Reviewerly/exHarmony huggingface dataset:
A Semi-Stack Approach for Accurate Network Traffic Classification Using Multi-View Stacking

Abstract The network traffic classification is used as a basis of network management related works, from security monitoring and Intrusion Detection Systems (IDS) to Quality of Service (QoS). Many works have been carried out on this theme that proposed various approaches. Most of the proposed approaches utilize predefined class label provided by an expert to perform the network traffic classification. Nevertheless, the difficulty to acquire consistent, adequate and up-to-date ground truth for classifying network flows effectively is still as serious problem. This work addresses such an issue via multi-view stacking to fuse the metadata from heterogeneous types of semi-supervised classifiers for accurate classification thru the following steps. Firstly, the original data is represented as multi-view using dimensionality reduction methods, with the aim to have strong discrimination capability. Secondly, integrate different semi-supervised learning algorithms from an ensemble learning perspective, to deliver a better stability and quality classification output. Finally, propose N-fold cross validation on metadata for training the meta-classifier, to produce the final class decision and predict the unlabelled traffic data. Experiment results on four existing network traffic datasets provide the best average accuracy mark is above 96% on all datasets and the best stability performance is up to 99.50%.
The Use of Irregular Precise Observations for Parametric Adaptation of a Discrete Stochastic Filter

The estimation of the state of dynamic stochastic systems using the methods of filtration theory assumes the exact a priori setting of the parameters of the equation of the system and the probabilistic characteristics of its noises, which in practice are known approximately or change in time randomly. In this regard, the algorithm of adaptive estimation of one of the most critical parameters of the discrete stochastic filter - the dispersion matrix of noises acting on the system is considered for measuring and information systems that use, in addition to continuous noisy observations, also accurate observations at random times. The advantage of the proposed algorithm is the possibility of analytical determination of this dispersion matrix based on current precise observations and, as a consequence, a significant increase in the overall accuracy of the evaluation of the perturbed state vector of the system. A numerical example illustrating the possibility of effective practical use of the developed algorithm is given.
The extraction of maize lodgingregionsin UAV imagesusingdeepfully convolutional neural network

Abstract This paper presents a method for automatic extraction of maize lodging regions in unmanned aerial vehicle (UAV)visible images. A deep fully convolutional neural network is used to achieve the purpose.Firstly, images are collectedby using UAV remote sensing, and then the network is trained and validated on a total of more than 20000 labelled images, and finally, the network is able toaccurately extractlodging regions. Experimental results demonstrate the effectivenessof the method and F 1 score reaches 89.5% on test dataset. This study makes a major contribution to the assessment of maize lodging disaster.
Implementing Interactive Information Technologies When Learning Integral Calculus in Teaching Further Mathematics

The article observes the effectiveness of various strategies and methods of organizing classes using IT, while studying the section Integration of Further mathematics course. The Fundamental theorem of integral and differential calculus offers 1st-year students a learning platform to study vocational courses. Many students do not cope with the subject, though. An active use of information technologies available in the educational process enable them refine the course successfully. The students learn certain definitions using original interactive computer games. Individual work is arranged using various applications of LearningApps.org. web service. The students learn geometric applications of integrals via Maple graphical capabilities. The educational experiment was run at Sholom-Aleichem Priamursky State University, Moscow State University of Civil Engineering, and Omsk State Technical University. The participants are the students of the Institute of Electrical Engineering, Electrical Engineering, Information Systems and Technology, Applied Mathematics and IT. There were 60 students in the control group, whereas there were 65 students in the experimental group. The survey resulted in a qualitative student academic performance in the experimental group larger by 17%. This confirms an assumption that using interactive technologies in the learning process enhances cognitive performance, stimulates students’ self-study, and guides them to acquire real, not by-the-book knowledge.
An Investigation into the Usage-trends of Canada’s Research Computing Clouds

Cloud platforms are gaining more footprint in many Information Technology sectors, including research computing. Infrastructure as a service (IaaS) remains a popular cloud delivery model for research computing clouds, enabling researchers to create isolated computing, storage, and network environments on demand. Despite the maturity of the IaaS model, cloud providers face several challenges due to the lack of insight into usage-trends of their platforms, including sub-optimal VM placement and arbitrary selection of adopted technologies. These problems become more difficult in the research computing domain, where the resource requirements and nature of computational workloads of VMs are highly diverse.In this paper, we illustrate the value of usage-trends' analysis in VM placement decisions and technology adoption choices by IaaS providers. We examine the usage-trends of four of Canada's research computing clouds, presenting our analysis of usage metrics that were collected since their inception and up to early 2019. We focus on examining the usage metrics with potential in guiding VM placement and technology adoption choices, namely the lifetime of VMs, the frequency of VMs' creation, and the VMs' resource allocations.
Implementation of Naive Bayes Classifier Algorithm in Classification of Civil Servants

