Reviewerly/exHarmony huggingface dataset:
Hadoop-Based Medical Image Storage and Access Method for Examination Series

Under the background of electronic medical data, doctors use electronic images to replace the traditional film for diagnosis, and patients can view examination images at any time through various electronic means. The storage and frequent reading of massive data bring new challenges. Given the characteristics of the size and quantity of image files generated by different examination types, different merging strategies are proposed to improve the storage performance of the files; according to the characteristics of medical data with examination as the basic unit, a two-level model combined with medical imaging information is proposed. The indexing mechanism solves the problem that SEQ files cannot be read randomly without an index; given the time characteristics of data access, an improved 2Q algorithm is proposed to cache the prefetched files and the read files in different cache queues, which improves the efficiency of file reading. In the experimental comparison, the proposed algorithm surpasses the baseline method in storage and access performance.
Activity Recognition for Shepherding

Activity recognition for shepherding is a way for an artificial intelligence system to learn and understand shepherding behaviours. The problem we describe is one of recognising behaviours within a shepherding environment, where a cognitive agent (the shepherd) influences agents within the system (sheep) through a shepherding actuator (sheepdog), to achieve an intent. Shepherding is pervasive in everyday life with AI agents, collections of animals, and humans all partaking in different forms. Activity recognition in this context is the generation of a transformation from sensor stream data to the perceived behaviour of an agent under observation from the perspective of an external observer. We present a method of classifying behaviour through the use of spatial data and codify action, behaviour, and intent states through a multi-level classification mapping process.
Software Anti-patterns Detection Under Uncertainty Using a Possibilistic Evolutionary Approach

Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that could deteriorate the software maintainability and evolution. Despite the high number of existing detection methods, the issue of class label uncertainty is usually omitted. Indeed, two human experts may have different degrees of uncertainty about the smelliness of a particular software class not only for the smell detection task but also for the smell type identification one. Thus, this uncertainty should be taken into account and then processed by detection tools. Unfortunately, these latter usually reject and/or ignore uncertain data that correspond to software classes (i.e. dataset instances) with uncertain labels. This practice could considerably degrade the detection/identification process effectiveness. Motivated by this observation and the interesting performance of the Possibilistic K-NN (PK-NN) classifier in dealing with uncertain data, we propose a new possibilistic evolutionary detection method, named ADIPOK (Anti-patterns Detection and Identification using Possibilistic Optimized K-NNs), that is able to deal with label uncertainty using some concepts stemming from the Possibility theory. ADIPOK is validated using a possibilistic base of smell examples that simulates the subjectivity of software engineers’ opinions’ uncertainty. The statistical analysis of the obtained results on a set of comparative experiments with respect to four state-of-the-art methods show the merits of our proposed method.
A Nonlocal Denoising Framework Based on Tensor Robust Principal Component Analysis with ℓ<sub>p</sub> norm

This paper have given a nonlocal denoising framework based on tensor robust principal component analysis with ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> norm for color image and video (NDFCIV), which have following three features: (1) it is capable of processing zero-mean Gaussian noise, impulse noise and any other noise that is created by mixing the two for color image and video at same time. (2) Meanwhile, nonlocal denoising strategy is adopted to promote the effectiveness of the denoising framework. (3) Moreover, we present a non-convex constraint method which can get more exact low rank tensor recovery result and enhance the denoising effect of the framework further. The experimental results demonstrate the effectiveness of the proposed denoising framework.
Performance improvement of extreme multi-label classification using K-way tree construction with parallel clustering algorithm

eXtreme Multi-Label Classification (XMLC) is the particular case of Multi-Label Classification, which deals with an extremely high number of labels. The main goal is to learn extreme classifier which extracts the subset of relevant labels from extremely large label space. In an extreme environment, one of the big issues is to deal with an extreme number of features, labels and instances which affects the performance of the classifier. The high dimensional feature space and label space makes existing approaches intractable in terms of data scalability, data sparsity, training and prediction cost. The appropriate input representation technique can be used to maintain the interdependency among labels and correlation between feature space and label space. The proposed approach called "K-way Tree based eXtreme Multi-Label Classifier (KTXMLC)" works on tree based classifier to maintain correlations using feature-label input representation technique and node partitioning using a clustering algorithm. KTXMLC constructs multi-way multiple trees using a parallel clustering algorithm, which leads to fast computational cost. KTXMLC outperforms over the existing tree based classifier in terms of ranking based measures on six datasets named Delicious, Mediamill, Eurlex-4K, Wiki10-31K, AmazonCat-13K, Delicious-200K.
Density based semi-automatic labeling on multi-feature representations for ground truth generation: Application to handwritten character recognition

