Reviewerly/exHarmony huggingface dataset:
A Unifying Coalgebraic Semantics Framework for Quantum Systems

As a quantum counterpart of labeled transition system (LTS), quantum labeled transition system (QLTS) is a powerful formalism for modeling quantum programs or protocols, and gives a categorical understanding for quantum computation. With the help of quantum branching monad, QLTS provides a framework extending some ideas in non-deterministic or probabilistic systems to quantum systems. On the other hand, quantum finite automata (QFA) emerged as a very elegant and simple model for resolving some quantum computational problems. In this paper, we propose the notion of reactive quantum system (RQS), a variant of QLTS capturing reactive system behavior, and develop a coalgebraic semantics for QLTS, RQS and QFA by an endofunctor on the category of convex sets, which has a final coalgebra. Such a coalgebraic semantics provides a unifying abstract interpretation for QLTS, RQS and QFA. The notions of bisimulation and simulation can be employed to compare the behavior of different types of quantum systems and judge whether a coalgebra can be behaviorally simulated by another.
Automatic Generation of Web Applications for Information Systems

Abstract The use of automation tools for software development or so-called Computer Aided for Automatic Software Construction tools is a sign of a high level of maturity for the software development process, and is especially necessary when is needed to build information systems, where many parts or modules of a bigger projects have similar features and functionalities. Among others, Automatic Software Construction tools often include code generation software based on templates. Template-based code generation involves the automatic creation of source code or some code output corresponding to a predefined format called a template. In this article, a software architecture for automatic software construction is presented. The tool allows prototyping software modules, projects and functional code to different needs and purposes. The basis of this technical solution is the creation of a general template representation format, which includes many common source code, configuration data files, and a function library that allows you to interpret the input model and use the template for creating objects from different types based on the input data. The generation algorithm and the automatic integration method use a database model in order to define the main behavior of the generated software. The achieved results shown the creation of complete functional web applications, and since the complete source code is generated, is possible customize for improve or add different or specific functionalities.
Empowering Knowledge Graph Construction with Hyper-graph for Personalized Recommendation

Personalized Recommendation (PR) aims to generate user-specific items in response to users' preferences. While existing works have developed various personalized recommendation methods which are effective to some extent, they require setting the explicit and implicit feedback between users and items. In contrast to traditional Collaborative Filtering (CF)-based approaches that suffer from sparse data and cold-start issues, our proposal of mining the higher-order auxiliary information between users and items by combining the knowledge graph (KG) construction with hyper-graph, which is more expressive and semantically plausible. To address these issues, we propose employing hyper-graph to empower knowledge graph construction to express and integrate much richer semantic information for improving the performance of recommendation, namely Hyper-graph based Personalized Recommendation (HPR), for the personalized recommendation task. Our model consists of two components. First, we employ the hyper-graph to model the higher-order relations between users, through the calculation and analysis of the user-item interaction matrix, the users with the highest similarity are seemed as hyper-edges to construct the user hyper-graph. Second, we propose to use knowledge graph to capture and express auxiliary information for recommendation. The two components are integrated in a principled way for returning a more accurate result. Our experimental results on a MovieLens-lM and another Book-Crossing datasets suggest that our proposed HPR framework provides a promising improvement for the personalized recommendation task, and significantly outperforms the state-of-the-art methods. Moreover, the component for mining higher-order relations by hyper-graph is so flexible that can help to improve the performance of KG-based personalized recommendation models significantly.
Prior Dirichlet Distribution Based Feature Selection in Naive Bayes on High Dimensional Data Classification

Classification is an essential technology in data mining, which has been widely used in real-world problems. A massive amount of information is pouring into our lives since the development of science and technology. The data size and dimension are growing rapidly in various fields and challenging data mining and machine learning approaches. Feature selection reduces the dimension of data and selects significant features, which is an important research direction. However, some norm measures of feature datasets may be imprecise. Many feature selection methods have the characteristics of high cardinality and high computational complexity. Therefore, there is a strong need to study effective feature selection methods. Naive Bayes is a simple and effective learning theory that does not need various parameters. However, its conditional independence assumption limits its classification results in practical applications. Meanwhile, the curse-of-dimensionality brings noises and messy interaction between features. In this paper, we propose a feature selection method named as the Prior Dirichlet Distribution of Naive Bayes (PDDNB) which optimizes the Bayesian theory based on Dirichlet priori distribution. This method simplifies the computational ways to an analytical solution, which significantly improve the calculation efficiency. The executive experiments on the ALL-AML datasets and the other five real-world datasets turn out our method to effectively increase classification accuracy.
Rough-Bayesian approach to select class-pair specific descriptors for HEp-2 cell staining pattern recognition

