Reviewerly/exHarmony huggingface dataset:
The Hyperbolic Temporal Attention Based Differentiable Neural Turing Machines for Diachronic Graph Embedding in Cyber Threat Intelligence

Cyber Threat Intelligence (CTI) is an effective approach to solve cyber security problems, finding unknown threats is becoming a problem to be solved. Research based on threat intelligence knowledge graphs has gradually increased for its capability to capture entity characteristics and better predict unknown threats. Most of the current research focuses on Euclidean space, however, the Euclidean space is insufficient to capture the hierarchical information of the knowledge graph. In this paper, we propose a novel Hyperbolic Temporal Attention based Differential Neural Turing Machines for diachronic graph embedding framework (HTA-DNTM), which adopts a graph attention network model based on hyperbolic space, simultaneously uses multi-head self-attention to map the temporal graph into hyperbolic space and incorporates hyperbolic graph neural network and hyperbolic gated recurrent neural network, capturing the evolving behaviors and implicitly preserve hierarchical information simultaneously. Moreover, we demonstrate significantly improved performance over various approaches on CTI. A series of benchmark experiments illustrate HTA-DNTM has ability to generate higher quality than state-of-the-art word embedding models in CTI fields.
Consensus One-Step Multi-view Image Clustering Based on Low-Rank Tensor learning

Multi-view subspace clustering aims to divide a set of multi-source data into several groups according to their underlying subspace structure. Despite superior clustering performance in various applications, most existing methods directly construct noisy affinity matrices by self-representation, and isolate the processes of affinity learning, multi-view information and clustering. Both factors may cause insufficient utilization of multi-view information, leading to unsatisfying clustering performance. In this paper, we propose a novel consensus one-step multi-view clustering method based on lowrank tensor learning to address these issues. Low-rank tensor learning, consensus learning and labels learning in a unified framework. Through the three steps of mutual negotiation, the final clustering label is directly obtained. Experimental results on four benchmark datasets demonstrate that our method outperforms other state-of-the-art methods.
Using Mixed Reality in Online Learning Environments

Among the technological advancements of Industry 4.0, virtual reality (VR), augmented reality (AR), and mixed reality (MR) have been used in many industries to visualize and interact with digital twins in real-time. These technologies have also been used with building information modeling (BIM) and geographic information systems (GIS) throughout the life cycle of construction projects. Moreover, the integration of BIM and GIS has been proven to improve collaboration among stakeholders by allowing them to visualize information seamlessly between buildings and their surroundings. Following the COVID-19 pandemic, many undergraduate and graduate courses were moved online, which posed many challenges on instructors to deliver lectures in accessible and informative ways. Furthermore, many courses and curricula have been originally online and have not yet benefited from the full potential of MR in virtual classrooms. Therefore, the aim of this research is to evaluate how MR applications of integrated BIM-GIS can improve the online learning experience of construction management students. An experimental study was conducted with students to evaluate the effect of using AR on handheld devices and MR on head-mounted devices on their spatio-temporal reasoning and understanding skills.
Value Proposition Innovation in Startups

In highly dynamic situations, entrepreneurs build value propositions in resource-constrained conditions. The activity is set up as a series of experiments, with each one aimed at validating value proposition-related assumptions with customers. Validation entails interactions between potential customers and the startup team utilizing prototypes, which leads to the confirmation of current assumptions as well as the discovery of new insights that lead to more experiments. The main features of the value proposition identification model are highlighted in this chapter, and a novel value prioritizing approach is proposed.
Financial ERP System Management and Design Based on Grid Algorithm

With the popularization of the Internet and the greatly reduced cost of the Internet and the change of traditional computing with the performance of the computer, grid computing has gradually become an important trend in the development of supercomputing. Financial information management in grid computing environment is a considerable challenge due to the resource characteristics of its environment. A wide range of complex resources will greatly affect the performance of the computation. Grid financial information management is just like the financial management system in traditional distributed computing environment or cluster computing environment. This system has three functions: job request management, financial management and financial information allocation and scheduling, that is, users submit application resource request to network system through job management function; Identify and monitor the status of network resources, gather information about resource usage during running jobs, and manage cluster resources. Receiving application resources Specify the resources required by the job based on the policy. Financial ERP system based on grid algorithm is necessary and an inevitable choice to comply with the development of The Times.
Spatiotemporal flow features in gravity currents using computer vision methods

