Reviewerly/exHarmony huggingface dataset:
Guided Inductive Logic Programming: Cleaning Knowledge Bases with Iterative User Feedback

Domain-oriented knowledge bases (KBs) such as DBpedia and YAGO are largely constructed by applying a set of predefined extraction rules to the semi-structured contents of Wikipedia articles. Although both of these large-scale KBs achieve very high average precision values (above 95% for YAGO3), subtle mistakes in a few of the underlying ex- traction rules may still impose a substantial amount of systematic extraction mistakes for specific relations. For example, by applying the same regular expressions to extract per- son names of both Asian and Western nationality, YAGO erroneously swaps most of the family and given names of Asian person entities. For traditional rule-learning approaches based on Inductive Logic Programming (ILP), it is very difficult to detect these systematic extraction mistakes, since they usually occur only in a relatively small subdomain of the relations’ arguments. In this paper, we thus propose a guided form of ILP, coined “GILP”, that iteratively asks for small amounts of user feedback over a given KB to learn a set of data-cleaning rules that (1) best match the feedback and (2) also generalize to a larger portion of facts in the KB. We propose both algorithms and respective metrics to automatically assess the quality of the learned rules with respect to the user feedback.
Label Clustering for a Novel Problem Transformation in Multi-label Classification

Document classification is a large body of search, many approaches were proposed for single label and multi-label classification. We focus on the multi-label classification more precisely those methods that transformation multi-label classification into single label classification. In this paper, we propose a novel problem transformation that leverage label dependency. We used Reuters-21578 corpus that is among the most used for text categorization and classification research. Results show that our approach improves the document classification at least by 8% regarding one-vs-all classification.
Enhancing the Security and Efficiency of Resource Constraint Devices in IoT

IoT is spreading rapidly from industry to domestic application such as home automation and gathering sensitive data through IoT devices hence the security of these devices and the data transmission is of great importance. Because of the resource constraint devices, lightweight cryptographic protocol plays an important role. Therefore, implementing the zero-knowledge proof along with the hyper elliptic curve cryptography enhances the security and efficiency in the resource constraint devices.
SynSemClass Linked Lexicon: Mapping Synonymy between Languages

This paper reports on an extended version of a synonym verb class lexicon, newly called SynSemClass (formerly CzEngClass). This lexicon stores cross-lingual semantically similar verb senses in synonym classes extracted from a richly annotated parallel corpus, the Prague Czech-English Dependency Treebank. When building the lexicon, we make use of predicate-argument relations (valency) and link them to semantic roles; in addition, each entry is linked to several external lexicons of more or less “semantic” nature, namely FrameNet, WordNet, VerbNet, OntoNotes and PropBank, and Czech VALLEX. The aim is to provide a linguistic resource that can be used to compare semantic roles and their syntactic properties and features across languages within and across synonym groups (classes, or ’synsets’), as well as gold standard data for automatic NLP experiments with such synonyms, such as synonym discovery, feature mapping, etc. However, perhaps the most important goal is to eventually build an event type ontology that can be referenced and used as a human-readable and human-understandable “database” for all types of events, processes and states. While the current paper describes primarily the content of the lexicon, we are also presenting a preliminary design of a format compatible with Linked Data, on which we are hoping to get feedback during discussions at the workshop. Once the resource (in whichever form) is applied to corpus annotation, deep analysis will be possible using such combined resources as training data.
Settings of Reverb Processors from the Perspective of Room Acoustics

The issues of reverberation in acoustic architecture and music production share the same theoretical core; nevertheless the first one has been scientifically researched in depth while the second one remains at technical and experimental crossroads. It could be stated that the ISO 3382 parameters were proposed in the
Acquaintance with Natural Language Processing for Building Smart Society

Natural Language Processing (NLP) deals with the spoken languages by using computer and Artificial Intelligence. As people from different regional areas using different digital platforms and expressing their views in their spoken language, it is now must to focus on working spoken languages in India to make our society smart and digital. NLP research grown tremendously in last decade which results in Siri, Google Assistant, Alexa, Cortona and many more automatic speech recognitions and understanding systems (ASR). Natural Language Processing can be understood by classifying it into Natural Language Generation and Natural Language Understanding. NLP is widely used in various domain such as Health Care, Chatbot, ASR building, HR, Sentiment analysis etc.
Implementation and performance analysis of dynamic partitioning of graphs in Apache Spark

In recent years data collection, management and usage have grown to an extremely large extent.Almost all the major fields like information technology, medical science, transport, retail are dependent on data analysis for growth.All necessary technologies needed for data gathering are getting developed exponentially.All the devices from highend aeroplanes to household equipment are becoming smarter and collecting huge data every second.Now the biggest challenge is to analyze this huge data and retrieving useful information which will help in the major decision-making process.One of the most discussed problems related to the processing of graph data is partitioned.Balanced partitioning of the graph is a popularly known NPcomplete problem that has a large set of applications.
Can We Predict the Change in Code in a Software Product Line Project?

