Reviewerly/exHarmony huggingface dataset:
Collimated Whole Volume Light Scattering in Homogeneous Finite Media

Crepuscular rays form when light encounters an optically thick or opaque medium which masks out portions of the visible scene. Real-time applications commonly estimate this phenomena by connecting paths between light sources and the camera after a single scattering event. We provide a set of algorithms for solving integration and sampling of single-scattered collimated light in a box-shaped medium and show how they extend to multiple scattering and convex media. First, a method for exactly integrating the unoccluded single scattering in rectilinear box-shaped medium is proposed and paired with a ratio estimator and moment-based approximation. Compared to previous methods, it requires only a single sample in unoccluded areas to compute the whole integral solution and provides greater convergence in the rest of the scene. Second, we derive an importance sampling scheme accounting for the entire geometry of the medium. This sampling strategy is then incorporated in an optimized Monte Carlo integration. The resulting integration scheme yields visible noise reduction and it is directly applicable to indoor scene rendering in room-scale interactive experiences. Furthermore, it extends to multiple light sources and achieves superior converge compared to independent sampling with existing algorithms. We validate our techniques against previous methods based on ray marching and distance sampling to prove their superior noise reduction capability.
A Stability-Enhanced Cyclegan for Effective Domain Transformation of Unpaired Ultrasound Images

As a cost-effective, non-invasive and radiation-free medical imaging modality, ultrasonic imaging is widely used in clinical diagnosis. However, the database bias is commonplace among different medical centers. Deep learning based ultrasound image analysis algorithms are usually data driven, rendering high requirements on the uniformity of image datasets. In this paper, we propose a stability-enhanced cycle-consistent generative adversarial network (CycleGAN) method with well detail preservation for the domain transformation to normalize ultrasound images from various medical centers. To stabilize the training process of CycleGAN model, we adopt spectral normalization for 1-Lipschitz continuity to reduce model oscillation. Besides, two time-scale update rule and label smoothing strategy are also utilized to maintain the balance between generators and discriminators for further stability enhancement. Moreover, our method applies skip connections to preserve ultrasound image details and prevent resolution loss during the domain transformation process. Experiments were conducted on clinical thyroid and carotid image datasets acquired from several medical centers. Massive results demonstrate that our proposed model is easier to reach a steady state when training, outstanding 50% from the basic CycleGAN model. Compared with representative algorithms, our proposed method reaches the state-of-the-art performance, with a 11.3% decrease in the mean absolute error and a 9.8% increase in the structural similarity. Hence, our proposed algorithm has a strong capacity of the domain transformation in ultrasound images to reduce the database bias for uniformly distributed datasets. We believe that our method can contribute to the development of the ultrasound image analysis and computer aided clinical diagnosis.
Volume Approximation Using Kinect Sensor

Kinect has already contributed to object detection, 3D modeling, autonomous navigation, and scene mapping studies. This research aims to use Kinect’s ability to collect depth data by approximating the volume capacity of an open-top subject and producing a 3D representation of it based on the data collected from Kinect and image processing. The experiment included taking the subject’s ground truth volume and comparing it to the system’s volume output. Using Linear Regression for the data interpretation indicates that the device created is reliable enough to produce a correlation coefficient of 0.9621. A significant positive association of the two datasets: experimental volume and theoretical volume. The prototype arrived with an average error rate of 9.003%, implying that the system can get accurate results.
Natural Language Interface to Database by Regular Expression Generation

Many of the information systems are static because the codes of the Structured Query Language (SQL) are written by the experts, and adding new queries is impossible without the help of experts. Natural Language Interface to Database (NLID) solves this problem. NLID is an idea where non-experts can retrieve information from a database by simply writing queries using natural language text. In NLID, a natural language input is executed to generate an appropriate SQL statement, and this SQL statement is executed to retrieve the information from the underpinning database as output. Therefore, retrieval from underpinning data by NLID is an important research area in database and NLP for easy and convenient information retrieval. In this paper, we propose an NLID system for fast and convenient information retrieval using pattern matching. The pattern matching is performed using the dynamic generation of regular expressions. We develop a student information system for the case study and measure the performance of the proposed approach.
Research on the Construction of Computer Basic System Based on Information Recognition Technology

research-article Share on Research on the Construction of Computer Basic System Based on Information Recognition Technology Authors: Lina Gao Bohai University, China Bohai University, ChinaView Profile , Lin Zhang Bohai University, China Bohai University, ChinaView Profile Authors Info & Claims ICASIT 2021: 2021 International Conference on Aviation Safety and Information TechnologyDecember 2021 Pages 514–518https://doi.org/10.1145/3510858.3510999Published:04 March 2022Publication History 0citation5DownloadsMetricsTotal Citations0Total Downloads5Last 12 Months5Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
EqNet-L: A new representation learning method for mathematical expression

