Reviewerly/exHarmony huggingface dataset:
Satisfaction and Implication of Integrity Constraints in Ontology-based Data Access

We extend ontology-based data access with integrity constraints over both the source and target schemas. The relevant reasoning problems in this setting are constraint satisfaction—to check whether a database satisfies the target constraints given the mappings and the ontology—and source-to-target (resp., target-to-source) constraint implication, which is to check whether a target constraint (resp., a source constraint) is satisfied by each database satisfying the source constraints (resp., the target constraints). We establish decidability and complexity bounds for all these problems in the case where ontologies are expressed in DL-LiteR and constraints range from functional dependencies to disjunctive tuple-generating dependencies.
Incremental Fuzzy Association Rule Mining for Classification and Regression

The aim of mining fuzzy association rules is to find both the association and the casual relationships between the itemsets.With the arrival of dynamic data, the fuzzy association rules should be updated in real time.However, most of the existing algorithms must remine the updated database and can only be applied in classification.This paper proposes an incremental fuzzy association rule mining algorithm to solve classification and regression problems.First, the sliding window is adopted to divide the fuzzy dataset.Second, the dynamic fuzzy variable selection algorithm is adopted to select variables for reducing the search space of the fuzzy association rule mining.Finally, in each sliding window, the result of variable selection is used to incrementally mine the causal fuzzy association rules with the fuzzy Eclat algorithm.When new data are added, the process judges whether concept drift occurs, and if so, the rule set is updated; otherwise, the original rule set is still applied.The weights of the rules are calculated to find the evolving relationship.The simulation result shows that this algorithm can improve accuracy and efficiency.
Manifold Distance-Based Over-Sampling Technique for Class Imbalance Learning

Over-sampling technology for handling the class imbalanced problem generates more minority samples to balance the dataset size of different classes. However, sampling in original data space is ineffective as the data in different classes is overlapped or disjunct. Based on this, a new minority sample is presented in terms of the manifold distance rather than Euclidean distance. The overlapped majority and minority samples apt to distribute in fully disjunct subspaces from the view of manifold learning. Moreover, it can avoid generating samples between the minority data locating far away in manifold space. Experiments on 23 UCI datasets show that the proposed method has the better classification accuracy.
Selection and Prioritization of Software Requirements Applying Verbal Decision Analysis

During the software development process, the decision maker (DM) must master many variables inherent in this process. Software releases represent the order in which a set of requirements is implemented and delivered to the customer. Structuring and enumerating a set of releases with prioritized requirements represents a challenging task because the requirements contain their characteristics, such as technical precedence, the cost required for implementation, the importance that one or more customers add to the requirement, among other factors. To facilitate this work of selection and prioritization of releases, the decision maker may adopt some support tools. One field of study already known to solve this type of problem is the Search-Based Software Engineering (SBSE) that uses metaheuristics as a means to find reasonable solutions taking into account a set of well-defined objectives and constraints. In this paper, we seek to increase the possibilities of solving the Next Release Problem using the methods available in Verbal Decision Analysis (VDA). We generate a problem and submit it so that the VDA and SBSE methods try to resolve it. To validate this research, we compared the results obtained through VDA and compared with the SBSE results. We present and discuss the results in the respective sections.
An Application of Markov Chain for Predicting Rainfall Data at West Java using Data Mining Approach

Markov chain model is a stochastic process to determine the transition probability of a state space based on a previous state. We can use a stationary distribution of first order-Markov chain model to determine the long terms probability rainfall phenomena. A rainfall data in West Java area has a big data because we can have a large rainfall data from many cities and regencies both of in spatial and time series observations. Furthermore, in this paper, we demonstrate an application of Markov chain using a Data Mining approach to get the knowledge as a pattern for description and prediction the monthly rainfall data in wet seasons December-January-February (DJF) using Knowledge Discovery in Database (KDD) method through pre-processing, data mining process and post-processing. We simulate the monthly rainfall data from the year 1981-2017 using four-state spaces: low (0), medium (1), high (2), and very high (4). The result of Markov chain shows that the probability of occurrence rainfall phenomena for four state spaces are: low (22.62%), medium (24.86%), high (25.46%), and very high is 27.05%. It means that West Java area over the long term condition will have a very high rainfall probability.
Approximability of Constant-horizon Constrained POMDP

