Reviewerly/exHarmony huggingface dataset:
Privacy preserving method for knowledge discovered by data mining

In spite of its success in a wide variety of applications, data mining technology raises a variety of ethical concerns which include among others privacy, intellectual property rights, and data security. In this paper, the author focuses on the privacy problem of unauthorised use of information obtained from knowledge discovered by secondary usage of data in clustering analysis. To address this problem, the author proposes the use of a combination of isometric data transformation methods as an approach to guarantee that data mining does not breach privacy. The three transformation methods of reflection, rotation, and translation are used to distort confidential numerical attributes for the purposes of satisfying the privacy requirements, while maintaining the general features of the cluster in clustering analysis. Experimental results show that the proposed algorithm is effective and provide acceptable values for balancing privacy and accuracy.
Cryptographic key management scheme for supporting multi-user SQL queries over encrypted databases

Database outsourcing is getting more popular bringing in a new standard, called database-as-a-service, where an organisation's database is stored in cloud. In such a setting, both access control and data confidentiality plays an important role, particularly when a data owner likes to publish his data for external use. Any cloud provider promises the security of its platform, while the execution of solutions to ensure confidentiality of the data stored in cloud databases is left to the data owner. The state-of-the-art solutions deal few preliminary issues with aid of SQL queries on encrypted data. In this paper, we propose a novel cryptographic key management scheme that combines data encryption and key management and supports multi-user SQL queries over encrypted databases. Our approach shows the proposed solutions for enforcing access control and for ensuring confidentiality of data. The experimental results obtained in this paper show the performance of proposed scheme.
Tangible Cultural Analytics: The Adoption of Recommender Systems Within Cultural Research

The prospect of implementing recommender systems within the context of cultural research has not been explored nearly as much compared to implementation in e-commerce websites and applications. Recommender systems allow for users to be shown new objects either based upon object similarity or based upon what the algorithm thinks the user will like – which can be derived from user feedback and comparing the user to other similar users. This paper discusses how a recommender system could benefit an augmented reality application that enables 3D viewing of artifacts – as part of the Tangible Cultural Analytics (TCA) project at Ryerson University’s Synaesthetic Lab. This paper outlines four recommender systems: 1) content-based filtering, 2) collaborative filtering, 3) cluster models 4) search based models, and 5) hybrid models; discussing the pros and cons to each. Ultimately, a content-based model without the user profile aspect was chosen for this stage in the prototype. This model showed us just how much potential these recommender systems have when helping cultural researchers uncover new relationships and pieces of history through the study and comparison of artifacts.
Social Spider Optimization Algorithm for Effective Data Classification: An Application of Stock Price Prediction

Presently, data classification has become a hot research topic, intends to categorize the data into a predefined number of classes. The data classification problem has been considered as a NP hard problem and various optimization algorithms are introduced to solve it. Social spider optimization (SSO) algorithm is originally developed to resolve continuous and optimization problems. In line with, it has been altered to manage different kinds of optimization process and also to be employed for data investigation. And, some other studies are also investigated the use of SSO algorithm in different domains. In this paper, we introduce new SSO algorithm particularly applicable for data classification process. In order to validate the performance of the SSO algorithm, a real time problem of stock price prediction (SPP) is employed. For experimentation, the results are validated by testing the SSO algorithm against four datasets such as Dow Jones Index (DJI) dataset, three own datasets gathered from Yahoo finance on the basis of daily, weekly and yearly. The empirical result states that the proposed algorithms perform well and it is noted that the classification performance of the SSO algorithm better than the compared methods.
Configuration

User-configurable data can be used to control the firmware behavior with the same firmware code. The configuration data is designed to be updatable and mutated by the end user, whereas the firmware code is typically only editable by the platform manufacturer. As such, the protection of configuration data is different from that of the firmware code.
A neural-based re-ranking model for Chinese named entity recognition

