Reviewerly/exHarmony huggingface dataset:
Decision Tree Induction: Using Entropy for Attribute Selection

This chapter examines some alternative strategies for selecting attributes at each stage of the TDIDT decision tree generation algorithm and compares the size of the resulting trees for a number of datasets. The risk of obtaining decision trees that are entirely meaningless is highlighted, pointing to the importance of a good choice of attribute selection strategy. One of the most widely used strategies is based on minimising entropy (or equivalently maximising information gain) and this approach is illustrated in detail.
EFFICIENT IMAGE ENCRYPTION SCHEMES BASED ON DEVELOPING AES ALGORITHM WITH FUZZY FUNCTION

This paper suggests three image encryption schemes based on developing ofAdvance Encryption Standard (AES) algorithm with the employment of fuzzy function for diagnoses medical images. In the three proposed image encryption methods, the image is firstly scrambled using the: a) First Algorithm: start with applied AES algorithm then the result modulated with Fuzzy function; .b) Second Algorithm: start with modulated Fuzzy function then the result applied AES algorithm. c) Third Algorithm: star with combine between modulated Fuzzy function with each operations of AES algorithm. The usage of a fuzzy map as a pre-processing scrambling stage along with the AES algorithm gives the advantages of both the noise immunity from the fuzzy map and the security encryption from the AES algorithm. Furthermore, the Triangular Membership Function (TMF) parameters are utilized as additional extra keys that improve the security of the image encryption methods. The analysis results of the security as well as evaluate the efficiency of developed algorithms detect that the cipher text image acquired is the similar as the plaintext image and fuzzy set theory was suitable for apply as round function in the design of other block image ciphers. Moreover, the security properties, demonstrated that our designs were highly secure and robust against possible image cryptographic attacks. Finally, the NIST statistical test for randomness as well as performance comparison between proposed schemes with identical methods ciphers image revealed that the proposed algorithms were quite secure, efficient, and faster than the conventional block ciphers. Key words: Image encryption, Advance Encryption Standard (AES), Fuzzy set theory, Triangular Membership Function (TMF), Peak Signal-to-Noise Ratio (PSNR), National Institute of Standards and Technology (NIST).
Onboard reasoning and other applications of the logic-based approach to the moving objects intelligent control

This article provides the theoretical background and practical case studies of the application of reasoning and other logic-based approaches to the moving objects control. Modern moving objects, both manned and unmanned, utilise computers as their 'onboard brain'. Since planes, spacecraft, cars, trucks and trains must demonstrate flexible and safe behaviour in various situations, it seems prospective to use intelligent control means instead of rigid control logic dispersed in a program source code. This article is concerned with the possible implementation of onboard intelligence. In contrast to the popular use of neural networks, the logic-based approach is based on clear and exact control rules with strict responsibility. Thus, formal specification and verification methods can be utilised. The article describes the real-time control algorithm logic (RTCAL) for the above-mentioned purposes. We also present case studies of reasoning at the design and operation stages for providing the fault tolerant control of a spacecraft.
Reports on Conferences, Institutes, and Seminars

This column begins with an overview of an Alma ebook management project from the Electronic Resources Minnesota conference in 2019. Then, conference presenters discuss organizational restructuring from their presentation at the Charleston Conference 2019, which is followed by two more reports from Charleston 2020 conference sessions highlighting an affordable course materials initiative and how to balance user preferences and convenience with protecting a user’s private information. Finally, a report from the first Core Conference discusses the new Unsub tool and how two libraries incorporated it.
Design and Applications of Learner Model Based on Behavior Analysis

As a deep integration of information science and education, learning behavior analysis is one of the hot topics to realize personalized study.A lightweight learner model is proposed to generate customized quiz for individuals, which is along with an explicit and an implicit update algorithm.While learning interest is a three-level queue which is inferred from learning behavior and learning level is a binary vector which is inferred from test result.Commentary is provided on two fronts, concerning the extreme and unexpected cases.Through the analysis of simulation results, it is confirmed that the model is helpful to enhance the outcomes of online learning.
Data Visualization for Supporting Linguists in the Analysis of Toxic Messages

