Reviewerly/exHarmony huggingface dataset:
Addressing Imbalanced Classes Problem of Intrusion Detection System Using Weighted Extreme Learning Machine

The main issues of the Intrusion Detection Systems (IDS) are in the sensitivity of these systems toward the errors, the inconsistent and inequitable ways in which the evaluation processes of these systems were often performed.Most of the previous efforts concerned with improving the overall accuracy of these models via increasing the detection rate and decreasing the false alarm which is an important issue.Machine Learning (ML) algorithms can classify all or most of the records of the minor classes to one of the main classes with negligible impact on performance.The riskiness of the threats caused by the small classes and the shortcoming of the previous efforts were used to address this issue, in addition to the need for improving the performance of the IDSs were the motivations for this work.In this paper, stratified sampling method and different cost-function schemes were consolidated with Extreme Learning Machine (ELM) method with Kernels, Activation Functions to build competitive ID solutions that improved the performance of these systems and reduced the occurrence of the accuracy paradox problem.The main experiments were performed using the UNB ISCX2012 dataset.The experimental results of the UNB ISCX2012 dataset showed that ELM models with polynomial function outperform other models in overall accuracy, recall, and F-score.Also, it competed with traditional model in Normal, DoS and SSH classes.
Self-awareness in Software Engineering

Background : Self-awareness has been recently receiving attention in computing systems for enriching autonomous software systems operating in dynamic environments. Objective : We aim to investigate the adoption of computational self-awareness concepts in autonomic software systems and motivate future research directions on self-awareness and related problems. Method : We conducted a systemic literature review to compile the studies related to the adoption of self-awareness in software engineering and explore how self-awareness is engineered and incorporated in software systems. From 865 studies, 74 studies have been selected as primary studies. We have analysed the studies from multiple perspectives, such as motivation, inspiration, and engineering approaches, among others. Results : Results have shown that self-awareness has been used to enable self-adaptation in systems that exhibit uncertain and dynamic behaviour. Though there have been recent attempts to define and engineer self-awareness in software engineering, there is no consensus on the definition of self-awareness. Also, the distinction between self-aware and self-adaptive systems has not been systematically treated. Conclusions : Our survey reveals that self-awareness for software systems is still a formative field and that there is growing attention to incorporate self-awareness for better reasoning about the adaptation decision in autonomic systems. Many pending issues and open problems outline possible research directions.
Navigation Aids in Collaborative Virtual Environments: Comparison of 3DML, Audio, Textual, Arrows-Casting

For Collaborative Virtual Environments (CVEs), many interaction techniques are developed.Depending on the purpose of the collaborative work, techniques of interaction and manipulation change from one application to another.There is no general, good and efficient solution for all the collaborative systems.In addition, people in CVEs also use communication channels to share task goals, task decomposition and task progress.Therefore, awareness and communication are usually considered as important instruments to complete collaborative task.In this paper we present a comparative study of user performance with assembly task across four different guidance navigation aids i.e. 3-Dimensional Map-Liner (3DML), Audio, Textual and Arrows-Casting.Dijkstra's algorithm is used for shortest path selection while making the assembly task in CVEs.We conducted experiments on different college students to check the performance of the students.We reported the results of a precise experiments containing of 24 virtual teams of 48 individual students.Overall results showed that students performed task faster using the arrows while they were slow with audio support in navigation.
The ‘connectaholic’ behind the curtain: medical student use of computer devices in the clinical setting and the influence of patients

Abstract Background The use of mobile devices such as tablets and laptops by students to support their learning is now ubiquitous. The clinical setting is an environment, which lends itself to the use of mobile devices as students are exposed to novel clinical scenarios that may require rapid location of information to address knowledge gaps. It is unknown what preferences students have for these devices and how they are used in the clinical environment. Methods In this study we explored medical students’ choices and their use of different devices in their first year of clinical attachments. We sought to evaluate learners’ experiences with these devices using a mixed methods approach. All students newly entered into the clinical years were given the option of a MacBook Air or iPad. We surveyed these students using an online survey tool followed by individual semi-structured interviews to explore survey findings in more depth. Results Students owned a multitude of devices however their preferences were for the 11 in. MacBook Air Laptop over the iPad mini. Students made constant use of online information to support their clinical learning, however three major themes emerged from the interview data: connection and devices (diverse personal ownership of technology by students and how this is applied to source educational materials), influence and interaction with patients (use of any device in a clinical setting) and influence and interaction with staff. In general students preferred to use their device in the absence of patients however context had a significant influence. Conclusions These mobile devices were useful in the clinical setting by allowing access to online educational material. However, the presence of patients, and the behaviour of senior teaching staff significantly influenced their utilisation by students. Understanding the preferences of students for devices and how they use their preferred devices can help inform educational policy and maximise the learning from online educational content.
Part 2:- quality assurance mechanisms for digital forensic investigations: Knowledge sharing and the Capsule of Digital Evidence (CODE)