As the executor of the transfer of civil servants, BKD (Badan Kepegawaian Daerah) is required for professionals in carrying out their duties in mutation positions for civil servants. This study implements a mutation system for civil servants by using Naive Bayes Classifier Algorithm. This study is implemented in Secretariat District Regency of Pelalawan, Riau Indonesia, This system uses some attributes such as age, total work period, education background and implemented on a web based application. This approach is an approximation to Bayes theorem which is the principle of statistical opportunity to combine previous knowledge with new knowledge. This system is very effective classification algorithm (get the right results) and efficient which can be a basic reason in mutation civil servants.
Probabilistic Metric Based Multidimensional Scaling

Multidimensional scaling is a well-known dimensionality reduction method and useful for summarizing the latent vital structure of the complex data. However, conventional multidimensional scaling is based on Euclidean distance between a pair of objects; there is a limitation to adjust the model to various similarity structures of real data. Therefore, this paper proposes a probabilistic metric based multidimensional scaling. For the probabilistic metric, t-norms in a probabilistic metric space and a kernel-based metric are employed. Several numerical examples are demonstrated to show a better performance of the proposed method.
Adaptive Neural Connections for Sparsity Learning

Sparsity learning aims to decrease the computational and memory costs of large deep neural networks (DNNs) via pruning neural connections while simultaneously retaining high accuracy. A large body of work has developed sparsity learning approaches, with recent large-scale experiments showing that two main methods, magnitude pruning and Variational Dropout (VD), achieve similar state-of-the-art results for classification tasks. We propose Adaptive Neural Connections (ANC), a method for explicitly parameterizing fine-grained neuron-to-neuron connections via adjacency matrices at each layer that are learned through backpropagation. Explicitly parameterizing neuron-to-neuron connections confers two primary advantages: 1. Sparsity can be explicitly optimized for via norm-based regularization on the adjacency matrices; and 2. When combined with VD (which we term, ANC-VD), the adjacencies can be interpreted as learned weight importance parameters, which we hypothesize leads to improved convergence for VD. Experiments with ResNet18 show that architectures augmented with ANC outperform their vanilla counterparts.
The Regulation of Blockchain in Africa

Blockchain and blockchain technology have captured the imagination of the world. It is being used increasingly more in business and has found its way into the legal profession as well. Blockchain as such has immense potential and can certainly be extremely beneficial. However, since it is such a dynamic, innovative, and recent development, there is a need for regulation of this phenomenon. Regulation will bring more clarity, protection, and assurance. One of the main objectives of a blockchain, however, is to move away from centralised control, so the issue of regulation is a sensitive and complex one. Regulators and policymakers find it difficult to maintain the balance between effective regulation and allowing blockchain to fulfil its potential.
Operation Command Decision Modeling and Simulation Research Based on Agent Technique

Command Decision Simulation (CDS) is an important and difficult issue in the field of simulation. Research on CDS can give support to all kinds of simulation. The thesis studies the practical use of Agent based on technique method in CDS. According to the emphasis on simulation function based on Agent technique method, the essay puts forward the system structure vision of CDS entity and focuses on command decision Agent operation modeling method based on action radix, and also deigns smart algorithm of command decision Agent based on action radix.
Fast Hand-Object Interaction Using Gesture Guide Optimization

In this paper, we present a fast approach to model the interactions between the human hand with virtual objects. Given a detailed hand model, we adopt a gesture guided optimization to find the stable poses for manipulating the virtual object. During this process, we identify user’s gesture with predefined proposition-based descriptions script and re-tracked hand pose, then generate the contact points using the gesture prior. Begin with these points, we compute the stable pose for interaction and then obtain plausible looking motion. In practice, our real-time algorithm can perform common manipulation in less than 20 ms (run on a single CPU) with virtual objects.
Transporting Real World Rigid and Articulated Objects into Egocentric VR Experiences