In the field of machine learning, symbolic reasoning has not made substantial progress in the past decades compared to deep learning. Researchers believe that combining deep learning may help, and the key is that the neural network needs to learn the features in the mathematical expressions. EQNET was explicitly created for the representation learning of mathematical expressions. Although it has made significant progress in learning representations of mathematical expressions, we believe that there is still much work that can be done. On this basis, we propose EQNET-L. Its structure is mainly based on EQNET, and We applied Dropout in network, the module STACKED-SUBEXPAE is proposed to make sure the network could learn more semantic information. Compared with the previous models, EQNET-L achieved better results in training and testing with the same hyperparameters as EQNET.
The Role of Education and Awareness in Tackling Insider Threats

Insider threats present a major concern for organizations worldwide. As organizations need to provide employees with authority to access data to enable them to complete their daily tasks, they leave themselves open to insider attacks. This chapter looks at those who fall into the category which can be referred to as insiders and highlights the activity of outsourcing which is employed by many organizations and defines the term insider threat while pointing out what differentiates an accidental threat from a malicious threat. The discussion also considers various methods of dealing with insider threats before highlighting the role education and awareness plays in the process, the importance of tailoring awareness programs, and what the future holds for insider threats within organizations.
Multi-agent Systems: Future Initiatives

Multi-agent systems (MAS), as the name suggests, refer to the collaborative and collective engagement of two or more agents to make decisions out of mutual or shared interests. These agents interact with each other to target a problem and give it to fruition. Typical manifestations of MAS are exhibited in the form of self-sustaining driving, multi-robotic manipulation, gaming platforms, automatic tutoring to name a few. With the rapid growth and evolution of computational intelligence algorithms, the future of MAS seems to be ripe with a host of promising avenues. This chapter highlights the possible incarnations of MAS in the near future.
Deep Face Recognition for Intelligent Video Surveillance at Electrical Substations

Robust face recognition (FR) in real-world surveillance videos is a challenging but important issue due to the need of practical applications such as security monitoring at electrical substations. While the performance of current FR systems has been significantly boosted by deep learning technology due to its high capacity in learning discriminative features, they still tend to suffer from variations in pose, illumination, occlusion, scale, blur or low image quality in real-world surveillance videos. In this paper, we propose a novel framework which integrates face detection and recognition with tracking. Extensive experiments validate the effectiveness of the proposed framework. Our method outperforms previous SOTAs on three public datasets, i.e., LFW, CFP and AgeDB. Moreover, on the challenging testing datasets collected from the electrical substation surveillance system, the proposed method achieves an average accuracy of 91.4%.
Advanced Applications on Bilingual Document Analysis and Processing Systems

Today, rapid digitization requires efficient bilingual non-image and image document classification systems. Although many bilingual NLP and image-based systems provide solutions for real-world problems, they primarily focus on text extraction, identification, and recognition tasks with limited document types. This article discusses a journey of these systems and provides an overview of their methods, feature extraction techniques, document sets, classifiers, and accuracy for English-Hindi and other language pairs. The gaps found lead toward the idea of a generic and integrated bilingual English-Hindi document classification system, which classifies heterogeneous documents using a dual class feeder and two character corpora. Its non-image and image modules include pre- and post-processing stages and pre-and post-segmentation stages to classify documents into predefined classes. This article discusses many real-life applications on societal and commercial issues. The analytical results show important findings of existing and proposed systems.
STRIDE: Secure Traffic Reporting Infrastructure based on Distributed Entities

