Reviewerly/exHarmony huggingface dataset:
E-Transformation in Higher Education and What It Coerces for the Faculty

With the phenomenal developments in information and communication technologies, higher education has been facing an unprecedented challenge that affects all the stakeholders. Faculty is no exception. The authors synthesize the demographic, economic, and pedagogical factors that lead to a paradigm shift in higher education and the global trends in digital technologies that impel digital transformation in higher education. They then provide a snapshot of how higher education institutions respond to this challenge and change, and the impact of these factors on the roles and competencies of faculty that need to be covered in faculty development initiatives in the digital age. Finally, examples of faculty development programs and initiatives that address the digital competencies of faculty are provided along with a summary of faculty development models for teaching and learning in the digital age.
Image Enhancement Using Morphological Operations: A Case Study

Morphology consists of two words: morph+ology. Morph means transformation, and ology means a branch of study. Image transformation may take place due to various factors. One of such factors is noise. Hence, morphology is considered to be an image transformation technique which is used to remove imperfections in an image caused by noise. Morphology finds its application in the representation of shape of a region, describing skeletons, surface deviation analysis, and other surface metrological applications. Morphological operations are performed using erosion and dilation algorithms, and the results are verified using MATLAB R2013a.
Automated Community Feedback and Monitoring Assistant

There is a need for social media platforms to provide a streamlined and seamless experience to share one’s comments on any event or content. The need for community moderation is ever increasing, as well as the need to make the best use of any and all of the huge data generated, in an ethical and responsible manner. The proposed system allows users to mark their responses either by text or by a speech-to-text conversion tool. Thereon, the system classifies the data and initiates corresponding action workflows. Once the data is found not to be platform abuse, the content is reviewed for any sentiment analysis information that could be extracted. In real time, users can express their opinions freely, responsibly, adhering to community standards of information sharing, as well as allow concerned advertising groups to monitor trends and make informed decisions with the user generated data. The system enforces community moderation standards with least manual effort and processes the content uploaded instantly, to provide valuable insight and add commercial value.
Motivational Factors Affecting Computer Science Enrollment

Internationally, the interest in science, technology, engineering, and math, or STEM, has led many countries to enact education policies aimed at generating more attention to STEM careers among its primary and secondary students. Driven by the supposed need for more STEM field workers, the movement has perhaps failed to look closely at what it is that drives the need and where the actual needs lie. Within the STEM career field lies jobs from nurse to field biologist to civil engineer to computer programmer [7]. However, there is also a large demand for computer science, field that is often swallowed up by the larger STEM interest. According to the Bureau of Labor and Statistics [2], computing jobs will make up 66% of all science, technology, engineering and math (STEM) jobs between 2014-2024 in the United States. However, only 8.48% of college STEM majors are in the computer science (CS) field [2] and there are over 500,000 unfilled computing jobs in the United States alone, but less than 50,000 CS graduates [3]. With the documented need for more CS workers, matched with very little primary and secondary CS education research and slow movements to enact policy, we have reached a critical point where it is important to understand more about what factors motivate students to enter the CS career field and how educators can use these factors to help direct and support more students to fill the important job sector that so desperately needs skilled employees. Using the expectancy-value theory (EVT) as a framework, this poster presents a study that looks at factors that could serve as predictors for enrollment in high school CS electives. EVT posits that individuals who are given options, particularly around course and career choices, make their decision based on how they expect to perform at the task and what value they see it having in their life. Value can be further broken into utility value, intrinsic value, attainment value, and cost [4, 5, 9]. This framework for understanding student motivations for STEM has been used and validated multiple times [1, 6, 8], so this study hopes to build on that proven reliability to understand student motivations with regard to CS. Using the High School Longitudinal Study of 2009 (HSLS:2009) This poster presents a work-in-progress that looks at what factors during the ninth grade (first year secondary) year are predictors of a student's enrollment in any CS course during high school.
Toward a continuous authentication system using a biologically inspired machine learning approach

Smartphones have recently seen massive growth in usage and become a repository for many types of personal information. The privacy and security are primary concerns for their usage, where there is a need to provide seamless and continuous authentication systems(CASs) for smartphones. We introduce in this work a proof-of-concept design and a case study for using a biologically-inspired and hardware-friendly CAS. Our proposed design adopts a hybrid Liquid State Machine (LSM) approach to perform automatic features extraction and multi-modal fusion scheme for users' interaction. Our work establishes the design concepts for future on-chip and explains why LSM can be a promising approach for fast, adaptive and reliable hardware CASs. The experimental part reveals our proof-of-concept testing results against the golden standard features. Furthermore, The results indicate a promising future for such design and a potential biologically-inspired CASs.
Towards textualising analytic provenance for visual analytics using natural language generation

