Reviewerly/exHarmony huggingface dataset:
An Approach Towards the Development of Scalable Data Masking for Preserving Privacy of Sensitive Business Data

Large amount of digital data is generated rapidly all around the globe. Providing security to digital data is the crucial issue in almost all types of organizations. According to the Identity Theft Resource Center, there were 8069 data breaches between January 2005 and November 2017 [1]. In the year 2018, 477 cases registered about data breach [2]. In just three months of 2019, 145 such cases are already noticed [GK et al. in A study on dynamic data masking with its trends and implications. 38(2), 0975–8887, 3], and it continues to grow. Protecting the digital sensitive data from data breaches is the need of the hour. The main objective is to protect the privacy of individuals and society which is becoming crucial for effective functioning across businesses. Privacy enforcement today is being handled primarily through government monitored regulations and compliances. To overcome the limitations of existing masking methods, researcher proposed a non-zero random replacement masking method. Researcher has successfully developed a scalable data masking model which can be used for various data types—CSV, JSON, XML, and relational databases. To evaluate the proposed method, researcher used an internationally recognized UCI repository which is an open source of secondary data, out of 436 datasets available on the site; researcher selected five different datasets of various business domains. The selected business data is under five different categories—healthcare, social media, bank marketing, bank finance, and stock market. The researcher also contemplated about volume of datasets. Researcher applied three types of masking—substitution, shuffling, and proposed method on the selected datasets. The original dataset and masked datasets are classified by classification metric. Performance parameters measured on four different classifiers delivered sizeable variations. With respect to data samples used for analysis, results strongly augmented that the proposed data masking method can be used across the business-critical domains. The results strongly emphasize that the proposed model is the solution which not only protects the sensitive data but also maintains the usability, accuracy, and sensitivity.
About control of a neuron as an elementary unit of multilayer artificial neural networks with variable signal conductivity

Abstract The report is devoted to a possibility of a control of each Hopfield’s neuron as an elementary unit of the neural network with variable signal conductivity used for transport scheduling tasks. It is set that direct control of the neuron is impossible in terms of initial data available for initializing the neural network. Also the result of some control algorithms based on statistical computations and signals dynamic is described below.
KNN Classification for CBIR with Color Moments, Connected Regions, Discrete Wavelet Transform

The difficulty of finding some specific images from a database creates a need for a system that applies the Content-Based Image Retrieval (CBIR) concept. Publicly, a CBIR system is a system that able to search and retrieve the desired images that have similarity in both visual characteristics and relevance terms. The proposed CBIR system is using three different feature extraction methods that is combined with the KNN Classification. The color feature will be extracted by Color Moments, the shape feature will be extracted by Connected Regions, and the texture feature will be extracted by Discrete Wavelet Transform. The images that will be used are animal images and separated into 10 classes. The KNN Classification will classify the query image first into a specific class before starting any retrieval process. The results from the system show that Color Moments has a precision of 45.8%, Connected Regions has a precision of 34.1%, and Discrete Wavelet Transform has a precision of 44.5%. Meanwhile, the combination of feature extraction methods without KNN Classification has a precision of 37.1%, while when combined with KNN Classification, the system has a precision of 68.7% with 100% classification accuracy. The experiment shows that the system needs KNN Classification to increase its usability and retrieval accuracy.
A SYSML-Based Approach for Requirements Risk Management and Change Control

Requirements are the collection of the needs of customer and requirement engineering is used for defining, analyzing and specifying these requirements. It is an important phase of software life cycle. When requirements are poorly defined, then it leads to various problems like system failure, delays, and cost overruns. Complexity of the system makes requirements more important and increases the chance of risk in requirements. Model driven approach enhances the understanding of the system in effective way as it contains the models, which is the main artifact during software system development. This paper proposes a model driven approach for defining the requirements with their related risk in efficient way. It helps in understanding the stakeholders who play an important role in development of the software system. A SysML(System Modeling Language) based requirement engineering approach is used for explaining the requirements graphically along with their associated risk. The validation of proposed approach is done through a case study of patient monitoring system.
Quantum Key Distribution