Partially Observable Markov Decision Process (POMDP) is a fundamental framework for planning and decision making under uncertainty. POMDP is known to be intractable to solve or even approximate when the planning horizon is long (i.e., within a polynomial number of time steps). Constrained POMDP (C-POMDP) allows constraints to be specified on some aspects of the policy in addition to the objective function. When the constraints involve bounding the probability of failure, the problem is called Chance-Constrained POMDP (CC-POMDP). Our first contribution is a reduction from CC-POMDP to C-POMDP and a novel Integer Linear Programming (ILP) formulation. Thus, any algorithm for the later problem can be utilized to solve any instance of the former. Second, we show that unlike POMDP, when the length of the planning horizon is constant, (C)C-POMDP is NP-Hard. Third, we present the first Fully Polynomial Time Approximation Scheme (FPTAS) that computes (near) optimal deterministic policies for constant-horizon (C)C-POMDP in polynomial time.
Hybrid Item-Item Recommendation via Semi-Parametric Embedding

Nowadays, item-item recommendation plays an important role in modern recommender systems. Traditionally, this is either solved by behavior-based collaborative filtering or content-based meth- ods. However, both kinds of methods often suffer from cold-start problems, or poor performance due to few behavior supervision; and hybrid methods which can leverage the strength of both kinds of methods are needed. In this paper, we propose a semi-parametric embedding framework for this problem. Specifically, the embedding of an item is composed of two parts, i.e., the parametric part from content information and the non-parametric part designed to encode behavior information; meanwhile, a deep learning algorithm is proposed to learn two parts simultaneously. Extensive experiments on real-world datasets demonstrate the effectiveness and robustness of the proposed method.
Design and Implementation of Computer Aided Physical Education Platform Based on Browser/Server Architecture

Based on the B/S mode network development technology, this paper studies the design and implementation of computer-aided physical education platform. According to requirement analysis of the platform, this study carries out the overall design and detailed design of the platform, compares and analyzes the teaching effects of computer-aided physical education platform and the traditional teaching method. The research results show that: the computer-aided physical education platform runs stably and realizes the expected design functions, it can enrich the teaching content, enhance students' initiative awareness, stimulate their interest in learning, effectively improve the teaching effect, and the paper confirms the feasibility and effectiveness of the system. This research provides a reference for the application of computer-aided physical education platform in physical education and has certain practical significance.
Penerapan Teknik SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Objektivitas Berita Online Menggunakan Algoritma KNN

Amount of information in the form of online news needs to be balanced with the ability of readers to sort or classify subjective or objective news. So that a special system is needed that can be used for online news objectivity classification so that it can help readers to pick up subjective or objective news. This research proposes the development of techniques in machine learning to help sort out news objectivity automatically based on the content of the news. The algorithm proposed is K-Nearest Neighbor (KNN) algorithm. News samples obtained from kompas.com by scrapping occur imbalance classes where the number of objective news and subjective news are not balanced. So that it can affect the performance of the classification algorithm. One technique to overcome the imbalance class is to apply the Synthetic Minority Over-sampling Technique (SMOTE) technique.. SMOTE is the generation of minority data as much as the majority data. This study compares the performance of KNN algorithm without SMOTE and the performance of KNN algorithm with SMOTE. Based on the results of the study by applying a variety of neighboring k values, namely 1, 3, 5, 7 and 9, it was found that the application of SMOTE could improve the accuracy of the KNN algorithm at values ​​k = 1 and k = 3 with an average increase of 3.36. At values ​​k 5, 7 and 9 the algorithm experiences an average decrease in accuracy of 6.67.
Unsupervised Learning of Monocular Depth and Ego-Motion using Conditional PatchGANs

This paper presents a new GAN-based deep learning framework for estimating absolute scale awaredepth and ego motion from monocular images using a completely unsupervised mode of learning.The proposed architecture uses two separate generators to learn the distribution of depth and posedata for a given input image sequence. The depth and pose data, thus generated, are then evaluated bya patch-based discriminator using the reconstructed image and its corresponding actual image. Thepatch-based GAN (or PatchGAN) is shown to detect high frequency local structural defects in thereconstructed image, thereby improving the accuracy of overall depth and pose estimation. Unlikeconventional GANs, the proposed architecture uses a conditioned version of input and output of thegenerator for training the whole network. The resulting framework is shown to outperform all existing deep networks in this field and beating the current state-of-the-art method by 8.7% in absoluteerror and 5.2% in RMSE metric. To the best of our knowledge, this is first deep network based modelto estimate both depth and pose simultaneously using a conditional patch-based GAN paradigm.The efficacy of the proposed approach is demonstrated through rigorous ablation studies and exhaustive performance comparison on the popular KITTI outdoor driving dataset.
Exponential slowdown for larger populations: The (μ + 1)-EA on monotone functions