• The proposed method selects a set of relevant descriptors for each pair of classes. • Final feature set for multiple classes is formed considering all pairs of classes. • decision theory and rough set are used to assess the relevance of descriptors. • The efficacy of the proposed method is demonstrated on several HEp-2 cell databases. • Significant increase in accuracy is noted employing class-pair specific descriptors. One of the important problems in computer-aided diagnosis of connective tissue disease is automatic recognition of staining patterns present in HEp-2 cells. In this regard, the paper introduces a novel approach for the recognition of staining patterns by HEp-2 cell indirect immunofluorescence image analysis. The proposed method assumes that a fixed set of local texture descriptors or scales may not be effective for classifying staining patterns into multiple classes. A particular set of descriptors or scales may be significant for classifying a pair of classes, but may not be relevant for other pairs of classes. The proposed approach, therefore, first selects a set of local texture descriptors under appropriate scales for each class-pair, and then forms the final feature set for multiple classes from the relevant descriptors of all possible pairs of classes. A novel framework, termed as Rough-Bayesian model, is introduced to evaluate the relevance of a descriptor and/or a scale. It is based on the merits of rough sets and Bayes decision theory. During the selection of relevant descriptor and/or scale, the proposed method takes care of the presence of both noisy pixels in an HEp-2 cell image and noisy HEp-2 cell images in a staining pattern class. The support vector machine is used to predict the staining patterns present in HEp-2 cell images. The performance of the proposed method, along with a comparison with state-of-the-art methods, is demonstrated on several HEp-2 cell image databases. An important finding is that the accuracy for classifying HEp-2 cell images is significantly increased if class-pair specific descriptors under appropriate scales are considered, instead of selecting a uniform set of descriptors and scales for multiple classes.
Determination of refractive index on three mediums based on the principle of refraction of light

Abstract This study aims to develop a tool to determine the refractive index and height of objects in three media based on the principle of light refraction. The method used was an experimental method. The results showed the refractive index values of the three media were quite accurate when compared to the absolute refractive index. The relationship of the angle of the incident ray was directly proportional to the angle of the refractive ray. The angle of light produced will be greater if the value of the given beam angle was greater. Thus, the set of refractive index laboratory tools developed is said to be good and suitable to be implemented in schools to explain the concept of the refractive index of light.
Local Search with Groups of Step Sizes

Local search methods for continuous optimization problems tend to be sensitive to the choice of step sizes in their search directions. This paper presents the Local Search with Groups of Step Sizes (LSGSS) method, a derivative-free method that reactively updates groups of promising step sizes for each problem coordinate. The experiments demonstrate LSGSS could find the best solutions for each large-scale benchmark problem when compared to classical methods.
Novel Approach by Fuzzy Logic to Deal with Dynamic Analysis of Shadow Elimination and Occlusion Detection in Video Sequences of High-Density Scenes

Monitoring of high-density images from video sequences provides an important potential for crowd detection and classification. In fact, shadow presence from video sequences causes detection failures of results or mistakes in interpretation. In this contribution, we present an automatic system to deal with shadow elimination based on extracting the vector size of the movement and detection of occlusion management with the Fourier series approach because of the position and orientation of the camera, speed magnitude and visual tracking of crowd scenes, and mathematical morphology of discrete data in a non-linear approach. The model consists of distinctive real objects from fused data and crowded scene caused by shades, which often has consequence such as the failure counting and grading of vehicle and people, while dealing with traditional methods. As we reveal, our technique is principally appropriate for UMN and PETS data to eliminate shadow nuisance and detection occlusion with quite good performance. For classification, we use two classes in each image, for each category of events detected by fuzzy logic. Although this comes down to easy system modeling, as it relates to the use of fuzzy rules. The provided results advocate in favor of our method in terms of effectiveness and precision. Indeed, the proposed approach provides the ability to segment more specific objects, such as people and vehicles in real-time space. The results were compared to other methods, namely Covariance Matrices for Crowd Behavior, Social Force Method, and Ground Truth Technique.
WITHDRAWN: Attributes of trustworthiness in fourth factor authentication