Despite potential numerous benefits, the field-wide sharing of knowledge in digital forensics is arguably still yet to be attained. Achieving this has attracted much practitioner and academic debate, yet solutions to two fundamental hurdles have yet to arguably be addressed; ‘how do we share knowledge’, and ‘what do we share’. Currently there a few viable protocols in place which tackle either of these issues forming a barrier to field-wide sharing. The focus of this work is to address the latter issue and guide practitioners on what content must be shared for any data to be of value to fellow professionals. This paper proposes the Capsule of Digital Evidence (CODE), a framework designed to set out the required elements for the sharing of reliable digital forensic knowledge. The CODE structure and its requisite contents are examined along with its applicability for supporting field-wide knowledge sharing in digital forensics.
An Evolutionary Approach to Class Disjointness Axiom Discovery

Axiom learning is an essential task in enhancing the quality of an ontology, a task that sometimes goes under the name of ontology enrichment. To overcome some limitations of recent work and to contribute to the growing library of ontology learning algorithms, we propose an evolutionary approach to automatically discover axioms from the abundant RDF data resource of the Semantic Web. We describe a method applying an instance of an Evolutionary Algorithm, namely Grammatical Evolution, to the acquisition of OWL class disjointness axioms, one important type of OWL axioms which makes it possible to detect logical inconsistencies and infer implicit information from a knowledge base. The proposed method uses an axiom scoring function based on possibility theory and is evaluated against a Gold Standard, manually constructed by knowledge engineers. Experimental results show that the given method possesses high accuracy and good coverage.
Fast dynamic nonparametric distribution tracking in electron microscopic data

In situ transmission electron microscope (TEM) adds a promising instrument to the exploration of the nanoscale world, allowing motion pictures to be taken while nano objects are initiating, crystalizing and morphing into different sizes and shapes.To enable in-process control of nanocrystal production, this technology innovation hinges upon a solution addressing a statistical problem, which is the capability of online tracking a dynamic, timevarying probability distribution reflecting the nanocrystal growth.Because no known parametric density functions can adequately describe the evolving distribution, a nonparametric approach is inevitable.Towards this objective, we propose to incorporate the dynamic evolution of the normalized particle size distribution into a state space model, in which the density function is represented by a linear combination of B-splines and the spline coefficients are treated as states.The closed-form algorithm runs online updates faster than the frame rate of the in situ TEM video, making it suitable for in-process control purpose.Imposing the constraints of curve smoothness and temporal continuity improves the accuracy and robustness while tracking the probability distribution.We test our method on three published TEM videos.For all of them, the proposed method is able to outperform several alternative approaches.
Implementation and Rehabilitation Application of Sports Medical Deep Learning Model Driven by Big Data

A large number of unlabeled and limited style data greatly reduces the reuse possibility of existing motion sequences. Effective classification and fragment splicing have become an important way of data reuse. Aiming at these two problems, this paper focuses on the great success of deep learning in the field of graphics and iconography. Based on the theory of Restricted Boltzmann Machine (RBM), a spatio-temporal feature extraction model for human skeleton medical motion sequences is established. The research results are mainly manifested in three aspects. (1) In this paper, stack factor decomposition spatiotemporal feature model and discriminate RBM are used to construct semi-supervised combination model. (2) The underlying model firstly uses the idea of weight decomposition to construct the three channel generative RBM model; and then it extracts the abstract temporal and spatial characteristics of the original motion sequence. Furthermore, it identifies the behavior style of the current input segment at the top using the discriminate RBM model. Finally conduct the stylistic statistics of the whole motion sequence in the voting space. (3) An unsupervised similar frame detection model is constructed by using 3D convolution RBM's perception of human adjacent joints' linkage. In this way, Candidate frames for constructing graph model nodes are obtained. Trajectory and style switching control is realized based on attitude similarity screening criteria. The simulation experiment verifies the superiority and reliability of the algorithm, and it is effectively applied in rehabilitation training.
Cryptocurrencies and the Evolution of Banking, Money, and Payments

This chapter discusses cryptocurrencies in the context of a historical overview of the evolution of money, banking, and the payment system. The chapter is organized as follows. Section I introduces the topic. Section II addresses money, payment, and payment intermediation. Section III sets out the evolution of commercial banking to facilitate national and global networks for book-based payments. Section IV addresses both electronic banking as a form of payment intermediation and the availability to the public of central bank balances as a challenge to payment intermediation. Section V examines the challenge cryptocurrencies present to state-issued currency, payment intermediation, and the roles of banks in the payment systems. The conclusion points at an irony: even as a challenge to banking, cryptocurrencies emerged as an outgrowth of an enhancement to banking.
An enhanced particle swarm optimization algorithm

