Reviewerly/exHarmony huggingface dataset:
A Fuzzy Linguistic Programming for Sustainable Ecotourism Activities

Ecotourism is a strategy that guarantees the sustainability of the earth's natural resources and promotes local people's economic development while maintaining and safeguarding their social and cultural integrity. However, the activities may not be suitable for sustainable environmental conditions. Therefore, activity selection is important in tourism management. In this paper, fuzzy linguistics Prolog is used to match ecotourism activities and suitable regions. Bousi ~ Prolog is generated from classic fuzzy prolog that allows to operate with both fuzzy linguistic and linguistic tools to guide the Prolog structures into programming model phrases that can be very useful to the linguistic resources.
Automatic Ontology Population Using Deep Learning for Triple Extraction

Ontology is a kind of representation used to represent knowledge in a form that computers can derive the content meaning. The purpose of this work is to automatically populate an ontology using deep neural networks for updating an ontology with new facts from an input knowledge resource. In this study for automatic ontology population, a bi-LSTM-based term extraction model based on character embedding is proposed to extract the terms from a sentence. The extracted terms are regarded as the concepts of the ontology. Then, a multi-layer perception network is employed to decide the predicates between the pairs of the extracted concepts. The two concepts (one serves as subject and the other as object) along with the predicate form a triple. The number of occurrences of the dependency relations between the concepts and the predicates are estimated. The predicates with low occurrence frequency are filtered out to obtain precise triples for ontology population. For evaluation of the proposed method, we collected 46,646 sentences from Ontonotes 5.0 for training and testing the bi-LSTM-based term extraction model. We also collected 404,951 triples from ConceptNet 5 for training and testing the multilayer perceptron-based triple extraction model. From the experimental results, the proposed method could extract the triples from the documents, achieving 74.59% accuracy for ontology population.
Seamless Interactive Program Verification

Deductive program verification has made considerable progress in recent years. Automation is the goal, but it is apparent that there will always be challenges that cannot be verified fully automatically, but require some form of user input. We present a novel user interaction concept that allows the user to interact with the verification system on different abstraction levels and on different verification/proof artifacts. The elements of the concept are based on the findings of qualitative user studies we conducted amongst users of interactive deductive program verification systems. Moreover, the concept implements state-of-the-art user interaction principles. We prototypically implemented our concept as an interactive verification tool for Dafny programs.
A Study on Mispronunciation Detection Based on Fine-grained Speech Attribute

Over the last decade, several studies have investigated speech attribute detection (SAD) for improving computer assisted pronunciation training (CAPT) systems. The predefined speech attribute categories either is IPA or language dependent categories, which is difficult to handle multiple languages mispronunciation detection. In this paper, we propose a fine-grained speech attribute (FSA) modeling method, which defines types of Chinese speech attribute by combining Chinese phonetics with the international phonetic alphabet (IPA). To verify FSA, a large scale Chinese corpus was used to train Time-delay neural networks (TDNN) based on speech attribute models, and tested on Russian learner data set. Experimental results showed that all FSA's accuracy on Chinese test set is about 95% on average, and the diagnosis accuracy of the FSA-based mispronunciation detection achieved a 2.2% improvement compared to that of segment-based baseline system. Besides, as the FSA is theoretically capable of modeling language-universal speech attributes, we also tested the trained FSA-based method on native English corpus, which achieved about 50% accuracy rate.
Applications of micro size anorganic membrane of clay, zeolite and active carbon as filters for peat water purification

Abstract Ceramic membrane is one of technology membrane which used as a filtration. Filtration process is the easy way to solve the problem for water purification. Ceramic membrane was used as filtration for peat water. Peat water is the surface water of peat soils that contain high organic matter as well as high enough iron, acidic taste with a pH of 3-5 and a low level of hardness. Therefore, one of the managements of peat water is by using ceramic membrane. The goal of this study is making ceramic membrane from clay, zeolite and activated carbon as a filter for peat water. The research was conducted by using ceramic membrane with composition ratio of 80:10:10; 70:20:10; 60:30:10; 50:40:10, respectively, with the temperature of combustion varied from 600, 700 and 800 Degree Celsius, respectively. The ceramic membrane applied as a filtration for peat water. The permeats from filtration process using ceramic membrane had been analyzed parameter which indicate the good quality of peat water. This study obtained the highest Fe2+ removal efficiency in peat water by 100 percent, the highest Mn2+ removal efficiency of 99.94 percent, followed by a TDS removal efficiency of 82.58 percent and turbidity 95.65 percent.
A Novel Deep Learning Based Approach for Breast Cancer Detection