Visual Analytics allows analysts to process large amounts of data for understanding and decision making. The analytical thinking, domain knowledge and experience of the analysts are major contributing factors during the Sensemaking process. Analytical Provenance deals with capturing, representing and utilizing the Sense-making process. This is needed for reflection, training and audit purposes. Existing automated methods fail to capture semantic meaning within the analysis process, while manual annotation by itself is disruptive to the analysis process. We propose automatically generated textual summaries of the Sensemaking process. We present a novel solution driven by the Natural Language Generation(NLG) domain which consists of generating textual narratives of the Sensemaking process. Our pilot study shows the potential for understanding the sensemaking process by generating textual narratives and sets ground work for further research.
Automatic Generation of Description for Images Using Recurrent Neural Network

In this paper, a generative long short-term memory (LSTM) model for generating description of the image is implemented. Automatic generation of description that describes the content of a given image is a fundamental problem in artificial intelligence. This kind of work is achieved by connecting two different domains like computer vision and natural language processing. The solution proposed here makes use of deep learning. A deep learning framework known as Keras is used which uses TensorFlow for the backend process. TensorFlow is a framework used to do a series of operations in a chain. The general technique is to feed the features of an image to the model, which is capable of generating text of length less than or equal to a predefined caption length. The dataset Flickr30 K is used to train the model. The InceptionV3 is used to extract features of the images. BLEU metric is used to measure the accuracy of the description that is generated for that image using LSTM model.
IMPROVED ELECTRONIC MAIL CLASSIFICATION USING HYBRIDIZED ROOT WORD EXTRACTIONS

Content based spam filter prevents spam mail from successful delivery to the targeted host using Bayesian probability approach. Unfortunately, spammers deceived content based filters by coming up with sophisticated means of circumventing detective pattern of developed content filters, manipulating and rearranging spam mail suspicious terms/content to fool such filters, since content based spam filters only work effectively, if the suspicious terms are lexically and grammatically correct. However, this paper proposes word stemming combined with Bayesian probability approach to regain spam-free inbox in the electronic mail infrastructure. The hybridized technique was used to detect modified suspicious terms by examining the base root of the misspelled or modified manipulated suspicious words/terms and reconverting them to the correct token or near correct token and examine as such. The implementation of the algorithm when tested with direct and manipulated spam mail content was able to successfully identified spam mail with manipulated suspicious terms and 99% of the tested  known manipulated suspicious terms spam mail were identified and classified as spam. However manipulated spam mail is of no effect in hybridized word stemming combined with Bayesian probability spam filter approach. The algorithm is effective, accurate, prevent false classification and negate spammer's innovation.
HLR Framework Development for Continuous Integration

Home Location Register (HLR) and Home Subscriber Server (HSS) are the master databases which manages Second Generation (2G), Third Generation (3G) and Fourth Generation (4G) networks. The Continuous Integration (CI) process requires engineers to integrate application features into a single repository frequently. In this paper, an automated HLR framework development for testing the 2G, 3G, 4G or LTE and IMS application features is presented. The HLR framework helps to install and configure the HLR test machines. This framework helps to automate the configuration management on the test machines using Jenkins server to run Sanity and Regression. The HLR Framework Development for Continuous Integration is going to reduce the manual efforts by automating the task of Continuous Integration process such as installation, configuration, execution and generation of a report.
Real time face detection using haar-like feature method and local binary pattern method

Face detection is one of the important roles in face identification. There are some difficulty factors in the process. Therefore, some methods are developed. This study aims to compare Haar-Like Feature method and Local Binary Pattern (LBP) method for face detection. Samples are 25 people in the Electric Engineering Department. Based on the result, Haar-Like Feature method is more accurate than LBP method. Haar-Like method can detect 20 faces from 25 faces with success rate of 80% while LBP can detect 14 faces from 25 faces with success rate of 56%. From this research, it is taken that both methods are having in trouble detecting face from someone that use glasses, have dark skin, or having facial hair.
Statistics for Bench Research

