Reviewerly/exHarmony huggingface dataset:
Learning State Switching for Multi-sensor Integration

This paper takes a fresh approach towards multi-sensor multi-agent integration for achieving improved performance using a control theoretic view of “state-switching”. The state-switching problem is formulated as a multi-agent reinforcement learning task for maximizing expected payoffs over time solved using a value iteration algorithm. Specifically, we use a problem of tracking an unknown object with unknown (motion) dynamics using manipulators, defined based on the well-known sawyer one-handed manipulator. We demonstrate that our multi-agent reinforcement learning based state switching algorithm shows superior performance compared to using individual sensors. Our trained agent is also further validated by transferring from simulation to a real experimental setup.
Probability of caused factors stroke disease use link and reliability functions

Abstract Diabetes mellitus disease is disease which abnormal metabolism for a long time, because pancreas can not be able to produce insulin hormone be enough, or because body can not be able to use insulin hormone has been produced by effective. A stroke occurs if the flow of oxygen-rich blood to a portion of the brain is blocked. Without oxygen, brain cells start to die after a few minutes. Sudden bleeding in the brain also can cause a stroke if it damages brain cells. These objectives are finding significant factors which cause diabetes mellitus disease and determine ordinal regression model. Ordinal regression model is used to look for probability and reliability functions of a patient has stroke disease. The method used to three link functions, that are logit link function, normit link function, and cloglog link function. Testing of homogeneity prediction result of link functions uses linear hypothesis test. Factors caused diabetes mellitus are body mass index, high density lipoprotein, and albuminuria. These factors cause to diabetes mellitus and stroke could be used to prevent diseases, in order to all persons are healthy and happy. The result that probability of a patient with macroalbuminuria has stroke greater than microalbuminuria and a patient with microalbuminuria has stroke greater than normal. Probability of patient with macroalbuminuria by logit, normit, and clogloc link functions is decrease, respectively. Probability of patient with microalbuminuria by logit, normit, and cloglog link functions is increase, respectively. Reliability of a patient with macroalbuminuria, normal, and microalbuminuria have stroke, respectively, is decrease. Reliability of patient with macroalbuminuria by logit, normit, and clogloc link functions, respectively, is increase. Reliability of patient with microalbuminuria by logit, normit, and clogloc link functions, respectively, is zero. All of link function methods yield estimation probability value is the same. AIC value of logit link function, normit link function, and cloglog link function are, respectively, 167.6826, 168.3965, and 169.6107. These results are same by the result of linear hypothesis analysis that AIC values are not different meanwhile their AIC values are not equal. Therefore, logit model, normit model and cloglog model could be used to predict probability with result almost same.
Method for Assessing the Applicability of AI Service Systems

Takeuchi, Hironori Yamamoto, ShuichiroIn this study, we consider the application of Artificial Intelligence (AI) technologies to enterprise functions and evaluate the viability of the AI service system. To assess applicability, we introduced conditions by modeling the business task and the AI service system by using the Enterprise Architecture approach. Through investigation, we confirmed that the proposed conditions were appropriate to the target business domain to ensure that the AI service system is relevant.
Information Technology Consulting Firms’ Readiness for Managing Information Security Incidents

Because of the increase in the number and scope of information security incidents, proper management has recently gained importance for public and private organizations. Further challenges in this area have resulted from new regulations, such as the General Data Protection Regulation (GDPR) and the Directive on Security of Network and Information Systems (NIS), as well as a tendency to outsource vital services to subcontractors. This study addresses the lack of empirical studies in the field and focuses on information security incident management at information technology (IT) consulting firms. Specifically, it examines challenges due to their exposed position and new regulations. The contribution of the paper is twofold. First, it provides valuable insight into the experiences and challenges of Swedish IT consulting firms. Second, it proposes criteria for classifying an information security incident that can equip decision-makers with a solid and assessable basis for incident management. The results emphasize further improvements in employee awareness, incident classification, and systemic governance, thereby integrating corporate policy making, information security incident management, and information system leadership.
Prevention and Detection of SQL Injection Attacks Using Generic Decryption