Chinese named entity recognition (CNER) is different from English named entity recognition (ENER). There is no specific delimiter in Chinese text to determine the words in a sentence. Besides, the combination of Chinese text has a strong arbitrariness. These special cases usually bring more errors to the Chinese NER (CNER). We propose a re-ranking model based on BILSTM network and without using any other auxiliary methods. Our approach uses N-best generalised label sequences that are produced by baseline model as input and feeds them into our re-ranking model for modelling the context within the generalised sequences. The optimal output sequence is obtained by comprehensively considering the result of baseline model and re-ranking model. Experimental results show that our model achieves better F1-score on Bakeoff-3 MSRA corpus than the best previous experimental results, which yields a 0.97% improvement on F1-score over our neural baseline model and a 0.22% improvement over the state-of-the-art CNER model.
An Immersive Approach based on Two Levels of Interaction for Exploring Multiple Coordinated 3D Views

The interaction with multiple 3D visualizations in 2D conventional displays lacks usability and does not guarantee the usefulness the extra dimension provides. Immersive visualization techniques can potentially fulfill these gaps by providing 3D visualizations and novel 3D interactions simultaneously. In this paper, we propose a new approach for interacting with composite and multiple coordinated visualizations in immersive virtual environments. We use a 3D-WIMP-like concept, i.e., virtual cubes (Spaces), for encapsulating views, which the user can freely control in the virtual environment. We compared our approach with a desktop version to evaluate its performance when dealing with composed tasks.
Data Containers

Nature and human civilization are about collections. Families collect relatives, cities collect people acting together, houses collect people and their belongings, math set theory uses collections for relation formulas, galaxies collect stars, atoms collect elementary particles, and so on. It is thus no surprise that computer languages, intended to model real-world scenarios, must be able to model collections as well.
SNCHAR : Sign language Character Recognition

Hard of hearing and unable to speak individuals convey among themselves utilizing gesture based communication yet typical individuals think that it’s hard to comprehend their language. Utilizing two hands frequently prompts lack of definition of highlights because of covering of hands. Our undertaking goes for making the essential stride in crossing over the correspondence hole between typical individuals and tragically challenged individuals utilizing Sign language. Powerful augmentation of this undertaking to words and typical statements may not just cause the deaf and dumb individuals to impart quicker and simpler with external world, yet in addition give a lift in creating self-sufficient frameworks for comprehension and supporting them. Gesture based communication is the favored technique for correspondence among the hard of hearing and the meeting debilitated individuals everywhere throughout the world. Acknowledgment of communication via gestures can have shifting level of achievement when utilized in a computer vision or some other techniques. Communication via gestures is said to have an organized arrangement of signals where each motion is having a particular significance. We propose a solution to this problem as SNCHAR will allow easy interaction between the deaf and the hearing impaired people and the ones who are not. Here SN stands for Sign language, CHA for Character, and R for Recognition system. The project "SNCHAR: Sign language Character Recognition" system is a python based application. It uses live video as input, and predicts the letters the user is gesturing in the live feed. It captures the frames, and recognizes the area of hand gesture by looking for skin color intensity object. It separates the gesture area from the rest of the frame, and feeds that part to our pre-trained model. This pre-trained model, using the hand gesture as input predicts a value that represents an alphabet. This alphabet is displayed on the screen. User can hear the text predicted on the screen by pressing “P” on the keyboard. The predicted text can be erased if required by using “Z” from the keyboard
Performant Retrieval Image using Rectangular Mask and Combination of Color, Texture and Shape Descriptors

The evolution of computer technologies has led to the growth of digital images, which has made the search for similar images in this volume of data a very important research component. Since several works have proposed image search systems entitled CBIR (Content-Based Image Retrieval). This paper presents a new and powerful method for creating CBIR in order to improve the accuracy of search through visual content. The originality of our method lies in its invariance to the rotation of images queries. She consists of applying rectangular masks of different size on the image, and extracting the color descriptor from the visible region on the mask, and then combining the result descriptor to the Uniform Local Binary Pattern (ULBP) texture features and add canny edge features. We compare the query features to the extract ones, using metric distance. We evaluate our techniques using Corel1K and Ukbench dataset. The average precision measured gives good results comparing to the others existing retrieval systems.
Quantum State Filtering of Dual-rail Photons with Fiberized Plasmonic Metamaterial

