Reviewerly/exHarmony huggingface dataset:
Vehicle Depth Estimation Based on Monocular Camera in Extreme Weather

Forward vehicle ranging is an important function in ADAS technology such as adaptive cruise control and forward collision warning system, which can help drivers make accurate judgments on impending danger in time to avoid accidents. At the same time, traffic scenarios in bad weather lead to frequent traffic accidents due to situations such as obstructed vision. In order to adapt the front vehicle ranging to multiple scenarios and increase the generalization performance of the ranging algorithm, a deep estimation network based on generative adversarial network is designed for weather scenarios such as rain, snow and fog. First, images of different weather scenes are input in the generator network, using the attention mechanism and the masking operation of the images. Then, feature extraction is performed on the images, and the encoder results in the normal weather depth estimation network are used as the network true values for training to ensure that the generator network can generate image features consistent with normal weather. Finally, the discriminator is used to output the depth map. The results show that the algorithm is highly adaptable to extreme weather such as rain, snow and fog.
Improved Joint Estimation for Body-Mounted Motion Capture Sensors Using Human Kinematics Prior Knowledge

Measurement uncertainty is affected by several factors, including sensor resolution. In circumstances where uncertainty varies across features (e.g., distance to transducer), confidence in sensor results is particularly affected, reducing their applicability in applications such as joint estimation through body sensors. In this manuscript, a model for joint estimation based on millimeter wave point cloud data and prior knowledge of human body structure is discussed. The proposed model uses structure and kinematic information about the measured entity to reduce measurement uncertainty, even when the expected uncertainty bounds change according to distance to the transducer. This is achieved by augmenting a sensor's processing stage with additional estimation constraints using prior knowledge, and using intersection of error bounds to improve estimated values. For a body-mounted motion tracking sensor performing joint estimation, our model is compared to empirical results of two 3D convolutional neural networks, one associated with a head-mounted millimeter wave sensor, and the other associated with a body-mounted millimeter wave sensor. The joint estimation model is found to outperform the neural-network based estimator in both mounting scenarios, resulting in reduced estimation error as confirmed by an external sensor that provides ground truth.
Simulated Leakage Power Analysis Attack of the Trivium Stream Cipher

In this paper, the theoretical basis for power analysis attacks on the Trivium utilizing leakage current as a side-channel are presented. The analysis is first established in the case of an ideal implementation without the presence of algorithmic noise, and goes on to examine the potential sources of algorithmic noise present in the system, deriving worst case scenario figures of the Pearson Correlation Coefficient (PCC). The same analysis is then performed under the presence of FDSOI random body bias countermeasures. The theoretical analysis are contrasted with simulations of the Trivium cipher under the assumption of a noiseless side-channel, showcasing the theoretical feasibility of utilizing leakage current as a means to attack the cryptosytem.
Perception System based on Cooperative Fusion of Lidar and Cameras

This paper proposes a novel sensor fusion method capable of detection and tracking of road users under nominal as well as in border cases of system operation. The proposed method is based on a sensor-agnostic Bayesian late fusion framework, augmented with an optional exchange of detector activation information between sensors, referred to as cooperative feedback. Experimental evaluation confirms that we obtain competitive detection and tracking performance in normal operation. The main benefit of the proposed method is in cases of sensor failure where, due to the probabilistic modeling, we observed significant improvements of both detection and tracking accuracy over the state of the art.
Wolaytta-English Cross-lingual Information Retrieval using Neural Machine Translation

The necessity of a system that supports query-based searching with the presence of more than 7,154 world languages also increases as the accessibility of information across languages increases. The most challenging task is gathering knowledge in various natural languages, which calls for a lot of resources like databases and digital libraries and others. Nowadays, accessing information in multiple languages is also one of the problems. In this multilingual world it is common to see language barriers while seeking information in our native language. This is especially true for under-resourced Ethiopian language, specifically for Wolaytta language. Therefore, the main goal of Cross-lingual Information Retrieval (CLIR) is to enable users submitting a query in one language and retrieve relevant documents in another language. The development of Wolaytta-English CLIR enables Wolaytta language speakers to write queries in their native tongue in order to access and retrieve the various information sources that are available in both Wolaytta and English. The translation of the query phrases from Wolaytta to English is done using the Neural Machine Translation (NMT) approach. To do this, a total of 31,102 Wolaytta and English parallel corpus had been gathered, pre-processed and employed in NMT and CLIR experiments at the sentence level. For a bilingual run of Wolaytta and English, the suggested system achieved an average precision of 0.75 and recall of 0.73. Though the performance of the system was affected by the size and quality of the corpus, the result obtained for the small corpus is encouraging. Improving the performance of the CLIR system by implementing stemmer and POS tagger for Wolaytta language will be the future work.
Data Transmission Based on a Unified Digital Recognition Technology

