Reviewerly/exHarmony huggingface dataset:
Adding feature selection on Naïve Bayes to increase accuracy on classification heart attack disease

Abstract Feature selection in the classification model has a role to choose relevant and interconnected features in the data mining task. in the medical world, feature selection can help the classification model in predicting heart attack. Naive Bayes is one of the most popular classification learning methods that can help to predict patients in helping paramedics to make decisions. The addition of feature selection in the form of backward elimination can increase the accuracy of Naïve Bayes by 89.45% from 84.29% previously. The results of this study indicate the accuracy of a backward selection method in predicting heart attack is quite high in adding accuracy.
Dimensionality Reduction Based on Multilocal Linear Pattern Preservation

Manifold learning-based methods, such as LLE, capture the geometry of the data based on the assumption that the local structure of a manifold is linear. However, these methods may extract an inaccurate local structure when the nonlinearity of the data is obvious. In this paper, we propose a novel dimensionality reduction method with the ability to characterize the locally nonlinear geometry of the data by multilocal linearity. Specifically, we first construct a local area for each data point. And based on the overlapping of local areas, each data point will belong to and be linearly reconstructed from several local areas. Next, the set of linear coefficients used to reconstruct the data point constitutes the multilocal linear pattern (MLLP) which is used to characterize the local geometry of the data. Geometrically, the MLLP of a data point represents the hyperplanes in different directions passing through the current point. And the locally nonlinear surface where the data point is located is approximated by these hyperplanes, which is more accurate to reflect the geometry of the data. Then, MLLP is preserved to the embedding data space, and the dimension-reduced data can be obtained by minimizing the reconstruction errors. Finally, experiment results on various datasets demonstrate the effectiveness of the proposed method.
Securing the Internet of Things: An Ongoing Challenge

The challenges, bugs, and trouble spots that we've had in building and securing the Internet of Computers can provide a starting point for analyzing the challenges that will be manifested in this brave new world. Members of the IT community have their work cut out here.
Research on compensating stitching testing for convex aspherical surface

Aspherical optical elements bring much benefit to modern optical systems, due to its significant role in improving optical performance by compensating residual aberrations, and decreasing system’s weight. However, manufacturing aspherical surfaces in high accuracy is usually limited by the ability of surface metrology, especially for convex aspherical surfaces. In this paper, we propose a compensating stitching method combining computer generated hologram (CGH) compensating testing and stitching testing to achieve the full aperture map of convex aspherical surfaces. Firstly, we introduce our self-developed mathematical models in the compensating stitching method, including CGH design model, subapertures’ alignment model and stitching model. Then the performance of our proposed method is demonstrated with compensating stitching testing of an off-axis convex aspherical mirror with its aperture of 332mm×144mm. The experimental result shows that the convex aspherical surface can be tested in full aperture with our proposed method effectively, which demonstrates that our proposed surface testing method opens a new path for the testing of convex aspherical surfaces.
Parking Lot Exploration Strategy

This paper presents methods of parking lot exploration with an autonomous vehicle. Parking lot exploration is a subsystem of an autonomous parking system which performs the exploration of the parking lot, the parking space detection and the parking maneuver itself. The first task is to go around the parking lot until a free parking space is found. In this paper, the parking lot exploration is presented in details and the basics of parking space detection with the help of a LiDAR (Light Detection and Ranging) sensor are given. Firstly, the trapezoidal cell decomposition based method is detailed. This method consists of cell decomposition with traditional image processing, creating the adjacency matrix that describes the adjacency graph and finally determining graph traversal from the adjacency graph. After that, Voronoi diagram based method is also presented and the two methods are compared in several aspects. Finally, the second subsystem of the autonomous parking system is discussed, where a parking space detection method is also presented.
Analyzing Different Domains using Data Mining Techniques

In the advance universe of fast progress, today we are having gigantic measures of information in our archives. Since the information is enormous it needs the technique by which the information can be effectively investigated inside a brief timeframe. Consequently, it is the data mining systems which aides in doing so. It utilizes a few sorts of examples for data recovery which aides in further examination. The determination of right system helps in diminishing the time and builds the throughput. This paper gives a look over the data mining applications such as agriculture, transportation, botany and zoology. It attempts to help people in various fields by saving their time. It is an analysis that helps poor farmers to predict the seed varieties, animal and plant classes and also helps in predicting variations in transportation facilities. Also better results can be observed if different algorithms are combined together to predict the results.
pubHeal - A Decentralized Platform on Health Surveillance of People