A huge number of labeled samples are required as training data to construct an efficient recognition mechanism for an optical character recognition system. Although samples of characters can be easily collected from available manuscripts, they often lack class labels, especially for ancient and local alphabets. The creation of a training dataset requires a great number of characters manually annotated by experts. It is a costly and time-consuming process. To considerably reduce the human effort required in the construction of training datasets, a novel semi-automatic labeling method is proposed in this work under the assumption that there are no initial labeled samples. The proposed method performs an iterative procedure on a nearest neighbor graph that views samples in multiple feature spaces. In each iteration, an expert is first called upon to label a relevant unlabeled sample that is automatically selected from the highest density area of unlabeled samples. Then, the manually annotated label is propagated to the neighbor samples with safe conditions based on sample density and multi-views. The procedure is repeated until all unlabeled samples are labeled. The labeling procedure of the proposed method is evaluated on MNIST, Devanagari, Thai, and Lanna Dhamma datasets. The results show that the proposed method outperforms state-of-the-art labeling methods, achieving the highest labeling accuracy. In addition, it can handle outlier samples and deal with alphabets that include visually similar characters. Moreover, the recognition performance of the classifier trained by using the semi-automatically generated training dataset is comparable with that classifier trained by actual ground truth.
Understanding User Understanding: What do Developers Expect from a Cognitive Assistant?

Software development is a complex endeavor that depends on a wide variety of contextual factors involving a large amount of distributed information such as technology-related tasks, software operating environments and stakeholder requirements. Most of this context is implicit and captured in the developers' minds (tacit) or distributed through volumes of documentation. Developers have to maintain mental models of this variety of tasks and information as they produce the software. As a result, context can be easily lost or forgotten and developers often use adhoc approaches while finishing the project. We present in this paper the preliminary results of a study that aims at analyzing qualitatively whether supporting software developers with a chatbot during task execution can improve the overall development experience. The chatbot can assist the developers in executing different tasks based on implicit contextual information. We propose an implementation to explore the viability of using textual chatbots to assist developers automatically and proactively with software development project activities that recur. We believe that understanding the interaction of developers with the systems supported by chatbots is key to improving the developer experience and advancing software engineering practices by providing needed timely support for developers.
Single-Shot Phase Measuring Profilometry Based on Quaternary Grating Projection

In this paper, we propose a new single-shot three-dimensional (3D) measuring method based on quaternary grating projection. In traditional binary grating phase measuring profilometry (PMP), a multi-step or color fringe pattern are usually used to extract the sinusoidal fringes. In our proposed method, by using the DLP4500’s 2-bit gray coding mode, the grayscale is quaternary. The three non-zero grayscales cyclically arranged in equal width, and the fourth grey value is 0 which is not encoded in the fringe pattern but represents the shadow information in the deformed pattern, where a quaternary grating is encoded. When the DLP4500 projects the quaternary grating onto the measured object, the charge coupled device (CCD) captures the corresponding deformed pattern synchronously. Three frames of binary deformed patterns with 1/3 duty cycle and a relative displacement of 1/3 period can be decomposed by the segmentation algorithm proposed in this paper. Three sinusoidal deformed patterns with a 2π/3 shift-phase can be obtained by extracting the fundamental frequency of the three binary deformed patterns correspondingly, and the 3D shape of the object can be reconstructed by PMP. Experimental results show the effectiveness and feasibility of the proposed method. Because the DLP4500 only needs 2-bit coded grating for projection, the refresh rate of the projected grating is as high as 1428 Hz, which will have a broad application prospect in real time and fast online measurement.
Real Estate Management System based on Blockchain

Real Estate management in India as well as in many parts of the world is a very inefficient process. Developing a secure central system that not only accelerates the process of land registration but also makes it efficient will be effective. This paper presents a blockchain-powered real estate management system that will provide a transparent, secure, and efficient system for real estate management. This system will include all the departments related to real estate management. It will store all the transactions on a distributed permissioned blockchain which will be very secure and will not be prone to hacking and this can be automated to a great extent. The system will be centralized connecting all the departments and will be decentralized for data storage. It is a practical solution to the real estate management problem.
AlignPIN: Indirect PIN Selection For Protection Against Repeated Shoulder Surfing

