Reviewerly/exHarmony huggingface dataset:
Image Registration Algorithm for Sequence Pathology Slices Of Pulmonary Nodule

Registration of pathological section images is an important part of three-dimensional reconstruction of sections. In this paper, a registration method was proposed to solve the problem of mismatching of pathological section images of pulmonary nodules. Firstly, rough matching is performed, and the feature points are extracted according to the scale-invariant feature transform (SIFT) algorithm. Then fast sparse coding (FSC) is used for fine matching to eliminate mismatched pairs. The algorithm presented in this paper is applied to the registration between sequence sections of pulmonary nodules. The experimental results show that the algorithm can effectively find more matching point pairs, accurately remove the false matching point pairs, and significantly improve the registration accuracy.
A Dyadic Particle Filter for Price Prediction

The most difficult task in financial forecasting is the accurate price prediction based on previous values. Two cases are studied: stock price prediction and flight price prediction. A dyadic particle filter is proposed that is based on sequential importance resampling. This dyadic particle filter captures the dynamic evolution of a pair of latent vectors. In stock price prediction, one latent vector is defined for each stock. This latent vector is paired with a market segment latent vector introduced for each group of companies of the same category. Both latent vectors capture the hidden information of the stock market and reinforce the state estimation procedure. This hidden information influences strongly the performance of the particle filter, yielding more accurate prediction of stock prices than the state-of-the-art techniques. For flight price prediction, the pair of latent vectors corresponds to route and destination, respectively. Given the price range of each flight, promising results are disclosed.
Extraction of Basic-Level Categories Using Dendrogram and Multidendrogram

Cognitive agents should be equipped with computational methods enabling autonomous extraction of internal cognitive structures, i.e., supporting organization of large datasets and facilitating interaction with human users. In this research, we focus solely on the extraction of basic-level categories. We propose a computational approach that allows the agent to develop, through individual interaction with the external world, provisional clusters (using hierarchal clustering techniques) and filter them, using predefined measures of basic-levelness (inspired by psycholinguistic research). We focus on analysing the behaviour of two proposed computational approaches, namely dendrogram and multidendrogram, to establishing provisional clusters. Further, the approach is extensively studied through an array of simulations. Obtained results highlight that the proposed methods can be used to extract basic-level categories.
Fine-Grained Geolocalization of User-Generated Short Text based on Weight Probability Model

Recently, the fine-grained geolocalization of User-Generated Short Text (UGST) has become increasingly important. Existing methods can not make full use of the location information in the UGSTs. Besides, existing works only consider the importance of terms for all locations, but do not distinguish the importance of the same term in different locations. To solve these problems, we propose a fine-grained geolocalization method based on a weight probability model (FGST-WP). The method mainly includes three parts: 1) Using the reverse maximum match algorithm to filter out UGSTs that do not contain any location indicative information. 2) Building coupling of terms and locations and adopting a mixed weight strategy to assign weights to terms. 3) Calculating the probability of non-geotagged UGST posted from each location and selecting k locations according to the top-k probabilities. Experiments on ground-truth datasets prove the superior performance of FGST-WP.
An Automated Technique to Recognize and Extract Images from Scanned Archaeological Documents

"Pompei: pitture e mosaici" is a valuable set of volumes containing over 20,000 historical annotated images of the archaeological site of Pompeii, Italy. Our project consists of extracting, archiving, analyzing and classifying all the image data from a digitized version of these books. In this paper, we describe a method that automatically locates and separates graphical elements such as maps, drawings, paintings and photographic images from text. We also introduce our ongoing work on the interpretation of the retrieved data.
Reconfigurable Mission Plans for RPAS

This paper deals with the problem of formally defining and specifying Mission Plans for Remotely Piloted Aircraft Systems (RPAS). Firstly, the profile of RPAS missions is highly variable and different from those of commercial flights. Route variability from the planned route is frequent due to operating conditions and, especially, contingencies. For this reason, RPAS Mission Plans should be reconfigurable: they should allow the nominal plan to be modified during flight time. Secondly, aviation authorities may require the ability to operate in an autonomous mode in response to Command and Control (C2) link losses. As a result, RPAS Mission Plans should specify all possible routings and behaviors in greater detail. The Reconfigurable Mission Plan concept introduced in this paper expands on current flight plans by providing a level of description that improves predictability and allows for reconfiguration, contingency handling, and higher levels of automation and pilot assistance. The paper presents a detailed discussion of RPAS contingency handling and develops a formal specification of the Reconfigurable Mission Plan concept. The paper also develops algorithms for dynamically configuring Mission Plan routes that might mitigate the effect of contingencies. Finally, the whole proposal is validated with a prototype implementation and a proof of concept.
Neighborhood Interaction Attention Network for Link Prediction