Traditional consumer virtual reality (VR) methods for facilitating interaction-e.g.controllers-limit immersion as they provide minimal tactile feedback and do not accurately represent real-world interactions with physical objects. To address these limitations, we propose an egocentric tracking pipeline in which physical rigid and articulated objects are tracked in a moving first-person camera, attached to a VR Head Mounted Display (HMD), and their behaviour used to interact with their virtual counterparts. The tracking pipeline uses a neural network, VRProp-Net+, to predict model parameters from RGB images of unconstrained scenes.
Projection-Free Methods

In this chapter, we present conditional gradient type methods that have attracted much attention in both machine learning and optimization community recently. These methods call a linear optimization (LO) oracle to minimize a series of linear functions over the feasible set. We will introduce the classic conditional gradient (a.k.a. Frank–Wolfe method) and a few of its variants. We will also discuss the conditional gradient sliding (CGS) algorithm which can skip the computation of gradients from time to time, and as a result, can achieve the optimal complexity bounds in terms of not only the number of calls to the LO oracle, but also the number of gradient evaluations. Extension of these methods for solving nonconvex optimization problems will also be discussed.
Deep Learning Technique for Serving Visually Impaired Person

The concepts like machine learning and artificial intelligence have been adopted in the real time applications due to their exceptional features and absolute algorithmic advantages. Deep learning is a one of research area in machine learning totally concerned with algorithmic techniques inspired by the structure and functions of the human neural system. It has created a niche for itself in learning processes involving multiple level of data representation and abstraction, thereby helping in making sense of data having several forms such as images, sound and text and finally revealing the feature level hierarchies. Deep learning techniques show higher level of accuracy and robustness in results and are independent of human crafted features. The Paper is divided into three main parts, first part identifies the need to develop the system for Visually Impaired person. Second part deals about various Deep learning techniques used for computer vision and object detection. And third part consist the proposed system for serving visually impaired person in doing daily chores.
Towards an Immersive Virtual Simulation for Studying Cybersickness during Spatial Knowledge Acquisition

Cybersickness is one of the challenges that has prevented the widespread adoption of Virtual Reality (VR) and its applications. Due to its importance, there have been extensive studies on understanding and reducing cybersickness. Inspired by previous work that has sought to reduce cybersickness by applying the blurring effect and reducing the field of view of VR scenes, we present a simulation with controllable peripheral blur in the VR image. The simulation can be used to study cybersickness and spatial knowledge acquisition in an engaging virtual walking scenario.
Disease Classification with LSTM and Logistic Regression Models using POS Tagging Categories

In the past time, patients able to know illness when seeing a doctor only and patient-doctor communication is an important component of patient care without an intense focus on skills of communication between doctor and patient. But the information on diseases at the present time are published on the internet and almost contents consist of symptoms, causes and prevention and treatment methods including a largely private data of illness transforms into an increasingly public experience. So internet health information is used to improve health literacy in the patient- physician relationship. The research uses the information of diseases from the internet which presents by using Thai language for determining of 24 diseases to develop tool which is the model for helping to health literacy an increasingly essential competency required of today's citizens by using LSTM and Linear Regression algorithms which use word embedding as the Bag of words and POS tagging categories found that both algorithms show high accuracy by using POS tagging in 1, 2 and 4 categories.
Agility in Instructional Design. Strengthening of Digital Skills in Incoming Students at FaCENA-UNNE

In a context of permanent evolution, Higher Education Institutions must plan and develop actions that promote the acquisition of the media, digital and information skills necessary for the insertion of new professionals in a world where Information Technology and Communication (ICT) is increasingly dominant. The article presents an agile educational proposal aimed at facilitating access to ICT and training in its use. The SCRUM-based agile framework that integrates the ADDIE Instructional Design Model is described in the Sprint. The proposal is validated in a course for incoming students of the year 2019. The implementation of the applied innovative strategy is analyzed and evaluated, identifying itself as successful in complex contexts such as education.
Counterexamples to the Maximum Speed/Energy IMC Theory – A New Geometric Consideration Incorporates the Multivariable Perfect Control Defined in the LTI Discrete-Time State-Space Framework

In this paper, the counterexamples to the general theory related to the state-feedback IMC plants are given and investigated. It is shown, that the maximum-speed multivariable perfect control schemes do not necessitate the maximum-energy of the control input variables. In that way, the IMC peculiarities involving the nonzero perfect control closed-loop poles are indicated, which constitutes a solid counterweight to the well-known general system theory. A new geometric approach results in counterexamples for both full and non-full rank plants, giving rise to the introduction of a complete analytical paradigm in the nearest future. Numerical examples confirm the intriguing issues covering the paper.
Assessing Personality Traits of Team Athletes in Virtual Reality