Enabling effective care of public has always been the major target across both government and public industries. Access to correct and complete medical history of a patient and having it available at a required time is lacking. There is a requirement of efficient system so that all the available data of the patients is managed, secure and available at the required moment. pubHeal is a permissioned decentralized system for distribution and storing of personal information of patients and related entities like hospitals, researchers. This system is created with the aim that during disaster events all data and past medical history of the patient should be available with the medical practitioners so as to perform better treatment and care. This also aims to help medical researchers with the identification and mitigation of infectious diseases. The system is based on Hyperledger fabric framework which is permissioned blockchain.
A Conceptual Model of Antecedents of Emergent Leadership in Agile Teams

Self-organization is a key proposition of agile software development (ASD). Despite this stark emphasis, there is little understanding how self-organization actually works. Drawing on established findings in general group research, we propose emergent leadership, that is taking on leadership roles without formal authority, to be an enabler of effective self-organization in ASD teams. By contextualizing extant knowledge, we develop a theoretical model of antecedents of emergent leadership in ASD teams and how ASD practices reinforce these relationships. Our model is of value to research by increasing understanding which factors make agile teamwork effective.
A Blockchain Supported Solution for Compliant Digital Security Offerings

Ethereum launched in 2015 ushering a sea change over its predecessors in it’s ability to tokenise an asset. This was a technical innovation and an Initial Coin Offering (ICO) boom ensued, peaking in 2017. The legal and compliance requirements of tokenisation failed to keep step in these early stages, but were eventually brought to bear after the ICO bubble burst, forcing technological liberalism to confront regulatory realities. The Digital Security Offering (DSO)—a name change intended to reflect full compliance—was coined. However, truly executing a fully compliant DSO remained elusive for many. In this chapter we navigate the regulatory landscape for DSOs and construct a compliant blockchain solution, using it to support the DSO capital raise for a product named Talketh in December of 2018. The journey discusses the key compliance concerns of Know-Your-Customer (KYC), Anti-Money-Laundering (AML), Custody, Tokenisation and onward secondary trading as part of a Distributed Exchange (DEX).
Software Engineering Education of Classical Computing vs. Quantum Computing

An ever-increasing amount of tasks have become too complex to calculate with classical computers in reasonable time, such as molecular interactions or traffic optimizations. Quantum computing (QC) is currently one of the most promising approaches to handle those mentioned challenges. Based on the effects of quantum mechanics (e.g. superposition or entanglement), it may lead to a completely new range of solutions. This goes along with changes in software engineering (SE) and education. Therefore, students of computer science have to build up some new technical competencies in QC and surrounded fields (e.g. physics) to understand the characteristics of SE for QC. This paper identifies the target state, i.e. the required technical competencies that master students of computer science should have in this field of knowledge. In addition, it also analyzes the current state, i.e. the technical competencies that graduates of the bachelor's program in computer science have in relation to SE for QC. With a structured competence analysis, the competencies are collected, documented and compared. It shows that the current state does not correspond to the required target state and that further technical competencies are required. The analyses only relate to students at the University of Applied Sciences Kempten (Germany).
Continuous Authentication of Smartphone Users via Swipes and Taps Analysis

Nowadays, smartphones are used for getting access to sensitive and private data. As a result, we need an authentication system that will provide smartphones with additional security and at the same time will not cause annoyance to users. Existing authentication mechanisms provide just a one-time user verification and do not perform re-authentication in the process of further interaction. In this paper, we present a continuous user authentication system based on user's interaction with the touchscreen in conjunction with micromovements, performed by smartphones at the same time. We consider two of the most common types of gestures performed by users (vertical swipes up and down, and taps). The novelty of our approach is that swipes and taps are both analyzed to provide continuous authentication. Swipes are informative gestures, while taps are the most common gestures. This way, we aim to reduce the time of impostors' detection. The proposed scheme collects data from the touchscreen and multiple 3-dimensional sensors integrated in all modern smartphones. We use One-Class Support Vector Machine (OSVM) algorithm to get a model of a legitimate user. The obtained results show that the proposed scheme of continuous authentication can improve smartphone security.
Schnorr-like identification scheme resistant to malicious subliminal setting of ephemeral secret

In this paper we analyze security of Schnorr Identification Scheme (IS) against subliminal setting of ephemeral secrets. We introduce a new strong security model, which allows the adversary to learn or set ephemeral values on the side of the prover. In this model, we define an IS scheme to be secure, if such an adversary, playing role of a verifier, cannot later impersonate the prover. The model primarily reflects a scenario, where the random number generator used for ephemeral secrets has been maliciously implemented or integrated. After showing that the original Schnorr IS is not secure in our model, we propose a modification, immune to such malicious activity. We prove the security of the modified construction in our new strong model. To prove the construction is practical, we provide an implementation and performance comparison with the original construction.
Complexity Scalable Bitplane Image Coding With Parallel Coefficient Processing