Pseudo-Boolean monotone functions are unimodal functions which are trivial to optimize for some hillclimbers, but are challenging for a surprising number of evolutionary algorithms. A general trend is that evolutionary algorithms are efficient if parameters like the mutation rate are set conservatively, but may need exponential time otherwise. In particular, it was known that the (1+1)-EA and the (1+λ)-EA can optimize every monotone function in pseudolinear time if the mutation rate is c/n for some c<1, but that they need exponential time for some monotone functions for c>2.2. The second part of the statement was also known for the (μ+1)-EA. In this paper we show that the first statement does not apply to the (μ+1)-EA. More precisely, we prove that for every constant c>0 there is a constant μ0∈N such that the (μ+1)-EA with mutation rate c/n and population size μ0≤μ≤n needs superpolynomial time to optimize some monotone functions. Thus, increasing the population size by just a constant has devastating effects on the performance. This is in stark contrast to many other benchmark functions on which increasing the population size either increases the performance significantly, or affects performance only mildly. The reason why larger populations are harmful lies in the fact that larger populations may temporarily decrease the selective pressure on parts of the population. This allows unfavorable mutations to accumulate in single individuals and their descendants. If the population moves sufficiently fast through the search space, then such unfavorable descendants can become ancestors of future generations, and the bad mutations are preserved. Remarkably, this effect only occurs if the population renews itself sufficiently fast, which can only happen far away from the optimum. This is counter-intuitive since usually optimization becomes harder as we approach the optimum. Previous work missed the effect because it focused on monotone functions that are only deceptive close to the optimum.
Prioritization of Software Functional Requirements: Spanning Tree based Approach

Requirements prioritization shows significant role during effective implementation of requirements.Prioritization of requirements is not easy process particularly when requirements are large in size.The current methods of prioritization face limitations as the current prioritization techniques for functional requirements rely on the responses of stakeholders instead of prioritizing requirements on the basis of internal dependencies of one requirement on other requirements.Moreover, there is need to classify requirements on the basis of their importance i.e. how much they are needed for other requirements or dependent on other requirements.Requirements are first represented with spanning trees and then prioritized.Suggested spanning tree based approach is evaluated on requirements of ODOO ERP.Requirements are assigned to four developers.Time estimation with and without prioritization are calculated.The difference in time estimation with prioritization and without prioritization shows the significance of prioritization of functional requirements.
MMSE: A Multi-Model Stacking Ensemble Learning Algorithm for Purchase Prediction

Analyzing users' consumption habits and preferences with big data technology, merchants can collect valuable data about users' consumption habits which is significant for them to make decisions, with the purpose of improving profitability and optimizing user experience. In this paper, we propose a Multi-Model Stacking Ensemble (MMSE) algorithm to deal with the problem of personalized commodities recommendation. The main components of our algorithm are the data analysis and the model construction. As for the data analysis, we apply data processing technologies to construct the data features. Specifically, we put forward a novel feature model which involves 6 clusters of features, and design a sampling algorithm to balance the ratio of positive and negative samples by the k-means clustering and undersampling. As for the model construction, we establish a two-layer model. In the first layer, we train four different ensemble algorithms as the basic classifiers, and in the second layer, we take XGBoost algorithm as the combiner classifier. Finally, we evaluate the proposed model on the users-commodities behavior data of Alibaba's M-Commerce platform. It performs better than single ensemble learning algorithms, achieving the performance of F1-score 10.97%.
Highly Efficient Follicular Segmentation in Thyroid Cytopathological Whole Slide Image

In this paper, we propose a novel method for highly efficient follicular segmentation of thyroid cytopathological WSIs. Firstly, we propose a hybrid segmentation architecture, which integrates a classifier into Deeplab V3 by adding a branch. A large amount of the WSI segmentation time is saved by skipping the irrelevant areas using the classification branch. Secondly, we merge the low scale fine features into the original atrous spatial pyramid pooling (ASPP) in Deeplab V3 to accurately represent the details in cytopathological images. Thirdly, our hybrid model is trained by a criterion-oriented adaptive loss function, which leads the model converging much faster. Experimental results on a collection of thyroid patches demonstrate that the proposed model reaches 80.9 $$\%$$ on the segmentation accuracy. Besides, 93 $$\%$$ time is reduced for the WSI segmentation by using our proposed method, and the WSI-level accuracy achieves 53.4 $$\%$$ .
Analysis of Student-at-Risk of Dropping out (SARDO) Using Decision Tree: An Intelligent Predictive Model for Reduction