We demonstrate quantum state filtering of dual-rail photons through single-photon interference on a fiberized plasmonic metamaterial, exploiting different optical response of the metamaterial to symmetric and anti-symmetric superpositions of double-path wavefunction of single-photons.
ITNT Proceedings Preface

Proceedings of the V International Conference on Information Technology and Nanotechnology (ITNT-2019); 21-24 May 2019, Samara, Russia D V Kudryashov 1,2 , E S Kozlova 1,2 1 Image Processing Systems Institute - Branch of the Federal Scientific Research Centre “Crystallography and Photonics” of Russian Academy of Sciences, Molodogvardeyskaya str. 151, Samara, Russia, 443001 2 Samara National Research University, Moskovskoe Shosse 34, Samara, Russia, 443086 e-mail: red_ko@smr.ru Abstract. The proceedings of the V International Conference on Information Technology and Nanotechnology contain 251 papers. The topics discussed include computer optics, nanophotonics, image processing, Earth remote sensing, data science, mathematical modeling of physico-technical processes and systems.
Reviewer Acknowledgements

Applied Finance and Accounting [AFA] would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether AFA publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 5, Number 2Adina Criste, “Victor Slavescu” Centre for Financial and Monetary Research, Romanian Academy, RomaniaAndrey Kudryavtsev, The Max Stern Yezreel Valley Academic College, IsraelAnna Viktorovna Kravchuk, Academy of the State Penitentiary Service, UkraineFabio Rizzato, University of Turin, ItalyFeng Jui Hsu, National Taichung University of Science and Technology, TaiwanFlorin Peci, University of Peja, KosovoGheorghe Morosan, Stefan Cel Mare University Suceava Romania, RomaniaHajar Jahangard, Central Bank of Iran (CBI), IranIoan Bogdan Robu, Alexandru Ioan Cuza University of Iasi, RomaniaJayendra S. Gokhale, Embry-Riddle Aeronautical University, USALingesiya Kengatharan, University of Jaffna, Sri LankaLuca Sensini, University of Salerno, ItalyLuo Yongli, Houston Baptist University, United StatesMarco Muscettola, Independent researcher, ItalyMawih Kareem Alani, Dhofar University, OmanMohamed Jalloh, Economic Community of West African States (ECOWAS), NigeriaNicoleta Radneantu, Romanian – American University, RomanianNikolay Patonov, European Polytechnical University, BulgariaNoriaki Okamoto, Rikkyo University, JapanPeibiao Zhao, Nanjing University of Science and Technology, ChinaShahram Fattahi, Razi University,, IranVineet Chouhan, Sir Padampat Singhania University, IndiaVolodymyr Vysochansky, Uzhhorod National University, UkraineYu Peng Lin, University of Detroit Mercy, USAZi-Yi Guo, Wells Fargo Bank, N.A., USA
Risks to the Public

RISKS items seem to be burgeoning, making it very difficult to devote detailed accounts in the six pages that our recent SEN sections are occupying. We try to emphasize those items here that have the most content relating to software engineer- ing and system engineering. Safety issues continue to recur, and security issues are always rampant. Fodder for this section continues to manifest itself.
Inequalities for permanents and permanental minors of row substochastic matrices

In this paper, some inequalities for permanents and permanental minors of row substochastic matrices are proved. The convexity of the permanent function on the interval between the identity matrix and an arbitrary row substochastic matrix is also proved. In addition, a conjecture about the permanent and permanental minors of square row substochastic matrices with fixed row and column sums is formulated.
An Innovative Video Summary and Pattern Mining using VSP Algorithm

