Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud Security Up for Auction: a DSIC Online Mechanism for Secure IaaS Resource Allocation

The lack of security is still one of the main factors that are blocking the full migration towards the Cloud Computing paradigm. More businesses would be attracted by the adoption of the Cloud model if the Cloud Infrastructure Providers (CIP) start increasing their investment in security solutions and demonstrating more explicitly the potency of their infrastructures in protecting customers' data and services. However, implementing security solutions is usually costly, and does not necessarily generate higher revenues. One way to reduce the cost of security investments would be to rent the Cloud secure resources to customers in a competitive fashion. The CIP could place her added value of security up for auction, and customers would place their bids along with their resource provisioning requests. In this paper, we propose an online mechanism that performs the allocation of the CIP's resources to customers in a security-oriented auction-based fashion. The mechanism is Dominant-Strategy Incentive-Compatible (DSIC), i.e., it guaranties the truthfulness of the bidders. The mechanism aims at allocating the resources to customers who valuate their security the most, and shows an acceptable performance compared to the famous offline Vickrey-Clarke-Groves (VCG) truthful mechanism.
Updates of View in Relational Databases*

The paper presents the editing technology of multi-table data views in databases of relational type. This technology removes the constraint that establishes conformance of the edited view tuple with the table (relation) tuple of the database. This technology is relevant in the automated creation of user applications. The application should work with the data view that is with the table formed out of the set of database relations with the queries of "join-select-projection" type. The requirement for such applications is the implementation of data editing functions: addition, deletion and modification of tuples in the table. In accordance with the executed editing, the application should convert the original database so that the result of edition is preserved under the iterated view formation.
Comparison Between the e-Learning Readiness of Educators and Learners in South African Schools

In this paper, e-learning readiness of the South African schools in previously disadvantaged schools in Gauteng were investigated. A twenty-nine (29) item questionnaire was used to obtain the data. The new learning method is being introduced and will be implemented by Gauteng Department of Education. The data obtained from the questionnaires was then analyzed by using a STOPE model. The results obtained by using STOPE show that the learners are 78% ready as compared to 74% of educators. The five-point likert scale showed a 3.91 average learners’ readiness as well as a 3.69 average educator’s readiness. Thus, it is evident that the educators are generally less receptive to adopting e-learning than the learners.
Warm-Dense-Matter State of Iron Generated by Intense Heavy-Ion Beams

The hydrodynamic behavior of a cylindrical solid target, heated by the intense heavy-ion beams, was simulated. The target was of the LAboratory PlAnetary Science (LAPLAS)-type geometry, as described by Tahir (LAPLAS). An iron target irradiated by the intense heavy-ion beams, with parameters available from the planned High Intensity Accelerator Facility or Facility for Antiproton and Ion Research, Society for Heavy Ion Research, Darmstadt, Germany, will transform to the warm-dense state, similar to the situation that exists in the planet interior. The dependence of the achievable state of iron on the outer-shell material and beam location was investigated with Medusa-code simulations. The simulation results show that the compression of inner iron is significantly influenced by the density-to-mass ratio of the outer-shell material and the location of the beam heating. Using the LAPLAS geometry, a uranium beam of 800 MeV/u, and $1.5\times 10^{11}$ particles per pulse, our simulations show that the warm-dense-matter regime for iron is obtained. Details depend on the yet unknown equation of state and will be measured when this beam intensity is available in a couple of years.
Performance Analysis of Machine Learning Classifiers for Intrusion Detection

Modern tactical wireless network (TWN) communication technologies are not only capable of transmitting voice but also capable of transmitting data. Due to such capabilities, TWN have high security requirements as any security breach can lead to detrimental effects. Hence, securing such an environment is not only a requirement but also a virtual prerequisite to the network centric warfare operational (NCW) theory. One key to securing this environment is to promptly and accurately recognize information warfare attacks directed to the network and respond to them. This is achieved using intrusion detection systems (IDS). However, false detection of nodes in hostile environment remains a major problem that need to be addressed. Recently, machine learning methods and algorithms have shown applicability and are growing research area for cyber security and intrusion detection. Conversely, several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. The question then becomes, which one amongst these machine learning algorithms have the potential to enhance or address IDS issues in TWN. In this paper, seven machine learning classifiers are analyzed; Multi-Layer Perceptron, Bayesian Network, Support Vector Machine (SMO), Adaboost, Random Forest, Bootstrap Aggregation, and Decision Tree (J48). WEKA tool was used to implement and evaluate the classifiers. The results obtained indicate that ensemble-based learning methods outperformed single learning methods when we consider the detection accuracy metrics; AUC, TPR, and FPR. However, ensemble classifiers tend to be slower in in terms of build time and model test time.
Cost-Benefit Analysis of Distributed Energy Storage in Distribution Grids with Renewables