Today, Internet services have become an indispensable part of everyone’s life. With Internet becoming platform to offer services, traditional core services like banking and ecommerce are now being provided in Internet platform, and making security for these services have become necessary. The openness of Internet has increased risk. In certain huge organization’s cyber security, it is usual to notice major issues because of vulnerabilities in their system or software. By performing a depth analysis, it has come to know that the cause of vulnerabilities is either the developers or the design process. Research studies suggest that more than 80% of the active Web sites are vulnerable to SQL injection. The Web sites that unnecessarily expose server information have been recorded as nearly 67%. The Web sites that does not secure session cookies is approximately 50%. About 30% of the Websites secure their communications, for sending sensitive information of users. Hence, a study on mitigation of attacks is compelling. SQL injection targets interactive Internet services that employ database and aims to exploit vulnerability occurring at database layer in the three-tier architecture. This paper proposes a solution to detect and prevent SQL injection attacks using a model which is inspired from the concept of generic decryption. Results obtained confirm the extraordinary performance of the proposed scheme against the existing prevention techniques.
Optimal Representation of Quantum Channels

This work shows an approach to reduce the dimensionality of matrix representations of quantum channels. It is achieved by finding a base of the cone of positive semidefinite matrices which represent quantum channels. Next, this is implemented in the Julia programming language as a part of the QuantumInformation.jl package.
An Extended Approach for the Automatic Solution of Tangram Puzzles Using Permutation Heuristics

The Tangram is a geometric puzzle composed of seven polygonal pieces that can be combined to form different patterns. In combinatorial optimization, the task of solving Tangram puzzles is known to be NP-hard. In this paper, we present an extension of a recent computational method for the automatic solution of Tangram puzzles. The original work considers the largest-first heuristic, in which the pieces are positioned inside the puzzle region following a sequence from the largest to the smallest in area size. We present three additional permutation heuristics that generate different sequences to guide the pieces positioning inside the puzzle region. The effectiveness of the proposed heuristics is indicated by the application of the extended method on the solution of different Tangram puzzles. Combining the executed experiments, the extended method solved 93.33% of the patterns included in a dataset in an average time of 53.0s, while the original implementation solved 86.67% of the same dataset in an average time of 51.4s.
Pathologist at work

The aim of this chapter is to take a glimpse at the pathologist's work and how can Artificial Intelligence be used in order to ease her/his job. After the cancer suspicion has risen in Chapter 2, and the diagnostic has been somehow confirmed by medical imagistics, now we are covering four important tasks: we see how we can build the tumor's pattern, how Artificial Intelligence merges with histology, with immunohistochemistry, and finally with genetics.
Application of Multi-layered Thresholding Based on Stack of Regions for Unevenly Illuminated Industrial Images

Binarization of unevenly illuminated and natural images usually cannot be conducted using typical global thresholding methods. Due to changes of the local contrast and potential presence of some other distortions the application of more computationally demanding adaptive methods is necessary. Nevertheless, to find balance between the global and adaptive methods, some relatively fast region based approaches might be considered providing satisfactory results. Since typical issues for unevenly illuminated industrial images are quite similar to some distortions which may be found in degraded document images, in view of lack of industrial image databases, all numerical experiments have been conducted using recently developed challenging Bickley Diary dataset. Results of experiments, verified for sample unevenly illuminated industrial images containing text information, are promising and confirm usefulness of the proposed multi-layered approach based on the use of stack of regions.
Punctuation Restoration System for Slovene Language

Punctuation restoration is the process of adding punctuation symbols to raw text. It is typically used as a post-processing task of Automatic Speech Recognition (ASR) systems. In this paper we present an approach for punctuation restoration for texts in Slovene language. The system is trained using bi-directional Recurrent Neural Networks fed by word embeddings only. The evaluation results show our approach is capable of restoring punctuations with a high recall and precision. The F1 score is specifically high for commas and periods, which are considered most important punctuation symbols for the understanding of the ASR based transcripts.
Design of a Drag and Touch Multilingual Universal CAPTCHA Challenge

In order to protect sensitive data from bots, service providers protect their Web sites through some of the CAPTCHA tests. This test ensures that protected resources are accessible to only legitimate human users and no computer program (bot) gets their access. With a growing number of Web sites and Web portals offering more and more sensitive data for access by its legitimate human users coupled with the advances in techniques used to break CAPTCHA challenges, more robust and complex CAPTCHA challenges were developed. Though some of these tests offer the desired level of security, however, they often are inaccessible to a large population of the Web sites and also suffer from other usability issues. This paper presents a new CAPTCHA challenge based on the mouse motion event and is also usable on touch screens. This test can be made highly secure, lightweight, multilingual, and universal and does not suffer from any usability issues. The test can be coded for English or any other regional language, therefore, overcoming the language barrier of the CAPTCHA challenges. Experimentation with the proposed model implementation of the CAPTCHA challenge has proved its robustness, diversity, lightweight, and usability.
Face Stylized Modeling for Virtual Character