Interactions between neighborhoods of two target nodes are often regarded as important clues for link prediction. In this paper, we propose a novel link prediction neural model named Neighborhood Interaction Attention Network (NIAN), which is able to automatically learn comprehensive neighborhood interaction features and predict links in an end-to-end way. The proposed model mainly consists of two attention layers. A node-level attention is designed to extract latent structure features of nodes in target neighborhoods. Based on the latent node features, a neighborhood-level attention is proposed to learn neighborhood interaction features by considering different importance of pair-wise interactions. The superiority of NIAN is demonstrated by extensive experiments on 6 benchmark datasets against 12 popular and state-of-the-art approaches.
Transformable Lighting Conditions in Learning VR Environments

The following topics are dealt with: computer aided instruction; learning (artificial intelligence); pattern classification; educational institutions; data mining; neural nets; decision trees; power engineering computing; computer science education; image classification.
Lightweight Elliptical Curve Cryptography (ECC) for Data Integrity and User Authentication in Smart Transportation IoT System

Data integrity and data security are major issues while transferring data in any Internet of Things (IoT) application. The sensor node as an edge node or end node collects information from the physical environment which is to be preprocessed and sent to the nearby server or cloud server for storage or for further processing. Because of the constrained resources at the edge node, it is essential to perform edge analytics with methods which utilize less power or can be performed with less computational complexity with limited memory. In this scenario, the data security related methodology should use lightweight encryption method for providing data security to avoid the attacks on data integrity while transmitting. In our Smart Goods Transport IoT system, we are using lightweight encryption method Elliptical Curve Cryptography (ECC) using ElGamal encryption scheme to provide strong authentication and data security while in transit.
Using Resources Competition and Memory Cell Development to Select the Best GMM for Background Subtraction

Background subtraction is an essential step in the process of monitoring videos. Several works have proposed models to differentiate the background pixels from the foreground pixels. Mixtures of Gaussian (GMM) are among the most popular models for a such problem. However, the use of a fixed number of Gaussians influence on their results quality. This article proposes an improvement of the GMM based on the use of the artificial immune recognition system (AIRS) to generate and introduce new Gaussians instead of using a fixed number of Gaussians. The proposed approach exploits the robustness of the mutation function in the generation phase of the new ARBs to create new Gaussians. These Gaussians are then filtered into the resource competition phase in order to keep only ones that best represent the background. The system tested on Wallflower and UCSD datasets has proven its effectiveness against other state-of-art methods.
Author Set Identification via Quasi-Clique Discovery

Author identification based on heterogeneous bibliographic networks, which is to identify potential authors given an anonymous paper, has been studied in recent years. However, most of the existing works merely consider the relationship between authors and anonymous papers, while ignore the relationships between authors. In this paper, we take the relationships among authors into consideration to study the problem of author set identification, which is to identify an author set rather than an individual author related to an anonymous paper. The proposed problem has important applications to new collaborator discovery and group building. We propose a novel Author Set Identification approach, namely ASI. ASI first extracts a task-guided embedding to learn the low-dimensional representations of nodes in bibliographic network. And then ASI leverages the learned embedding to construct a weighted paper-ego-network, which contains anonymous paper and candidate authors. Finally, converting the optimal author set identification to the quasi-clique discovery in the constructed network, ASI utilizes a local-search heuristic mechanism under the guidance of the devised density function to find the optimal quasiclique. Extensive experiments on bibliographic networks demonstrate that ASI outperforms the state-of-art baselines in author set identification.
Continuous Experimentation for Software Organizations with Low Control of Roadmap and a Large Distance to Users: An Exploratory Case Study

With the increasing popularity of A/B testing and other experimentation practices in web systems, companies from a range of different domains are starting to look at continuous experimentation as a way to guide product development and feature prioritization. Research in continuous experimentation traditionally focused on companies that have easy access to user data and that have a high degree of control of the product roadmap. However, little research has been conducted to understand how companies that have a low control of roadmap and have a large distance to the users, such as consultancy companies, can benefit from continuous experimentation practices. To address this problem, we performed an exploratory case study with a software consultancy company combined with a validation procedure with four additional companies. The contribution of this work is three-fold. First, we devised a model to classify a company in the terms of the distance to users and the control of roadmap. Second, we show how control of roadmap and distance to user impacts continuous experimentation. Finally, we present several perceived challenges and benefits of continuous experimentation for companies and directions for future work.
Method of gap combined with fuzzy logic controller designed for mobile robot navigation