In this paper, a low-input and fast-deployment application framework based on the video recognition technology is proposed for the data transmission for cross-system or isolated equipment without proper interfaces. The technical work of this paper mainly includes two parts. Firstly, a YOLO-based deep learning extraction and recognition algorithm is designed to solve a unified digital recognition for both printed style digital and tube style ones. Secondly, aiming at the problem of deviation of recognition area caused by video displace or rotation, we propose a correction technology based on image registration. Experimental results and practical application results show the effectiveness of the technology.
A digital diploma verification and sharing scheme based on Blockchain

Currently, Some educational institutions are gradually offering online learning resources, With large number of digital diplomas, Because of the insecurity of the privacy nature of the digital diploma itself, And the different institutions that may not recognize each other's diplomas, Bringing on a series of challenges, The invariance and privacy of blockchain provides a solution to the circulation of digital diplomas, Constructing a digital diploma sharing scheme based on alliance chain technology, Using a distributed file system, storage schemes, Addressing the range of challenges facing centralized storage or traditional cloud storage — illegal tampering or deletion, Thus, the distributed trust mechanism of digital diploma is realized. In addition, in order to relieve the chain storage pressure of the digital diploma stored in the blockchain, the digital diploma file itself is stored in the IPFS (InterPlanetary File System) to store the summary value of the digital diploma into the blockchain, effectively alleviating the chain storage pressure. At the same time, digital diploma has the need of confidentiality and signature recognition. The proposed scheme is suitable for digital diploma blockchain system through anonymous authentication and encryption by using multi-center ring signature encryption scheme, and hardly affects the speed of alliance chain blockchain generation.
Foggy Lane Line Detection Based on an Improved Dark Channel Prior and the Canny Operator

Lane line detection is an important part of intelligent driving. However, the road pictures taken in haze weather have low clarity, and the loss of detailed information brings great difficulties for lane line detection. The classical defogging algorithm cannot meet the accuracy requirements of lane line detection, so this paper proposes a foggy lane line detection algorithm for improving the dark channel and Canny operator. First, the foggy image is divided into the area of interest, and then use the quadruple tree multilevel search to obtain the accurate value of atmospheric light. The restored image is image-enhanced to improve the effect of fog removal; The images are then preprocessed for edge detection using the modified Canny operator, and we use a hybrid filter instead of Gaussian filter. The maximum inter-class variance (OSTU) algorithm obtains the adaptive optimal threshold and improves the experimental performance of the algorithm. Finally, the polar angle constraint uses the Hough transformation to accurately fit the lane lines. Experiments show that the improved defogging algorithm obtains higher image contrast, clearer image, and the edge detects more preserved lane line information, and improves the recognition accuracy of vehicle lane lines.
Target tracking algorithm based on improved image hash and multiple search areas

Moving target tracking is one of the important research topics in the field of computer vision. Aiming at the problem of tracking failure caused by illumination intensity change and similar background interference, a target tracking algorithm based on improved image hash and multi search area is proposed. Firstly, the image hash algorithm is improved, and the image hash algorithms based on cyclic comparison and threshold segmentation are designed, which are applied to track the target to obtain two hash sequences; Secondly, multiple search areas with different sizes are taken near the target position in the previous frame, and two candidate frames most similar to the target are selected by comparing the Hamming distance in the search area; Finally, the final target frame is obtained by fusing the two candidate frames, and whether to update the target is judged according to the threshold. The experimental results show that the algorithm can track the target stably and accurately under the illumination change and background interference, and the tracking success rate is 95.3%.
Orientation Estimation for Airplane Targets in SAR Images Based on Keypoint Detection

Object detection in aerial images is a challenging task since objects distribute in arbitrary orientations under the top view of remote sensing images (RSIs). Recent methods exploit oriented bounding boxes (OBB) to represent oriented objects accurately. However, airplane targets in different orientations appear very differently in Synthetic Aperture Radar (SAR) images due to the complicated imaging mechanism and various scattering conditions. Most SAR image datasets for airplanes are annotated with horizontal bounding boxes (HBB) and related research focuses on HBB without orientation information. This paper proposes a method for predicting the orientation of airplane targets in SAR images based on keypoints detection. Specifically, the object detection module is adopted to generate proposal regions of airplane targets in the first stage. Then, the proposal regions are utilized in the orientation prediction module to detect the head and tail of an airplane. The proposed method provides semantic orientation information for airplane targets in SAR images labeled by HBB. The experiments are conducted on Gaofen-3 (GF-3) dataset with labels of aircraft keypoints, and the accuracy of orientation estimation is achieved 85.8%. The Mean Average orientation Error is 18.24°. Furthermore, the orientation estimation model is applied on SAR Aircraft Detection Dataset (SADD) to supplement orientation information for airplane targets.
Ship Detection for SAR Remote Sensing Images based on Coordinate-Enhanced Attention and Long-Short-Range Spatial Semantic Fused Context