Very fast image and video codecs are a pursued goal both in the academia and the industry. This paper presents a complexity scalable and parallel bitplane coding engine for wavelet-based image codecs. The proposed method processes the coefficients in parallel, suiting hardware architectures based on vector instructions. Our previous work is extended with a mechanism that provides complexity scalability to the system. Such a feature allows the coder to regulate the throughput achieved at the expense of slightly penalizing compression efficiency. Experimental results suggests that, when using the fastest speed, the method almost doubles the throughput of our previous engine while penalizing compression efficiency by about 10%.
Comparing Vision-based to Sonar-based 3D Reconstruction

Our understanding of sonar based sensing is very limited in comparison to light based imaging. In this work, we synthesize a ShapeNet variant in which echolocation replaces the role of vision. A new hypernetwork method is presented for 3D reconstruction from a single echolocation view. The success of the method demonstrates the ability to reconstruct a 3D shape from bat-like sonar, and not just obtain the relative position of the bat with respect to obstacles. In addition, it is shown that integrating information from multiple orientations around the same view point helps performance. The sonar-based method we develop is analog to the state-of-the-art single image reconstruction method, which allows us to directly compare the two imaging modalities. Based on this analysis, we learn that while 3D can be reliably reconstructed form sonar, as far as the current technology shows, the accuracy is lower than the one obtained based on vision, that the performance in sonar and in vision are highly correlated, that both modalities favor shapes that are not round, and that while the current vision method is able to better reconstruct the 3D shape, its advantage with respect to estimating the normal's direction is much lower.
Isotropy Maximization Loss and Entropic Score: Accurate, Fast, Efficient, Scalable, and Turnkey Neural Networks Out-of-Distribution Detection Based on The Principle of Maximum Entropy

Current out-of-distribution (OOD) detection approaches require cumbersome procedures that add undesired side effects to the solution. In this paper, we argue that the low OOD detection performance of neural networks is due to cross-entropy SoftMax loss anisotropy and extreme propensity to produce low entropy (high confidence) posterior probability distributions in frontal disagreement with the Principle of Maximum Entropy. Consequently, we propose IsoMax, a loss that is isotropic (distance-based) and produces high entropy (low confidence) posterior probability distributions despite still relying on cross-entropy minimization. Additionally, we propose a speedy Entropic Score for OOD detection. IsoMax loss works as a seamless SoftMax loss drop-in replacement that keeps the overall solution accurate, fast, efficient, scalable, and turnkey. Our experiments indeed confirmed that neural networks OOD detection performance may be extremely improved without relying on techniques such as adversarial training or validation, data augmentation, ensembles methods, generative approaches, model architectural changes, metric learning, or additional classifiers or regressions. The results also showed that our straightforward approach is competitive against state-of-the-art solutions besides avoiding previous methods undesired drawbacks.
Infographic Techniques in Professional Training of Master’s Degree Students of the Humanities University

The article shows how modern sociocultural situation determines new requirements for an educational paradigm: active use of information and communication technologies, different forms of distant and electronic learning and immediate involvement of educational process participants in processing powerful information streams. In this regard, innovative educational technologies associated with visual thinking proved to be highly relevant and efficient. The paper focuses on infographics, which allows algorithmization of big data processing. Relying on personal experience of using this technology, the authors develop methodology to create iconographic images of different complexity satisfying goals and tasks of educational process.
Nucleus Decomposition in Probabilistic Graphs: Hardness and Algorithms

Finding dense components in graphs is of great importance in analysing the structure of networks. Popular frameworks for discovering dense subgraphs are core and truss decompositions. Recently, Sarıyüce et al. introduced nucleus decomposition, which uses <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> -cliques contained in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> -eliques, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s &gt; r$</tex> , as the basis for defining dense subgraphs. Nucleus decomposition can reveal interesting subgraphs that can be missed by core and truss decompositions. In this paper, we present nucleus decomposition in probabilistic graphs. The major questions we address are: How to define meaningfully nucleus decomposition in probabilistic graphs? How hard is computing nucleus decomposition in probabilistic graphs? Can we devise efficient algorithms for exact or approximate nucleus decomposition in large graphs? We present three natural definitions of nucleus decomposition in probabilistic graphs: local, global, and weakly-global. We show that the local version is in PTIME, whereas global and weakly-global are #P-hard and NP-hard, respectively. We present an efficient and exact dynamic programming approach for the local case. Further, we present statistical approximations that can scale to bigger datasets without much loss of accuracy. For global and weakly-global decompositions we complement our intractability results by proposing efficient algorithms that give approximate solutions based on search space pruning and Monte-Carlo sampling. Extensive experiments show the scalability and efficiency of our algorithms. Compared to probabilistic core and truss decompositions, nucleus decomposition significantly outperforms in terms of density and clustering metrics.
Exploiting the GBVS for Saliency aware Gaze Heatmaps

