Reviewerly/exHarmony huggingface dataset:
Conceptual Design of an Accident Prevention System for Light Water Reactors Using Artificial Neural Network and High-Speed Simulator

Eight years after the Fukushima accident, the last missing pieces of the jigsaw puzzle for light water reactor (LWR) safety have been put together. In the United States, the nuclear power industry has implemented diverse and flexible strategies to prevent and mitigate severe accidents. In this technical note, the author presents a conceptual design of an online accident prevention system (APS). The proposed concept takes advantage of the fact that the progression of a severe accident caused by an unplanned evolution of the fission dynamics in LWRs, which may be due to mechanical failures, human errors, or external events, progresses significantly slower than events in many other industries, such as chemical explosions or transportation accidents. The APS will make rapid diagnostics of any ongoing event by artificial intelligence and subsequently make immediate predictions using a high-speed simulation code. Should the severe accident lead to core degradation or off-site release, the operators will use all available means including diverse and flexible coping strategies (known as FLEX) to prevent it from happening. Full development and implementation of this APS will greatly enhance nuclear safety in the fight against global warming.
Camera-Aware Image-To-Image Translation Using Similarity Preserving StarGAN for Person Re-Identification

Person re-identification is a crucial task in intelligent video surveillance systems. It can be defined as recognizing the same person from images of a person taken from different cameras at different times. In this paper, we present a camera-aware image-to-image translation using similarity preserving StarGAN (SP-StarGAN) as the data augmentation for person re-identification. We propose the addition of an identity mapping term and a multi-scale structural similarity term as additional losses for the generator. SP-StarGAN can learn the relationship among the multiple cameras with a single model and generate the camera-aware extra training samples for person re-identification. We evaluate our proposed method on public datasets (Market-1501 and DukeMTMC-reID) and demonstrate the efficacy of our method. We also report competitive performance with the state-of-the-art methods.
Air-to-Ground Surveillance Using Predictive Pursuit

This paper introduces a probabilistic prediction model with a novel variant of the Markov decision process to improve tracking time and location detection accuracy in an air-to-ground robot surveillance scenario. While most surveillance algorithms focus mainly on controls of an unmanned aerial vehicle (UAV) and camera for faster tracking of an unmanned ground vehicle (UGV), this paper proposes a way of minimizing detection and tracking time by applying a prediction model to the first observed path taken by the UGV. We present a pursuit algorithm that addresses the problem of target (UGV) localization by combining prediction of used planning algorithm by the target, and application of the same planning algorithm to predict future trajectories. Our results show a high predictive accuracy based on a final position attained by the target and the location predicted by our model.
Generating a Cylindrical Panorama from a Forward-Looking Borehole Video for Borehole Condition Analysis

Geological exploration plays a fundamental and crucial role in geological engineering. The most frequently used method is to obtain borehole videos using an axial view borehole camera system (AVBCS) in a pre-drilled borehole. This approach to surveying the internal structure of a borehole is based on the video playback and video screenshot analysis. One of the drawbacks of AVBCS is that it provides only a qualitative description of borehole information with a forward-looking borehole video, but quantitative analysis of the borehole data, such as the width and dip angle of fracture, are unavailable. In this paper, we proposed a new approach to create a whole borehole-wall cylindrical panorama from the borehole video acquired by AVBCS, which provides a possibility for further analysis of borehole information. Firstly, based on the Otsu and region labeling algorithms, a borehole center location algorithm is proposed to extract the borehole center of each video image automatically. Afterwards, based on coordinate mapping (CM), a virtual coordinate graph (VCG) is designed in the unwrapping process of the front view borehole-wall image sequence, generating the corresponding unfolded image sequence and reducing the computational cost. Subsequently, based on the sum of absolute difference (SAD), a projection transformation SAD (PTSAD), which considers the gray level similarity of candidate images, is proposed to achieve the matching of the unfolded image sequence. Finally, an image filtering module is introduced to filter the invalid frames and the remaining frames are stitched into a complete cylindrical panorama. Experiments on two real-world borehole videos demonstrate that the proposed method can generate panoramic borehole-wall unfolded images from videos with satisfying visual effect for follow up geological condition analysis. From the resulting image, borehole information, including the rock mechanical properties, distribution and width of fracture, fault distribution and seam thickness, can be further obtained and analyzed.
Preserving Honest/Dishonest Users’ Operational Privacy with Blind Interference Calculation in Spectrum Sharing System

