Reviewerly/exHarmony huggingface dataset:
A General Dynamic Sequential Learning Framework to Vehicle Trajectory Reconstruction Using Automatic Vehicle Location or Identification Data

Vehicle trajectory data derived from automatic vehicle location (AVL)/automatic vehicle identification (AVI) system provides key supports for intelligent transportation systems. Field collected vehicle trajectories are usually incomplete, driving the development of vehicle trajectory reconstruction approaches. The traditional methods have to impose simplifying assumptions, e.g., drivers choose the shortest path, because of the complex driver route choice behaviors and the dynamics of traffic propagation. To reduce the impact of the assumptions, few researchers have tried to apply emerging technologies, e.g., deep learning algorithms, to vehicle trajectory reconstruction, yet the technologies are only used to determine the optimal path from the candidate set, and these methods are merely suitable to either AVL data or AVL data. This paper proposed a general dynamic sequential learning framework to reconstruct vehicle trajectories. First, an Isolation Forest based ensemble learning model is developed for trip trajectory sequence extraction from an entire trip chain. Second, two dynamic sequential prediction models based on dynamic recurrent neural network (RNN) are tailored to predict AVL and AVI trajectory respectively. The prediction models can learn the intrinsic patterns from the complete vehicle trajectories to reconstruct incomplete vehicle trajectories. The global positioning system (GPS) data gathered from simulation and the automatic license plate recognition (ALPR) data collected from real-world ALPR system in the downtown area of Kunshan, China were used to evaluate the performance of the proposed method. The results show that the proposed method achieves superior performance for vehicle trajectory reconstruction compared with the traditional methods for both types of data. The impacts of different sampling rates and traffic conditions on the model performance are also discussed.
View Loss Evaluation and Keyframe Reselection for Active Aerial Visual Reconstruction

In this paper, we propose a keyframe reselection method based on the field-of-view loss evaluation for active visual reconstruction with photography constraints using UAVs. To obtain the pose and position shift of the actual active vision base coordinate to the planned ones, a particle filter optimization method that utilizes the quartered depth histogram is presented. To evaluate the view loss of the captured images relative to the photogrammetric-constraint planning, a set of quantitative criteria is conducted. The keyframe reselection algorithm is improved based on the above works, which aims to keep images that are more in line with the photogrammetric constraints. The experimental results show that the proposed method can effectively enhance the effect of reconstruction.
Application of Small Sample Information Processing in High Dimensional Space

Often times it is difficult to collect sufficient large dataset to support the test or validation of given hypotheses. Information diffusion is an effective way to mitigate the issue. Research results have been reported on information diffusion in one-dimensional space and two-dimensional Euclidean space. This paper attempts to extend the research to three-dimensional space. The main idea is to decompose a three-dimensional problem to two-dimensional problems using the golden section rule. A case study on a real-world small dataset analysis is provided to demonstrate the effectiveness of the proposed approach.
OUP accepted manuscript

In his recent book, Reality+, the philosopher David Chalmers argues that we are unable to know if we are all living in a simulated virtual universe: virtual realities are, he claims, genuine realities. Ever since 2009 when Satoshi Nakamoto (who may or may not be a real person) unleashed Bitcoin on what we assume is “the world”, lawyers have been wondering how to tell the difference between real and virtual money, between real assets and virtual assets; the answer remains as elusive as Chalmers’ question. We are forced back to first principles: What is property? What is money? Can digital assets (if they are assets) be the subject of a trust? What does it mean to “own” something? What is the relationship between data and the medium on which it is stored? How can the rules of private international law be adapted to deal with valuable rights that have no situs? Can the infringement of such rights found a claim for trespass? Or conversion?
Model selection and signal extraction using Gaussian Process regression

We present a novel computational approach for extracting weak signals, whose exact location and width may be unknown, from complex background distributions with an arbitrary functional form. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our approach on the CERN open dataset from the ATLAS collaboration at the Large Hadron Collider, which contains the Higgs boson signature. Our approach is based on Gaussian Process (GP) regression - a powerful and flexible machine learning technique that allowed us to model the background without specifying its functional form explicitly, and to separate the background and signal contributions in a robust and reproducible manner. Unlike functional fits, our GP-regression-based approach does not need to be constantly updated as more data becomes available. We discuss how to select the GP kernel type, considering trade-offs between kernel complexity and its ability to capture the features of the background distribution. We show that our GP framework can be used to detect the Higgs boson resonance in the data with more statistical significance than a polynomial fit specifically tailored to the dataset. Finally, we use Markov Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of the extracted Higgs signature.
Research on Emerging Business Development of Energy Internet Enterprises