This research is to present a method for real human face stylized modeling. It can transform the 3D data of an actual face into a model with an anime style, which could be applied to virtual character production in CG. Moreover, the "stylization" here is limited: We would pick a few famous Japanese anime and utilize their characters' features to recreate a new face. The anime should be in natural styles that representatively depicted the faces of most Japanese, for example, the works of Kyoto Anime. As the image shows (Figure 1), our work focuses on how to transform the 3D facial data, specifically including three steps: 1. facial feature analyzation, 2. transforming relationship, and 3. modeling. We expect this system to complete automatic modeling in one hour to provide preliminary models to CG character makers for further manual improvement. Therefore, they do not need to spend more time in modeling from nothing but would be able to carry out artistic creation faster.
Attendance automatic recognition and learning behavior of web-based course attendance based on machine learning algorithms

At present, the automatic attendance mode of distance education is not conducive to the confirmation and analysis of information after class. In order to study the effective automatic recognition algorithm of remote education classroom, this study takes the educational classroom of intelligent innovation and entrepreneurship of Internet + as an example for analysis. Moreover, this paper adopts facial features as the basis of recognition, establishes corresponding positioning points, and constructs precise positioning methods for real-time feature capture. At the same time, the ASM algorithm is used to extract facial features, and the algorithm is improved to improve the extraction effect. In addition, this paper proposes Gabor-wavelet packet set and Gabor beamlet set for auxiliary recognition, which improves the recognition rate. Finally, this paper designs experiments to analyze the performance of the algorithm of this study. The results show that the proposed algorithm has certain practical effects and can provide theoretical reference for subsequent related research.
An Extended Framework for Context Modeling

This paper proposes an extended framework to describe a context for a human work environment. The framework consists of human and nonhuman elements, their inner states, and the interactions among them, with all these elements physically distributed in time and space. Moreover, it is noted that descriptive, normative, prescriptive, and formative representations of contexts should be distinguished. This enables us to use the proposed framework to describe past and future contexts in a standardized way so that we can share our contextual knowledge and design a context.
A Study of Cyber Security Issues in Sri Lanka

Cybersecurity is focused on helping the community to make knowledgeable decisions on its adaptation and mitigation. This survey evaluated the level of cybersecurity awareness and discernment amongst university students in Sri Lanka. The study was based on primary data collected through a questionnaire on awareness and the perception of cybersecurity from respondents at universities in Sri Lanka. The results indicated that experience and the level of cybersecurity awareness among university students in Sri Lanka are not significantly low, but there are some knowledge gaps with new threats. Further, the results showed that university students in Sri Lanka were able to identify cybercrime as a threat. These findings necessitate building awareness and developing capacity to improve student's knowledge on the cybersecurity subject especially if universities are to be used as a key focal point in cybersecurity awareness campaigns in Sri Lanka.
Prediction of High Recommendation Mobile Brands Using Sentiment Analysis

Digital world is growing very fact every day. It is the very important resource for generation of digital data. Technology is taking us to more sophisticated and user-centric applications in order to perform our daily requirements. Mainly, usage of social networks and online transactions is more preferred by people which generates huge data in terms of useful insights for future references. People are more comfortable giving their views, ideas, and reviews online instead of offline. These reviews are the source which if used in appropriate manner can help to understand needs of customers and help them in recommending some brands as per their requirements and help them in decision making. In this paper, we are using tweets of various mobile brands as our input. We then use naïve Bayes and support-vector machine supervised machine learning algorithms for prediction of sentiments and prediction of highly recommended brands. In our experiment, we got more accurate results for support-vector machine than naïve Bayes.
Applications of artificial intelligence for image enhancement in pathology

Pathology is finally profiting from the recent development of promising novel imaging techniques, as well as exploration of computational methods for extracting increased information from existing slides and conventional microscopy. This chapter focuses largely on the latter, looking at application of artificial intelligence (AI) methods for denoising, sharpening, super-resolution, and color mapping from unstained slides to generate virtually stained images. In addition, software-based tools have been developed to replicate histochemical and immunohistochemical results without having to perform the actual procedures. Finally, an example of an application of novel slide-free histology using stimulated Raman microscopy for intraoperative guidance during brain cancer surgery is presented remarkably; this method includes AI classification tools (cancer/not cancer) that surgeons can rely on directly.
Research on Domain Ontology Modeling and Formal Expression of Ocean Flow Field