Software programs are always prone to change for several reasons.In a software product line, the change is more often as many software units are carried from one release to another.Also, other new files are added to the reused files.In this work, we explore the possibility of building a model that can predict files with a high chance of experiencing the change from one release to another.Knowing the files that are likely to face a change is vital because it will help to improve the planning, managing resources, and reducing the cost.This also helps to improve the software process, which should lead to better software quality.Also, we explore how different learners perform in this context, and if the learning improves as the software evolved.Predicting change from a release to the next release was successful using logistic regression, J48, and random forest with accuracy and precision scored between 72% to 100%, recall scored between 74% to 100%, and F-score scored between 80% to 100%.We also found that there was no clear evidence regarding if the prediction performance will ever improve as the project evolved.
The selection of the relevant association rules using the ELECTRE method with multiple criteria

The extraction of association rules is a very attractive data mining task and the most widespread in the business world and in modern society, trying to obtain the interesting relationship and connection between collections of articles, products or items in high transactional databases. The immense quantity of association rules obtained expresses the main obstacle that a decision maker can handle. Consequently, in order to establish the most interesting association rules, several interestingness measures have been introduced. Currently, there is no optimal measure that can be chosen to judge the selected association rules. To avoid this problem we suggest to apply ELECTRE method one of the multi-criteria decision making, taking into consideration a formal study of measures of interest according to structural properties, and intending to find a good compromise and select the most interesting association rules without eliminating any measures. Experiments conducted on reference data sets show a significant improvement in the performance of the proposed strategy.
Medical vision: web and mobile medical image retrieval system based on google cloud vision

The application of information technology is rapidly utilized in the medical system. There is also a massive development in the automatic method for recognizing and detecting objects in the real world. In this study, we present a system called Medical Vision which is designed for people who has no expertise in medical. Medical Vision is a web and mobile-based application to give an initial knowledge in a medical image. This system has 5 features; object detection, web detection, object labeling, safe search, and image properties. These features are run by embedding Google Vision API in the system. We evaluate this system by observing the result of some medical images which inputted into the system. The results showed that our system presents a promising performance and able to give relevant information related to the given image.
Psychological and creative aspects of the concerter’s work in the process of choral conducting education of the future teacher of music art

The article examines the process of concertmaster professional work in higher education institutions in choral conducting classes, explores the peculiarity of the concertmaster professional activity within the discipline, which is the leading in the conducting-choral education system, systematized and specified the concertmaster professional requirements in the musical-pedagogical process, generalized and formulated psychological and creative aspects of the concertmaster’s work; the concept of professional concertmaster’work is proposed in the conditions of work in higher education institutions at the classes of choral conducting. In the choir conductor training system, the artistic and pedagogical efficiency of the classes is created by the efforts of two people – a teacher and a concertmaster. Despite the apparent unity, interpenetration and the organic continuity of each individuals work, their official status is significantly different: the teacher is always considered the main, leading person in the educational process, and concertmasters, according to the staffing schedule, belong to the so-called "teaching and support staff". Such the official definition puts them in a minor position. We will try to prove the principle error of this point of view in this article. Consequently, the problem of this work is the contradiction between the concertmaster official status and the real functions of his professional activities. Bythe content of his work, he is a full complement of the creative and pedagogical process, while the statute of "support" staff creates an incorrect orientation and causes some underestimation of the concertmaster’s work. The actual content of the teacher-concertmaster work and the truth of the significance of his activity in the system of conducting-choral education are still notsufficiently recognized at the official level. Therefore, the purpose of this work is to substantiate the true value of the concertmaster activity in the musical-pedagogical process and to form the concept of the concertmaster professional work in the modern musical-pedagogical process. The subject of the work is related directly to everyday pedagogical practice and it is stipulated. That’s why, the practical significance of the work is obvious and does not require additional explanations.
Towards Evaluating and Simulating Keyword Queries for Development of Interactive Known-item Search Systems