The goal of this research is to provide linguists with visualisations for analysing the results of their hate speech annotation.These visualisations consist of a set of interactive graphs for analysing the global distribution of annotated messages, finding relationships between features, and detecting inconsistencies in the annotation.We used a corpus that includes 1,262 comments posted in response to different Spanish online new articles.The comments were annotated with features such as sarcasm, mockery, insult, improper language, constructivity and argumentation, as well as with level of toxicity ('not-toxic', 'mildly toxic', 'toxic' or 'very toxic').We evaluated the selected visualisations with users to assess the graphs' comprehensibility, interpretability and attractiveness.One of the lessons learned from the study is the usefulness of mixed visualisations that include simple graphs (Bar, Heat map) -to facilitate the familiarisation with the results of the annotated corpus together with more complex ones (Sankey, Spider or Chord) -to explore and identify relationships between features and to find inconsistencies.
Postgraduate training system and scientific and pedagogical activity in assessments of French PhD students

Discussed are issues of postgraduate training system in France. The European system of doctoral training was always effective, had a great diversity of programs, wide opportunities for the graduates. It set trends for postgraduate training systems all over the world. France is the second economy of the European Union, producing almost a fifth of the European Union’s GDP. This country is among the leaders that allocate a large number of funds to finance the research sector. In 2018, France’s gross domestic spending on R&amp;D was 2.3%. In this regard, the social survey of French PhD students was conducted. They were asked to answer questions about the effectiveness of the French postgraduate training system as a whole and to assess the social and psychological side of the doctoral education process. We considered such aspects of the problem as the competitiveness of the French doctoral school in comparison with other countries, employment in the scientific and pedagogical area, the perception of the research fellow, and the obstacles that arise in the process of writing the thesis. The empirical basis of the study is a survey conducted in 2019–2020. The sample consisted of 149 people. It is noted that in general, future candidates of sciences tend to get into the research environment to continue working as full-fledged researchers. The analysis also showed that despite the good funding, there are certain problems in the field of science that concern the current generation of PhD students.
A Study on the Cross-Curricular Approaches in Competency-Based Curriculum: Focusing on Estonia, Finland, Australia and Canada

The purpose of this study was to analyze the cross-curricular approaches that appear in the competency-based curriculum and to derive implications for the future Korean national curriculum development. For this, literature research was conducted to analyze foreign national curriculum and related materials on Estonia, Finland, Australia, and BC Canada. As results, in terms of the status, it is reinforcing its status by revealing the cross-curricular competencies or themes in the curriculum structure or statement, and supporting implementation. In terms of elements and contents, cross-curricular competencies and themes are specified, and the linkage between competencies, cross-curricular themes, and subjects learning are improved. In the operation method, topic-oriented and integrated approaches, contextual learning in connection with life, and competency evaluation are emphasized. Based on main results, four implications were proposed in terms of the status, elements and contents, and the operation method of the cross-curricular approaches.
An empirical study on fall-detection using K-means based training data subset selection

In the era of fourth industrial revolution or Industry 4.0, Computational Intelligence, Data Science and smart Information and Communication technology (ICT) are gaining huge importance in our society.Governments around the world started relying heavily on information and communication technologies to build smart and hyper-connected infrastructures that allow cities to provide better services to people and reduce energy consumption, as the global urban population is growing substantially.These intelligent technologies allow cities to introduce new ways of monitoring the atmosphere, buildings, street lighting, traffic, crowds, crime, etc.In building a smarter and more intelligent city around the world, Computational intelligence and ICT are the underlying technologies; but if not wisely applied, ICT can be a major environmental problem.At present, about 2 percent of global greenhouse gas (GHG) emissions are accounted for by the global ICT industry.This footprint is expected to increase dramatically to about 14 percent by 2040, according to a recent report.Intelligent use of advances in ICT will assist in minimizing GHG while still achieving its goals.ICT is theoretically able to reduce the carbon footprint in other fields by a factor of 10, based on the Global e-Sustainability Initiative (GeSI) report.ICT technologies such as Green ICT, the Internet of Things (IoT) and Artificial Intelligence (AI) can play important roles, not just in making our environment smarter, but also greener and more sustainable.
A Simulation Process of Watermarking Algorithm to Noisy Images for Copyright Protection with DWT, SVD and DCT

Currently we are living in web era where anything can be copy in a few seconds so there is a most important issue present that is plagiarism, there are lots of content which is easily copied from internet and user represent those work as their own work. There is lots of algorithms are available which are able to protect the content but all those algorithms are not in terms of noise effect on RGB color image. So in this paper basically we present a system which is able to resolve the issue of copy rightwith reduction in noise issue.According to our proposed approach basically three approaches are using, according to this approaches will not utilize the complete system, we are using only those apart which are require rest are simply truncate.According to our proposed approach basically we design an algorithm which will take the input RGB noisy image and for reduction of noise we will use SVD, DCT and DWT.Now as per the image watermarking we also calculate the pixel value of each image and for extraction of those images using SVD and DWT.
Machine learning and big data

