Reviewerly/exHarmony huggingface dataset:
Mining Disproportional Frequent Arrangements of Event Intervals for Investigating Adverse Drug Events

Adverse drug events are pervasive and costly medical conditions, in which novel research approaches are needed to investigate the nature of such events further and ultimately achieve early detection and prevention. In this paper, we seek to characterize patients who experience an adverse drug event, represented as a case group, by contrasting them to similar control group patients who do not experience such an event. To achieve this goal, we utilize an extensive electronic patient record database and apply a combination of frequent arrangement mining and disproportionality analysis. Our results have identified how several adverse drug events are characterized in regards to frequent disproportional arrangements, where we highlight how such arrangements can provide additional temporal-based information compared to similar approaches.
Keypoints Detection for Stroke Order of Chinese Characters

Chinese character is written in a specific stroke order. However, it is hard to determine the stroke order of a Chinese character. In this paper, we propose a keypoint detection method to predict the stroke order of a Chinese character. The experiment result shows that we can find a rough rule to determine the stroke order of all Chinese characters.
Particle Swarm optimization based Neural Network Model for Chaotic Time Series Forecasting

Training model weight is a challenging research topics in the field of chaotic time series modeling and analysis, and intelligent optimization algorithm provides an effective solution to this kind of problem. Aiming at the nonlinear characteristics of dynamic systems, this paper proposes an improved particle swarm optimization (IPSO) based neural network to forecast the chaotic time series. The novel particle swarm optimization (PSO) algorithm is used to optimize the output weight matrix of a neural network, and the hybrid model is available for the chaotic time series modeling process. To enhance the generalization ability and prevent over-fitting, the objective function of this paper is the weighted sum of the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm of the loss function and the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm of the output weights. Our work improves the original PSO algorithm by improving the initialization strategy of the population. Furthermore, some new strategies are proposed to disturb the population, which can increase population diversity and enhance search performance. To verify the efficiency, we evaluate the IPSO based forecasting model with some other intelligent optimization algorithm based models. And simulations show the IPSO based model achieves the highest forecasting accuracy on both Lorenz data and Beijing air quality index data, which testifies the validity of the proposed model in chaotic time series forecasting.
A Two-Phase Approach for Semi-Supervised Feature Selection

This paper proposes a novel approach for selecting a subset of features in semi-supervised datasets where only some of the patterns are labeled. The whole process is completed in two phases. In the first phase, i.e., Phase-I, the whole dataset is divided into two parts: The first part, which contains labeled patterns, and the second part, which contains unlabeled patterns. In the first part, a small number of features are identified using well-known maximum relevance (from first part) and minimum redundancy (whole dataset) based feature selection approaches using the correlation coefficient. The subset of features from the identified set of features, which produces a high classification accuracy using any supervised classifier from labeled patterns, is selected for later processing. In the second phase, i.e., Phase-II, the patterns belonging to the first and second part are clustered separately into the available number of classes of the dataset. In the clusters of the first part, take the majority of patterns belonging to a cluster as the class for that cluster, which is given already. Form the pairs of cluster centroids made in the first and second part. The centroid of the second part nearest to a centroid of the first part will be paired. As the class of the first centroid is known, the same class can be assigned to the centroid of the cluster of the second part, which is unknown. The actual class of the patterns if known for the second part of the dataset can be used to test the classification accuracy of patterns in the second part. The proposed two-phase approach performs well in terms of classification accuracy and number of features selected on the given benchmarked datasets.
Data Mining based Researcher's Hub:An Online Platform for Searching and Booking Research Instruments

The research ecosystem in India is at a developing stage. This ecosystem provides researchers with the benefit of greater career flexibility, better feedback on their work and improved reproducibility. Many organizations are keeping no stone unturned to develop this ecosystem. But still, there is a kind of unawareness among researchers regarding the availability of research facilities, instruments, techniques, etc needed for the development of their research work. In this paper, a system/ platform is proposed which will assist scientists, researchers, users from National R&D laboratories and industries to book slots for specific instruments available in research centers all over India to take samples. Users who want to rent their research instruments for offering sampling service can also avail the services of this platform. The platform will help small educational institutions and industries to overcome their inability to procure and maintain research and analytical instruments.
“Homenu”: An Interactive Projection Cooking Assistant

In recent years, people are increasingly concerned about the safety and health of their diet, which leads to more people preferring to cook for themselves. However, young people do not have complete cooking skills because of poor experience. This paper focuses on their cooking problems and applies user research approach to concluding the pain points of cooking behavior for this cohort of young-adult users. The purpose is to provide users with easy, intuitive, and interesting cooking instructions. Based on the interactive projection technology, we developed a cooking interactive projection system named “Homenu”, which has a lightweight and natural interaction.
Image Registration Method for Temporal Subtraction Based on Salient Region Features