Assessment of personality traits is highly relevant in team sports in order to analyze the performance of an athlete under pressure when in competitive situations, for team-strategic decisions, to optimize command transmission, and ultimately to understand top-level performers. It further facilitates the development and application of personalized exercises, coaching to improve performance in competition, and can be considered a valuable criterion for talent scouting and development. The current state of the art method to assess personality traits in sports relies on validated questionnaires. However, these often provide non-sport-specific, subjective self-reported information and lack the ability to measure how these characteristics are reflected in context-based performance.We developed a virtual reality (VR) tool for the assessment of personality traits in team sports, in our case for soccer. An evaluation of this tool within a study with 24 subjects yielded a benchmark of its immersion through user experience and provided an objective description of athletes’ personalities based on performance indicators extracted from activity-tracking. Within the tool, we implemented two realistic virtual soccer environments to assess the motivational orientation of soccer players (i.e. action- and state-orientation) which we discerned from the gold standard questionnaire.Results show that user experience and presence of the implemented virtual environments scored significantly higher compared to benchmark measurements. Additionally, a significant difference between the two groups of action and state-oriented athletes could be observed. Measures of failure rate, pass accuracy, number of perceived opponents, and achieved bonus goals are parameters that differ significantly among the two athlete groups. These findings show that VR technology is applicable for the assessment of athletes’ motivational orientation and thus demonstrate the feasibility of virtual environments as functional game scenario-based assessment tools for athletes.
Disambiguation Techniques for Freehand Object Manipulations in Virtual Reality

Past work in augmented reality has shown that temperature-associated AR stimuli can induce warming and cooling sensations in the user, and prior work in psychology suggests that a person’s body temperature can influence that person’s sense of subjective perception of duration. In this paper, we present a user study to evaluate the relationship between temperature-associated virtual stimuli presented on an AR-HMD and the user’s sense of subjective perception of duration and temperature. In particular, we investigate two independent variables: the apparent temperature of the virtual stimuli presented to the participant, which could be hot or cold, and the location of the stimuli, which could be in direct contact with the user, in indirect contact with the user, or both in direct and indirect contact simultaneously. We investigate how these variables affect the users’ perception of duration and perception of body and environment temperature by having participants make prospective time estimations while observing the virtual stimulus and answering subjective questions regarding their body and environment temperatures. Our work confirms that temperature-associated virtual stimuli are capable of having significant effects on the users’ perception of temperature, and highlights a possible limitation in the current augmented reality technology in that no secondary effects on the users’ perception of duration were observed.
Squishy Volumes: Evaluation of Silicone as Camera-less Pressure-Based Input for 3-Dimensional Interaction

Low cost sensors and materials are increasingly of interest to designers for developing new ways to gather 3-Dimensional input. Silicone is a low cost material with capabilities of a variety of forms and sizes, thereby facilitating flexible construction. Given these properties, users can construct unique input solutions for a variety of applications. However, aside from other existing methods of measuring volume deformation, molded silicone (without added components inside the silicone and without added external cameras) for volumetric input has not been largely explored. In this paper we present an evaluation that investigated the parameters of silicone as volumetric input. The silicone volume has no added components inside making it easy to construct and use, however some external but small, flexible, and portable low-cost components are used for deformation measurement. We present the 3-dimensional input results as a function of the physical pressure on the silicone by the volume of silicone. Researchers can use these input metrics to design a silicone-based device with desired size and thickness to achieve the desired sensitivity and resolution of input for their application.
On cryptographic anonymity and unpredictability in secret sharing

We revisit the notions of cryptographic anonymity and share unpredictability in secret sharing, introducing more systematic and fine grained definitions. We derive tight negative and positive results characterizing access structures with respect to the generalized definitions.
Logic-Based Smart Contracts

With the increasing popularity and diversity of Blockchain systems, smart contracts were introduced as a necessity to automatically execute certain operation depending on the occurred events. The programming languages used in defining the triggering events and their consequent actions depends on the Blockchain implementation. In this article, we will investigate the advantages and suitable scenarios for using a formal approach in defining smart contracts. We will use a particular python implementation of a Blockchain and add the interface with a Prolog component for defining and querying smart contracts.