Authentication is the process of recognizing a person based on his credentials. As human beings maintain social relationships and depend on trust since the evolution of mankind, authentication is possible by his physical and biological characteristics. If the user is not known personally, identification can be done through vouching by a trusted person. In case of online transactions, it requires trust to be built ultimately between the users and the resource providers like financial organizations, social networking websites. This trust has to be built not only between the users and the resource providers in person but also through electronic devices like PCs, laptops and mobile phones, which is done by providing valid credentials like user ids and passwords, thus proving that the person they are relating to is genuine or not. In this paper, we propose few attributes to measure the trustworthiness of the authentication mechanism implemented by the user. We have defined metrics like reliability and authenticity and tested with different transaction scenarios at various stages and the results are elucidated.
Method for Re-finding Mobile Phone Documents Based on Feature Knowledge Graph

People often need to find documents that previously saved on their mobile phones. When searching a document, sometimes people cannot recall the exact name of it, but can remember some objects in it, such as the figures, tables or formulas in the documents, the source of the documents, or related topics. And the existing mobile phone document management software cannot associate the content of the document with itself. Therefore, how to extract document information into attributes tags to associate them with documents and how to re-find the expected documents based on these tags are practical and valuable. This paper proposes a method for re-finding mobile phone documents based on feature knowledge graph. First, we use four features to describe the documents and extract tags to form attributes. Then we link the attributes with the documents to form the document knowledge graph and propose a calculation algorithm of the four tags’ weight. Finally, we propose the sorting and re-finding method based on the feature knowledge graph. By using personal mobile phones data, the experiment results verify the effectiveness and efficiency of this method.
Understanding Linux kernel vulnerabilities

Protecting the Linux kernel from malicious activities is of paramount importance. Several approaches have been proposed to analyze kernel-level vulnerabilities. Existing studies, however, have a strong focus on the attack type (e.g., buffer overflow). In this paper, we report on our analysis of 1,858 Linux kernel vulnerabilities covering a period of Jan 2010-Jan 2020. We classify these vulnerabilities from the attacker’s view using various criteria such as the attacker’s objective, the targeted subsystems of the kernel, the location from which vulnerabilities can be exploited (i.e., locally or remotely), the impact of the attack on confidentiality, system integrity and availability, and the complexity level associated with exploiting vulnerabilities. Our findings indicate the presence of a large number of low-complexity vulnerabilities. Most of them can be exploited from the local system, leading to attacks that can severely compromise the kernel quality of service, and allow attackers to gain privileged access
Transporting a heavy object on a frictional floor by a mobile manipulator based on adaptive MPC framework

This work tackles a task to transport an object by a mobile manipulator to the designated location. Heavy objects that the robot cannot lift up and needs to push or drag are targeted. It is assumed that the mass property of the object and the frictional characteristics between the object and the floor are unknown. In addition, a caging strategy of loosely holding the object is employed in order to reduce the required motor torques. Thus, a challenge is how to deal with uncertainties accompanied with interactions between the robot, the object and the ground. A control system that stands on a combination of an instantaneous equilibration model of a planar frictional movement and the adaptive model predictive control (MPC) to modify the model parameters during the operation is proposed and implemented on a mobile manipulator. Efficacy of the proposed method was verified through experiments, in which even objects that are equipped with casters and thus have largely different characteristics from the supposed friction model were successfully moved to the desired destination.
Bayesian Optimal Experimental Design for Inferring Causal Structure

Inferring the causal structure of a system typically requires interventional data, rather than just observational data. Since interventional experiments can be costly, it is preferable to select interventions that yield the maximum amount of information about a system. We propose a novel Bayesian method for optimal experimental design by sequentially selecting interventions that minimize the expected posterior entropy as rapidly as possible. A key feature is that the method can be implemented by computing simple summaries of the current posterior, avoiding the computationally burdensome task of repeatedly performing posterior inference on hypothetical future datasets drawn from the posterior predictive. After deriving the method in a general setting, we apply it to the problem of inferring causal networks. We present a series of simulation studies, in which we find that the proposed method performs favorably compared to existing alternative methods. Finally, we apply the method to real data from two gene regulatory networks.
A clustering algorithm based on the weighted entropy of conditional attributes for mixed data

