Reviewerly/exHarmony huggingface dataset:
Research on Automatic Knowledge Acquisition Technology for Software Fault Diagnosis

The traditional way of knowledge acquisition is that the knowledge engineer obtains the knowledge acquired from the knowledge source into the knowledge base through software. After the knowledge base is established, it is mainly updated by the deletion method and the completion method. Not only does it take time and effort, but it is not guaranteed for correctness and is inefficient for troubleshooting. Here is another way to gain knowledge - automatic knowledge acquisition. Establish an automated process of knowledge acquisition based on machine learning, realize the automatic acquisition process of software fault diagnosis knowledge, improve the human-computer interaction process of knowledge acquisition process, and realize the accuracy and reusability of knowledge.
ANALISIS USABILITY MENGGUNAKAN METODE WEBUSE PADA WEBSITE KITABISA.COM

Website is a collection of pages that contain information, videos, audio, images and the like that are interconnected with one another and can be accessed today by using a browser that is connected to the internet. Usability is the ability of an application or website that can be used easily to achieve the goals of the user itself. Why usability is important if an application or website has good usability, the user will be able to easily use it, besides easy to use usability must also be easy to learn by the new user and of course the application or website must run according to its function and no errors occur when the user uses it. Surely this usability has become standard at ISO 9241-11: 2018. Every website has its own usability level, the higher its usability value, the better the usability value means that the application or website is easily understood by its users and its users can find what they are looking for in a short time, and errors that occur are very minimal. This study aims to find out whether the website has been able to meet the needs of its users or not and whether the user experiences complications when using it or not. In Indonesia there are also many platforms that provide distribution or fundraising online and can be monitored for movement, such as example bookisa.com , this platform has been around since 2013 but it is still a social movement and it was only in 2014 that the book was switched to focus on online donations and in 2018 the book was able to channel more than 500 billion to those in need. This research uses the webuse method. The study analysed by sampling 20 people and the results of Good on the four variables, with the highest variable being Content, Organization, and Readability variable which got score 0.77, as well as the lowest variable is design user interface score of 0.70.
The effects of reward systems and organizational structure on tacit knowledge sharing

Sharing of tacit knowledge is not without its own challenges.It proposed here that to encourage tacit knowledge sharing, Rewards Systems, and Organizational Structure must be present.This proposition tested among ICT employees in Amman, Jordan.The data collected were analyzed using Pearson correlations and multiple regression, and the results indicated that organizational structure significantly related to tacit knowledge sharing.
A Novel Approach Towards Tamper Detection of Digital Holy Quran Generation

Quran phrases are found in many Arabic websites. Lamentably, many mistakes and typos appear on most of the websites embedded with Quran texts. Therefore, it becomes very difficult to recognize the legal document of the religious book, whether the online document is tampered or not. Hence, verifying the Quran expression has become a crucial issue for most of the online users who read the digital copy. We propose a novel approach for the tamper detection of a digital document of the Holy Quran. We have implemented a desktop application, having modified UI that utilizes Jaro-Winkler distance and Difflib function as String Edit distance algorithm to highlight the words in the Holy Quran for the verification purpose. A reliable and trustworthy Quran database was taken for testing. The results obtained from the application show higher performance. The system achieved the detection accuracy of 95.9% and 95% by Jaro-Winkler and Difflib, respectively along with the precision of 93.29% and 96% in the case of diacritics. Additionally, F-score is 93.22% and 96.41% obtained by Jaro-Winkler and Difflib, respectively in the case of no diacritics.
Research on Industrial Software Testing Knowledge Database Based on Ontology

Industrial software testing is the key part before the release of industrial software. The software test engineers without industrial knowledge and manufacturing experience cannot test for its unique functional performance requirements and industry characteristics. Therefore, the industrial software testing knowledgeis studied basedon ontologyinthis paper. The knowledge of industrial software testing domain is represented by the ontology whichis constructed by the improved seven-step method. Andthe graphdatabase is usedto store knowledge. The management of industrial software testing knowledge is standardized by the Industrial Software Testing Knowledge Base, which is helpful to improve the quality and efficiency of industrial software test.
Educational technologies as a means of developing students' independence