Suppose we want to estimate the probability that an experiment that can be characterized by a vector (x1,…,xn) will result in a success, when there is a large amount of data concerning a variety of experiments. In such cases we often consider procedures that do not assume a particular probability model regarding the vector and its probability of success. Such procedures, which rely on the large amount of data available, are the subject of this chapter.
Disconnected connectivity and the information and communication technology for development (ICT4D) initiatives in Bangladesh

This paper presents insights on the use of Information and Communication Technology for Development (ICT4D) in Bangladesh. It uses qualitative research approach and incorporates perspectives of Information and Communication Technology (ICT) users, ICT4D project officials, government officials, ICT experts, and development experts. It examines three cases that use e-learning (learning using electronic device) as an informal means of learning. Thematic data analysis shows that ICT4D initiatives follow a top-down approach which underestimates aspirations of end-users to adopt ICT. Lack of social acceptance of ICT4D often undermines considerable economic gains earned by technopreneurs (internet-based entrepreneurs). Notions of disconnectedness are instilled by ICT4D project priorities that limit rural people's conceptualisation of ICT, disconnecting them from sensing broader perspectives of it. Exclusion of academicians in knowledge encoding process for e-learning, and development of ICT4D discourses reflecting incomplete scenarios are the other instances of disconnectedness.
Goodbye Intuition

Goodbye Intuition (2017 - 2020) is an artistic research project on improvisation. With Goodbye Intuition we seek to challenge our roles and artistic preferences as improvising musicians by improvising with "creative" machines. In our project the machines can both take the role of a performer for us to play with and they can be extensions of our own instruments. They can become both our duet partners and they can be additions, expansions or augmentations of our sound. Playing is core in our investigation, and it is based on this experience we try to articulate thoughts and answers to the following questions: - How do we improvise with "creative" machines, how do we listen, how do we play? - How will improvising within an interactive human-machine domain challenge our roles as improvisers? - What music emerge from the human-machine improvisatory dialogue? The project’s artists are Andrea Neumann (GER), Morten Qvenild (NOR) and Ivar Grydeland (NOR). The artist Sidsel Endresen (NOR) is our observer, commentator, critic and discussion partner. Additionally, musician, composer and researcher Henrik Frisk (SWE), writer, musician, composer David Toop (UK), musician and researcher Anna Lindal (S), musician Lasse Marhaug (N), Christian Wallumrød Ensemble (N), and director and writer Annie Dorsen (US) contributes to the project. Humans at Norwegian Center for Technology in Music and the Arts (NOTAM) are our technological collaborators.
A Metropolis within Gibbs algorithm for knowledge discovery in language assessments

The reduced reparameterised unified model (RRUM) has been frequently used in language assessment studies. The objective of this research is to advance an MCMC algorithm for the Bayesian RRUM. The algorithm starts with estimating correlated attributes. Using a saturated model and a binary decimal conversion, the algorithm transforms possible attribute patterns to a multinomial distribution. Along with the likelihood of an attribute pattern, a Dirichlet distribution is used as the prior to sample from the posterior. The Dirichlet distribution is constructed using Gamma distributions. Correlated attributes of examinees are generated using the inverse transform sampling. Model parameters are estimated using the Metropolis within Gibbs sampler sequentially. Two simulation studies are conducted to evaluate the performance of the algorithm. The simulation studies show that as sample size increases, the measure of accuracy increases. The algorithm developed in this research is implemented in R.
Learning context-dependent word embeddings based on dependency parsing

Word embeddings constitute the basic methods of text representation. Whether they are the inputs to a machine learning algorithm or the features used in a natural language processing application, embeddings have proven helpful in solving various text processing tasks. In natural language texts, contextual information exerts a crucial influence on the semantics of word representations. In current research, most training models are based on shallow textual information and do not fully exploit deep relationships in sentences. To overcome this problem, this paper proposes the dependency-based continuous bag-of-words model which integrates the dependency relationships between words and sentences into the context with weights, thereby increasing the influence of specific contextual information on the prediction of target words. This method increases the abundancy of word context information and enhances the semantics of word embeddings. The experimental results show that the proposed method highlights semantic relations and improves the performance of word representations.
Generating polarization-entangled photon pairs in domain-engineered PPLN

Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text P. S. Kuo, V. B. Verma, T. Gerrits, S. W. Nam, and R. P. Mirin, "Generating polarization-entangled photon pairs in domain-engineered PPLN," in Conference on Lasers and Electro-Optics, OSA Technical Digest (Optica Publishing Group, 2019), paper JTu2A.42. Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
Understanding the Users Personal Attributes Similarity Across Online Social Networks