&lt;p&gt;In this paper, an enhanced stochastic optimization algorithm based on the basic Particle Swarm Optimization (PSO) algorithm is proposed. The basic PSO algorithm is built on the activities of the social feeding of some animals. Its parameters may influence the solution considerably. Moreover, it has a couple of weaknesses, for example, convergence speed and premature convergence. As a way out of the shortcomings of the basic PSO, several enhanced methods for updating the velocity such as Exponential Decay Inertia Weight (EDIW) are proposed in this work to construct an Enhanced PSO (EPSO) algorithm. The suggested algorithm is numerically simulated established on five benchmark functions with regards to the basic PSO approaches. The performance of the EPSO algorithm is analyzed and discussed based on the test results.&lt;/p&gt;
Augmenting Car Surrounding Information by Inter-Vehicle Data Fusion

Collecting vehicle surrounding information is a key issue for accident prevention and autonomous driving applications. Although GPS and 4G/LTE are widely accepted, it is still a challenge for a vehicle to get complete information of its surrounding vehicles. In this work, we consider the integration of multi-sensory data through V2V communications to help a vehicle to understand its complex surroundings. We propose a fusion algorithm that can integrate four types of sensory inputs: V2V communications, GPS, camera, and inertial data. We show that through such fusion, it is possible for a vehicle to visually see the driving states of its surrounding vehicles.
Exact dimensionality selection for Bayesian PCA

Abstract We present a Bayesian model selection approach to estimate the intrinsic dimensionality of a high‐dimensional dataset. To this end, we introduce a novel formulation of the probabilisitic principal component analysis model based on a normal‐gamma prior distribution. In this context, we exhibit a closed‐form expression of the marginal likelihood which allows to infer an optimal number of components. We also propose a heuristic based on the expected shape of the marginal likelihood curve in order to choose the hyperparameters. In nonasymptotic frameworks, we show on simulated data that this exact dimensionality selection approach is competitive with both Bayesian and frequentist state‐of‐the‐art methods.
Extractive NarrativeQA with Heuristic Pre-Training

Although advances in neural architectures for NLP problems and unsupervised pre-training led to impressive improvements on question answering and natural language inference, reasoning over long texts still poses a great challenge.Here, we consider the task of question answering from full narratives (e.g., books or movie scripts), or their summaries, tackling the NarrativeQA challenge (NQA; Kocisky et al. ( 2018)).We introduce a heuristic extractive version of the data set, which allows us to approach the more feasible problem of answer extraction (rather than generation).We develop models for passage retrieval and answer span prediction using this data set.We use pre-trained BERT embeddings for injecting prior knowledge into our system.We show that our setup leads to state of the art performance on summary-level QA.On narrativelevel QA, our model performs competitively on the METEOR metric.We analyze the relative contributions of BERT embeddings and the extractive model setup, and provide a detailed error analysis.
Generating Random Numbers from Biological Signals in LabVIEW Environment and Statistical Analysis

This paper explores the generation of random numbers, using electromyographic (EMG) signals collected from arm, elbow and finger movements of healthy individuals.The original signals were extracted from the Ninaweb database.The author designed a new discretization algorithm to convert these signals from floating point numbers to discrete values, and proposed a true random number generator (TRNG) structure that obtain the EMG signals with human arm and finger movements as noise sources.The proposed algorithm was applied to obtain and process real-time signals in the LabVIEW environment, and verified through NIST, TestU01, Scale index and autocorrelation tests.The results show that the discretization algorithms in TRNGs faced a huge data loss (70 %), while the designed algorithm with our structure lost no data and achieved 100 % efficiency in number generation.The research results prove the possibility of generating random numbers from biological signals.
Machine Learning: Mathematical Theory and Scientific Applications

This is essentially the transcript of the Peter Henrici Prize Lecture, given on July 15 at ICIAM 2019 in Valencia.The talk was designed to give a broad overview of• some of the current work on integrating machine learning with scientific modeling (e.g.physicsbased modeling) to address some of the most challenging problems in a variety of disciplines, and • some of the current work on building a mathematical theory of machine learning.There were two basic messages that I wanted to convey.First, in theoretical and computational science and engineering, a fundamental obstacle that we have encountered is our limited ability to deal with problems in high dimension.Machine learning has now provided new tools for
Sparsification of core set models in non-metric supervised learning