The problem of mobile robot navigation and obstacle collision is treated in this paper. We have proposed an algorithm which combines a method based on gap identification and a fuzzy control law. This strategy can deal with the cluttered environment. In fact, the suggested approach allows to find the most suitable free gap between obstacles in order to generate an optimal path to reach the target. The simulations made using this method have shown its validity and efficiency compared with previous works.
Blast furnace condition data clustering based on combination of T-distributed stochastic neighbor embedding and spectral clustering

T-distributed stochastic neighbor embedding (t-SNE) dimension reduction technique are combined with Spectral clustering algorithm to mine and analyze the historical condition data of the blast furnace, and the clustering results are divided into different furnace conditions. The nonlinear dimensionality reduction algorithm tSNE finds the rule in the data by recognizing the observed pattern based on the similarity of data points with multiple features. Most nonlinear techniques except tSNE can not retain local structure and global structure of the data. At the same time, the clustering problem of dataset is transformed into the optimal partitioning problem of graph by spectral clustering, which reduces the impact of blast furnace operating point drift. The test based on the field history library shows its accuracy. At the same time, the data of the blast furnace condition data can be visualized, and the difference between different furnace conditions is demonstrated, which provides convenience for the research of the blast furnace condition in the future. Finally, based on historical data, the well-trained model can also be used for prediction, and it is effective and convenient for the blast furnace operator to adjust the abnormal furnace condition.
Artificial Intelligence Aspects in Developed E-Material Formatting Application

Despite technology and screen use benefits in education providing and supporting, lots of users are having complains after long screen reading. It is based on the slower evolution of humans' perception system and reading paradigms to new reading conditions. Followed new public health problems of nowadays related to screen-reading, and users' needs are thought of new content-presentation improvement is need. Methodology: Literature research of AI approaches, app prototype descriptive analysis, and simple comparison analysis of app and theoretical AI approaches. Results: Newly developed application prototype for e-material formatting is created to improve screen-reading abilities and comfort and improve learning processes. The app works by using several AI approaches and elements: machine learning, perceptron, decision tree graphs, rule-based system, classification, and deep learning. The app collects data and analyses them based on the training database. After, app categorises data by decision tree method. Finally, it decides for formatting recommendation to suggest and make appropriate document formatting. The app receives users' feedback after use. Conclusions: The app uses all collected data for the deep learning process to improve personalised recommendations to create the user-centred design. Currently, it is a narrow range use app designed for e-study use on MOOC type platforms for user group without specific limitations or disabilities. The app is with several level formatting possibilities, including deeply personalised formatting. That create and provide more effective e-materials as it increases visual perception, legibility, readability, reading comprehension, and memorability of content. It is a learner-oriented education methodology with an AI approach.
Electronic records of obstetrics and gynecology encounter

This paper examines how use of electronic medical record (EMR) within professional understanding of medical model have bearings upon the health-care provisions for women. Through a qualitative, empirical study in four hospitals, we showed that understanding illness/health of married women were not solely driven by clinical signs and symptoms rather originate in the socio-economic context. We discuss how within doctor-patient interactions, EMR mediates doctor's actions by putting patient's physiological 'system', like reproductive system, in the centre. We find that EMR serve the needs of doctor by valuing their understanding and, beliefs over patient and discounting the patient's situational aspects. We unpack some doctor-patient interactions where patients' needs arising outside 'medical model' were met. Drawing on our findings, we argue for the need to update the understanding and approach within HCI community towards tools mediating health care provision of women by taking into account specificity of situations in design, not merely focusing on symptoms coming from disease taxonomies.
Personalized Book Recommendation Based on a Deep Learning Model and Metadata

Reading books is one of the widely-adopted methods to obtain knowledge. Through reading books, one can obtain life-long knowledge and maintain them. Additionally, if multiple sources of information can be obtained from various books, then obtaining relevant books is desirable. This can be done by book recommendation. There are, however, a number of challenges in designing a book recommender system. One of the challenges is to suggest relevant books to users without accessing their actual content. Unlike websites or blogs, where the crawler can simply scrape the content and index the websites for web search, book contents cannot be accessed easily due to copyright laws. Because of this problem, we have considered using data such as book records, which contains various metadata of a book, including book description and headings. In this paper, we propose an elegant and simple solution to the book recommendation problem using a deep learning model and various metadata that can infer the content and the quality of books without utilizing the actual content. Metadata, which include Library Congress Subject Heading (LCSH), book description, user ratings and reviews, which are widely available on the Internet. Using these metadata are relatively simple compared to approaches adopted by existing book recommender systems, yet they provide essential and useful information of books.
Scalable Collaborative Ranking for Personalized Prediction