Relationships between the features visually identified at the front of the flow’s current and parameters regarding its velocity and turbulence were observed in early experimental works on the characterization of gravity currents. Researches have associated front features, like lobes and clefts, with the flow’s turbulence, and have used these associations ever since. In more recent works using numerical simulations, these connections were still being validated for various flow parameters at higher front velocities. The majority of works regarding measurements at the front of a gravity current rely on the front’s images for making its analysis and establish relationships. Besides that, there is an interdisciplinary field related to computer science called computer vision, devoted to study how digital images can be analyzed and how these results can be automated. This paper describes the use of computer vision algorithms, particularly corner detection and optical flow, to automatically track features at the front of gravity currents, either from physical or numerical experiments. To determine the proposed approach’s accuracy, we establish a ground-truth method and apply it to numerical simulation results data sets. The technique used to trace the front features along the flow showed promising results, especially with higher Reynolds numbers flows.
Fixed Point Clip: A New Data Augmentation Method for Object Detection

Data augmentation is one of the most important ways to improve object detection performance. In this paper, we propose a new offline data augmentation method, this method obtains multiple new anchors and new labels by fixed-point clipping in the original anchor box, by retaining important semantic information, the detection performance of the occlusion object is improved. At the same time, we produced a marine fish dataset with 11 kinds of marine creatures, and we verify the effectiveness of the proposed method in this dataset. Experiments confirmed the effectiveness of this method on the marine fish dataset, in the total dataset, the mAP@0.5 of this method is 10% higher than the Baseline. In the Tuna class that is seriously occluded in the obstruction problem, the mAP@0.5 of this method is 0.25 higher than baseline, which is increased by 46%. It indicates that this method can not only perform well in normal object detection but also maintain the advantage in complex scenes.
INDEPENDENT WORK WITH EDUCATIONAL TEXT AS A MEANS OF INCREASING STUDENT PERFORMANCE

No abstract.
Deep Learning-Based Sequence Labeling for Information Extraction from Multiple Types of Textual Bridge Reports

A massive amount of data/information that is buried in unstructured data sources such as bridge rehabilitation reports offer opportunities to complement other data sources such as structural health monitoring data for improved prediction of future bridge conditions. Extracting these data/information from their sources is, however, challenging for two reasons: (1) utilizing textual data for analytics remains to be a challenge due to the inherently unstructured nature of these data; and (2) existing information extraction methods, which aim to extract structured data/information from text, are limited in their capabilities and generalizability (e.g., a method developed to extract certain information from one type of document may not perform well on another type). To address these gaps, this paper proposes a deep learning-based information extraction (IE) model that automatically recognizes and extracts bridge-related data/information (e.g., bridge deficiencies) from multiple types of unstructured textual sources and represents the extracted data/information in a structured format to support further data analytics. The proposed IE model utilizes deep learning-based BiLSTM; begin, inside, and outside (BIO) encoding for the phrase-level segmentation; and conditional random fields (CRF) for both word-level and phrase-level labeling for automatic sequence labeling. The paper discusses the proposed model and its performance results.
Generating Consistent and Diverse QA pairs from Contexts with BN Conditional VAE

One of the most challenging problems in the question answering (QA) area is the lack of high-quality labeled data. However, the cost of manually labeling a question-answer (QA) pair from the target text is very high. One way to solve this problem is to automatically generate QA pairs from the target text. In this paper, we propose the Batch Normalization conditional VAE-QA pair generation (BNCVAE-QAG) model to generate QA pairs for a given text. First, we employ the Batch Normalization (BN) layer to prevent Kullback-Leibler (KL) divergence from disappearing. We also design modules to extract spatiotemporal features from text contents, in addition, the self-attention mechanism is employed in the question decoder, which improves the accuracy and recall of results. Furthermore, we propose a question generalization mechanism to generate more QA pairs. We evaluate our BNCVAE-QAG model on several datasets. The experimental results show that our model has achieved an impressive performance improvement than the baseline.
A Swift Gaze Estimate Method Based On The Corneal Image System