Under the background of the rapid development of energy Internet, digital technology and entity industries are being deeply integrated. the production organization mode and operation mode are being gradually reshaped, new businesses, new formats and new models continuously appear and grow vigorously. Facing the gradual opening of the power market, energy Internet enterprises have entered the market. Facing the trend of the development of the times, in order to stay at the forefront of development, energy Internet enterprises need to actively develop emerging businesses, opportunities should be seized to meet challenges. Firstly, the background of current energy reform and internet development were analyzed, and the significance of energy Internet enterprises were defined to carry out emerging business under the new background. Then, the market demand was analyzed, the emerging business of energy Internet was proposed combined with the market demand, its connotation were respectively defined, the current situation and challenges of business development mode were understood. The types and competitiveness of energy Internet enterprises are researched and judged. Finally, the layout of emerging businesses was summarized.
Error matching elimination based on a local affine algorithm

Abstract Aiming at the shortcomings of orb algorithm using the traditional RANSAC (random sampling consistency) algorithm to eliminate mismatches, such as the high number of iterations when the sample size is large, and the proportion of error points also affects the fitting accuracy of the traditional algorithm, a rough and fine method to eliminate mismatches is proposed; The threshold screening method is used to select the optimal threshold and preliminarily eliminate a large number of wrong matching pairs to achieve the effect of rough elimination; Then the sub-window method is used to calculate the local affine matching under all windows, reduce the number of error points, and increase the correctness of the fitting model of RANSAC algorithm. The experimental results are compared with the traditional algorithm, and the matching accuracy is improved by 6% and combined with orb-slam2.
Comparative study of elastography and histopathology findings of breast lump at a tertiary hospital

ackground: Cancer breast has emerged as leading site of cancer in most urban populations of India. Breast sono- elastography is a non-invasive imaging technique which provides information on breast lesions. In present study we compared elastography and histopathology findings of breast lump and diagnostic accuracy of elastography. Material and Methods: Present study was a prospective observational study conducted in females patients, sonographically visible solid breast lesions, measuring less than 3 cm, classified as BI‑RADS 3 and 4. Results: During study period 126 female patients underwent USG elastography, followed by biopsy/surgery and histopathology reports were available. As per histopathology, 52 (41.72 %) were benign while rest 74 (58.73 %) were malignant. Age, BIRADS, Elastography Score and Strain ratio were significantly higher in malignant cases as compared to benign cases and difference was statistically significant (p&lt;0.001). According to Histopathological diagnosis, among benign lesions majority were fibroadenoma (77.03%) others were fibrocystic disease (9.46%), benign fibroepithelial lesion (6.76%), abscess (5.41%) and sclerosing adenosis (1.35%). while, among malignant cases majority were invasive ductal carcinoma (67.31%) followed by invasive mucinous carcinoma (13.46%), invasive poorly differentiated carcinoma (7.69%), ILC (5.77%), medullary ca (1.92%), papillary ca (1.92%) and phylloids (1.92%), Excellent scores were noted with combination of Ultrasound Score + Elastography Score + Strain Ratio as sensitivity, specificity, diagnostic accuracy, NPV, and PPV of 96.00 %, 96.05 %, 96.03 %, 94.12 % and 97.33 % respectively. Conclusion: Ultrasound elastography combined with strain elastography and ultrasound score have high sensitivity, specificity, and diagnostic accuracy in differentiating benign and malignant breast masses.
Input Selection for Bandwidth-Limited Neural Network Inference

Data are often accommodated on centralized storage servers. This is the case, for instance, in remote sensing and astronomy, where projects produce several petabytes of data every year. While machine learning models are often trained on relatively small subsets of the data, the inference phase typically requires transferring significant amounts of data between the servers and the clients. In many cases, the bandwidth available per user is limited, which then renders the data transfer to be one of the major bottlenecks. In this work, we propose a framework that automatically selects the relevant parts of the input data for a given neural network. The model as well as the associated selection masks are trained simultaneously such that a good model performance is achieved while only a minimal amount of data is selected. During the inference phase, only those parts of the data have to be transferred between the server and the client. We propose both instance-independent and instance-dependent selection masks. The former ones are the same for all instances to be transferred, whereas the latter ones allow for variable transfer sizes per instance. Our experiments show that it is often possible to significantly reduce the amount of data needed to be transferred without affecting the model quality much.
Miniature probability maps using resource limited embedded device for classification of histopathological images