Spectrum sharing has been gaining its popular adoption as a potential solution to improve spectrum utilization in future wireless systems. Both Federal Communications Commission (FCC) and European Telecommunications Standards Institute (ETSI) support dynamic spectrum access (DSA) as an enabling technology for spectrum sharing. To effectively realize DSA in practice, users (from both defense and commercial sectors) are required to share their (radio) operational information, which risks exposing their security, privacy, and business plan to unintended agents. Protecting users' operating information is hence the key to DSA's success. In this paper, taking the FCC's spectrum access system (SAS) as a study case, we investigate the operational privacy issue of Incumbent Users (IUs) and honest/dishonest Secondary Users (SUs). For the case of IUs and honest SUs, we propose a privacy-preserving scheme for DSA by leveraging encryption and obfuscation methods (PSEO). To implement PSEO, we introduce an interference calculation scheme that allows users to calculate an interference budget without revealing operational information (e.g., antenna height, transmit power, location...), referred to as the blind interference calculation scheme (BICS). BICS also reduces the computing overhead of PSEO, compared with FCC's SAS by moving interference budgeting tasks to local users and calculating it in an offline manner. To further save the overhead in calculating the interference map, we introduce a quantization method and optimize the grid sizes of the terrestrial area of interest. Additionally, for the case of IUs and dishonest SUs, we propose a “punishment and forgiveness” (PF) mechanism, which draws support from SUs' reputation scores (RSs) and reputation histories (RHs), to encourage SUs to provide truthful information. Theoretical analysis and extensive simulations show that our proposed PSEO and PF-PSEO schemes can better protect all users' operational privacy under various privacy attacks, yielding higher spectrum utilization with less online overhead, compared with state of the art approaches.
Eigenvector-Based Distance Metric Learning for Image Classification and Retrieval

Distance metric learning has been widely studied in multifarious research fields. The mainstream approaches learn a Mahalanobis metric or learn a linear transformation. Recent related works propose learning a linear combination of base vectors to approximate the metric. In this way, fewer variables need to be determined, which is efficient when facing high-dimensional data. Nevertheless, such works obtain base vectors using additional data from related domains or randomly generate base vectors. However, obtaining base vectors from related domains requires extra time and additional data, and random vectors introduce randomness into the learning process, which requires sufficient random vectors to ensure the stability of the algorithm. Moreover, the random vectors cannot capture the rich information of the training data, leading to a degradation in performance. Considering these drawbacks, we propose a novel distance metric learning approach by introducing base vectors explicitly learned from training data. Given a specific task, we can make a sparse approximation of its objective function using the top eigenvalues and corresponding eigenvectors of a predefined integral operator on the reproducing kernel Hilbert space. Because the process of generating eigenvectors simply refers to the training data of the considered task, our proposed method does not require additional data and can reflect the intrinsic information of the input features. Furthermore, the explicitly learned eigenvectors do not result in randomness, and we can extend our method to any kernel space without changing the objective function. We only need to learn the coefficients of these eigenvectors, and the only hyperparameter that we need to determine is the number of eigenvectors that we utilize. Additionally, an optimization algorithm is proposed to efficiently solve this problem. Extensive experiments conducted on several datasets demonstrate the effectiveness of our proposed method.
A Novel Comparison-Free 1-D Median Filter

This brief presents a novel high-speed low-cost comparison-free, 1D bit-level median filter. The filter can be customized by cascading different median units. The most prominent point is that a pre-set value is used during the process, and the partial median can be easily identified by observing the MSB. Therefore, the median can be obtained without any comparators. With this feature, the area cost drops significantly while the operating speed remains the same. The results show that when synthesized using TSMC 90nm CMOS process technology, our design can achieve 2000MHz, and the area cost is reduced by 15.72% as compared to a state-of-the-art design.
An Automatic Spelling Correction Method for Classical Mongolian

Classical Mongolian is suffering a serious misspelling matter due to its polyphonic alphabet. One Mongolian glyph can map to different letters, i.e., some letters display the same shape. This special encoding scheme makes the words very easy to be misspelled. About half to three quarters of the words are misspellings in the classical Mongolian text, which is mainly caused by confusion between letters of the same shape. Conventional spelling correction techniques cannot solve such errors with correct shapes well for they mostly focus on errors such as character insertion, deletion and transposition. In this work, we propose the intermediate codes to map the words of the same shape into a single shape-based intermediate representation. According to the corresponding shapes, the hybrid approach is then applied to get the correct spellings by integrating rules and neural representation model (context2vec). The experimental results show that this approach achieves the new state-of-the-art performance. In addition, we also develop an efficient and free Mongolian automatic correction system for the text editors.
Pedestrian Trajectory Prediction Using a Social Pyramid