This paper proposes a VSP-TREE algorithm that mines l associations from video. A tool is designed to annotate the Video sequences by giving appropriate values to the sequence and then these values are converted into two-dimensional datasets suitable for clustering. The datasets are clustered using innovative algorithm to form distinct group and known as summary candidate with user size, our system make summary by choosing important frame from candidate cluster and put them in original. A VSP-TREE Based Mining method is used find out frequent patterns occurrence in the video. Association mining algorithm used on clustered datasets with innovative method, Video sequential pattern Tree (VSP Tree) Structure to generate frequent patterns through efficient methodology called conditional search.
Chapter 18: Evaluation of Clustering Algorithms

In the literature of data clustering, a lot of algorithms have been proposed for different applications and different sizes of data. But clustering a data set is an unsupervised process; there are no predefined classes and no examples that can show that the clusters found by the clustering algorithms are valid (Halkidi et al., 2002a). To compare the clustering results of different clustering algorithms, it is necessary to develop some validity criteria. Also, if the number of clusters is not given in the clustering algorithms, it is a highly nontrivial task to find the optimal number of clusters in the data set. To do this, we need some cluster validity methods. In this chapter, we will present various kinds of cluster validity methods appearing in the literature.
The implementation of scaffolding on students’ writing ability in English online classes

The objectives of this research were to found out the improvement of the students' writing achievement before and after the implementation of scaffolding and also to found out what aspects of writing improved the most after the implementation of scaffolding at SMA N 1 Sendang Agung. The subject of this research was students of eleventh-grade (class XI IPA 1) in the academic year 2020/2021. This research was conducted virtually through Google Classroom. This research was a quantitative research and the method was one group pretest and post-test design. The analysis of SPSS 20 shows that there was an improvement of students' writing ability after the implementation of scaffolding. Beside, based on seeing the analysis of students' worked in pre-test and post-test on each aspect of writing, it could be found that content was the aspect that increased the most than other. Keyword: scaffolding, writing, online classes.
Latent Feature Word Representations to Enhance Topic Models for Text Mining Algorithms

Dealing with large number of textual documents needs proven models that leverage the efficiency in processing. Text mining needs such models to have meaningful approaches to extract latent features from document collection. Latent Dirichlet allocation (LDA) is one such probabilistic generative process model that helps in representing document collections in a systematic approach. In many text mining applications LDA is useful as it supports many models. One such model is known as Topic Model. However, topic models LDA needs to be improved in order to exploit latent feature vector representations of words trained on large corpora to improve word-topic mapping learnt on smaller corpus. With respect to document clustering and document classification, it is essential to have a novel topic models to improve performance. In this paper, an improved topic model is proposed and implemented using LDA which exploits the benefits of Word2Vec tool to have pre-trained word vectors so as to achieve the desired enhancement. A prototype application is built to demonstrate the proof of the concept with text mining operations like document clustering.
Halaman Depan Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 8 No. 1 2019

This article contains front-matter of Sisfokom, Volume 8 Number 1 March 2019, which includes a cover page, title page, editorial team, and table of contents.
A CASE BASED ANALYSIS OF ORGANIZATIONAL SECURITY GOVERNANCE DIMENSIONS: USER INVOLVEMENT, PROCESS INTEGRITY AND RESOURCE ALLOCATION

Incidents of cybersecurity breaches are widespread and on the rise.There is more and more evidence to believe that threats to information assets are better mitigated if security controls are reflective of specific organizational context.Organization security objectives are critical to a useful basis for strategic planning for comprehensive security.Based on a study, Mishra (2015) that identifies 23 objectives of organizational security governance (OSG), this study interprets the meaning of these objectives to operational, tactical and senior level executives in an organization.Based on the case study data, the result indicates that the importance of OSG objectives to different segments of employees is dependent on roles they play in organizations.A framework of underlying OSG dimensions: user involvement, process integrity, and resource allocation are proposed.Implications are drawn, and future research suggested.
Reviewer Acknowledgements