Currently Distributed Energy storage system (ESS) has a significant impact on the flexibility of medium/low voltage power distribution network to address the challenges due to growing power demand and increasing penetration of renewable energy resources. This work exploits and explicitly quantifies the potential benefit of optimal coordinated multiple ESSs to support the secure power supply of power distribution networks with distributed generations (DGs) by providing capacity services. The proposed model was compared against conventional methods and assessed in three case studies: different demand conditions, different sizing of ESSs and reactive power provisioning. The numerical result obtained from a set of simulation experiments clearly demonstrates various benefit of coordination of ESSs in supporting flexible and secure operation of power distribution networks, e.g. peak demand reduction, reactive power support.
In Situ Antenna Far Field Estimation Based on Equivalent Sources

This paper deals with a near-field to far-field transformation, based on source identification, which allows to predict the radiation of antennas in their operating environment. It is focused on an experimental set-up which carries out the near-field measurements of UHF antennas above the ground. The predicted far-field is compared to the one calculated with a commercial electromagnetic software.
The Experimental Comparison of Features for Hand Detection

Hand detection is critical in gesture recognition for conveying information or control commands between persons and computers. The accuracy of hand detection from images plays an important role in these applications. Extraction of effective features is the main factor in this task. The features should be discriminative, robust to different variations and easy to compute. This paper presents the experimental comparison of features commonly used in object detection, such as Haar-like features, a histogram of oriented gradient (HOG), and local binary pattern (LBP), using hand detection as the test platform. The adaptive boost (AdaBoost) cascade classification method is employed to combine "weak learners" to a strong classifier. The classifier was trained using 300 positive images, which are images containing the hand (region of interest (ROI)) and 10000 negative images, which are images that do not contain a hand on them. Different parameter combinations of the classifier are considered for comparative experiments. The performance of the classifier using Haar, HOG and LBP features were evaluated with 320 static test images. The results show that parameter combinations have significant effects on the hand detection accuracy, which also differ when different features are used.
Ontology-Based Knowledge Representation for Increased Skill Reusability in Industrial Robots

We assume that an intuitive means for the specification, re-use, modification and transfer of synchronized motions-both regarding the two arms of a dual-arm robotic system, as well as regarding the coordination of a user and a robot-is key in interactive and collaborative settings as they are currently targeted for industrial applications. We show, how our knowledge based approach to end-user programming of synchronized motions and other generalizable, robot-agnostic skills can support such specification of coordinated actions between two robot arms and explain how that could be extended to include coordination with a human user. We describe the underlying ontologies and possibilities to populate those with an interface for intuitive programming, and show the generality of our approach through a task transfer between different kinematics (different robots), where the user is supported through underlying reasoning about the fulfillment of certain parameters or constraints for the involved skills.
Alternative 2-D Search Table for the Specific Heat Ratio of Air Improving Near-Space Hypersonic Flow Field Calculations

The specific heat ratio of air is the key parameter in describing the hypersonic flow field. The existing search tables of its values indexed by temperature and air pressure are not suitable for hypersonic flow field calculation. Therefore, theoretical calculations of the hypersonic flow field set the specific heat ratio of air at a constant value resulting in large calculation errors in general. In this paper, a 2-D search table for the specific heat ratio of air based on velocity and height as search variables is proposed for hypersonic flow field calculations to replace the invalid temperature–pressure specific heat ratio of air. Taking the calculation of the stagnation temperature of the hypersonic flow field as an example, the theoretical calculation results and the measured data of the RAM-C hypersonic flight experiment are compared and discussed. A comparison of results shows that the proposed 2-D search table meets the computational requirements for hypersonic flow field calculations and provides a fast and accurate query database for the specific heat ratio of air and related parameters.
A Synergetic Voluntary Control for Exoskeleton based on Spinal Cord Mapping of Peripheral Bioelectric Activity