The current stage of development of society is characterized by the development of scientific and technological progress, the emergence of various innovative processes that affect the course of development of all spheres of life, including higher education. With the emergence of a competency-based approach and a corresponding change in educational goals, higher education institutions, fulfilling the requirements of the Federal state educational standards, should use innovative educational technologies in the preparation of students to form their professional competence. The purpose of the article is to consider the experience of implementing educational technologies as a tool for developing students' independence. The independence of the student is an integral part of the development of his competence. The article presents an examination of the concepts of “independence” and “educational technology” from various points of view. The dependence of independence on the use of innovative educational technologies in the educational process is traced. The stages of the process of organizing students' independent work using innovative educational technologies are highlighted. The presented study on identifying students' level of independence when studying the discipline “Teaching Technologies of the Teachers of the Past” allows us to conclude that students are more successful with the active implementation of educational technologies, since they make the process more active and creative, make the interaction process more efficient. The study was conducted over two years (in 2018 and 2019). We checked the level of independence of students before the introduction of innovative technologies in the study of the discipline "Technology of teaching teachers of the past" and after. The more often educational technologies are used in the educational process, the more students get used to independent work, the better it becomes. The use of educational technologies allows the formation of a highly educated competent specialist who independently and creatively solves professional problems.
Using Twitter Streams for Opinion Mining: A Case Study on Airport Noise

This paper proposes a classification model for opinion mining around airport noise based on techniques such as event detection and sentiment analysis applied on Twitter posts. Tweets are retrieved using the Twitter API either because of location or content. A dataset of preprocessed, with NLP techniques, tweets is manually annotated and then used to train an SVM (Support Vector Machine) classifier in order to extract the relevant ones from the obtained collections. The extracted tweets from the SVM classifier are fed to a lexicon-based classifier to filter out the false relevant and to increase precision. A lexicon-based sentiment classifier is then applied in order to separate positive, negative and neutral tweets. The sentiment classifier uses emoticons, polarity of words with subjective intensity, intensifiers, negation effect with dynamic scope, contrast effect and SWN to detect the sentiment of tweets in a hierarchical manner. The information present in the classified tweets is used for a statistical survey-like study.
PIN Authentication Using Multi-Model Anomaly Detection in Keystroke Dynamics

This paper investigates the design of anomaly detectors and feature sets for Personal Information Number (PIN) authentication on mobile devices. The work involved a selection of raw data feature sets that are extracted from mobile devices, such as finger area, pressure, and timestamp. A set of primary and secondary authentication features have been formulated, which are calculated from the raw data features. The anomaly detectors are based on the outlier concept, where an input PIN's calculated feature is classified as imposter value if it is outside an acceptable zone from a central value such as the mean or median of a set of training values. The Z-Score method is used as the distance function of the anomaly detectors, and three versions are investigated; the standard deviation-based Z-Score, the modified Z-Score which uses the Median-Absolute-Deviation (MAD) and the Average-Absolute-Deviation (AAD) Z-Score function. The three single models are combined into ensemble models. Experimental work resulted in a PIN dataset from 70 subjects, where the data included genuine and imposter PIN data. The primary and secondary authentication features dataset were calculated from the raw features dataset. The results showed that the merged AAD and MAD ensemble model achieved the lowest error rate.
App-generated digital identities extracted through Android permission-based data access - a survey of app privacy

Smartphone apps that run on Android devices can access many types of personal information. Such information can be used to identify, profile and track the device users when mapped into digital iden ...
Relaxed Weighted Path Order in Theorem Proving

Abstract We propose an extension of the automated theorem prover E by the weighted path ordering (WPO). WPO is theoretically stronger than all the orderings used in E Prover, however its parametrization is more involved than those normally used in automated reasoning. In particular, it depends on a term algebra. We integrate the ordering in E Prover and perform an evaluation on the standard theorem proving benchmarks. The ordering is complementary to the ones used in E prover so far. Furthermore, first-time presented here, we propose a relaxed variant of the weighted path order as an approximation of the standard WPO definition. A theorem prover strategy with a relaxed order can be incomplete, which is, however, not an issue as completeness can be easily regained by switching to a complete strategy. We show that the relaxed weighted path order can have a huge impact on an improvement of a theorem prover strategy.
Interpretational applications of artificial intelligence-based seismic fault delineation

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Multiple Secret Sharing with Simple Image Encryption

Multiple Secret Sharing (MSS) aims to secure the image transmission by improving the ambiguity on image contents. The former ( n , n )-MSS scheme generates n shared images from n secret images and reconstruct n recovered secret images from n shared images. This scheme hides the content of secret image by performing the eXclusive-OR (XOR) with specific masking coefficient. It exploits the Chinese Remainder Theorem (CRT) approach for generating the masking coefficient. However, the former scheme cannot work if n is odd. It overcomes the aforementioned problem by incorporating random image, transforming into nk encrypted secret images, and employing double masking coefficients. The presented MSS scheme utilizes an image encryption technique with simple chaotic maps for increasing the ambiguity of shared image content. The experimental results reveal that the proposed MSS method solves the problem on former MSS scheme and yields better performances.
Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study