This study examine and investigate possibility of employing machine vision techniques for Breast cancer identification. Initially, data preparation was performed for labelling followed by preprocessing for removing pectoral muscle and inherent noise. Digital Database for Screening Mammography (DDSM) an internationally available well received database, which contain 2620 cases of mammograms was incorporated for experimentation. VGG-16 was employed for feature extraction subsequently, SSD was incorporated for tumor detection. Once method was established results were compared with other published methods for validation. The results exhibit an accuracy of 96.2% on DDSM. Therefore, a method is proposed and developed and its effectiveness is demonstrated in term of breast cancer detection.
Game-Theoretic Analysis of an Incentivized Verifiable Computation System

Outsourcing computation allows a weak client to outsource its computation to a powerful server and receive the result of the computation. Verifiable outsourcing enables clients to verify the computation result of untrusted servers. Permissionless distributed outsourcing systems provide an attractive marketplace for users to participate in the system as a problem-giver who needs solution to a problem, or problem-solver who is willing to sell its computational resources. Verification of computation in these systems, that do not assume trusted computational nodes, is a challenging task. In this paper we provide a game-theoretic analysis of an incentivized outsourcing computation system, proposed by Harz and Boman [Harz et al. 2018] (HB), at WTSC 2018 (FC Workshop), and show that the system is vulnerable to collusion and Sybil attacks, that result in incorrect solutions to be accepted by the system. We also show that malicious computational node can succeed in polluting the blockchain. We propose modifications to the system that incentivizes honest behavior, and improve the system’s correctness guarantee. We provide a high-level analysis of the modified system using our game theoretic approach, and show the effectiveness of the proposed modifications.
Educational App for Android-Specific Users—EA-ASU

M-learning or mobile learning is the fastest-evolving learning technology, and it has ample opportunities in global learning technical industry. If the app is designed very well with respect to its implementation, then it will definitely fulfill the purpose of learning and discovery. In accordance with this context, we have aimed at designing of an educational app EA-ASU. The proposed app aims at teaching and self-learning, even by any person who does not even have any professional knowledge regarding this. EA-ASU models any real-world learning scenarios where any user can access the various digital collection of study materials. At the same time, user can modify or upload any content to the center repository. It has also been concluded that EA-ASU, when deployed among students, was found to be very beneficial to them and it was able to improve their learning capability at ease.
Face Recognition using Eigen-Faces and Extension Neural Network

In this paper, the extension neural network is used as a classification tool in the application of face recognition. The eigen-faces method is firstly adopted in order to extract the coefficients associated with the most important eigen-faces. Next, the role of ENN comes as a classifier or pattern recognition technique. The performance of ENN is found to be superior over the traditional Multi-Layer Perceptron (MLP) in several aspects. The accuracy of the ENN is higher than MLP with less memory and processing requirements. Moreover, the structure of ENN is simple and fully determined compared with the MLP structure. In addition, the learning speed of ENN is higher than MLP. As a consequence of its simple structure, ENN is easier to be extended to be able to recognize new persons by just adding neurons in the output layer. Furthermore, in this paper, the effect of the learning rate, on both the stability and the speed of learning, is examined. We concluded that there is some optimum value for the learning rate which, in general, depends on the data set.
Towards Better Railway Service: Passengers Counting in Railway Compartment

Counting passengers in railway compartments is an essential problem for improving service quality, user experience, public security, and disaster relief in the railway system. Considering many limitations in the compartment, the infrared sensor, 3D camera, etc. are not practical in this scene. Due to the flexibility and lower cost, solutions with standard cameras attract much attention in real applications. However, since the problem with scale variation in the narrow space is different from universal detection or counting problems, the specific benchmark of dataset and methods should be provided and proposed for this task. In this paper, we provide a passenger counting dataset. Relying on this dataset, we propose a passenger counting method. The solution contains a motion supervised multi-scale representation method which provides proposals against scale variation, a spatially-temporally enhanced counting which provides precise counting numbers, and a partial proposal method which conducts methods to be utilized in reality. With the proposed solution, the passengers counting task is solved in higher accuracy and practicable in the compartment environment. In experiments, the results show that all the modules in our solution are useful and efficient, and our method outperforms in comparison with others in the compartment scene.
Application of Blockchain Technology in the Integration of Management Accounting and Financial Accounting