Success in academic surgery requires a basic understanding of statistics. Without this understanding a surgeon-scientist will not be able to interpret scientific articles, develop a hypothesis, or analyze the data they collect in their research program. The purpose of this chapter is to provide a brief overview of the statistical methodsBench research, statistical methodsStatistical methods which are used in bench and clinical research.
Asymmetric Key Cryptosystem and Digital Signature Algorithm Built on Discrete Logarithm Problem (DLP)

The thoughts of data security prompt the advancement of Cryptography. At the end of the day, Cryptography is investigation of keeping data secure. In the advanced mark plot a message can be “marked” utilizing a secretly held decoding key. Anybody can confirm this mark utilizing the comparing freely uncovered encryption key. Marks can’t be fashioned, and an endorser can’t later prevent the legitimacy from claiming his mark. This has evident applications in “electronic mail” and “electronic assets exchange” frameworks. Cryptography, notwithstanding give secrecy, authenticity, integrity and non-revocation. The core of cryptography lies in the keys included and mystery of the keys used to encode or decode. Another vital issue is the key size that determines the strength of the key and complexity in executing brute force attack on the texts to recover the key. There have been different cryptographic algorithms suggested. And we give brief overview of mathematical back ground of public key cryptography. A novel the public key scheme and cryptographic signature system has been proposed and the security of system relies on difficulty of computing discrete logarithm problem over finite field.
Transfer of quantum continuous variable states based on coupled resonator waveguide network

A high fidelity quantum transfer scheme for continuous variable states based on coupled resonator waveguide network is presented. We first give the transfer scheme of an arbitrary superposition of coherent states by using single coupled resonator waveguide, next we give the transfer scheme of entangled coherent states by using two parallel coupled resonator waveguides, and finally give the transfer scheme of continuous variable cluster states by using four parallel coupled resonator waveguides. The fidelity of all schemes is up to 100%.
Grammar Inference Based on Passive Learning and Genetic Algorithm

The mathematical model of a deterministic finite automaton has a wide potential of application, for instance, in control systems. Some of that systems are not trivial and can be defined only in terms of formal language theory. In this paper, we propose a new model for grammar inference, i.e. synthesizing of a deterministic finite automaton by a list of positive and negative examples. We present the results of testing developed model on formal grammars of various complexity.
Classifier Ensembling: Dataset Learning Using Bagging and Boosting

Data mining is used to tackle the huge amount data which are preserved in the data warehouse and database, to chase desired information. Many data mining have been projected such as association rules, decision trees, neural networks, etc. from many years it takes its position in center. The efficiency of the real time or artificial is being enhanced using certain data mining techniques like bagging and boosting and AdaBoost by which the accuracy of the different classifiers is also efficient. Boosting and bagging are two widely used ensemble methods for classification. As the task is to increase the efficiency of the classifier, as per the past works it is always better to have combination of classifiers than to go for random guessing. From the available boosting techniques, the AdaBoost algorithm is one of the best techniques especially in the case when carrying out the branching type of tasks. In the following work, a classification technique is explained which considers the ensemble of classifiers for bagging and for boosting separately, where decision tree is used as the classifier for bagging and for boosting artificial neural network (ANN) is being used as the classifier. For the ensembling, the classifier MDT is being used. Sections are divided as Introduction, Literature Review, Problem Statement, Issues and Challenges, Research Methodology, and Analysis of the Work.
A Study on the Recognition of University Education Expertson Design Thinking Education

The purpose of this study is to explore the perception of Design Thinking education in order to develop Design Thinking education programs applicable to universities. The results of the recognition survey analysis are as follows: First, 95.7 percent of experts said they need Design Thinking education. Secondly, experts said that Design Thinking education is best suited for students of all grades and majors, as well as irregular courses. Third, factors that experts have difficulty in operating Design Thinking education include performance evaluation of Design Thinking education, diagnosis of capacity, and lack of awareness of universities and students. Fourth, experts presented four category in Design Thinking education: recognition and attitude, class environment, class content and teaching method.
Topic-based indexing of federated datasets

The increasing availability of open datasets calls for the adoption of novel query engines able to ease the retrieval of desired information from this huge amount of data. This paper proposes an in depth analysis of a topic-based indexing solution for building transparent, federated query engines for SPARQL. For the index construction, we assume the retrieval of RDF fragments from the distributed datasets while user queries are re-written by the engine with the support of the SERVICE keyword, recently introduced in SPARQL for distributed queries. To perform rewriting, we propose different strategies for discovering and selecting the dataset endpoints. The strategies are analyzed and compared throughout the paper by exploiting some benchmarks. The tests regard both the effectiveness and performance of the proposed strategies. The results show significant improvements if compared with other techniques.
Fyr