Efficient and intelligent traffic networks rely on the constant exchange of information between participants. For instance, navigation services benefit directly from the availability of real-time traffic information to suggest the most time-optimized and ecologically sustainable routes. This type of information is now commonplace and is formed based on extensive, microscopic movement profiles. This imposes direct constraints on the location privacy of users who implicitly or explicitly share such information. In this paper, we present STRIDE as a component of an ITS to gather real-time traffic information in a privacy-friendly manner, ultimately protecting data sources (i.e., users) against data misuse. Our architecture is designed around the concept of distributed trust, preventing attackers from tracking vehicles across the network, even if they succeed in compromising network components. We also achieve conformity to ETSI standards and conclude that real-world implementation of our architecture would be feasible. Thus, we evaluate STRIDE using SUMO and a real-world data set to analyze STRIDE's potential to provide accurate traffic information. Furthermore, we show that STRIDE ensures k-anonymity even in sparse traffic scenarios, eventually protecting location privacy of each vehicle.
Analysis of Seepage and Clogging Characteristics of Rock-Soil Porous Media Based on DEM Technology

The seepage fouling in the rock and soil porous media is essentially the result of the coupling effect of seepage and particles. Developed a lattice Boltzmann method suitable for coupled simulation of rock and soil seepage-particles-immersion motion boundary-DEM numerical calculation platform, and carried out a pore-scale simulation of the seepage and blocking process of rock and soil porous media, and analyzed the particle distribution of the porous media framework. Framework particle size, porosity, framework particle orientation and water pressure and other factors affect its permeability and fouling characteristics. The results show that: the spatial distribution of the framework particles in the porous media is different, and the permeability and fouling characteristics are also different; the smaller the particle size or porosity of the framework particles in the porous media, the more retained particles after fouling, and the more serious the fouling. The particle orientation has a certain influence on the fouling characteristics. The fouling is the most serious when the framework particles are strip-shaped and their long axis is perpendicular to the flow direction with the increase of water pressure, the distribution of the retained particles in the porous medium after fouling becomes more serious.
Unsupervised Anomaly Detection in the Wild

Unsupervised anomaly detection is often attributed great promise, especially for rare conditions and fast adaptation to novel conditions or imaging techniques without the need for explicitly labeled data. However, most previous works study different methods in a constrained research setting with a limited number of common types of pathologies. Here, we want to explore a more realistic setting and target the incidental findings in a large-scale population study with 10000 participants using a recent anomaly detection approach. Despite the difficulties in selecting a proper training set in such scenarios, we were able to produce promising quantitative results and detected 31 anomalies which were not reported previously. Evaluation by a radiologist revealed remaining open challenges when it comes to the detection of less conspicuous anomalies.
Web Scraping and Ethics in Automated Data Collection

As a Ph.D. student doing research in Big Data and Data Mining, the data collection is a big part of the process. Because I am also a programmer, the technique of Web Scraping became a fast and reliable method to collect the data that I needed. But once starting this process, a lot of legal and ethical questions appeared for me. Questions regarding the legality of the process itself or regarding what type of data can be collected and how. This paper does not try to represent legal advice but tries to present a perspective of how we could do data collection, by presenting techniques and what are the laws, rules and guidelines that can apply to web scraping and what we can do, to keep the whole process not only legal but also ethical.
Enhancing Requirements Completeness of Automated Driving System in Concept Phase

During the product lifecycle, a change in the requirements may involve expensive consequences on the system development. Nevertheless, having a decent understanding of the system and documenting the correct requirements from different perspectives help to minimize the changes caused by missing functionalities, therefore, reduce the development cost. Developing comprehensive understanding of the system in the concept phase promotes the completeness and reduces the requirements’ changes. That helps also to create a good model of the system and develop simulation and test cases, which reveal the bugs and the design issues early. This work focus on thinking strategy to answer the research questions, “How to analyze the Automated Driving System (ADS) to improve the requirements’ completeness”. ADS is a complex system that works in non-deterministic environment, in addition, it is a safety related system, meaning that, any malfunction during the operation can cause a harm to people or properties. Three systematic methods were investigated. First, identify the gaps of the stakeholders. It uses product life cycle to identify a list of internal and external stakeholders and then identify their expected needs. The second systematic process utilizes holistic thinking perspectives method to build a broad understanding of ADS and its neighbor systems. This process tries to direct the system definition using external, internal, progressive, quantitative and scientific perspectives. The last method deals with safety requirements identification tools.
Learning Time and Type Aware Representations for Urban Zones