Walking rehabilitation must be performed based on voluntary motion intention, and for this purpose, the development of a control method for an exoskeleton robot based on voluntary intention is investigated. This study proposes a method of exoskeleton robot control to estimate the voluntary lower limb muscle activities lost after a spinal cord injury (SCI). This method is based on the spinal cord mapping of the remaining muscle activities and its matching to the one obtained from healthy participants considering the muscle synergy of the whole body during the walking motion. By implementing the matching procedure at the spinal cord map level and incorporating information of reliable and unreliable spinal cord levels based on a diagnosis, the method has the potential to provide a maximally voluntary locomotion for people with SCI. We report an analysis of the synergy of the whole-body muscle activity during walking and its spinal cord mapping using non-negative matrix factorization and the computation of the transformation matrix to estimate the intended lower limb muscle activity from the remaining spinal cord activity. The implementation of the proposed method using the right leg of the hybrid assistive limb and walking experiments with a healthy participant are also reported.
Building a Cloud IDS Using an Efficient Feature Selection Method and SVM

Network traffic in cloud computing is characterized by a large volume of data, with potential high dimensionality and high levels of redundancy. “Big data” not only retard the execution process of intrusion detection systems (IDS) but they can also lead to unsatisfactory classification accuracy. The efficient correlation-based feature selection (ECOFS) approach proposed in this paper, can handle linearly and nonlinearly dependent data and it can eliminate redundant and irrelevant features. Its effectiveness has been evaluated through its employment in an intrusion detection system. A Libsvm-IDS has been built to operate using the features selected by the proposed ECOFS algorithm. The performance of the hybrid Libsvm-IDS + ECOFS approach has been evaluated using two well-known intrusion detection evaluation datasets, namely the KDD Cup 99 and the NSL-KDD. The evaluation results show that our ECOFS algorithm selects the smallest number of features, resulting in the lowest computational cost for the Libsvm-IDS, without any performance compromise. In fact, our algorithm has achieved higher accuracy compared with two well established methods.
Dual-Arm Coordinated Motion Planning and Compliance Control for Capturing Moving Objects with Large Momentum

Capturing a moving object with large momentum by a dual-arm robot is especially challenging because of the requirement of dual-arm coordinated motion planning for tracking the moving object, and the operational force control for contact and momentum transfer. In this paper, we present a dual-arm coordinated motion planning and compliance control method with a unique null-space projected relative Jacobian and relative operational force between the two arms. The proposed method is able to plan dual-arm capturing motion and control the capturing force without disturbing the tracking motion. We have also adopted a direct collocation trajectory optimization method to generate optimal trajectory to decrease the object's momentum with minimum effort. Simulation and experiment of dual-arm robots picking up a moving box on a mobile platform are carried out to verify the proposed method.
Virtual Reality Games: A Study About the Level of Interaction vs. Narrative and the Gender in Presence and Cybersickness

Virtual reality (VR) games have the potential to produce immersive experiences. To better explore the potential of VR games, it becomes necessary to understand what affects the player's presence in VR games. This work measures and compares the levels of presence and cybersickness in VR environments. Two games with different levels of interaction and narrative were compared. Presence and cybersickness were measured in a sample of 32 subjects using the IPQp questionnaire and a Portuguese version of the SSQ respectively. The results indicate that there were no differences in presence and cybersickness between the interaction and the narrative dimensions. To extend the study, the gender of participants was also considered an independent variable where we found significant differences in the metrics of presence and experienced realism, nausea and disorientation with female participants getting higher scores.
New Similarity Function for Test Case Prioritization in Model-Based Context

Test case prioritization (TCP) refers to the process, which evaluates test cases ranking within a test suite. Currently, the focus of TCP research lay mainly in code-based testing. However, the Model-Based Testing (MBT) context still need more investigation. This paper presents a new distance function for defining the degree of similarity between MBT generated test cases. These test cases are represented by labeled graphs, and their similarity is measured. In the paper, the function is applied in Hint-Based Adaptive Random Prioritization method, and improved fault-detection performance is presented.
MID: A Novel Contrast Metric for the MSER Detector