Supervised learning employing positive semi definite kernels has gained wide attraction and lead to a variety of successful machine learning approaches. The restriction to positive semi definite kernels and a hilbert space is common to simplify the mathematical derivations of the respective learning methods, but is also limiting because more recent research indicates that non-metric, and therefore non positive semi definite, data representations are often more effective. This challenge is addressed by multiple approaches and recently dedicated algorithms for so called indefinite learning have been proposed. Along this line, the Krĕin space Support Vector Machine (KSVM) and variants are very efficient classifiers for indefinite learning problems, but with a non-sparse decision function. This very dense decision function prevents practical applications due to a costly out of sample extension. We focus on this problem and provide two post processing techniques to sparsify models as obtained by a Krĕin space SVM approach. In particular we consider the indefinite Core Vector Machine and indefinite Core Vector Regression Machine which are both efficient for psd kernels, but suffer from the same dense decision function, if the Krĕin space approach is used. We evaluate the influence of different levels of sparsity and employ a Nyström approach to address large scale problems. Experiments show that our algorithm is similar efficient as the non-sparse Krĕin space Support Vector Machine but with substantially lower costs, such that also problems of larger scale can be processed.
Negative Space: Workspace Awareness in 3D Face-to-Face Remote Collaboration

Face-to-face telepresence promotes the sense of "being there" and can improve collaboration by allowing immediate understanding of remote people's nonverbal cues. Several approaches successfully explored interactions with 2D content using a see-through whiteboard metaphor. However, with 3D content, there is a decrease in awareness due to ambiguities originated by participants' opposing points-of-view. In this paper, we investigate how people and content should be presented for discussing 3D renderings within face-to-face collaborative sessions. To this end, we performed a user evaluation to compare four different conditions, in which we varied reflections of both workspace and remote people representation. Results suggest potentially more benefits to remote collaboration from workspace consistency rather than people's representation fidelity. We contribute a novel design space, the Negative Space, for remote face-to-face collaboration focusing on 3D content.
Data-Driven Logical Topology Inference for Managing Safety and Re-Identification of Patients Through Multi-Cameras IoT

As Internet of Things (IoT) develops, IoT technologies are starting to integrate intelligent cameras for managing safety within mental health hospital wards and relevant spaces, seeking out specified individuals from these surveillance videos filmed by the various cameras. Because monitoring is one of the important application of IoT based on distributed video cameras. In order to fine-grained re-identification of patients and their activities against the very low resolution, occlusions and pose, viewpoint and illumination changes, we propose a novel data-driven model to infer multi-cameras logical topology and re-identify patients captured by different cameras. In our model, we employ a Time-Delayed Mutual Information (TDMI) model in order to address multi-cameras logical topology inference. Additionally, we use a well-trained Deep Convolutional Neural Network (DCNN) to extract characteristics. Moreover, we employ a name-ability model to discover deep attributes and a classifier based on a structural output of attributes is designed to tackle the re-identification of patients, especially who possess psychiatric behaviour. In order to improve the present model's performance, we resort to the parallelized implementations. Experimental results show that our model possesses the best performance as compared to state-of-the-art model,especially, when the semantic restrictions are imposed onto the production of patients' specific attributes with structural output. Further, the deep learning model is used to produce characteristics when there is no supervision on the learning model of attributes.
The Effects of Augmented Reality Interaction Techniques on Egocentric Distance Estimation Accuracy

Recent developments in virtual environment applications allow users to interact with three-dimensional (3D) objects in virtual environments. As interaction with 3D objects in virtual environments becomes more established, it is important to investigate user performance with such interaction techniques within a specific task. This study investigated two interaction modes, direct and indirect, depending on how the users interacted with the 3D objects, by measuring the accuracy of egocentric distance estimation in a stereoscopic environment. Fourteen participants were recruited to perform an acquisition task with both direct pointing and indirect cursor techniques at three egocentric distances and three task difficulty levels. The accuracy of the egocentric distance estimation, throughput, and task completion time were analyzed for each interaction technique. The indirect cursor technique was found to be more accurate than the direct pointing one. On the other hand, a higher throughput was observed with the direct pointing technique than with the indirect cursor technique. However, there were no significant differences in task completion time between the two interaction techniques. The results also showed accuracy to be higher at the greatest distance (150 cm from the participant) than at the closer distances of 90 cm and 120 cm. Furthermore, the difficulty of the task also significantly affected the accuracy, with accuracy lower in the highest difficulty condition than in the medium and low difficulty conditions. The findings of this study contribute to the understanding of user-interaction techniques in a stereoscopic environment. Furthermore, developers of virtual environments may refer to these findings in designing effective user interactions, especially those in which performance relies on accuracy.