In this modern era of technology, everyone accessing the Internet is obsessed with social media. A User accesses different social media services to fulfill his diverse needs. For instance, Instagram is mainly used for sharing personal visual content while Twitter is known for finding latest news and trends, similarly Facebook for personal posts. Such services lead to the distribution of personal information of an Internet user on these platforms. In this paper, we build a framework to discover the relationship among the attributes of a user across the social media.We use different fuzzy string matching algorithms to find the similarities between the attributes. We extract the ‘name’ and ‘username’ from a publicly shared dataset and apply two character based and token based algorithms on these features. The results are indicative of the fact that only a limited number of users share the same name and username across the sites. On further analysis, it is found that although name and username of most of the users do not exactly match, they tend to be similar with the infinitesimal difference like; underscore, period, one digit numbers, etc. This study provides an analysis of the typical variations in names and usernames, which can further be studied for the extension to other social networks This profile will help in behavior analysis of a user, which will further help us to improve recommendations and analyze for criminal behavior and similar applications.
Vehicle tracking and speed estimation using view-independent traffic cameras

The traditional method's speed estimation accuracy depends on environmental changes and the new CNN technique accurately estimates speed only for quality video but input lacks, which leads to overfitting. Hence proposed method uses the Kalman filter and Hungarian algorithm to track detected vehicles for estimating the speed. First, vehicles are detected using the k-nearest neighbour (kNN) classifier-based background subtraction technique. Then logistic regression value is mapped to a binary value called decision threshold which accurately detects the vehicles. Secondly, each detected vehicle is tracked by a Kalman filter, then the Hungarian algorithm connects all the predictions and produces the tracking results. Finally, the vehicle speed is estimated by measuring the distance between the detected vehicle and the virtual line tracked vehicles on the ground plane. It also stores over-speeding vehicle images with speed. The proposed technique is verified with the BMVC standard database which shows a maximum error of ±2 kmph.
Emoji2019 Workshop Chairs' Welcome

research-article Share on Emoji2019 Workshop Chairs' WelcomeWWW '19: Companion Proceedings of The 2019 World Wide Web ConferenceMay 2019 Pages 415–416https://doi.org/10.1145/3308560.3316540Published:13 May 2019Publication History 0citation74DownloadsMetricsTotal Citations0Total Downloads74Last 12 Months10Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Halaman Depan Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 8 No. 2 2019

This article contains front-matter of Sisfokom Journal, Volume 8 Number 2 Year 2019, which includes a cover page, title page, editorial team, and table of contents.
Location-based Access Control in Neighborhood Geosocial Network: a Case of Emergency Assistance

To enforce safer virtual neighborhood environment, access control is essential to specify privileges or access rights to resources in neighborhood geosocial network applications. In the case of emergency circumstances, several concerns in implementing access control have been identified: (1) inefficient message sending to irrelevant recipients (2) location spoofing (3) gossip and rumors due to disclosing information to wrong audience. To address such potential risks, access control specifically tailored for neighborhood geosocial network is required. The objective of this paper is two-fold: First, to propose an access control using density-based and position-based location conditions for neighborhood geosocial network. Second, to design mySOS, a proof-of-concept prototype that incorporate the proposed access control to improve access control for emergency assistance in neighborhood geosocial network. To achieve the objectives, a combination of proposed self-organized segmentation algorithms (density-based) and resident engagement incentives (position-based) are implemented. This study contributes to a new way for access control that will determine user profile in accessing emergency assistance features in a neighborhood geosocial network application.
Implementation of Clustering Algorithms for Real Time Large Datasets

Now a day’s clustering plays vital role in big data. It is very difficult to analyze and cluster large volume of data. Clustering is a procedure for grouping similar data objects of a data set. We make sure that inside the cluster high intra cluster similarity and outside the cluster high inter similarity. Clustering used in statistical analysis, geographical maps, biology cell analysis and in google maps. The various approaches for clustering grid clustering, density based clustering, hierarchical methods, partitioning approaches. In this survey paper we focused on all these algorithms for large datasets like big data and make a report on comparison among them. The main metric is time complexity to differentiate all algorithms.
streamflow bias

This paper looks at potential future shifts in climate and streamflow for four river catchments in southern Ontario.The CRCM5-LE RCP 8.5 scenario projections of air temperature and precipitation were used as input in the Precipitation Runoff Modelling System (PRMS) to determine future streamflows.