Prediction of malignancy in histopathology images using CNN is mostly performed using cloud services suffers from network latency. We propose a novel, efficient method to classify whole slide histopathology images using modular and portable embedded devices to detect the presence of cell abnormality. The proposed method generates probability maps which indicates predictions so that a bird&#x2019;s-eye view of tissue malignancy can be obtained. The miniature map(mini-map) of histopathology image is the overview of binary class probabilities at the patient level. The computational overhead of device is reduced as well as prediction will be faster while using custom-trained model. The round trip time is also reduced as the computing occurs near the end-device itself. The obtained predictions in mini-map can be viewed in any portable device consuming minimum processing time as the size of the map is only few kilo-bytes. This method is found to be suitable to assist medical practitioners in patient diagnosis.
Design of interfaces for Dyslexia and Dyscalculia Applications

In the field of Human-Computer interaction, one of the decisive factors is the graphical interface; especially in applications in which the user has specific conditions that limit its operation and performance. This is the case of the Gabarato application, which facilitates the treatment of children diagnosed with dyslexia and dyscalculia who are cared for by Sinsoluka Center in Ecuador.During the development phase of the application, it was determined that there is a complexity to achieve specific usability interactions such as “ease of use”, “memorability” and “effectiveness”, especially with users who do not have the ability to operate peripherals of the application. computer and who present physical or cognitive difficulties to manipulate a conventional interface.Although in the preliminary studies the methodologies, standards, and protocols were met in the prototype tests; it was evident that the metrics used might not be effective; For this reason, the present work presents a proposal to measure usability interactions in children who present specific conditions; With these results, new versions of interfaces oriented to mobile devices were made.In this work, comparative data are analyzed that establish differences between the usability metrics with conventional users and users who present specific conditions or disabilities; Additionally, a discussion is offered regarding the elements that make up the interfaces and their relationship with usability heuristics and the fundamentals of human perception.
A Comparative Analysis on Image Caption Generator Using Deep Learning Architecture—ResNet and VGG16

Image caption generator generates the caption for a given image by understanding the image. The functionality is that it involves numerous concepts of computer vision to identify the image and to reciprocate the same in English. The challenging part of the caption generation is to understand the image and understand the image context and produce English description for the image. In our work, we compared the abilities of two deep learning architectures named VGG16 and ResNet50 for understanding the image and LSTM for generating the relevant caption for the image. The paper discusses about the usage of two deep learning architectures on generating the captions from the photograph. With the advancements in the deep learning techniques, the Flickr8k datasets are taken that have high dimensionality to compare the performance of the caption generated. The Flickr8k dataset has 8000 images where every image is grouped with five varied captions that determine the appropriate content of the image. The high computational power of the deep learning techniques is helpful to build models that can generate captions for picture. The two deep learning architectures performance is compared using BLEU score. The widely used applications of image caption generator are to describe caption for photograph so that blind can understand the image.
Significance of HCI in Cognitive Science

Cognitive science is an interdisciplinary and scientific study of the mind and its processes. Examining the nature, the tasks, and the functions of cognition takes place. Cognitive scientists study behaviour and intelligence, with a focus on how nervous systems represent, process, and transform information. Mental faculties of cognitive scientists include language, perception, memory, attention, reasoning, and emotion; to understand these faculties, cognitive scientists borrow from fields such as linguistics, psychology, artificial intelligence, philosophy, neuroscience, and anthropology. The typical analysis of cognitive science spans many levels of organization, starting from learning and decision to logic and planning; from neural circuitry to modular brain organization. The fundamental concept of cognitive science is that "thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures. The paper describes how the study of Cognitive science plays a vital role in building a successful Human- Computer Interaction system. Further, it also explains that why cognitive science is involved in this vast topic called Human-Computer Interaction.
THE SYSTEM OF CONTINUING EDUCATION AS A TOOL FOR DEVELOPING «HARD SKILLS» AND «SOFT SKILLS» FOR STUDENTS OF LAW SCHOOLS