Understanding and forecasting human movement paths are vital for a wide range of real world applications. It is not an easy task to generate plausible future paths as the scenes and human movement patterns are often very complex. In this paper, we propose a social pyramid based prediction method (SPP), which includes two encoders to capture motion and social information. Specifically, we design a social pyramid map structure for the Social encoder, which can differentiate the influence of other pedestrians in nearby areas or remote areas based on their spatial locations. For the Motion encoder, a mixing attention mechanism is proposed to combine the location coordinates and velocity vectors. The two encoded features are then merged and passed to the decoder which generates future paths of pedestrians. Our extensive experimental results demonstrate competitive prediction performance from our method compared to state-of-art methods.
Performance Analysis for Blockchain Driven Wireless IoT Systems Based on Tempo-Spatial Model

Blockchain has shown a great potential for Internet of Things (IoT) systems to establish trust and consensus mechanisms with no involvement of any third party. It has been not clear how the low complexity devices and the wireless communications among them can pose constraints on the blockchain enabled IoT systems. In this paper, we establish a fundamental analysis model to underpin the blockchain enabled IoT system. By considering spatio-temporal domain Poisson distribution, i.e., node geographical distribution and transaction arrival rate in time domain are both modeled as Poisson point process (PPP), we first derive the distribution of signal-to-interference-plus-noise ratio (SINR), blockchain transaction successful rate as well as overall throughput. Then, based on the analytical model, we design an optimal full function node deployment for blockchain system to achieve the maximum transaction throughput with the minimum full function node density. Numerical results validate the accuracy of our theoretical analysis and evaluate the relationship between blockchain full function node deployment and the density of IoT nodes.
New lower bounds on the size of (n,r)‐arcs in PG(2,q)

Abstract An ‐arc in is a set of points such that each line contains at most of the selected points. It is well known that ‐arcs in correspond to projective linear codes. Let denote the maximal number of points for which an ‐arc in exists. In this paper we obtain improved lower bounds on by explicitly constructing ‐arcs. Some of the constructed ‐arcs correspond to linear codes meeting the Griesmer bound. All results are obtained by integer linear programming.
Marine accident learning with fuzzy cognitive maps (MALFCMs) and Bayesian networks

Abstract Addressing safety is considered a priority starting from the design stage of any vessel until end-of-life. However, despite all safety measures developed, accidents are still occurring. This is a consequence of the complex nature of shipping accidents where too many factors are involved including human factors. Therefore, there is a need for a practical method, which can identify the importance weightings for each contributing factor involved in accidents. As a result, by identifying the importance weightings for each factor, risk assessments can be informed, and risk control options can be developed and implemented more effectively. To this end, Marine Accident Learning with Fuzzy Cognitive Maps (MALFCM) approach incorporated with Bayesian networks (BNs) is suggested and applied in this study. The MALFCM approach is based on the concept and principles of fuzzy cognitive maps (FCMs) to represent the interrelations amongst accident contributor factors. Thus, MALFCM allows identifying the importance weightings for each factor involved in an accident , which can serve as prior failure probabilities within BNs. Hence, in this study, a specific accident will be investigated with the proposed MALFCM approach.
D_dNet-65 R-CNN: Object Detection Model Fusing Deep Dilated Convolutions and Light-Weight Networks

In recent years, object detection has become a popular direction of computer vision and digital image processing. All the research work in this paper is a two-stage object detection algorithm based on deep learning. First, this paper proposes the Deep_Dilated Convolution Network (D_dNet). That is, by adding the operation of dilated convolution into the backbone network, in this way, not only the number of training parameters can be further reduced, but also the resolution of feature map and the size of receptive field can be improved. Second, the Fully Convolutional Layer (FC) is usually involved in the re-identification process of region proposal in the traditional object detection. This too “thick” network structure will easily lead to reduced detection speed and excessive computation. Therefore, the feature map before training is compressed in this paper to establish a light-weight network. Then, transfer learning method is introduced in training network to optimize the model. The whole experiment is evaluated based on MSCOCO dataset. Experiments show that the accuracy of the proposed model is improved by 1.3 to 2.2% points.
Robust Structure Learning of Bayesian Network by Identifying Significant Dependencies