Summary A novel definition for weighted entropy is proposed to improve clustering performance for small and diverse datasets. First, intra‐class and inter‐class weighted entropies for categorical and numeric conditional attributes are respectively developed using the mathematical definition of entropy. Second, the weighted entropy is used to calculate cluster weights for mixed conditional attributes. A unique weighted clustering algorithm that adopts entropy as its primary description term, after integrating the corresponding distance calculation mechanism, is then introduced. Finally, a theoretical analysis and validation experiment were conducted using the UC‐Irvine dataset. Results showed that the proposed algorithm offers high self‐adaptability, as its clustering performance was superior to the existing K‐ prototypes, SBAC, and OCIL algorithms.
Ontology Based Privacy Preservation over Encrypted Data using Attribute-Based Encryption Technique

The web documents are automatically interacting to discover the information by web mining, which is one of the applications of Cloud Computing (CC) technologies.These documents may be in the form of structured, semi-structured, or unstructured formats.In current web technologies, the Semantic Web is an extension for better enabling the people and computers to work together, where the information is well defined.Before storing the data to the cloud server, data owners should encrypt their data for privacy and security concerns.At the same time, the end-user, who is finding the data related to specific keywords, suggests the research on searchable encryption technique.In this research work, fine-grained authorization of search was achieved by developing the Attribute-Based Encryption (ABE) search technique, which is under the distribution of multiple attribute authorization.Finally, to validate this approach, an experimental study is conducted on Wikipedia as an ontology with existing techniques.This research applies the Attribute based encryption and search method for the effective search and improve security in the cloud.Access policy, cipher text and secret key is developed based on the Attribute selected from the data.The Lagrange interpolation method is applied for the search process and registration key is applied to access the data.The privacy preserving efficiency of the proposed model is 99.2 % and existing Hierarchical-ABE method has 96 % efficiency.
Assessment of characteristics of surface solar irradiance on consecutive days using a self‐organizing map and clustering methods

Abstract The assessment of surface solar irradiance as a resource is a major research topic in renewable energy. A method for assessing the characteristics of global horizontal solar irradiance (GHI) at the surface on consecutive days is proposed in this work. A self‐organizing map and cluster analysis are used as core analysis techniques in the proposed method. Using this method, the regional and seasonal characteristics of GHI for five consecutive days are analysed. One hour accumulated GHI data from ground observation stations in Japan are used. Time series assigned to each node on the topological map of the self‐organizing map are characterized by strength and variability. The probabilities of occurrence of each node on the map differ between stations. Then, using the probabilities as a feature vector for quantifying the characteristics of each observation station, a cluster analysis is conducted to divide stations into clusters according to the dissimilarity of the features. The results show that there are three major regions in which the station characteristics are similar in Japan. Additionally, another cluster analysis is conducted to investigate the seasonal characteristics of the occurrence of time series patterns. It is found that consecutive cloudy days occur frequently in winter and during the rainy season, whereas consecutive clear days occur frequently in spring and summer.
Medicine Identification System on Mobile Devices for the Elderly

This research develops an application that helps the elderly to identify medicine from a mobile image, to reduce confusion in taking medication, and thus to reduce the rate of medication errors. The data used in this research are collected from the medicine blister packs for the elderly consisting of 14 types of medicine, which are taken with the smartphone cameras and amounting to a total of 56,000 single medicine blister pack images for image classification model training. For object detection model training, there are a total of 21,000 single medicine blister pack images with added multiple medicine blister pack images amounting to 120 images from the image dataset. Text recognition is used to identify the medicine type using Keras-OCR. For all experimental results in the image classification model experiments reveal that the MobileNet V2 with 14-class detection has the highest accuracy at 93.79 percent. The object detection model is the MobileNet V1 with the highest mAP of 0.875 with the Average Precision with 0.5 IoU and 0.75 IoU at 0.998 and 0.91, respectively.
A Novel Machine Annotated Balanced Bangla OCR Corpus

We present a balanced and 100% machine annotated Bangla OCR corpus of nearly eight and a half million characters. This is a “first-of-its-kind” effort for the Bangla language. Although Bangla is a top five most frequently used language in the world, it is still considered to be a resource-poor language since even the most basic language processing tools are not available. This corpus goes a long way in mitigating this shortcoming. Also, it is important to mention that this is a continuation of our previous work in building a synthetic corpus for training Bangla OCRs and is an intermediate step in our effort to build a true gold-standard corpus for Bangla OCRs—our ultimate goal. The paper not only discusses the corpus, but also the entire processing pipeline—from scanning pages to identifying lines to segmenting words and finally characters—which are then annotated using a homegrown OCR. We also present a comprehensive corpus characteristics and specification review—demonstrating that we have built a corpus which is very nearly balanced—a very desirable characteristic of any corpus design.