Authentication schemes need to be designed such that they can provide security against observational attacks. Many modern day services use Personal Identification Numbers (PINs) to authenticate their users. However, conventional PIN interfaces provide minimal to no security against observational attacks such as shoulder surfing. We propose AlignPIN, an Indirect PIN selection technique which is resistant to shoulder surfing. We present a detailed analysis to show that AlignPIN is effective in providing resistance against not only casual shoulder surfing, but also sophisticated shoulder surfing and repeated shoulder surfing. To test the usability of AlignPIN, we present a comprehensive usability analysis through data collected over the course of a twelve day usability study done with both young and old participants. The combined results of the security analysis and the practical usability study show that AlignPIN can be used as a PIN selection technique in public places.
Assessing Legacy Collections for Scientific Data Rescue

Widespread investments in facilitating reuse and reproducibility of scientific research have spurred an increasing recognition of the potential value of data biding in unpublished records and legacy research materials, such as scientists’ papers, historical publications, and working files. Recovering usable scientific data from legacy collections constitutes one kind of data rescue: the usually urgent application of selected data curation processes to data at imminent risk of loss. Given growing interest in data-intensive research, and a concomitant movement toward computationally amenable collections in memory institutions, scientific data repositories and collecting institutions would benefit from systematic approaches to assessing and processing legacy collections with the specific goal of retrieving reusable or historically valuable scientific data. This paper suggests a preliminary framework for assessing legacy collections of research materials for the purpose of data rescue. Developed through three case studies of agricultural research collections held by the United States Department of Agriculture’s National Agricultural Library, this framework aims to guide data rescue initiatives in agricultural research centers, and to provide conceptual framing for emerging conversations around data rescue across disciplines.
Light Field Style Transfer with Local Angular Consistency

Style transfer involves combining the style of one image with the content of another to form a new image. Unlike traditional two-dimensional images which only capture the spatial intensity of light rays, four-dimensional light fields also capture the angular direction of the light rays. Thus, applying style transfer to a light field requires to not only render convincing style transfer for each view, but also to preserve its angular structure. In this paper, we present a novel optimization-based method for light field style transfer which iteratively propagates the style from the centre view towards the outer views while enforcing local angular consistency. For this purpose, a new initialisation method and angular loss function is proposed for the optimization process. In addition, since style transfer for light field is an emerging topic, no clear evaluation procedure is available. Thus, we investigate the use of a recently proposed metric designed to evaluate light field angular consistency, as well as a proposed variant.
Automatic Grading of Knee Osteoarthritis from Plain Radiographs Using Densely Connected Convolutional Networks

In this paper, we consider densely connected convolutional networks and their applicability to the problem of assessment of knee osteoarthritis (OA) severity in the five-point Kellgren-Lawrence scale. First, we use trained from scratch Single Shot Detector (SSD) to localize knee joint areas in radiographs. Then, we apply DenseNets to quantify OA stages in the images of detected knee joints. We consider networks of different depths, trained both from scratch and pre-trained on the ImageNet dataset and fine-tuned in the images from Osteoarthritis Initiative dataset (OAI). Also, different loss functions are examined to understand which one gives the best training results. In the knee joint localization task, we obtain an accuracy of 94.03% under the Jaccard index threshold of 0.75. Also, our classifier outperforms the current state-of-the-art with accuracy of 71% in the classification task.
On a Waring's problem for Hermitian lattices

Assume E is an imaginary quadratic field and O is its ring of integers. For each positive integer m, let Im be the free Hermitian lattice of rank m over O having an orthonormal basis. For each positive integer n, let SO(n) be the set of all Hermitian lattices of rank n over O that can be represented by some Im. Denote by gO(n) the smallest positive integer g such that each Hermitian lattice in SO(n) can be represented by Ig. In this paper, we shall provide an explicit upper bound for gO(n) for all imaginary quadratic fields E and all positive integers n.
Predicting Opioid Overdose Readmission and Opioid Use Disorder with Machine Learning

Opioid use disorder (OUD) is a medical condition associated with problematic patterns of opioid use that cause interpersonal and social impairment. This research demonstrates how supervised machine learning can be used to predict patients at risk of hospital readmission following opioid overdose, and to predict patients at risk of developing OUD. Two labeled datasets were built from deidentified hospital data provided by a Level I Trauma Center Hospital. Several machine learning models were constructed (logistic regression, random forest, support vector machine, AdaBoost, XGBoost) and validated with 10 iterations of 10-fold cross validation. The XGBoost classifier can sufficiently predict patients at risk for OUD (AUC = 0.78, precision = 0.71, recall = 0.53). This work can assist providers in determining appropriate preventive care and resources for at-risk patients.
Performance Analysis of Incremental Learning Strategy in Image Classification