Background Falls are a common health problem, which in the worst cases can lead to death. To develop reliable fall detection algorithms as well as suitable prevention interventions, it is important to understand circumstances and characteristics of real-world fall events. Although falls are common, they are seldom observed, and reports are often biased. Wearable inertial sensors provide an objective approach to capture real-world fall signals. However, it is difficult to directly derive visualization and interpretation of body movements from the fall signals, and corresponding video data is rarely available. Objective The re-enactment method uses available information from inertial sensors to simulate fall events, replicate the data, validate the simulation, and thereby enable a more precise description of the fall event. The aim of this paper is to describe this method and demonstrate the validity of the re-enactment approach. Methods Real-world fall data, measured by inertial sensors attached to the lower back, were selected from the Fall Repository for the Design of Smart and Self-Adaptive Environments Prolonging Independent Living (FARSEEING) database. We focused on well-described fall events such as stumbling to be re-enacted under safe conditions in a laboratory setting. For the purposes of exemplification, we selected the acceleration signal of one fall event to establish a detailed simulation protocol based on identified postures and trunk movement sequences. The subsequent re-enactment experiments were recorded with comparable inertial sensor configurations as well as synchronized video cameras to analyze the movement behavior in detail. The re-enacted sensor signals were then compared with the real-world signals to adapt the protocol and repeat the re-enactment method if necessary. The similarity between the simulated and the real-world fall signals was analyzed with a dynamic time warping algorithm, which enables the comparison of two temporal sequences varying in speed and timing. Results A fall example from the FARSEEING database was used to show the feasibility of producing a similar sensor signal with the re-enactment method. Although fall events were heterogeneous concerning chronological sequence and curve progression, it was possible to reproduce a good approximation of the motion of a person’s center of mass during fall events based on the available sensor information. Conclusions Re-enactment is a promising method to understand and visualize the biomechanics of inertial sensor-recorded real-world falls when performed in a suitable setup, especially if video data is not available.
Modelling Specialized Knowledge With Conceptual Frames: The TermFrame Approach to a Structured Visual Domain Representation

We describe an emerging knowledge base for karstology developed in line with the frame-based approach with data for three languages, English, Slovene and Croatian. An annotation framework was developed to identify the definition elements, semantic categories, relations and relation definitors in definitions of karst concepts extracted from specialized corpora. A multi- layered annotation was performed for sets of validated English and Slovene definitions. We present the distribution of semantic categories and typical definition frames for the most prominent semantic categories: surface and underground landforms, hydrological forms and geomes, for English and Slovene. The definition frames specify the typical properties of concepts we expect to be described, and in our case they were initialized by domain experts and then verified through corpus data. The structured domain representation resulting from the annotated corpus allows us to compare knowledge structures between languages, generate ideal definitions and experiment with domain visualisations, graphs and maps of geolocations.
Towards Real-Time, Multi-View Video Stereopsis

We present a real-time, multi-view video stereopsis (RTMVS) algorithm. This algorithm processes five synchronized video streams from cameras of a stationary camera array using a commodity laptop computer equipped with an Nvidia GPU. It provides 3D visualization of a dynamic scene from a chosen viewpoint at the video frame rate. In RTMVS, 3D surfaces are represented as a set of triangles anchored on a sparse set of 3D feature points. The computationally intensive Structure-from-Motion (SfM) algorithm is executed as an initial step. Feature points in each video stream are tracked using a KLT tracker. Triangles will be updated only when at least one vertex moves from its current position. The algorithm will redetect features every X frame. Epipolar geometry and Trifocal tensor are also exploited to accelerate sparse feature point matching and track filtering. Compared to a dense point cloud multi-view stereopsis baseline algorithm, RTMVS reduces the processing time per frame from 30s down to less than 44 ms.
Finger-Drawn Signature Verification on Touch Devices Using Statistical Anomaly Detectors