Searching for memorized images in large datasets (known-item search) is a challenging task due to a limited effectiveness of retrieval models as well as limited ability of users to formulate suitable queries and choose an appropriate search strategy. A popular option to approach the task is to automatically detect semantic concepts and rely on interactive specification of keywords during the search session. Nonetheless, employed instances of such search models are often set arbitrarily in existing KIS systems as comprehensive evaluations with reals users are time demanding. This paper envisions and investigates an option to simulate keyword queries in a selected "toy'' (yet competitive) keyword search model relying on a deep image classification network. Specifically, two properties of such keyword-based model are experimentally investigated with our known-item search benchmark dataset: which output transformation and ranking models are effective for the utilized classification model and whether there are some options for simulations of keyword queries. In addition to the main objective, the paper inspects also the effect of interactive query reformulations for the considered keyword search model.
Real Time Attendance System using One Shot Learning for Face Recognition

Attending lectures plays an important part in the intellectual development of a student.Hence, attendance is given such great value in our education system.It is a useful measure of evaluating the performance of a student.Thus, we propose a real time attendance system which identifies and then recognizes students using their facial features.This system will record and mark attendance of all the students present in the classroom with the help of advancements in machine learning.The various technologies used in face recognition include Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Siamese Neural Network and many more.We will be working with the lattermost that is, Siamese neural network for One shot learning.The steps described in this paper include capturing video and converting it into a picture, face detection, face recognition using feature extraction and classification.Multiple cameras ensure accuracy and accountability of the system.This automated system can transform the manual and tedious work of taking attendance to a simpler and automated one.
A novel system for early detection of breast cancer using area and entropy features of malignant tumour

Computer aided detection and classification system has been developed to detect breast cancer at an early stage by predicting the area and texture of malignant tumours. Noise removal and image enhancement is carried out in the pre-processing stage by using adaptive median filter and contrast limited histogram equalisation techniques. Improved watershed segmentation technique with appropriate internal and external markers, have proved to be an efficient approach in detecting the region of interest. The detected tumours are classified using feedforward artificial neural network that are trained using textural features. Area and entropy features extracted from malignant tumours aids in early detection of breast cancer by categorising malignant tumours as belonging to stage I or stage II. The overall efficiency of the system, for identifying stages of malignant tumour is 92%, which has been identified to be high when compared to all existing systems. Mammogram images from Mammographic Image Analysis Society (MIAS) database was used for training the system and efficiency of the system was tested using real time hospital images.
Oil Phase Velocity Measurement of Oil-Water Two-Phase Flow with Low Velocity and High Water Cut Using the Improved ORB and RANSAC Algorithm

Abstract Velocity is an important parameter for fluid flow characteristics in profile logging. Particle tracking velocimetry (PTV) technology is often used to study the flow characteristics of oil wells with low flow velocity and high water cut, and the key to PTV technology is particle matching. The existing particle matching algorithms of PTV technology do not meet the matching demands of oil drops in the oil phase velocity measurement of oil-water two-phase flow with low velocity and high water cut. To raise the particle matching precision, we improved the particle matching algorithm from the oriented FAST and the rotated BRIEF (ORB) feature description and the random sample consensus (RANSAC) algorithm. The simulation and experiment were carried out. Simulation results show that the improved algorithm not only increases the number of matching points but also reduces the computation. The experiment shows that the improved algorithm in this paper not only reduces the computation of the feature description process, reaching half of the computation amount of the original algorithm, but also increases the number of matching results, thus improving the measurement accuracy of oil phase velocity. Compared with the SIFT algorithm and the ORB algorithm, the improved algorithm has the largest number of matching point pairs. And the variation coefficient of this algorithm is 0.039, which indicates that the algorithm is stable. The mean error of oil phase velocity measurement of the improved algorithm is 1.20 %, and the maximum error is 6.16 %, which is much lower than the maximum error of PTV, which is 25.89 %. The improved algorithm overcomes the high computation cost of the SIFT algorithm and achieves the precision of the SIFT algorithm. Therefore, this study contributes to the improvement of the measurement accuracy of oil phase velocity and provides reliable production logging data for oilfield.
Multimodal Intelligent Wheelchair Interface