While diagnosis imaging is an indispensable technology in medical field, it causes an increase in the burden to radiologists. In recent years, computer-aided diagnosis (CAD) system for supporting a radiologist has been developed to solve this problem. Temporal subtraction, which is one of CAD, is a technique to generate images emphasizing temporal changes in lesions and facilitates diagnosis of radiologists. To make a temporal subtraction image, image registration technique is required. In this chapter, we propose an image registration method for image registration of current image and previous image to generate temporal subtraction images in a short time. The proposed method consists of three steps: (i) segmentation of the region of interest (ROI) using position information of the spine based on anatomical information, (ii) using global image matching to select pairs of previous image and current image in which the same portion is depicted, and iii) final image matching based on salient region features (SRF). We performed our registration technique to the synthetic data and confirmed usefulness of the proposed method. The rotated synthesis image gives TP 100.0% and FP 12.16%. The synthesis image obtained by applying a Gaussian filter gives TP 70.40% and FP 0.00%. The synthesis image obtained by adding artificial pseudo-lesion region gives TP 99.45% and FP 17.89%. The synthesis image obtained by adding random noise of 5% gives TP 83.05% and FP 16.95%. Furthermore, radiologist conducted comparative experiments without and with temporal subtraction images created by proposed method. As a result, radiologist showed high reading performance by using temporal subtraction images.
Local unitary equivalence of the SLOCC class of three qubits

Classification of multipartite pure states via the local unitary (LU) equivalence or the stochastic local operations and classical communication (SLOCC) equivalence is a fundamental task in investigating multipartite entanglement. It is shown in the seminal paper (2000 Phys. Rev. A 62 062314) that pure states of three qubits can be partitioned into six SLOCC classes. We explore here a necessary and sufficient condition for LU equivalence of pure states for any SLOCC class via Acín et al's Schmidt coefficients (ASC) of three qubits in (2000 Phys. Rev. Lett. 85 1560). We show that the LU equivalent states in W SLOCC class have the same ASC irrespective of phases while there are generally two different ASCs for the LU equivalent states in GHZ SLOCC class (irrespective of phases for some subclass). Consequently, we obtain a complete LU classification of three qubit pure states in the sense of ASC. Moreover, we discuss the choice among the different ASCs and then propose an approach to find a unique ASC for any given three qubit pure state. At last we analyse the entanglement/separability of the marginal states of the two inequivalent genuine entangled SLOCC classes. We find that any bipartite marginal states of the W SLOCC class are entangled, and that of the GHZ SLOCC class has eight different cases which are depend on the ASCs.
Adherent mist and raindrop removal from a single image using attentive convolutional network

Temperature-difference-induced mist and environmentally-induced raindrops adhering to glass products such as windshield and camera lens, can often block vision and severely degrade the image seen through the lens. Despite posing a challenge to various vision systems, including autonomous driving and security surveillance, this problem has not received sufficient attention from researchers. In this work, we discuss the image degradation caused by adherent mist and raindrops. An attentive convolutional network is designed to visually remove the adherent mist and raindrops from a single image. Considering the variations in coexistence and reginal characteristics of adherent mist and raindrops, we propose interpolation-based pyramid attention blocks to perceive spatial information at different scales without rigid priors. Experiments show that the proposed method can improve the visibility of severely degraded images in real-world scenarios, both qualitatively and quantitatively. Further application experiments demonstrate that this practical problem is critical to high-level vision situations.
Model Comparison and Individual Open Source Community Development

This is an overview of the training in open source improvement, with specific accentuation on the secluded extensibility interfaces inside a few of the best activities, including Apache, Eclipse, Mozilla Firefox, Linux portion, and the World Wide Web and numerous other logical research-related programming. The issue here is to examine the plan strategies of the open source advancement. This issue is examined by looking at of four models of improvement of open source programming given Nakakoji; Gacek, Lawrie and Arief; Sharma, Sugumaran and Rajagopalan; Feller and Fitzgerald. Exploratory examination is to be directed to clarify the open and free source software development wonder. In the wake of contrasting the single individual model cooperation with an open and free source people group with four different models, the nature of the model is adequate as the reason for this examination and finished up to be an appropriate structure for research in FOSPHost. The single individual model interest to an open and free source people group is picked as hypothetical reason for the examination of FOSPHost. Despite the fact that this model is less explicit than different models, it is extensive and adaptable enough with the end goal of this exploration.
Stereo Matching Using Discriminative Feature-Oriented and Gradient-Constrained Dictionary Learning