The use of behavioral biometrics in user authentication has recently moved to new security application areas, one of which is verifying finger-drawn signatures and PIN codes. This paper investigates the design of anomaly detectors and feature sets for graphic signature authentication on touch devices. The work involved a selection of raw data feature sets that are extracted from modern mobile devices, such as finger area, pressure, velocity, acceleration, gyroscope, timestamp and position coordinates. A set of computed authentication features are formulated, derived from the raw features. The proposed anomaly detector is based on the outlier method, using three versions of the Z-Score distance metric. The proposed feature sets and anomaly detectors are implemented as a data collection and dynamic authentication system on an Android tablet. Experimental work resulted in collecting a signature dataset that included genuine and forged signatures. The dataset was analyzed using the Equal-Error-Rate (EER) metric. The results for random forgery and skilled forgery showed that the Z-Score anomaly detector with 3.5 standard deviations distance from the mean produced the lowest error rates. The skilled forgery error rates were close to random forgery error rates, indicating that behavioral biometrics are the key factors in detecting forgeries, regardless of pre-knowledge of the signature's shape.
A Method to Identify the Current Mood of Social Media Users

Mood of a person changes frequently during a day. A mood can be categorized as happy, sad, calm and angry. Most of the people today share their daily activities, opinions, feelings regularly in social media. Identification of current mood will be useful for recommendation systems to change or elevate moods. Thus, this proposed system identifies the current mood of a person by mining their social media contents such as posts, comments, image posts and emoticons. In the proposed solution, a score for the current mood is calculated in two stages; a score for text contents (images with text and posts/comments) and score for emoticons were computed. Posts made within a 24-hour period will be considered for the current mood and scores for multiple posts are combined using a temporal weighted average. Text classification is done using a 1D Convolution Neural Network and emoticon classification is performed using a survey. Finally, an overall accuracy of 85% is achieved.
Assessing presence in virtual environments: adaptation of the psychometric properties of the Presence Questionnaire to the Portuguese populations

Virtual Reality applications have the goal of transporting their users to a given virtual environment (VE). Thus, Presence is a consensual metric for evaluating the VEs' effectiveness. The present study adapts the Presence Questionnaire (PQ) for the Portuguese-speaking population, maintaining the validity of the contents and concepts, to ascertain the psychometric properties of the instrument.The adaptation to Portuguese was achieved through the standard adaptation process of translation and back-translation process. The sample consisted of 451 individuals (268 males and 183 females). Factor reliability ranged from 0.63 to 0.86. Confirmatory factor analysis produced a theoretical model of 21 items distributed among seven factors, where the covariance between some residual item errors was established. The fit indices obtained were χ2/df=2.077, GFI =0.936, CFI =0.937, RMSEA =0.049, P [RMSEA ≤0.05], MECVI =1.070. Results obtained allowed us to consider that the adapted Portuguese version of the PQ, with 21 items, forms a robust and valid questionnaire whose use is recommended to evaluate Presence in virtual reality research programmes, provided that they use samples of the Portuguese language (Europe).
The CNN and DPM based approach for multiple object detection in images

With the development of intelligent device and social media, the bulk of data on Internet has grown with high speed. There are so many important aspect in image processing, object detection is one of the international demanded research field. Multiple object detection is an important concept in object detection. In object detection extracting the features and handling the occlusion are two most important components. A Region-based Convolution Neural Network (R-CNN) has achieved great success in extracting the region based features which may used for extracting multiple regions from the images and Deformable Part Based Model (DPM) improve the ability for handling the occlusion. Occlusion handling is nothing but when multiple objects are near to each other that time some objects are not detected so this drawback will be handled by DPM. Existing method not performing well in the aspect of detecting multiple objects. In this paper R-CNN and DPM are to be integrated to detect multiple objects. By combining these two models we are able to notice every single object with high accuracy.
MDD based case tool for Automatic Generation of ChatBot

This article presents the process Automatic source code generation for ChatBot using the Model Driven Development (MDD) for Facebook Messaging platform. We propose the use of metamodel to define the ChatBot, including the option of consuming external REST services. Two test scenarios are presented: the first one is a ChatBot oriented to the sales of pizzas and hamburgers, which requires to obtain external information (REST services) on the ingredients and the search for commercial establishments that satisfy the options defined by the user. Our second test scenario is aligned with elements of IoT (Internet Of Things), specifically manages the sensors of a Sense Hat on a RaspBerry Pi 3 card, considering sensors such as: humidity, temperature, pressure, accelerometer and an actuator represented by a Led matrix. Finally, the results of each of the ChatBot are shown. these are automatically generated from the conceptual specification. The Chatbots are deployed on the Facebook platform validating the consumption of information from external REST services.