Fyr is an open source systems programming language that combines the versatility of C with the ease and safety of modern application programming languages such as Java and Go. Fyr uses manual memory management to support systems where memory is a scarce resource, especially networked IoT devices. Fyr guarantees memory safety and thread safety. These safety guarantees are built on a combination of compile-time type checking, control-flow checking and runtime reference counting where necessary. We show that Fyr allows for more flexibility and simpler code than Rust, especially when working with heap data structures.
UML Modeling for Preserving Sensitive Information Based on k-Means Clustering Approach

This paper proposes the UML modeling approach to facilitate the software engineers in order to implement the cryptographic PPkDC (Privacy Preserving k-means Data Clustering) algorithm based on Object Oriented methodology. Industrial applications for securing sensitive information to be mined have now become the need of different organizations. This paper applies the k-means data clustering approach on to securing sensitive information. The authors have demonstrated the Object Oriented modeling of the proposed approach with the help of visual modeling language platform UML. It applies clustering algorithm on the data encrypted by Pailler homomorphic cryptosystem and UML modeling of the proposed approach has also been given in this research work with the help of different UML sequence, class, activity diagram. Authors have represented the UML approach of software design regarding PPkDC implementation to different data sources. The proposed approach provide the blueprint for constructing and documenting the UML model for the proposed system for providing the cost and time efficient framework for securing sensitive data to be mined accidently or intentionally.
Extraction of features from video files using different image algebraic point operations

In the human-computer interaction (HCI) field, facial feature analysis and extraction are the most decisive stages which can lead to a robust and efficient classification system like facial expression recognition, emotion classification. In this paper, an approach to the problem of automatic facial feature extraction from different videos are presented using several image algebraic operations. These operations deal with pixel intensity values individually through some mathematical theory involved in image analysis and transformations. In this paper, 11 operations (point subtraction, point addition, point multiplication, point division, edge detecting, average neighborhood filtering, image stretching, log operation, exponential operation, inverse filtering, and image thresholding) are implemented and tested on the images (video frames) extracted from three different self-recorded videos named as video1, video2, video3. The videos are in .avi, .mp4 and .wmv format respectively. The work is tested on two types of data: grayscale and RGB (Red, Green, Blue). To assess the efficiency of each operation, three factors are considered: processing time, frames per second (FPS) and sharpness of edges of feature points based on image gradients. The implementation has been done in MATLAB R2017a.
Designing for Visual Data Exploration in Multi-Device Environments

Multi-device environments have an enormous potential to enable more flexible workflows during our daily work. At the same time, visual data exploration is characterized as a fragmented sensemaking process requiring a high degree of flexibility. In my thesis, I am aiming to bring these two worlds into symbiosis, specifically for sensemaking with multivariate data visualizations and graph visualizations. This involves three main objectives: (i) understanding the devices' roles in dynamic device ensembles and their relations to exploration patterns, (ii) identifying mechanisms for adapting visualizations for different devices while preserving a consistent perception and interaction, and, finally, (iii) supporting users and developers in designing such distributed visualization interfaces, e.g., through specific guidelines. As specific contributions, it is planned that (i) and (ii) emerge into a design space, while (iii) leads to a set of heuristics. So far, I was able to extensively work on the first objective as well as to touch on the other two.
rGUIDANCE – alignment confidence score computation in R

R has become the toolbox of many researchers in ecology and evolutionary biology, both fields increasingly integrating molecular phylogenetic information.This modular toolbox allows to implement pipelines from sequence retrieval and alignment, to phylogeny estimation and high-end visualization of results.One essential step within this pipeline is currently missing from the R package ecosystem: detection of unreliably aligned regions within multiple sequence alignment (MSA).Although alignments are fundamental to phylogenetically informed analyses, they often contain extended unreliable regions.The alignment confidence score GUIDANCE demonstrated high accuracy in detecting such regions of low quality.Here we introduce the R package rGUIDANCE, which fully implements the alignment confidence algorithms GUIDANCE, HoT and GUIDANCE2.We will demonstrate the core functionality of rGUIDANCE and how it can be easily integrated into a phylogeny inference pipeline.rGUIDANCE is a free and open-source R package, available via GitHub at https://github.com/FranzKrah/rGUIDANCE//