Ben Daiab, Xiaotong Shena, Junhui Wangb & Annie Quc* a School of Statistics, University of Minnesota , Minneapolis , MN ; b School of Data Science, City University of Hong Kong , Kowloon Tong, Hong Kong ; c Department of Statistics, University of Illinois at Urbana Champaign , Champaign , IL
Appearance Flow Completion for Novel View Synthesis

Abstract Novel view synthesis from sparse and unstructured input views faces challenges like the difficulty with dense 3D reconstruction and large occlusion. This paper addresses these problems by estimating proper appearance flows from the target to input views to warp and blend the input views. Our method first estimates a sparse set 3D scene points using an off‐the‐shelf 3D reconstruction method and calculates sparse flows from the target to input views. Our method then performs appearance flow completion to estimate the dense flows from the corresponding sparse ones. Specifically, we design a deep fully convolutional neural network that takes sparse flows and input views as input and outputs the dense flows. Furthermore, we estimate the optical flows between input views as references to guide the estimation of dense flows between the target view and input views. Besides the dense flows, our network also estimates the masks to blend multiple warped inputs to render the target view. Experiments on the KITTI benchmark show that our method can generate high quality novel views from sparse and unstructured input views.
NICE: Neural In-Hospital Cost Estimation from Medical Records

Estimating in-hospital costs from medical records is an important task with many applications such as accountable care. Existing methods for this task usually rely on manual feature engineering which needs massive domain knowledge, and do not exploit the textual information in medical records, e.g., diagnosis and operation texts. In this paper, we propose a neural in-hospital cost estimation (NICE) approach to estimate the in-hospital costs of patients from their admission records. Our approach can exploit the heterogeneous information in records, such as patient features, diagnosis/operation texts, and the diagnosis/operation IDs, via a multi-view learning framework. In addition, since different words, diagnoses and operations have different importance for cost estimation, we propose a hierarchical attention network to select important words, diagnoses and operations for learning informative record representations. Extensive experiments on a real-world medical dataset validate the effectiveness of our approach.
Decision trees using local support vector regression models for large datasets

Our proposed decision trees using local support vector regression models (tSVR, rtSVR) aim to efficiently handle the regression task for large datasets. The learning algorithm tSVR of regression models is done by two main steps. The first one is to construct a decision tree regressor for partitioning the full training dataset into k terminal-nodes (subsets), followed which the second one is to learn the SVR model from each terminal-node to predict the data locally in a parallel way on multi-core computers. The algorithm rtSVR learns the random forest of decision trees with local SVR models for improving the prediction correctness against the tSVR model alone. The performance analysis shows that our algorithms tSVR, rtSVR are efficient in terms of the algorithmic complexity and the generalization ability compared to the classical SVR. The experimental results on five large datasets from UCI repository showed that proposed tSVR and rtSVR algorithms are faster than the standard SVR in training the non-linear regression model from large datasets while achieving the high correctness in the prediction. Typically, the average training time of tSVR and rtSVR are 1282.66 and 482.29 times faster than the standard SVR; Furthermore, tSVR and rtSVR improve 59.43%, 63.70% of the relative prediction correctness compared to the standard SVR.
Efficient Moth-Flame-Based Neuroevolution Models

This chapter proposes a new efficient moth-flame-embedded multilayer perceptrons (MLP) neuroevolution model to deal with classification problems. Moth-flame optimizer (MFO) is one of the effective swarm-based metaheuristic methods inspired by the natural direction-finding behaviours of moth insects and their well-known entrapment phenomena when they circulate the non-natural lights and flames. MFO is capable of demonstrating a very promising performance in terms of exploration and exploitation inclinations. The proposed MFO-MLP model is extensively substantiated on 16 benchmark datasets, and the results are compared to well-known methods such as particle swarm optimizer (PSO), population-based incremental learning (PBIL), differential evolution (DE), and genetic algorithm (GA). The obtained results indicate the efficacy of the MFO-embedded neuroevolution model as a potential method in dealing with classification cases.