With the continuous acquisition of multi-source marine data, the issue of semantic heterogeneity among ocean flow field phenomena had become the main difficulty for data sharing and analyzing of ocean flow field. For the reason that there was still no complete semantic shared knowledge system for ocean flow field at present, based on extensive reference to marine expertise and related standards, with the idea of ontology, the paper established the hierarchical system of ocean flow field domain knowledge. Through the semantic analysis of the relationship between concepts and attributes of ocean flow field domain ontology and the space-time relationship between ocean flow field instances, the paper proposed an ontology expression model based on a six-tuple, and constructed a basic structure that integrated concepts, attributes, relationships and instance sets together. And through extended modeling key words for Web Ontology Language (OWL), taking the western boundary current as an example, the paper gave the formal expression and description for semantic information based on OWL. The paper’s research could provide a theoretical basis for knowledge sharing and data mining in the field of ocean flow field.
Implementation of Digital Signature on QR Symbol by Area Division Using Rhombic Sub-cells

QR Code can be easily exploited for impersonation and forgery. One countermeasure is to implement a digital signature on the QR Symbol. From the viewpoint of compatibility, digital signatures must be separable and implemented independently of application data. Several methods of doing this have been proposed, but all have serious drawbacks. We have overcome these by developing a method for embedding rhombic sub-cells in the peripheral area of square cells to generate separate storage areas without affecting compatibility; these rhombic sub-cells can carry the digital signature. In this paper, we report on the proposed symbol structure and a method to implement the Elliptic Curve Digital Signature Algorithm in QR Symbols.
Access Control Vulnerabilities Detection for Web Application Components

In this paper we review the research progress of the mainstream approaches of detecting access control vulnerabilities and classify them based on the key techniques for web application components. And we compare different detection methods, analyze their advantages and flaws. Then we discuss the experimental results of relevant detection tools for realistic usage. Finally, we summarize the general framework of detection method and provide future research directions in this area.
Quantitative 3D Reconstruction from Scanning Electron Microscope Images Based on Affine Camera Models

Scanning electron microscopes (SEMs) are versatile imaging devices for the micro- and nanoscale that find application in various disciplines such as the characterization of biological, mineral or mechanical specimen. Even though the specimen’s two-dimensional (2D) properties are provided by the acquired images, detailed morphological characterizations require knowledge about the three-dimensional (3D) surface structure. To overcome this limitation, a reconstruction routine is presented that allows the quantitative depth reconstruction from SEM image sequences. Based on the SEM’s imaging properties that can be well described by an affine camera, the proposed algorithms rely on the use of affine epipolar geometry, self-calibration via factorization and triangulation from dense correspondences. To yield the highest robustness and accuracy, different sub-models of the affine camera are applied to the SEM images and the obtained results are directly compared to confocal laser scanning microscope (CLSM) measurements to identify the ideal parametrization and underlying algorithms. To solve the rectification problem for stereo-pair images of an affine camera so that dense matching algorithms can be applied, existing approaches are adapted and extended to further enhance the yielded results. The evaluations of this study allow to specify the applicability of the affine camera models to SEM images and what accuracies can be expected for reconstruction routines based on self-calibration and dense matching algorithms.
Private Identity Agreement for Private Set Functionalities

Private set intersection and related functionalities are among the most prominent real-world applications of secure multiparty computation. While such protocols have attracted significant attention from the research community, other functionalities are often required to support a PSI application in practice. For example, in order for two parties to run a PSI over the unique users contained in their databases, they might first invoke a support functionality to agree on the primary keys to represent their users. This paper studies a secure approach to agreeing on primary keys. We introduce and realize a functionality that computes a common set of identifiers based on incomplete information held by two parties, which we refer to as private identity agreement, and we prove the security of our protocol in the honest-but-curious model. We explain the subtleties in designing such a functionality that arise from privacy requirements when intending to compose securely with PSI protocols. We also argue that the cost of invoking this functionality can be amortized over a large number of PSI sessions, and that for applications that require many repeated PSI executions, this represents an improvement over a PSI protocol that directly uses incomplete or fuzzy matches.
Burr Registration and Trajectory Planning of 3D Workpiece Using Computer Vision

This paper proposes an effective way to identify the burr using image processing technique. The proposed method uses a simple mirror setup to capture 3D details of the burr. A set of image processing algorithm is used to calculate the dimensions of the burr. The burr dimensions are verified using the coordinate measuring machine. These details are then used to generate the trajectory for robot deburring. The deburring path generated is verified using ABB RobotStudio simulation software. The experimental result shows that the proposed approach provides an efficient method for robotic deburring.