The article raises the actual problem of development of continuing education on the basis of the law University. The author substantiates the prerequisites for changing the system of professional education for training specialists with a set of « hard skills «and» soft skills. The key features of the system of continuing education are highlighted. We consider a three-level model of the working environment that generates requirements for the level of training of a specialist in the field of law. The article presents the experience of implementing the system of continuing professional education of the Russian state University of justice. The author proves the necessity of continuing preofe4ssional education as a tool to form soft skills and hard skills that are the main resource at the labor market. The experiences of the efficiently organized system of continuing education at the Ural Branch of the Russian State University of Justice, providing graduates’ demand at all levels of professional working environment is presented.
Educational Practices and Management Information System in Managing Driving School on Website-Based GTC

Course institutions are indispensable to support people’s education by taking courses that can explore their potential. Global Training Centre (GTC) is a driving school in Sragen. However, the current registration system still uses manual means, and data management has not been computerized, so that the performance is less effective and efficient. This research aims to design a web-based management information system in managing the driving school at GTC. The software development method uses the waterfall method by utilizing UML (Unified Modelling Language) consisting of ERD, case diagram, activity diagram, and wireframe. The results of website development research are to provide ease of participants in registering to the course and facilitate GTC in managing course institutions to produce more optimal services.
Space counting method of crucible bubbles based on video images

This paper proposes a crucible bubble space counting method based on image slices. A digital microscope is used to photograph the crucible transparent layers of different depths to construct a discrete three-dimensional space in the depth direction. The bubble image is enhanced, segmented, flooded and filled to obtain a binary image of the bubble on the two-dimensional plane. Aiming at the problem of low accuracy and long time-consuming traditional Hough circle detection algorithm, an improved random Hough transform bubble detection algorithm is proposed. The algorithm is used to obtain the center and radius parameters of the bubbles, and then the multi-layer bubbles in the three-dimensional space of the crucible transparent layer are counted. Compared with the traditional method, the single-frame bubble recognition rate of this method is more than 95%, and the detection time is reduced by 55% compared with the traditional algorithm. Compared with the actual crucible bubble count, the multi-layer bubble count has a correct rate of more than 95%.
When Diversity Meets Hostility: A Study of Domain Squatting Abuse in Online Banking

In today’s digital era, a large number of users rely on banking websites to perform financial transactions. The widespread adoption of online banking and the monetary value associated with each user account make banking websites a potential target for domain squatting. Domain squatting is a common practice in which malicious actors register internet domain names which are similar to popular domains. In this work, we study the prevalence of domain squatting abuse that exploits inconsistent internet domain names used by popular banks across several countries including US, UK, Australia, Germany, China and India. An attacker exploits the inconsistencies present in the domain names to generate similar looking domains and use them for malicious purposes such as domain takeover, malware propagation, click fraud, phishing, stealing traffic, distribution of ads and malware.In this paper, we present the first context-free grammar (CFG) based algorithm that models inconsistencies in domain names of banking websites and use it to generate candidate domains. We also provide a comprehensive categorization technique to classify candidate domains into four different categories: defensive, malicious, suspicious and unrelated. Our study reveals that more than 3,000 domains that are either malicious or suspicious, targeting popular banks across different countries around the world. Further, we noticed prevalence of three forms of domain squatting, namely comboTLDsquatting, full-name squatting and brandname squatting. We found that most of the malicious and suspicious domains are instances of comboTLDsquatting. Our work shows that only few organizations are protecting their brands against domain squatting abuse by performing defensive registration. Further, our study identified different strategies used by malicious actors during domain registration in order to evade detection from security researchers and trick victims into disclosing their credentials. In particular, we discover that malicious actors use similar words, same TLDs, grammar rules and registrar for registering domains which are used in benign domains.
Near real-time event detection for watershed monitoring with CANARY

Near real-time data-driven watershed monitoring aids regulatory compliance and improved water quality. CANARY helped identify suspected illicit dumping events.
Research on the Application of Cloud Computing Technology in Computer Data Processing

In the information age, advanced Internet technology and cloud computing technology have been widely used in all walks of life, which have brought great convenience to people's work and life. Cloud computing technology can not only provide a good platform for computer data processing, but also provide a platform for users to use cloud computing to process data and information, and provide security for data transmission. However, due to the particularity of the computer network environment and the characteristics of cloud computing technology, there are still some security issues that need to be resolved. This article will combine the related concepts and content of cloud computing technology to analyze the role and specific application of cloud computing technology in computer data processing. The author puts forward the problems existing in the application of current cloud computing technology. In order to better play the value of cloud computing technology in computer data processing, the author of this article has given effective measures to ensure cloud computing security, strengthen research and development of hybrid cloud computing, develop corresponding programs, and improve performance.