles. For the images of different wavebands obtained by different types of sensors in the same scene, this paper uses the existing SAR images and optical images of different wavebands to register the different source images based on SIFT algorithm. Experimental results show that the algorithm has a good effect in the registration work between different source images.
A Multi-channel Target Detection Algorithm Based on OFDM Communication Signal

The research in this paper is based on OFDM signal, providing a target detection algorithm and an improved compensation algorithm. By using subcarriers of OFDM signal, this paper presents a super-resolution joint estimation algorithm of angle and distance (JADE). The actual data results show that the improved compensation algorithm has strong suppression of velocity dimension expansion; the simulation results confirm that JADE can achieve distance super-resolution, which is better than traditional target detection algorithms in performance.
Virtual and Augmented Reality as Educational Tools for Modern Quantum Applications

Emerging immersive technologies have seen recent integration in both industry and education. Summarising current literature integrating quantum topics and immersive technologies has highlighted a lack of conclusive evidence indicating significant educational benefit. Despite this, the paper suggests a foundation for development in this area which is awaiting further investigation.
Low Cost Real-time Eye Tracking System for Motorsports

Eye-tracking technology can be used to determine reaction time and cognitive abilities of a subject based on response time to visual changes and analysis of focus points and duration. In the context of motorsports, quantifying driver visual data adds to the wealth of information required for the marginal performance gains that make up motorsport competition. Specifically, the gaze of drivers can be used to analyze and assess driving abilities and improve driving performance. Despite eye trackers being perfectly suited for this purpose, their high cost may impede their penetration in the motorsports industry. Therefore, we demonstrate a low-cost wearable real-time eye tracking system for motor sport applications. This system was mounted on a racing helmet to monitor the driver's gaze in real-time. A dual-camera system was used to capture both eye image and front view. The eye image was processed using OpenCV-Python to determine the gaze coordinates. Moreover, the gaze position was mapped into the front view to determine the wearer's line of sight. The eye tracking helmet was tested and demonstrated good accuracy, where the deviation angle was 3.8 degrees. These promising results further add to the growing body of knowledge in low cost eye trackers.
A Physical Reservoir Computing Model Based on Volatile Memristor for Temporal Signal Processing

Reservoir computing has emerged as a practical paradigm of implementing neural network algorithms on hardware for high-efficient computing. With the concept of reservoir computing, various electronic' dynamics can be harvested as computational resources, which has received considerable attention in recent years. Volatile memristor is an emerging memristive device that exhibiting interesting biomimetic behaviours such as short-term memory. Moreover, its conductance state can be varied by historical stimulation. In this work, a reservoir computing model using TiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</inf> -based volatile memristor as processing core is proposed. The volatile memristor is measured and characterised, followed by using the discrete model to approximate the behaviours of the volatile memristor. Finally, a parallel volatile memristor reservoir computer is simulated based on the volatile memristor model. This model is evaluated by a waveform classification. The results (normalized root mean square error is 0.15 when using 10 volatile memristors) indicate the feasibility of using the physical behaviours of volatile memristor for constructing reservoir computers.
A Delay-Based Reservoir Computing Model for Chaotic Series Prediction

Conventional computers based on Von Neumann architecture are unable to process complex sequential data with high efficiency. This work investigates why Delay-based Reservoir Computing (DRC) is preferred this architecture, by performing Mackey-Glass chaotic time series prediction. The outcome of the prediction and the role of the Memory Capacity (MC) for such system are presented, with simulations done in MATLAB Simulink. This developed algorithm performs with a Mean Square Error (MSE) of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1.2314\times 10^{-3}}$</tex> per predicted digit.
Optimal Launch Bound Selection in CPU-GPU Hybrid Graph Applications with Deep Learning

Graph algorithms, which are at heart of emerging computation domains such as machine learning, are notoriously difficult to optimize because of their irregular behavior. The challenges are magnified on current CPU-GPU heterogeneous platforms. In this paper, we study the problem of GPU launch bound configuration in hybrid graph algorithms. We train a multi-objective deep neural network to learn a function that maps input graph characteristics and runtime program behavior to a set of launch bound parameters. When applying launch bounds predicted by our neural network in BFS and SSSP algorithms, we observe as much as 2.76× speedup on certain graph instances and an overall speedup of 1.31 and 1.61, respectively. Similar improvements are seen in energy efficiency of the applications, with an average reduction of 14% in peak power consumption across 20 real-world input graphs. Evaluation of the neural network shows that it is robust and generalizable and yields close to a 90% accuracy on cross-validation.
Discrete Haar Wavelet Transform Hardware Design for Energy-Efficient Image Watermarking

Digital watermarking is a technique for hiding data by embedding a watermark in the host image to protect the illegal distribution of multimedia data. We propose a two-dimensional Discrete Haar Wavelet Transform (DHWT) based on image water-marking architecture. The performance evaluation of our DHWT-based architecture uses a set of standard test images. The proposed approach is robust against recurrent attacks in the literature and outperforms the state-of-the-art in energy and area savings. The energy and area savings reached <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.824\times$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.344\times$</tex> , respectively.