Sparse coding has been widely utilized in the field of image processing and proved to have great accuracy. The unsupervised learning ability of sparse coding makes it possible to find a group of over-complete basic vectors to represent samples more efficiently. This method is also applied to the field of stereo matching. However, most stereo matching method based on sparse coding prefer to consider each pixel of the image equally instead of segmenting the images with similar features thus ignoring the optimal sparse domain. We propose a stereo matching algorithm based on dictionary learning using discriminative feature and gradient constrain in this paper. First, we introduce a dictionary learning method based on super-pixel patches. We select matching pairs on super-pixel patches for the dictionary learning. Simultaneously, we embed the gradient information of images into the training data to further improve the discriminating ability of the dictionary. Then, the matching cost of matching points is measured by its sparse representation over the learning dictionary. Semi-global cost aggregation and a winner-takes-all strategy are adopted to calculate the initial disparity maps. Finally, a disparity filling method is proposed based on the distance and color information between the mismatched points and its surrounding points. The superiority of the proposed method is demonstrated by comparison with other methods on the Middlebury datasets.
The Mathematical Applications of Majorization Inequalities to Quantum Mechanics

We supplement some foundational theorems which connect majorization inequalities with quantum mechanics. To begin, we give the sufficient condition for bi-partite pure states entanglement transformation from the control to the controlled states which are impossible to be transformed even by catalysts. Furthermore, a sufficient and necessary condition about the existence of an entangled assisted state is discussed. Also, the sufficient and necessary condition to give rise to the same given ensemble for a group of vectors is demonstrated. At last, owing to the unique distinguishability of orthogonal states, it is natural to establish local distinguishability of two bi-partite orthogonal quantum states by using ensembles of majorization in LOCC.
National Cultural Symbols Recognition Based on Convolutional Neural Network

In order to solve the problem that the process of manually identifying national symbols is extremely tedious and the recognition effect is not satisfactory, the paper uses the TensorFlow framework to build a convolutional neural network to identify domestic symbols simply and efficiently. In this paper, the classified Zhuang ethnic symbol pictures are labeled and normalized to make a data set, and then during the training process, the loss value between the prediction result and the correct answer is continuously reduced to train a convolution layer, pool The convolutional neural network of the visualization layer, the fully connected layer, and the SoftMax layer. Finally, the images are classified by the SoftMax layer. The experimental results show that after a lot of training, the model has been more robust, and the recognition rate of 15 symbol types can reach 89%, which is faster and more accurate than the manual recognition process.
ATM Theft Investigation Using Convolutional Neural Network

Image processing in a surveillance video has been a challenging task in research and development for several years. Crimes in Automated Teller Machine (ATM) is common nowadays, in spite of having a surveillance camera inside an ATM as it is not fully integrated to detect crime/theft. On the other hand, we have many image processing algorithms that can help us to detect the covered faces, a person wearing a helmet and some other abnormal features. This paper proposes an alert system, by extracting various features like face-covering, helmet-wearing inside an ATM system to detect theft/crime that may happen. We cannot judge theft/crime as it may happen at any time but we can alert the authorized persons to monitor the video surveillance.
Locating and extracting call number under complex color background

Aiming at the current call number extraction technology that only deals with the useful area, which is greatly affected by complex color background factors, a method for locating and extracting call numbers from randomly collected images of books on the shelf under a complex color background is designed. According to the color characteristics at the edge of the call number label, the horizontal projection analysis is used to analyze the image features, filter out other color background interference, and locate the call number area. Combine mathematical morphology and iterative global threshold segmentation algorithm to segment and extract the call number area. The experimental results show that the algorithm's Intersection-over-Union (IOU) index reaches 89.91% and the Precision index reaches 96.11%. Compared with the commonly used algorithms, they are respectively increased by 29.94% and 31.08%, which can effectively extract and segment the call number area without being affected by complex color background, with high efficiency and strong anti-interference.
Automatic Extraction of Lesions and Hepatic Structures in Liver using Segmentation Techniques

Computed Tomography is an important instrument that can be used to analyze the images of the human organs such as liver. It is helpful to diagnose certain liver diseases and applicable for surgical planning. In some cases, it seems to be difficult to obtain the accurate shape and intensity of the liver. In this paper, a hybrid approach is proposed by combining the seed point selection method and connected component method with watershed algorithm to obtain a better accuracy and robustness of the CT images. The experimental result shows that the proposed hybrid approach is fast and efficient in detection with a high accuracy of 98.6 %.
Comprehensive Evaluation of Urban Health Level Based on Factor Analysis Algorithm and Systematic Clustering Algorithm