Being able to characterize urban zones and regions is an important prerequisite to understanding the structures, patterns and dynamics of urban areas. Recently, the idea of representation learning has been applied to approach this by learning a vector space for regions, where each zone/region is represented as a low-dimensional vector. However, most existing work has only focused on learning static zone embeddings. These, however, fail to reveal the spatial interaction between zones during different temporal contexts. To bridge this gap, we propose a time-aware embedding learning framework from a Heterogeneous Information Network perspective, where human mobility and geospatial semantics (e.g., place types) are integrated and the temporal dynamic between zones is considered explicitly. We experiment with real-world datasets from Manhattan, New York City to learn time-aware zone embeddings. The results demonstrate the superiority of zone embeddings regarding recognizing urban functional zones compared with baseline methods using multiple standard evaluation metrics. Moreover, qualitative analysis shows the ability of time-aware embeddings in uncovering spatial clustering patterns at different times. Potentially, these learned embeddings could be treated as pre-trained features of zones which can be later fine-tuned to support other themes such as urban vibrancy, urban structure, housing price prediction, and crime estimation.
Color Separated Restoration for Lightweight Single Image Super-Resolution

Recently, single image super-resolution (SISR) has remarkably progressed because of deep convolutional neural network (CNN) based methods. As CNN architectures grow deeper and wider, they achieve considerable reconstruction quality of super-resolved image. However, these very deep CNN have high computational costs, including large memory usage and slow inference speed. To address these issues, numerous lightweight and efficient SISR methods have been proposed, but they have limitation because of small number of network parameters. To improve the capability of lightweight networks, we introduce a new framework (color separated restoration framework) that separately reconstructs each color channel. However, this separation constrains the full use of information in an image; thus, we develop a color content fusion (CCF) layer to solve this problem. The CCF layer efficiently fuses separated features and generates favorable features for each color from the fused features. We also propose attention-based feature decomposition, which enables effectively to reduce the number of parameters by dividing features into two parts while enriching feature representation with an attention mechanism. Extensive experimental results show the superiority of our methods over other state-of-the-art lightweight SR methods.
Dr-Darts: Differentiable Structure Search with Dynamic Regularization

Differentiable Structure Search (DARTS) has become one of the mainstream paradigms of neural architecture search. However, its optimization process is affected by many interference factors, and its performance level is not stable. Chu et al. point out that skip connections natively have an unfair advantage in the exclusive competition which primarily leads to dramatic performance collapse. This work mitigated this phenomenon by adding self-feedback dynamic regularization in the search phase. In practice, the optimizer will provide the current schema feedback information for the regular term during each search epoch, and guide the regular coefficient λ to break the suppression effect of the skip connections. At the same time, we have carried out a verification experiment on the CIFAR and ImageNet dataset and obtained excellent experimental results, which proves that this work can effectively solve the problem of " collapse " and improve the performance of the search network.
Adversarial attack for deep-learning-based fault diagnosis models

Deep-Iearning-based fault diagnosis methods have been proved effective in recent years. With lots of convolutional-neural-network-based models being raised, deep learning is gaining attention in the industrial field. However, recent research shows that deep-learning-based methods are easily influenced by disturbances which are mixed in input signals intendedly. Once disturbances are included, the neural networks will be forced to give wrong predictions at output. To the best of our knowledge there have not been researches in the field of fault diagnosis. And to investigate the influence of disturbances introduced by adversarial attacks, we conduct experiments on several commonly seen models in cases of both white-box and black-box attack. Our results show that when either white-box or black-box adversarial attacks are applied the models are vulnerable and fail in detecting faults. Also, the results can help us improve the robustness of the models, as well as better understand the learnt features shared by the models.
MACSQ: Massively Accelerated DeepQ Learning on GPUs Using On-the-fly State Construction

The current trend of using artificial neural networks to solve computationally intensive problems is omnipresent. In this scope, DeepQ learning is a common choice for agent-based problems. DeepQ combines the concept of Q-Learning with (deep) neural networks to learn different Q-values/matrices based on environmental conditions. Unfortunately, DeepQ learning requires hundreds of thousands of iterations/Q-samples that must be generated and learned for large-scale problems. Gathering data sets for such challenging tasks is extremely time consuming and requires large data-storage containers. Consequently, a common solution is the automatic generation of input samples for agent-based DeepQ networks. However, a usual workflow is to create the samples separately from the training process in either a (set of) pre-processing step(s) or interleaved with the training process. This requires the input Q-samples to be materialized in order to be fed into the training step of the attached neural network. In this paper, we propose a new GPU-focussed method for on-the-fly generation of training samples tightly coupled with the training process itself. This allows us to skip the materialization process of all samples (e.g. avoid dumping them disk), as they are (re)constructed when needed. Our method significantly outperforms usual workflows that generate the input samples on the CPU in terms of runtime performance and memory/storage consumption.