Deep learning-based image classification model learns from the fixed and specific training dataset. For the generalization and adaptation of human learning behaviour, some of these models adapted incremental learning to enhance the learning and knowledge from updated and incremented dataset. An incremented dataset can be in form of increment of examples or new class dataset images. This incremented dataset is learned by deep learning models by two incremental learning strategies i.e. sample-wise and class-wise. This paper proposes a performance analysis methodology and experimentally analyze the performance of these incremental learning strategies in CNN based image classification model on prepared incremented dataset. The evaluation of performance of these deep image classification model on classification performance metrics such as accuracy, precision, recall and f1 score shows that these model's learning and classification capabilities are increased with incremental learning on the incremented dataset. Two different incremental learning shows the relation between performances of model to the increment of dataset.
Optimization of Tilt Angle of a PV System to Get Maximum Generated Power: a Case Study

The amount of solar irradiance falls onto photovoltaic (PV) panels depends highly on the tilt angle between the panels and the horizontal plane. Therefore, this angle must be chosen carefully so that the panels can absorb the largest possible amount of the solar beam. It is more convenient for the panels to be installed with the fixed tilt angle once a month or year as the cost and complexity of the solar tracking systems are high. This research determines monthly and yearly optimum tilt angles for 50 KWp solar system in Duhok city, Duhok Polytechnic University campus (Latitude/Longitude: 36.862, 42.980). Moreover, this work examines the performance of the system with different PV technologies which are Crystalline Silicon, Cadmium Telluride (CdTe), and Copper Indium Selenide (CIS) to determine which one is more convenient for Duhok weather. This work uses the PVGIS simulation tool, and the result shows that the 32.7 degree is the optimum tilt angle for fixed annually adjustment for the mentioned city. While the monthly optimum tilt angles were different for each month (the lowest tilt angle was 1.5? observed in June and the highest tilt angle was 63.9? recorded in December). Regarding PV technologies, the CdTe types generated more annual PV energy than other mentioned types, followed by CIS which produced more output PV energy than Crystalline Silicon.
Detection and Localization of Abnormalities in Surveillance Video Using Timerider-Based Neural Network

Abstract Automatic anomaly detection in surveillance videos is a trending research domain, which assures the detection of the anomalies effectively, relieves the time-consumed by the manual interpretation methods without the requirement of the domain knowledge about the anomalous object. Accordingly, this research work proposes an effective anomaly detection approach, named, TimeRide Neural network (TimeRideNN), by modifying the standard RideNN using the Taylor series such that an extra group of rider, named as timerider, is included in the standard rider optimization algorithm. Initially, the face in the videos is subjected to face detection using the Viola Jones algorithm. Then, the object tracking is performed using the knocker and holoentropy-based Bhattacharya distance, which is a modification of the Bhattacharya distance using the knocker and holoentropy. After that, the features, such as object-level features and speed-level features of the objects, are extracted and the features are employed to the proposed TimeRideNN classifier, which declares the anomalous objects in the video. The experimentation of the proposed anomaly detection method is done using the UCSD dataset (Ped1), subway dataset and QMUL junction dataset, and the analysis is performed based on accuracy, sensitivity and specificity. The proposed TimeRideNN classifier obtains the accuracy, sensitivity and specificity of 0.9724, 0.9894 and 0.9691, respectively.
Guaranteeing Admissibility of Abstract Argumentation Frameworks with Rationality and Feasibility Constraints

Abstract This paper considers the problem of under what circumstances an aggregation rule guarantees admissible sets of arguments that represent a good compromise between several extensions, i.e., sets of arguments of abstract argumentation frameworks each provided by a different individual. We start by showing that the preservation results of Dung’s admissibility, graded admissibility and strong admissibility during the aggregation of extensions are negative. To overcome such negative results, we define a model for extension aggregation that clearly separates the constraint supposed to be satisfied by individuals and the constraint that must be met by the collective decision. Using this model, we show that the majority rule guarantees admissible sets on profiles that satisfy a variant of Dung’s admissibility, as well as profiles of extensions with some specific characteristics.