Applied Economics and Finance (AEF) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether AEF publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 6, Number 5Ali Massoud, Sohag University, EgyptAndrey Kudryavtsev, The Max Stern Yezreel Valley Academic College, IsraelAyoub Taha Sidahmed, SIU, SudanDyah Wulan Sari, Airlangga University, IndonesiaFarhat Iqbal, University of Balochistan, PakistanGetamesay Bekele Meshesha, Debre Berhan University, EthiopiaHedieh Shadmani, Fairfield University, USAIan McFarlane, University of Reading, UKIulia Lupu, Victor Slavescu” Centre for Financial and Monetary Research, Romanian Academy, RomaniaKembo Bwana, College of Business Education, TanzaniaMagdalena Radulescu, University of Pitesti, RomaniaMagdalena Zioło, University of Szczecin, PolandMahmoud Mohammed Sabra, Al Azhar University-Gaza, PalestineMamdouh Abdelmoula M. Abdelsalam, Minufiya University, EgyptMarco Mele, University of Teramo, ItalyMarco Muscettola, Independent Researcher-Credit Risk Manager, ItalyNicolas Afflatet, University of the Federal Armed Forces, GermanyOltiana Muharremi Pelari, University of Vlora, AlbaniaPatrycja Kowalczyk-Rolczynska, Wroclaw University of Economics, PolandPayal Chadha, University of Wales Prifysgol Cymru, KuwaitRajeev Rana, APB Govt. P.G. College, IndiaRichard Nguyen, Alliant International University, USARomeo Victor Ionescu, Dunarea de Jos University, RomaniaSebastian Schich, Organisation for Economic Coopertaion and Development (OECD), FranceSzabolcs Blazsek, Universidad Francisco Marroquín, GuatemalaWoodrow Clark II, Clark Strategic Partners, United States, USAY. Saidi, M’sila University, AlgeriaZi-Yi Guo, Wells Fargo Bank, N.A., USAZuzana Janko, San Francisco State University, USA Nikki GibbsEditorial AssistantOn behalf of,The Editorial Board of Applied Economics and FinanceRedfame Publishing9450 SW Gemini Dr. #99416Beaverton, OR 97008, USAURL: http://aef.redfame.com
Community Partnership Program (CPP/PKM) High School Teachers in Bandung City and District in E-Learning Based Data Collection Activities

The purpose of the Subject Teachers Consultation (STC/MGMP) will be if the teachers are skilled, accustomed and able to develop various activities that are able to support teacher competencies. One of the activities carried out was to participate in the Community Partnership Program (PKM) program development activities with LPTKs such as Pasundan University. The STC included in the Community Partnership Program (CPP/PKM) is the Bandung and City Mathematics STC. Problems with both partners are: (1) Some teachers do not know SPSS and its benefits in research, (2) Understanding of some teachers in collecting data according to the formulation of research problems is still lacking, (3) Some teachers do not know e-learning applications that use video conference. This problem has an impact on the inhibition of research and an increase in the rank of teachers. Approach methods used to overcome problems with training, mentoring, and mentoring. The two partners' solution-resolution procedures will be carried out by the team as follows: (1) Training, mentoring and mentoring in using SPSS features, (2) Training in using e-learning applications (3) Training, mentoring, and mentoring in collect data according to the formulation of research problems. CPP was attended by 14 Mathematics Teachers from the City of Bandung, and 17 Mathematics Teachers from Bandung Regency who were each from the Bandung City and Regency STC. The first meeting resulted in the following conclusions: (1) The quality of CPP in formulating problems, testing instruments, using SPSS features, and video conferencing conducted was very good, (2) The ability of teachers to understand, master, and train in making either test or non-test research instruments are in a pretty good category, (3) the teacher's ability to input data according to data or the scale of the data is in a good category, (4) The teacher's ability to use video conferencing to get guidance and assistance in doing the work is in good category