Due to the lack of sufficient appearance features of objects and the interference of complex background, it is a challenge to detect small-scale ships in SAR remote sensing images. To remedy these problems, we propose a small ship object detection algorithm by means of coordinate-enhanced attention mechanism and long-short-range spatial semantic fused context. Based on YOLOX, we introduce the spatial information into the channel attention to construct an attention-aware feature extraction network, which improves the focusing capability to small ships and suppresses background interference. The long-short-range spatial semantic fused context extraction module is used to capture the multi-scale environmental information around the objects with different receptive fields while integrating the overall semantic cues of the scene, so as to enhance the spatial and semantic feature representation of small ships. The experiments show that our method with the average precision 76.17% on LS-SSDD-V1.0 improves the detection performance of small-scale ships in SAR remote sensing images effectively.
Vehicle Detection in High-resolution SAR Image Using Saliency Visual Attention Method

Benefiting from the characteristics of low-cost, small-size, and operational capacity of all-day and all-weather, high-resolution millimeter-wave (mmWave) synthetic aperture radar (SAR), is widely used in advanced driving assistance system (ADAS). In this paper, inspired by human visual attention mechanism, a saliency visual attention algorithm is first proposed to suppress background noise while prominent foreground targets. After generating the saliency map, a watershed-based SAR image segmentation algorithm is used to Figure out the candidate regions. Finally, image morphology analysis and connected components analysis is adopted to reduce false alarms and detect the vehicle in the imaging scene. Experimental results based on measured data in a 77GHz automotive radar platform show that the proposed method is well worked and has incremental performance in parking plot scenes.
Real-Time Contactless WiFi Based Room Detection of Sitting and Standing Human Motions

In the field of healthcare, Human activity monitoring has recently been gaining widespread attention. The ability to monitor human activities is being applied to assist the care of vulnerable people. These monitoring systems can allow elderly people to live more independent lives within their own homes without residing in care facilities. The implementation of monitoring systems can relieve the strain on family members and/or caregivers from frequent visits to check on vulnerable people's well-being. The work of this paper proposes a contactless real-time monitoring system to detect if a person is sitting or standing. The contactless feature works by using machine learning to classify the propagation of RF signals as they travel through the atmosphere. The propagation data is collected using local devices and then uploaded to the cloud. A dashboard is used to download the data from the cloud and provide information on the output of the monitoring system. The system uses data filtering techniques to observe the patterns of propagation and establish if movements have taken place. If movements are detected then the data is passed to a trained AI model. The AI model will classify the movements as Sitting or Standing. The training of the AI model included applying 10-fold cross-validation to the training data to test performance. The Random Forest algorithm achieved an accuracy of 90.75 % and was used to build the AI model.
SemiSegPolyp: Semi-Supervised Polyp Segmentation using Graph Signals

Polyps are one of the major causes of colorectal cancer. Polyp Segmentation is a fundamental task for computer aided gastrointestinal disease detection. Nowadays, it is very essential to develop an intelligent system for early diagnosis and detection of polyps which could result in successful treatment. Unlike the existing deep learning segmentation methods which required huge amount of labeled data, this paper presents a semi-supervised segmentation method called SemiSegPolyp relies on the graph signal processing (GSP) which requires few labeled data. The proposed approach includes many steps: instance segmentation, texture polyp features for the graph's nodes, graph construction baed on K-nearest neighbors, and semi-supervised semantic segmentation based on the Total Variation Minimization tool. We evaluate our method on two popular polyp datasets: CVC-ClinicDB and Kvasir-SEG. SemiSegPolyp outperforms semi-supervised and supervised methods although the use of small amount of labeled data.
Pruning-based Neural Network Reduction for Faster Profiling Side-Channel Attacks

Deep learning-based side-channel attacks (DL-SCA) has drawn significant interest in academic research. Despite the significantly superior results in practice, very few effort have been made in order to reduce time-consuming process of DL-SCA. The difficulty to define optimal hyperparameters and to train deep neural networks may become a serious limitation for their practical applications. Therefore, in this paper, we propose an improved pruning-based surgery as a way to reduce the size of neural networks, decreasing training time while keeping the profiling attack performance, especially on targets with countermeasures. The results show that the proposed method is 9 times faster for the MLP network and 6.38 times for the CNN network compared to the original surgery method applied to SCA.
RETRACTED: Application of Convolutional Neural Network in Modern Technology Field and Improvement by Time-space Version