With the development of China's comprehensive strength, people are paying more and more attention to health. However, the indicators affecting health factors are different in different regions. This article reviews the main factors affecting health, proposes an index-based factor analysis method, and establishes appropriate health factor indicators. After consulting the data of “Shandong Statistical Yearbook”, this article standardized the collected data according to the data analysis method, and used the factor analysis method to reduce the dimensions of many indicators. Finally, this article uses factor analysis to obtain the comprehensive score of the principal component factors, and uses the factor score to quantify the comprehensive value of the health factor level of each prefecture-level city, and obtains the corresponding health factor level ranking. The experimental results show that Qingdao ranked first, followed by Jinan, followed by Weifang, and finally Laiwu. This paper uses a systematic clustering analysis based on SPSS to cluster similar cities in 17 cities together and divide them into four categories by factor analysis. Then, this article distinguishes them through factor analysis and provides suggestions for improving health factor scores.
Planning with Subjective Knowledge in a Multi-Agent Scenario

The AI community has always been interested in designing intelligent agents which function in a multi-agent arrangement or a man-machine scenario. More often than not, such settings may require agents to work autonomously (or under intermittent supervision at the least) in partially observable environments. Over the last 10 years or so, the planning community has started looking at this interesting class of problems from an epistemic standpoint, by augmenting the notions of knowledge and beliefs to AI planning. In this paper, we present a system that synthesizes plans from the primary agent’s perspective, based on its subjective knowledge, in a multi-agent environment. We adopt a semantic approach to represent the mental model of the primary agent whose uncertainty about the world is represented using Kripke’s possible worlds interpretation of epistemic logic. Planning in this logical framework is computationally challenging, and, to the best of our knowledge, most of the existing planners work with the notion of knowledge, instead of an agent’s subjective knowledge. We demonstrate the system’s capability of projecting beliefs of the primary agent on to others, reasoning about the role of other agents in the prospective plans, and preferring the plans that hinge on the primary agent’s capabilities to those which demand others’ cooperation. We evaluate our system on the problems discussed in the literature and show that it takes fractions of seconds to search for a plan for a given problem. We also discuss the issues that arise in modeling dynamic domains with the representation our system employs.
An Adaptive Cognitive Model to Integrate Machine Learning and Visual Streaming Data

In this paper, we present our current work towards developing a context aware visual system with capabilities to generate knowledge using an adaptive cognitive model. Our goal is to assist people in their daily routines using the acquired knowledge in combination with a set of machine learning tools to provide prediction and individual routine understanding. This is useful in applications such as assistance to individuals with Alzheimer by helping them to maintain a daily routine based on historical data. The proposed cognitive model is based on simple exponential smoothing technique and provides real time detection of objects and basic relations in the scene. To fulfill these objectives we propose the integration of machine learning tools and memory based knowledge representation.
A Novel Group-based Swarm Optimizer for Large-Scale Optimization

In this paper, a novel group-based swarm optimizer (GSO) is proposed for large scale optimization. Inspired from the group learning behavior in human society, the hierarchical learning are involved in GSO. In the mass learning stage, GSO randomly picks three particles to form a study group and then adopts the competitive mechanism to update the members of studing group. In the hierarchical learning phase, elite particles, the better fitness value, are directly retained for the next iteration. Worst particles learn two dominant particles to further search for more promising areas in the team at the same time. Another particles utilize differential evolution to explore search space. Then, theoretically and empirically analysis of GSO are presented and the exploration abilities of proposed method are performed compared the most popular particle swarm optimizer. Further, a set of experiments on two widely used large-scale benchmark sets demonstrate that GSO achieves better performance on large scale problems than several state-of-the-art algorithms.
Pancreas and Duodenum—Automated Organ Segmentation

Zarychta, PiotrThe goal of this preliminary research is to present an automated segmentation of abdominal organs: pancreas and duodenum. The paper shows the automatic extraction of pancreas and duodenum in clinical abdominal computed tomography (CT) scans. The proposed method allows building a feature vector that automates and streamlines the fuzzy connectedness (FC) method. All described steps of the presented methodology have been implemented in MATLAB and tested on clinical abdominal CT scans. The atlas based segmentation combined with the FC method gave Dice index results at the following level: 70.18–84.82% for pancreas and 68.90–88.06% for duodenum.