Blockchain is one of the most popular technologies nowadays. The birth of blockchain technology has solved the problem of reliability of accounting data. The accounting industry should seize the opportunity and actively use blockchain technology to promote industry innovation. At the same time, accounting professionals must also improve their professionalism to adapt to industry changes. The two major branches of modern accounting, financial accounting and management accounting, have become disconnected in practice. Both of them need to rely on different information processing systems for their actual operation. This is not economical for the enterprise, and it is also not conducive to management accounting to play its function of serving the economic management of the enterprise. To this end, the in-depth analysis of the differences between management accounting and financial accounting, and the introduction of “enterprise economic value added”, the unification of the theoretical differences between the two is of great significance to the development of the accounting field. This paper introduces the current research status of accounting information distortion and accounting fraud through the review and analysis of domestic and foreign literatures. Based on the research purpose of this article, it analyzes the characteristics and advantages of blockchain technology, and the impact of blockchain technology on the accounting industry The current status of research on the impact of issues such as research has determined the research theme, ideas, and methods. It also expounds the theoretical basis that will be used to study and build the credibility guarantee mechanism of the accounting information system based on blockchain technology, and then defines and divides the related concepts. This paper uses the basic principles of blockchain, information asymmetry theory, and moral hazard as the theoretical basis. Through questionnaire surveys, it is believed that the current accounting information system makes the original transaction information and the accounting information processing process unreliable, and the quality is worrying. The study found that only 14.5% of the qualified accountants in the experiment. The extreme shortage of accountants, especially senior accounting professionals, is the primary factor restricting the implementation of advanced management accounting methods in China on a large scale.
Fast and Efficient Model for Real-Time Tiger Detection In The Wild

The highest accuracy object detectors to date are based either on a two-stage approach such as Fast R-CNN or one-stage detectors such as Retina-Net or SSD with deep and complex backbones. In this paper we present TigerNet - simple yet efficient FPN based network architecture for Amur Tiger Detection in the wild. The model has 600k parameters, requires 0.071 GFLOPs per image and can run on the edge devices (smart cameras) in near real time. In addition, we introduce a two-stage semi-supervised learning via pseudo-labelling learning approach to distill the knowledge from the larger networks. For ATRW-ICCV 2019 tiger detection sub-challenge, based on public leaderboard score, our approach shows superior performance in comparison to other methods.
Cloud Computing Security

The aim of this paper was to analyze the issue of the Cloud security solutions as well as to design a secure access for a demonstrative cloud application. Several security items have been chosen for a secure access to the Cloud; moreover, the application has been implemented to Heroku, from Sales force company, with the use of the existing cloud technologies. The created application shows the possibilities of the application's development with the usage of the security items.
Human Computer Interaction using Hand Gestures and Voice

Human-Computer Interaction (HCI) exists everywhere in our everyday life. It is usually achieved by using a physical controller such as a mouse, keyboard or touch screen. This hinders the users natural experience as it creates a barrier between the user and computer as well as they are costly and take up lots of space on the desk. There are few hand tracking systems available in the market, but they are complex and expensive. The development of a marker-less hand tracking system and gesture recognition with low-cost hardware is presented here. The proposed system is a simple but effective method that allows rapid manual tracking despite the complex background. This system eliminates motion blur and is able to detect gestures like clicking and also hand tracking. The detected gesture is converted into specific functional inputs like clicking and mouse movement to control other applications. A voice module is integrated to receive voice commands from the user and perform it. This enables intuitive HCI and interactive motion gaming.
CogniKit

This paper presents CogniKit; an extensible tool for human cognitive modeling. It is based on the analysis, classification and visualization of eye tracking data such as gaze points, fixation count and duration, saccades, gaze transition and stationary entropy, heat maps, areas of interests, etc. These are further processed, analyzed and classified for detecting higher level human cognitive factors such as cognitive processing styles and abilities. CogniKit comprises of two main components: i) a software application that collects and processes low- and highlevel eye gaze data metrics in real-time; and ii) an extensible interactive workbench for storing, analyzing, classifying and visualizing the collected eye gaze data. We developed an example application to demonstrate the use of CogniKit within a practical scenario.
Image Reconstruction from Local Descriptors Using Conditional Adversarial Networks

Many applications rely on the local descriptors extracted around a collection of interest points. Recently, the security of local descriptors has been attracting increasing attention. In this paper, we study the possibility of image reconstruction from these descriptors, and propose a coarse-to-fine framework for the image reconstruction. By resorting to our gradually reconstructing network architecture, the novel multiscale feature map generation algorithm, and the strategically designed loss functions, our proposed algorithm can recover the images with very high perceptual quality, even partial descriptors are provided only. Extensive experimental results are reported to show its superiority over the existing algorithms. Our study implies that the local descriptors contain surprisingly rich information of the original image. Users should pay more attention to sensitive information leakage when using local descriptors.
Implementation of Aspect-oriented Business Process Models with Web Services