The advancement of the country is exceptionally reliant on the education of its citizens, which is an incontestable truth of the worldwide perspective.Education assumes a focal part of developing an individual to become a productive citizen and is the most important factor contributing to the progress of the nation.However, children in developing countries have denied their right to education by dropping out for different reasons.Student-At-Risk of Dropping Out commonly known as SARDO is a term coined by the Philippines' Department of Education, defined as, a student who is likely to become a candidate to drop out.Philippine Statistics Authority had conferred that there is a total of 36, 238 people ages from six to twenty-four who are out-of-school.This poses a predicament if this will continue without even attempting to intervene or at least reduce it gradually, to the governing body (DepEd) and ripple its effect to the entire country.In this paper, a classification data mining technique such as decision tree was used to generate a rule-based classifier.Based on results, there were three extracted rules from the decision tree generated using partykit and rpart libraries as a predictive model, a basis for forecasting who among the enrolled students will be a dropout.The first rule is, if a student has an experience being retained from previous grade level/s, he/she is more likely to drop.The second is, if he/she has not experienced retention but his/her number of siblings is greater than six, most probably he/she will drop from school.The last rule is if a student has not experienced retention nor has greater than six siblings, but his/her general average of previous grade level is fairly satisfactory and satisfactory, then he/she will probably stop from schooling.Having this intelligent predictive model, the educational institution with its stakeholders can now have a proactive measure on addressing to reduce the inflation of dropout rate of which is the sole purpose of why this study was conducted.
Dissipative generators, divisible dynamical maps, and the Kadison-Schwarz inequality

We introduce the concept of Kadison-Schwarz-divisible dynamical maps. It turns out that it is a natural generalization of the well-known CP-divisibility which characterizes quantum Markovian evolution. It is proved that Kadison-Schwarz-divisible maps are fully characterized in terms of time-local dissipative generators. Simple qubit evolution illustrates the concept.
Certified answers for ordered quantum discrimination problems

We investigate the quantum state discrimination task for sets of linear independent pure states with an intrinsic ordering. These structured discrimination problems allow for a scheme that provides a certified level of error; that is, answers that never deviate from the true value by more than a specified distance and hence control the desired quality of the results. We obtain an efficient semidefinite program and also find a general lower bound valid for any error distance that only requires the knowledge of an optimal minimum-error scheme. We apply our results to the cases of quantum change point and quantum state anomaly detection.
Three-dimensional (3D) Dynamic Obstacle Perception in a Detect-and-Avoid Framework for Unmanned Aerial Vehicles

In this paper, a 3D dynamic obstacle perception is developed in a detect-and-avoid (DAA) framework for unmanned aerial vehicles (UAVs) or drones. The framework requires only an end point coordinate for collision-free path-planning and execution in an environment with dynamic obstacles. The sense portion of the DAA framework takes data from an mmWave sensor and a depth camera while the detect portion of the framework updates a probabilistic octree when static and dynamic obstacles are sensed. Perception of dynamic obstacle was achieved by implementing an algorithm that clears the sensor's field of vision before computing the occupied voxels and populating the probabilistic octree. The avoidance portion of the framework is based on rapidly-exploring random tree (RRT) but the framework is flexible to allow other types of planners. This work develops the DAA framework for a UAV in a dynamic 3D environment by modifying the MoveIt framework. The framework is implemented on a UAV platform equipped with an on-board computational unit. The simulation and indoor experiments were conducted, which show that the modified DAA framework with dynamic 3D obstacle perception can successfully sense, detect and avoid obstacle. Additionally, the proposed perception method reduced the path re-plan time.
FRAME SELECTIVE AND DYNAMIC PATTERN BASED MODEL FOR EFFECTIVE AND SECURE VIDEO WATERMARKING

Video watermarking enables the users to share the digital contents in public domain without any issue. However, video watermarking is also infected by various attacks that can even destroy the hidden watermark. The security, robustness and reliability are the key challenges of any Video watermarking technique. In this paper, such a robust and dynamic video watermarking framework is presented that can handle most of the existing issues. In this framework, the video is hiding on the selective frames and the preference of frames is done based on structural feature evaluation. After performing the frame selection, the featured analysis is accomplished on each frame to identify the effective cover blocks. The structure feature analysis based rules are applied to identify the most secure region over the frame. In the final stage, the histogram shift method is applied to perform data hiding. To improve the security, instead of hiding the complete image, the ECC (Elliptic Curve Cryptography) encrypted image is watermarked within the video frames. The analysis is performed on real time videos. The analysis results are generated in terms of MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity Measure) and BCR (Bit Correct Ratio) parameters. The results identified that the proposed model has improved the effectiveness, robustness and security against various attacks. The comparative results are also validated against the DCT and DCT-SVD based video watermarking methods.