With the development of intelligent manufacturing, the demand of the incoming Human-Machine Interaction such as the augment reality rapidly increasing. However, the existing interaction modes in the augment reality, rely heavily on the hands or head movement. The inflexible modes is inefficient in the busy work flow. In this paper, we propose a gaze estimation work based on the Corneal Image System which can improve the efficiency of the interaction. Several prior works have proved, single Corneal Image contains the subject’s gaze information. However, the quality of Corneal Image is always impacted by the color and texture of the iris or the light from the surrounding, is hard to be applied directly. In order to improve the quality of the Corneal Image, people usually import additional devices into their work, such as infrared camera or eye tracker. These extra devices cause their gaze estimation works to become cumbersome and hard to be re-implemented commonly. Our gaze estimation work requires no additional device, can be seamlessly integrated into the AR domain with the help of the AprilTag mark. An AprilTag mark contained in an eye image, is distinct enough to be recognized, meanwhile, owns the hybrid pose relationship information between the eye, camera, and the focused AprilTag mark. The gaze can be inferred through the rigid body coordinate transformation naturally from this relationship. Many experiments have demonstrated that our approach is much easier to be re-implemented than the previous Corneal Image System based gaze computing works, at the same time, have the near performance to the state of the art.
DBSwin: Transformer based dual branch network for single image deraining

Rain streaks severely affect the perception of the content and structure of an image so high-performance deraining algorithms are needed in order to eliminate the effects of various rain streaks for high-level computer vision tasks. Although much progress has been made with existing deraining methods, the task of single image deraining remains challenging. In this paper, we first point out that existing Transformers lack sufficient ability to capture channel attention which restricted the ability of models in deraining. To improve the performance of deraining model, we propose a dual branch deraining network based on Transformer. One branch uses dense connections to connect Transformer modules which embed the attention of a composite channel. This branch captures channel attention more finely to learn the representation of rain streaks features. The other branch first obtains features at different scales by gradually expanding the receptive field, then uses these features to obtain attention for regional features, and finally uses the attention to guide the model to focus on areas of high rain streaks density and large scales. By fusing these two branches, the model is able to capture channel attention more finely and to focus on regions of high rain streaks density and large scales. The extensive experimental results on synthetic and real datasets demonstrate that the proposed method outperforms most advanced deraining methods.
Approximation Speed of Quantized Versus Unquantized ReLU Neural Networks and Beyond

We deal with two complementary questions about approximation properties of ReLU networks. First, we study how the uniform quantization of ReLU networks with real-valued weights impacts their approximation properties. We establish an upper-bound on the minimal number of bits per coordinate needed for uniformly quantized ReLU networks to keep the same polynomial asymptotic approximation speeds as unquantized ones. We also characterize the error of nearest-neighbour uniform quantization of ReLU networks. This is achieved using a new lower-bound on the Lipschitz constant of the map that associates the parameters of ReLU networks to their realization, and an upper-bound generalizing classical results. Second, we investigate when ReLU networks can be expected, or not, to have better approximation properties than other classical approximation families. Indeed, several approximation families share the following common limitation: their polynomial asymptotic approximation speed of any set is bounded from above by the encoding speed of this set. We introduce a new abstract property of approximation families, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> -encodability, which implies this upper-bound. Many classical approximation families, defined with dictionaries or ReLU networks, are shown to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> -encodable. This unifies and generalizes several situations where this upper-bound is known.
The Application of Machine Learning for Computational Electromagnetic Solver Selection

The field of Computational Electromagnetics (CEM) consists of several techniques used to solve the electromagnetic (EM) response of arbitrarily shaped conducting objects. Selecting the most efficient and accurate solver usually requires extensive user experience and depends on the geometry and setup of the problem. This work aims to simplify this process by using machine learning models to predict whether a given solver would be sufficiently accurate before simulation. The hybrid Method of Moments and Physical Optics technique (MoM-PO) is considered in this investigation. It is found that predicting the accuracy of the MoM-PO when calculating the far field, near field and surface currents is in fact possible with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> -scores of 98.23%, 97.61% and 92.26%, respectively. Classification models such as Logistic Regression, Decision Trees and Artificial Neural Networks are considered.
SemSegFPA: Semantic Segmentation Feature Pyramid Network with Attention Module