In software development, crosscutting concerns, such as security, audit, access control, authentication, logging, persistence, transaction, error handling etc. can be modularized using the aspect-oriented paradigm. In business process modeling, aspects have been used to reduce visualization complexity, increase reuse and improve model maintainability. There are techniques which address aspects in modeling and implementation phases of business process; however, these techniques adopt different semantic representations, hindering the integration of these phases into the BPM lifecycle. This work proposes an architecture for service discovery capable of selecting web services that implement crosscutting concerns and meet the goals established in the aspect modeling phase, executing them accordingly with a prioritization. A proof of concept to analyze the proposed architecture and generated artifacts was performed. Afterwards, the proposal was evaluated by means of an experiment. The results suggest that the definition of an operational goal enables the business specialists to concentrate on the modeling of the aspect without necessarily concerning its implementation, since a proper option for implementation is discovered during the execution of the process.
Bullfighting extreme scenarios in efficient hyper-scale cluster computing

Data centres are quickly evolving to support new demands for Cloud-Computing services. Extreme workload peaks represent a challenge for the maintenance of the performance and service level agreements, even more when operation costs need to be minimised. In this paper, we first present an extensive analysis of the impact of extreme workloads in large-scale realistic Cloud-Computing data centres, including a comparison between the most relevant centralised resource-managing models. Moreover, we extend our previous works by proposing a new energy-efficiency policy called Bullfighter which is able to keep performance key performance indicators while reducing energy consumption in extreme scenarios. This policy employs queue-theory distributions to foresee workload demands and adapt automatically to workload fluctuations even in extreme environments, while avoiding the fine-tuning required for other energy policies. Finally, it is shown through extensive simulation that Bullfighter can save more than 40% of energy in the aforementioned scenarios without exerting any noticeable impact on data-centre performance.
Machine Learning Algorithms for Breast Cancer CADx System in the Mammography

A computer aided diagnosis and detection system (CADx) enhances the early detection accuracy for breast cancer which in turn save women life all over the world. The existing early detection CADx systems are costly and demands highly complex algorithms with complicated computation thus unsuitable for developing countries. The segmentation process is a key issue in designing CADx system for breast cancer and the most computationally costing. Hence, the contribution of this paper is proposing two CADx systems for breast cancer in mammographic datasets that compromise between efficiency, speed, and cost. The segmentation step in the first system is performed by a Particle Swarm Optimization (PSO) Algorithm while it is performed by a Gaussian Mixture Model (GMM) in the second proposed system. Textural, statistical and shape features are extracted from segmented ROIs and input for the classifier to distinguish the mammograms if normal, benign or malignant. The proposed method utilizes the nonlinear support vector machines for classification. Experiments are conducted on mini MIAS database. The resulted accuracy rates based on 10-folds cross validation for classifying normal from abnormal are 85.4% and 82.4% for PSO and GMM respectively. Besides, the ratios become 89.5%, 87.5% for classifying malignant from benign for PSO and GMM respectively. On the other hand, the experimental results show the overall average classification accuracy of 81% and 85% when classifying normal, malignant or benign. Confusion matrix is additionally evaluation metric that was also used to check and assess the process comprehensive balanced performances for both PSO and GMM based methods.
Security and Privacy Education for STEM Undergraduates: A Shoulder Surfing Course Project

This Innovative Practice Full Paper presents a course project incorporating shoulder surfing. With the widespread use of technology in our everyday lives, our digital data is a hot commodity for cybercriminals. One of the easiest ways to access information is through the age-old technique of shoulder surfing, which refers to using direct observation, such as looking over individuals' shoulders at their screen or keyboard. Much research has been done on defense measures, user experiences, and self-reports of victimization. Inspired by this research, the authors designed a shoulder surfing course project for undergraduate students across multiple STEM disciplines. This paper shares details about the course project design and logistics, student and educator experiences, as well as lessons learned.
H<sub>∞</sub> Filter Design for Discrete-time Linear Positive Systems

In this paper, algorithms of limiting Kalman filters and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filters are proposed for a class of linear discrete-time positive systems. Every structure is analyzed being possible to formulate design conditions for multi-input multi-output systems by a set of linear matrix inequalities, discussion on asymptotic convergence of the estimation error, as well as on reflection on parameter constraints implying from a system positive structure. The filter structures are connected within feedback control output schemes in simulations, reflecting problem that not all state variables are available for measurement.