Abstract Based on the basic principle of convolutional neural networks, this paper gradually introduces the development of convolutional neural networks and its related application fields. We also analyze and compare the iterations and developments of convolutional neural networks in three most commonly used areas: medical treatment, face recognition and transportation. A large number of articles have been written to understand the variations and differences of convolutional neural networks in these three areas, such as the use of different training methods or different structures, such as the time-space convolutional neural networks that will be mentioned in the article. And the last part is the summary.
Comparison of Statistical Estimators for Estimating the Orders of Markov Chains

Abstract High order discrete Markov chain is essential to analyze the dependency structure of data sets. To apply Markov chain correctly, even though the true order is an unknown parameter, statisticians have developed multiple order estimators. It is natural to identify the strongest order estimators under different parameter combinations. Aim for evaluating the performance of estimators, we study four of them in this paper: Akaike information criteria (AIC), Bayesian information criteria (BIC), Maximal fluctuation estimation method (PS), and approximate χ 2 − distribution method ( D k ). We simulated C r × C transition matrices to generate word-count-based Markov sequences with the most straightforward initial distribution. We found PS and D k give more accurate discrete Markov order estimation. Although AIC and BIC are commonly applied, their performances are not the most accurate. The accuracy declines approximately exponentially as the Markov model gets more complex, i.e. r ≥ 1 and C ≥ 3. AIC’s accuracy is higher when the Markov chain length is relatively small, but D k yields a slightly higher accuracy under the same setting. PS give a more reasonable estimation when Markov order is the variable, i.e. 1 ≥ r ≥ 3. D k gives more reasonable estimations when the length L and alphabet size C are variable, i.e. 150 ≥ L ≥ 800 and 3 ≥ C ≥ 5.
Possible Implementations of Oracles in Quantum Algorithms

Abstract Quantum computing is an inspiring technic on solving complicate problems, which shows the superiority over the classical computing. Contemporarily, the quantum-based algorithms were invented in many purposes on solving those problems. The thing is some of the algorithms were composed with oracles, which can be treated as a black box. It can be worked out/analyzed in theoretical mathematical expressions, but it can never fall in the ground without those actual implementations. Therefore, this paper will illustrate the idea and possible implementations on some quantum algorithm. Among various algorithms, this research will choose those relatively famous quantum algorithms, i.e., Deutsch-Jozsa Algorithm, Shor’s Algorithm, and Grover’s Algorithm. Different algorithms have the different intrinsic logics, which means there is no general description for all solution in this problem, but all of those can achieve the goal according to the same framework. Since those oracles are black boxes that can only be analyzed by its behavior with different cases, and then based on the behaviors and cases, and in IBM quantum platform, one can fill out the oracle by manipulate with different quantum operators. Lastly, the figures of some the possible implementation of those oracles with the three quantum algorithms will be shown in the IBM quantum platform, as well as the histograms of probability distributions that can justify the correctness of the implementations. These results shed light on guiding further exploration of the design and analysis on quantum algorithms.
Research and application of detection platform for distribution primary and secondary fusion switchgear

Abstract China’s power enterprises are actively promoting the combinations of primary and secondary devices for distribution switchgear. However, these devices are still being tested separately, and the testing devices are bulky and occupy a large area. In this regard, we propose a new type of testing platform that can achieve three functions: testing for primary devices, testing for a system that integrates primary and secondary devices, and testing for distribution terminals. Hardware components, plug-in modules, and internal interfaces are designed in a standardized and compact manner, and genetic algorithms are used to automatically generate test samples. The integrated test platform for switch sets is miniaturized and portable, and its test environment is more in line with the field test, thus offering more efficient and reliable test results compared to traditional testing devices.
Customized AES for Securing Data in Sensitive Networks and Applications

Developing and improving encryption systems becomes a prerequisite for protecting sensitive data from ever-developing attacks. The Advanced Encryption Standard (AES) is the best recommended symmetric key block cipher for securing data of sensitive networks and applications. However, the AES may be penetrative to some attacks in the future. In this paper, we propose a customized AES to enhance the security of the AES algorithm to be hard to cryptanalysis and become safer to use in sensitive networks and applications whose security is of utmost priority, like military networks and applications. Security of customized AES will be improved by modification in two points; the first uses a new AES key expansion algorithm to remove dependencies between round keys and improve key expansion time, and the second point uses a key-dependent S-box for each round to overcome the problems caused by using a static AES S-box. Finally, results show that the Proposed AES provides a significant enhancement in security compared to the original AES.