Intelligent wheelchairs allow individuals to move more freely, safely, and facilitate users’ interaction with the wheelchair. This paper presents the results focused on the study and analysis of the state of the art related to topics as interaction, interfaces, intelligent wheelchairs and the analysis of the Intellweelsproject. The main goal is to create and implement a multimodal adaptive interface to be used as the control and interaction module of an intelligent wheelchair. Moreover, it will be important to have in mind the usability, by facilitating the control of a complex system, interactivity, by allowing the control using diverse kinds of input devices, and expansibility, by integrating easily with several intelligent external systems. This project features a complex input/output system with linked parameters simplified by a node system to create the input/output actions with automatic input recording and intuitive output association as well as a powerful, device-agnostic design, providing an easy way to extend the inputs, outputs and event the user interface. Results reveal a positive users’ feedback and a responsive way when using the multimodal interface in simulated environment.
Computational load reduction of the agent guidance problem using Mixed Integer Programming

This paper employs a solution to the agent-guidance problem in an environment with obstacles, whose avoidance techniques have been extensively used in the last years. There is still a gap between the solution times required to obtain a trajectory and those demanded by real world applications. These usually face a tradeoff between the limited on-board processing performance and the high volume of computing operations demanded by those real-time applications. In this paper we propose a deferred decision-based technique that produces clusters used for obstacle avoidance as the agent moves in the environment, like a driver that, at night, enlightens the road ahead as her/his car moves along a highway. By considering the spatial and temporal relevance of each obstacle throughout the planning process and pruning areas that belong to the constrained domain, one may relieve the inherent computational burden of avoidance. This strategy reduces the number of operations required and increases it on demand, since a computationally heavier problem is tackled only if the simpler ones are not feasible. It consists in an improvement based solely on problem modeling, which, by example, may offer processing times in the same order of magnitude than the lower-bound given by the relaxed form of the problem.
Sistem Pakar dalam Identifikasi Kerusakan Gigi pada Anak dengan Menggunakan Metode Forward Chaining dan Certainty Factor

Tooth decay in children is one of the most common problems found. This damage has a long-term effect on children's dental health. The limitation of expert doctor's service hours and the disproportionate number of doctors and patients makes the service to patients not optimal. Then we need an expert system to help the role of expert doctors in diagnosing tooth decay. One of them carried out treatment or prevention measures from the start. In this study forward chaining and certainty factor methods are used, in which this expert system can assist an expert in diagnosing tooth decay based on the symptoms experienced by the patient. The forward chaining method will be collaborated with the certainty factor method to calculate the accuracy of the type of tooth decay experienced. The use of these two methods aims to provide better results in overcoming or preventing tooth decay in children. From the test results obtained knowledge for patients in dealing with or preventing tooth decay with an accuracy rate of 91.20%. The application of an expert system can be used for early action in overcoming or preventing tooth decay in children.
BITA*: Business-IT alignment framework of multiple collaborating organisations

Businesses today must collaborate in a coordinated fashion. To collaborate, they must align their business processes and IT by complying to a common reference architecture. The common reference architecture that addresses their specific collaboration requirements is generally an adaptation of an existing generic reference architecture. However, a design framework for adapting reference architectures is lacking. In this paper we propose a design framework for aligning business processes and IT across diverse collaborating organisations in order to derive a more specific reference architecture from a generic one. We developed the design framework using the guidelines of ISO/IEC/IEEE standard for modelling design viewpoints and validated it in a real-life business case study. We developed an architectural design framework which we call BITA* that is composed of three coherent architectural design viewpoints. The BP2BP alignment viewpoint provides alignment modelling abstractions for business analysts to be used to align business collaboration processes. The IT2IT alignment viewpoint provides alignment modelling abstractions for software architects to be used to align distributed IT systems. The BP2IT alignment viewpoint provides alignment modelling abstractions for interdisciplinary teams of business and IT specialists for aligning the mapping of business collaboration processes and the underlying distributed IT. The modelling abstractions are applied in a case study to derive a reference architecture for meat supply chain transparency systems. A key challenge in developing the design framework is the difficulty of comparing models of business processes and IT that come from diverse organisations. Our main contribution is the set of modelling abstractions, which enabled us to represent business processes and IT in a uniform and comparable manner, and the systematic approach for applying the modelling abstractions. The framework is applied in the agri-food sector and needs to be evaluated further in multiple case studies from various application domains.