Quantum key distribution is an application of quantum cryptography which is based on quantum mechanics and optical physics. The word “quantum” means the smallest particle of matter and energy which inhibits unique special properties to make it different from normal matter. This chapter discusses underlying principles, and operations of quantum mechanics which are used to derive quantum key distribution protocols. This chapter also discusses elementary QKD protocols based on no cloning theorem and EPR correlations. Limitation of quantum key distribution is also discussed with reference to its implementation. Conceptual notes on quantum internet are also given.
Sales Data Monitoring Systems Telkom Indonesia Witel Samarinda Field Business Goverment and Enterprise

Business Government & Enterprise is a field in PT. Telkom Indonesia is engaged in a large scope, namely marketing products to businessmen, government agencies, and large companies that use internet access with different access to home internet. This field requires a reporting system and data monitoring to make employee performance more efficient. Where for reporting and monitoring contract data or sales will only be accessed through the website so that it makes it easier for employees to access and make performance more efficient. Monitoring System is a group of integrated elements that collect and analyze information from ongoing projects. The purpose of the monitoring system is that sales in the Business & Enterprise Business can continue to be monitored to the maximum.
Face Recognition with Kernel Principal Component Analysis and Support Vector Machine

Machine learning and pattern recognition recently become a hot topic in computing world. This is due to the fast-growing of resources as well as techniques that make it easier to solve machine learning and pattern recognition problems. Problems that require machine learning solutions may be very simple for humans but actually can be very complex for machines to solve them. Face recognition is amongst those problems. Almost all human can easily recognize others without require specific knowledge to do it, different from machines which require its. This paper discussed face recognition task using machine learning strategies which involved Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM) to identify person. KPCA extracted features from 2D image input and produced the important features of an image input. The extracted face features are recognized by SVM by classifying human face according to their stored identity in a database. SVM, which was basically a binary classifier worked by using one-against-one strategy to compare the face feature vector of a single test image to the stored face image in a face image database. Experiment results on grayscale images with size 92x112 pixels gave 96.25% of accuracy rate. Hence, KPCA and SVM for face recognition is a robust machine learning method.
Metaphors and Technologies for New Tangible User Interfaces

Tangible user interfaces create the possibility to provide users with an exploratory, expressive and flexible access to musical interaction. However, new technologies and metaphors can additionally open up further opportunities and mental models for the design of new interaction concepts. In the scope of my thesis I explore new metaphors by designing and building new interfaces which finally explore these ideas and concepts. In addition to the conceptual work, the development of new technologies, which are necessary for the implementation of these concepts, is a central part of the work. In this paper I present the prototypes COMB and StringTouch and discuss potential application areas for the implementation of core ideas regarding both concepts.
Study on Synthesis Methods for Real-Time Control of Car-Like Mobile Robot

This paper presents a comparative study of synthesis methods for real-time control of an unmanned car-like mobile robot while moving along a given trajectory. Three methods are considered: a feedback control using the PID controller, a model predictive control, and artificial neural networks. A comparison is based on computational experiments. A complex trajectory with crossing paths is considered for path tracking.
Design and Implementation of Hex Computer Game Platform

In order to improve the development efficiency of Hex game computer game program, the program which can load different game engines and carry out human-machine competitive as a test platform in the development process is designed and realized. Through pipeline communication to realize the interaction between the platform and the game program, design the Hex game referee terminal logic. This paper introduces the function of the program, designs the goal, and lists the treatment of the key problems in the program. The practical application proves that the game program runs fast and stable, and the design and realization of the man-machine game program has certain reference value to complete other game computer game programs.
An Integrated Design For Geospatial Analysis Based On WebGIS

In this paper, the web map application based on WebGIS was architected for geographic shapes creation and geospatial data analysis, providing a better and higher performance in an interactive mode. By integrating with geometric operation module provided by geospatial database, it is supported to create varied kinds of geometry shapes with complex structures and symmetric patterns in an easy way. The WebGIS application was designed in the form of the distributed architecture, providing visualization features of geometric operations and geospatial storages. It is feasible to support interactive features of geospatial analysis in an easy way, providing efficient security and better flexibility of map applications. The successful and efficient result indicates that it will bring scalable convenience and wide practicability in the area of complex geometric patterns design, including geographical regional planning and design.
VR or non-VR?