In this paper, we address semantic segmentation tasks through combining multi-level feature representations with attention mechanism. Different from previous works that only capture contexts from the last layer of the backbone, we propose Semantic Segmentation Feature Pyramid Network with Attention Module (SemSegFPA) to adaptively collect local features from different feature levels and effectively utilize those features that have different information to generate global context. Specifically, we create two types of attention modules for better information extracting, which respectively encodes important information in weights in spatial and channel dimensions. Our modules effectively improve the performance of the model with marginal extra cost. Our method outperforms SemSegFPN and achieves competitive performance compared to other mainstream methods, with the least runtime, on four challenging semantic segmentation datasets, i.e., ADE20K, PASCAL VOC 2012, and PASCAL Context, and LIP.
Deep Learning based Telugu Video Text Detection using Video Coding Over Digital Transmission

With the development of smart devices and high-speed internet, demands for the ever-increasing number of videos. Comparing conventional machine learning methods with in-depth Reading methods. The two independent steps used to decipher text is; the discovery of the Candidate text region and the division into categories. Then, the text texts received were. distributed through a few layers of CNN, which first produced a conversion feature map as well as Divide candidate circuits into text or non-text classes. Explored the proposed programs. On the data got from various Telugu videos. The data sets have been collected by collecting. videos from various Telugu channels. Text from the video provides useful information, which can create a default video index and retrieval system and integrated heuristic and in-depth learning-based approach Neural Network (CNN) to automatically output text to video. Two independent steps were used to extract the existing text; the discovery of the Candidate text region and the division into categories. In the first step, the circles are rectangular. It identified textual content using heuristics, which included morphology analysis and geometric issues. Then, the text of the accepted candidate text has transferred to a few layers of CNN, which first produced a convolutional feature map and then split the candidate regions into text or non-text classes. Video collection organized on the website is different. channels Telugu. 70 percent of the data have been used for network training while 30 percent Confirmed. Experiments have shown that our proposed approach has reached modernization functionality in our database.
Implementation of E-Health Services Using Machine Learning for Smart Cities

Algorithms based on machine learning can be used for analyzing large amounts of varied data with exact results. One can use machine learning in offering healthcare services in the areas of interpretation, prognostics, and diagnostics in healthcare services for smart cities. In today's world we are using information systems very widely in offering various health services, and also digitization of the patient's information generates large amounts of information/data. While performing these activities, the data is stored in many systems and in various formats. Thus, after making a detailed analysis, the authors came to know that about 80% of health-related data is still in unstructured format. While combining the large amounts of mottled data, it needs high computing power and fine methods that enable fast execution and automated designing of machine-learning algorithms, which enables the analysis of the complex health data with exact results. Offering of e-health service through machine learning for smart cities makes the services more proactive.
Frame Attention Mechanism and Transfer Learning Network for Classification on Motion Video

Video content classification has a wide range of application scenarios. In situations such as public place security and behavior prediction, the type of behavior of a person is inferred from the continuous images in the video. Video data adds the concept of time compared to image data, which inevitably increases the overall computational effort. A typical research tool uses 3D convolution for feature map extraction in the time domain, but such methods exhibit significant performance loss due to frame down sampling. Inspired by the human judgment of video content, this study computes features on a single frame by transfer learning and then encodes the position of the resulting set of features. An attention mechanism is used to determine the keyframe locations. Finally, a multilayer perceptron is combined to achieve video content classification. The results of the study show that with the chosen dataset. Our model outperforms the 3D convolutional model in discriminative accuracy, confusion matrix, and down sampling conditions.
Review of Blockchain Technology Applications in Construction Engineering and Management

Adoption of new technology is a relatively slow process in the construction industry. The growing number of cyber threats has raised a serious concern about the data transmissions. Blockchain technology (BCT) has been introduced to curb some of the privacy and security challenges. It is a technology that decentralizes authority and makes it almost impossible for external threats to alter the transmitted content. Thus, this paper aims to provide an in-depth review of the BCT articles in the construction industry, specifically for its applications in construction management. Five categories were identified for BCT in the construction industry after reviewing the existing literature: (1) secure data transmission and data ownership, (2) IT in construction, (3) quality information management, (4) stakeholders’ collaboration, and (5) construction payments and trust. The findings indicated that BCT presented high promises to improve the overall efficiency of the construction industry. Finally, insights for future research about BCT in the construction industry are provided.
PERSONAL DEVELOPMENT OF A PRESCHOOL CHILD IN MODERN SOCIO-CULTURAL SPACE

No abstract.