Analyzing visual perception in scene images is dominated by two different approaches: 1.) Eye Tracking, which allows us to measure the visual focus directly by mapping a detected fixation to a scene image, and 2.) Saliency maps, which predict the perceivability of a scene region by assessing the emitted visual stimulus with respect to the retinal feature extraction. One of the best-known algorithms for calculating saliency maps is GBVS. In this work, we propose a novel visualization method by generating a joint fixation-saliency heatmap. By incorporating a tracked gaze signal into the GBVS, the proposed method equilibrates the fixation frequency and duration to the scene stimulus, and thus visualizes the rate of the extracted visual stimulus by the spectator.
Developing a Conceptual Framework for Smart Teaching: Using VR to Teach Kids How to Save Lives

We describe a conceptual framework that teachers can utilize when embedding immersive virtual reality with HMDs in their classroom for educational purposes. The concept is based on an ongoing research project where an immersive virtual reality application is being produced and will be used in K-12 education for learning first aid. By using virtual reality, we redefine the way students learn first aid by exposing them to real-life scenarios in an immersive virtual environment where they feel a sense of presence, that otherwise would not have been possible due to resources, risks and ethical reasons. The conceptual framework we have developed is an attempt to close a gap between the focus on the benefits of this promising learning technology and the practical considerations and guidelines from a teacher’s perspective in an educational context. We believe that there is immense value in providing teachers with a framework they can use when embedding immersive virtual reality in the dynamic environment of classrooms.
A Methodology to Integrate Professional Skill Development throughout an ICT Curriculum

Several studies have reported on different methods of including the development of a professional skill (such as communication, collaboration) that have been effective at increasing the competency of an information and communication technology (ICT) student with that skill at a subject level. This paper proposes a methodology that can be used in the design of an ICT curriculum to examine the breadth and depth of professional skill development inclusion across a curriculum. The methodology is applied to a case study ICT curriculum to evaluate the extent of professional skill development. This methodology and the case study ICT curriculum can assist curriculum designers in the difficult task of integrating professional skill development within an ICT curriculum to the extent necessary to achieve professionally competent graduates.
Algorithm of Tunning Heating Source Thermophysical Parameters in Smart Home

A development of the efficient smart home energy management systems requires understanding of the heating behavior. Part and parcel of this process is thermal parameters of heating sources which define the effectiveness of energy usage. In this paper we introduce a novel approach for the estimation of thermophysical parameters of heating elements. Based on the formulation of dynamic heat transfer problem we present a model that describes the heating process in a building with multiple heating sources. Within this model we propose an algorithm of determination the effective thermophysical parameters, where historical temperature and aggregated energy data are used in order to estimate unknown parameters. Also we present the algorithms for data preparation and renormalization. The latter means a transition from integral building parameter of heating sources to separate parameters corresponding to particular room heating element. Due to occurred errors during the computation, the presence of some incorrect values in provided time series and the ambiguity of calculation scenarios, the resulting parameters can be tuned to achieve greater accuracy. To do that, we propose an approach which involves the multivariate minimization of loss function using the genetic algorithm. The whole algorithmic pipeline is discussed in this report.
Estimating Point-of-Gaze using Smooth Pursuit Eye Movements without Implicit and Explicit User-Calibration

Detecting the point-of-gaze in the real world is a challenging problem in eye-tracking applications. The point-of-gaze is estimated using geometry constraints, and user-calibration is required. In addition, the distances of the focused targets are variable and large in the real world. Therefore, a calibration-free approach without geometry constraints is needed to estimate the point-of-gaze. Recent studies have investigated smooth pursuit eye movements (smooth pursuits) for human-computer interaction applications, and we consider that these smooth pursuits can also be employed in eye tracking. Therefore, we developed a method for estimating the point-of-gaze using smooth pursuits without any requirement for implicit and explicit user-calibration. In this method, interest points are extracted from the scene image, and the point-of-gaze is detected using these points, which are strongly correlated with eye movements. We performed a comparative experiment in a real environment and demonstrated the feasibility of the proposed method.