In this paper, we aim to research how a choice of a gamification medium influences the users' psychological and behavioral experiences which are also gamification outcomes. We discuss gamification, its components, persuasive systems, cognitive absorption, perceived persuasiveness of gamified systems, as well as the influence of the psychological constructs on intention to play the game and intention to engage in sustainable behavior. Since we take a look at two types of virtual environments (non-immersive and immersive), we include concepts relevant for choosing an appropriate platform, such as sense of presence and simulator sickness, both of which have a significant impact on user experience. Based on the discussed theoretical background, we propose a research framework and a list of hypotheses to be empirically tested. We describe an experimental set up to test the hypotheses within the context of pro-environmental behavior. Finally, we present potential contributions and limitations of this research-in-progress.
Active Learning for Spam Email Classification

Deep learning has yielded state-of-the-art performance on text classification tasks. In this paper, a new neural network based on Long-Short-Term-Memory model is applied to classify spam emails. Using deep learning method to classify spam emails requires large amounts of labeled data. To solve this problem, active learning method is used to reduce labeling cost and increase model adaptability. In this paper, it is found that the new model performs better than standard CNNs and RNNs on email classification task, and active learning methods can match state-of-the-art performance with just 10% of the labeled data.
An Overall Study of Formulaic Expressions

Defining formulaic expressions is rather controversial and problematic because it encompasses a variety of theories and approaches such as idioms, collocation, proverbs, sayings, etc. For this reason, this paper presents a brief survey of the contrasting perspectives. It starts with earlier views of formulas focusing on the identification and definition of such phenomena and ends with a working definition for this research as an attempt to analyse this distinctive phenomenon. The aims of the present study are: first, finding an operational definition to the formulaic expressions. Second, establishing a historical overview of formulaic expressions. Finding out the differences between formulaic expressions and other types of formulas. The study hypothesizes the following: first, the formulaic expressions are collocated words formed by speakers for easiness. Second, formulaic expressions have an ancient history. Third, many differences are indicated between formulaic expressions and other formulas.
Using Stanford CoreNLP Capabilities for Semantic Information Extraction from Textual Descriptions

Automated extraction of semantic information from textual descriptions can be implemented by processing results of application of Stanford CoreNLP tools. This paper presents a sequence of processing steps and initial results of their application for two examples of a description of system’s functionality. The processing steps allow identifying main functional characteristics of the system and its operational domain. Results obtained as a result of application of the steps are compared with data obtained as a result of analysis by a developer. Application of Stanford CoreNLP parsers in certain cases can produce errors and can influence results of further processing. The comparison of the two results sets showed that variability of language constructs in descriptions affects an amount of implicitly expressed knowledge. Nevertheless, results of this research can be used as a start point of automated text processing for creation of analysis models.
Blockchain: A New Disruptive Innovation for Knowledge Risk Management

This chapter aims to examine blockchainBlockchain technology, which is a disruptive innovationDisruptive innovation of recent years, in the context of knowledge risk management (KRM). It discusses how tacit knowledge of blockchain can be retained in organizations and integrated to production or service process. The chapter examines the KRM process in three knowledge retention stages, namely knowledge acquisition, knowledge transfer and knowledge integration. The core argument of this study is organizations intending to adopt blockchain should improve their KRM capacity before initiating this technology in their business process, whilst this technology is still in its nascent years. Otherwise, there is a huge knowledge managementKnowledge management riskRisk for them because they may lose their first-mover advantage against their competitors if they lose their knowledge assets. Their pioneering role can be seized by their rivals.
Steganographic Approach to Enhance Secure Data Communication Using Nonograms

In the present scenario the security of data is of utmost importance. While cryptographic techniques render the data unintelligible, steganography conceals the presence of the data itself. There are many pre-existing methods of steganography that involve hiding encrypted data in various mediums such as images, videos, audio, etc. In this project, we are going to focus on data hiding in images. There are numerous ways to embed data into images out of which the most widely used is the least-significant-bit substitution, in this method the least-significant bits of each pixel are substituted with the data instead. Nonograms are Japanese grid-based puzzles which when solved produce a picture composed of square blocks. We are using the puzzle to divide the cover image into blocks and then we use k-means clustering to select the pixels for LSB substitution thereby going a step further and hiding the hidden data in RGB images.
A Robust Multilevel Security Mechanism against Geometric Attacks