Bayesian networks have long been a popular medium for graphically representing the probabilistic dependencies which exist in a domain. State-of-the-art tree-augmented naive Bayes (TAN) builds maximum weighted spanning tree to represent 1-dependence relationships between attributes. In this paper, we propose to optimize the structure of TAN applying heuristic search to sort attribute and filtering technique to remove weak conditional dependencies. Extensive experimental results on 35 data sets from University of California at Irvine (UCI) machine learning repository reveal that the proposed algorithm achieves competitive generalization performance and even outperforms higher-dependence BNCs like <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -dependence Bayesian network while retaining excellent strucutre complexity.
ASTRO Journals’ Data Sharing Policy and Recommended Best Practices

Transparency, openness, and reproducibility are important characteristics in scientific publishing. Although many researchers embrace these characteristics, data sharing has yet to become common practice. Nevertheless, data sharing is becoming an increasingly important topic among societies, publishers, researchers, patient advocates, and funders, especially as it pertains to data from clinical trials. In response, ASTRO developed a data policy and guide to best practices for authors submitting to its journals. ASTRO's data sharing policy is that authors should indicate, in data availability statements, if the data are being shared and if so, how the data may be accessed.
How energy consumption in the cloud data center is calculated

Energy efficiency has turned into an undeniably essential worry in data centers as a result of issues related to energy consumption, including capital costs, working costs, and natural effect. While energy wastage, because of imperfect utilization of facilities and IT hardware, has to a great extent been decreased using best-practice advances, tending to energy loss in IT equipment, still requires the plan and usage of energy-aware resource management frameworks. Various methods aimed to enhance the energy efficiency in data centers have been developed in the field related to the development of resource allocation. In fact, researchers and scientists utilize numerous ways to improve effectiveness for optimized power and performance: scaling virtual machine and server preparing abilities to minimize energy consumption, enhancing resource use through exploiting its heterogeneity and consolidating the workload. To accomplish these objectives, there is multiple proposes models, calculations, algorithms, techniques, and procedures that diminish energy use using virtual machine scaling, virtual machine sizing for CPU and memory, CPU frequency adjustment, and also equipment power control for resource allocation in the server-level. In this context, we present different formulas for calculating energy consumption in cloud data centers and we propose a simplified and completed deduced formula for energy consumption estimation.
A two-route CNN model for bank account classification with heterogeneous data

Classifying bank accounts by using transaction data is encouraging in cracking down on illegal financial activities. However, few research simultaneously use heterogenous features, which are embedded in the time series data. In this paper, a two route convolution neural network TRHD-CNN model, fed with two types of heterogeneous feature matrices, is proposed for classifying the bank accounts. TRHD-CNN adopts divide and conquer strategy to extract characteristics from two types of data source independently. The strategy is proved able in mining complementary classification characteristics. We firstly transfer the original log data into a directed and dynamic transaction network. On the basis of that, two feature generation methods are devised for extracting information from local topological structure and time series transaction respectively. A DirectedWalk method is developed in this paper to learning the network vector of vertices used for embedding the neighbor relationship of bank account. The extensive experimental results, conducted on a real bank transaction dataset that contains illegal pyramid selling accounts, show the significant advantage of TRHD-CNN over the existing methods. TRHD-CNN can provide recall scores up to 5.15% higher than competing methods. In addition, the two-route architecture of TRHD-CNN is easy to extend to multi-route scenarios and other fields.
A measurable website usability model: Case Study University of Jordan

Website usability is one of the most important quality factors which cannot be measured easily, because of its dependency on various other factors, which some of them are difficult to be measured. Literature shows several website usability models which do not include all usability aspects and shows the difficulty of measuring usability. This paper proposes a website hierarchical usability model with 9 major factors and 24 measurable criteria which are distributed and replicated among factors in hierarchical manner to achieve weight concept. Also this paper introduces a case study Jordan University Website with free tools to measure its usability.
New Features for Eye-Tracking Systems: Preliminary Results

Due to their large number of applications, eye-tracking systems have gain attention recently. In this work, we propose 4 new features to support the most used feature by these systems, which is the location (x, y). These features are based on the white areas in the four corners of the sclera; the ratio of the whites area (after segmentation) to the corners area is used as a feature coming from each corner. In order to evaluate the new features, we designed a simple eye-tracking system using a simple webcam, where the users faces and eyes are detected, which allows for extracting the traditional and the new features. The system was evaluated using 10 subjects, who looked at 5 objects on the screen. The experimental results using some machine learning algorithms show that the new features are user dependent, and therefore, they cannot be used (in their current format) for a multiuser eye-tracking system. However, the new features might be used to support the traditional features for a better single-user eye-tracking system, where the accuracy results were in the range of 0.90 to 0.98.