The need for secured data communication in today's world is a prime concern of every internet user facing various security assaults. To cope up in such a hostile environment, various researchers have proposed the use of multiple levels of securities which combine cryptography and steganography. The most common approach in literature uses spatial domain steganography technique coupled with symmetric key cryptography. Though, spatial domain steganography possess a rich PSNR value and a good perceptual quality however, it's robustness against geometric attacks is less. Therefore, this paper proposes a robust multilevel security mechanism which inculcates transform domain steganography technique combined with hierarchical visual cryptography. To enrich the data embedding capacity, Huffman compression is also added. The proposed mechanism is implemented in MATLAB-2012 and the result shows that the technique is far better against attacks than the other ones available in literature.
Out of Our Minds

With the exponential development rate of technology at the start of the 21st century, humanity faces data that it alone cannot process without the aid of even more technology. As we move into a post-human era, how can immersive, spatialized, and embodied media assist us in comprehending the effects of these agents, and reconsider our past conclusions? These are media that can communicate new perspectives or run complex simulations tirelessly. They can provide the scaffolding to test our analytical reasoning and process to potentially escape our cognitive biases, develop greater plasticity, or even test new forms of embodiment. We must also consider how they can be manipulated and weaponized, and the rights of our future digital selves, as we become subsumed by data. This chapter explores ontology and embodied media in a post-human paradigm.
Smart Security System Using Face Recognition on Raspberry Pi

Privacy and Security are two of the most important universal rights. To ensure security in our daily life through technology, a lot of research is going on. Among them facial recognition is a popular and well-established technology. In this technology, faces are detected and identified out of images and with the help of Internet of Things (IoT), it becomes even more useful and precise. Using face recognition and IoT, we aim to create a smart door, which secures the gateway on the basis of who we are. In our proof of the concept of such a smart security system, we have used Viola-Jones method to detect faces and Eigenfaces method to recognize people. A Raspberry Pi has been used as the microprocessor for ensuring low-cost and small size of the system. The door will open automatically for the known person upon receiving command from the processor. On the other hand, photo of the unknown will be uploaded to a webserver while an email is sent to the owner of the place containing a link to the image. From the website of the system, the owner can then allow/block entry and also trigger an alarm from anywhere in the world.
Development of the Edge Computing Platform with Dynamic Modular Configuration for an IoT Platform

We have been developing an edge computing platform that can coordinate with each computing resources of device, edge node and cloud for diversifying data can be modularized and actively deployed in cooperation within the wide area network. With these features, it is possible to flexibly deploy and work modules according to data characteristics and purpose of use and to provide various functions at appropriate network locations. This platform brings transparent processing environment for IoT devices according to a data characteristic or use purpose. This paper describes a dynamic control method for the platform based on multiple sensor information, and show implementation and evaluation by an actual application using camera devices.
Expanding Tracing Capabilities Using Dynamic Tracing Data

Software traceability enables gaining insight into artifact relationships and dependencies throughout software development. This information can be used to support project maintenance and to reduce costs, e.g. by estimating the impact of artifact changes. Many traceability applications require manual effort for creating and managing the necessary data. Current approaches aim at reducing this effort by automating various involved tasks. To support this, we propose an enrichment of tracing data by capturing interactions that influence the artifacts’ life-cycle, which we refer to as dynamic tracing data. Its purpose is to expand capabilities of traceability applications and to enable assistance in development tasks. In this paper, we present our research methodology and current results, most importantly a flexible and modular framework for capturing and using dynamic tracing data, as well as an example scenario to demonstrate a possible implementation and usage of the framework.
Extraction of Lexical Bundles used in Natural Language Processing Articles

Lexical bundles are indispensable for fluent academic writing. They might not constitute complete structural units but they occur very frequently in academic conversations, conference presentations and scientific articles. This paper shows how to collect a large database of lexical bundles from articles in the Natural Language Processing (NLP) domain. We first collect highly frequent N-grams from the ACL-ARC collection of NLP articles and then classify them into true or false lexical bundles using machine learning models trained from a set of manually checked bundles. In a verification experiment, our best model achieves an accuracy of 76 %. Using this model, we extract more than 18,000 lexical bundles from the ACL-ARC corpus, which we publicly release.
Automatic Keyword Extraction and Crossword Generation Tool for Indian Languages: SEEKH

With many schools exploring the transition to a digital curriculum for enhanced learning and evaluation procedures, the need for freely available, customizable tools to address these concerns has increased. More students will be able to reap the benefits of such resources if implementations are available in regional languages which are a medium of education in any schools. Comprehension of a passage of text can assessed by the reading of the given text. The generation and playing of the crossword in a recognized Indian Language could enhance learning experience of students studying in vernacular medium.