This paper presents a novel contrast measure for MSER region selection, termed Mean Intensity Difference (MID). The proposed metric is computed between the pixels of an MSER region and its surrounding pixel set. In this work we consider the complementary pixels within the bounding box of a region and alternatively the pixels of the first contour layer as surroundings. To evaluate the proposed contrast metric, a location retrieval task is performed. To this end, SURF descriptors are computed and the Bag-of-Words representation is used as global signature for each image. For the evaluation we use the Devon Island dataset, which is said to have one of the most Mars-like environments on Earth and which comes with GPS ground-truth data. We further integrate the contrast-based methods with the approach of Grid Adaptation. The experimental results show that our contrast metric outperforms state-of-the-art metrics, such as Perceptual Divergence, and yields better performance compared to random region selection. In this work we also evaluate the computational complexity of the methods.
Caching Placement Strategies for Dynamic Content Delivery in Metro Area Networks

Video-on-Demand (VoD) traffic explosion has been one of the main driving forces behind the recent Internet evolution from a traditional connection-centric architecture towards the new content-centric paradigm. To cope with this evolution, caching of VoD contents closer to the users in core, metro and even metro-access optical network equipment is regarded to be a prime solution that could help mitigating this traffic growth. However, the optimal caches placement and dimensioning is not univocal, especially in the context of a dynamic network, as it depends on various parameters, such as network topology, users behavior and content popularity. In this paper, we focus on a dynamic VoD content delivery scenario in a metropolitan network implementing different caching strategies. We evaluate the performance of the various caching strategies in terms of network-capacity occupation showing the savings in resource occupation in each of the network segments. We also evaluate the effect of the distribution of the storage capacity on the overall average number of hops of all requests. The obtained numerical results show that, in general, a significant amount of network resources can be saved by enabling content caching near to end-users. Moreover, we show that blindly providing caching capability in access nodes may result unnecessary, whereas a balanced storage distribution between access and metro network segments provides the best performance.
Temperature Control Design of Biological Sample Within Microscope

This paper deals with the design of a temperature control system for a biological sample. The need for maintaining a specific temperature of a biological sample is given by the viability of the microorganism depending on their temperature. Meeting the conditions for changing or maintaining the required temperature is especially necessary in cases such as monitoring the growth of microorganisms and temperature dependent mutations. In this paper, the design of the heating platform as well as the temperature control algorithm is presented and experimentally verified.
AIBO Robot Mortuary Rites in the Japanese Cultural Context*

In 1999 Sony released the AlBO Entertainment Robot, selling more than 150,000 units worldwide until 2006. By 2014, Sony had stopped offering upgrades and maintenance for the product, and owners were faced with the fact their pet-like robots would “die”. Some shrines and temples in Japan hold ningyo kuyō or mass funerals for dolls and other toys. At the suggestion of a small Japanese tech-repair company called A-Fun, one temple began offering a Buddhist funeral ceremony for AIBOs. Approximately 700 AIBOs have so far received a funeral service. This paper surveys A-Fun‘s maintenance services for old AIBOs, the AIBO funerals, and Sony's new 2018 AIBO release, in the cross-disciplinary context of human-machine relations in Japan and elsewhere. Drawing on the author's interviews with key actors, it articulates links between philosophy and neuroscience to explain tendencies toward zoomorphism in robot design. Perceiving presence (sonzai kan) and sensibility (kansei) in objects is a culturally contingent phenomenon. Whereas ways of conceiving the partly animate are largely absent from Western philosophy, in the case of AIBO ownership in Japan there is a reverential mindfulness of the technology's inherent contradictions.
Estimation of the Distribution Probability Density Acoustic Signals And Interferences, the Reconstruction Methods

In operation, methods of estimating probability density distributions are considered, which are urgent in the solution of the filtering issues of the useful information on the background of external acoustic noise in the telecommunications systems. Parametric and non-parametric methods of estimating probability densities are discussed, methods for determining an empirical distribution function for the case of a limited sample volume. It is shown that the approximation of the probabilities empirical data can be performed by the method of nuclear evaluations. Within this method, the estimate may be represented by the convolution of the core and the empirical density. It derives from the fact that the nuclear score is a result of a histogram of the histogram evaluation. It has been shown that reconstruction of the distribution function as a polynomial in the system of functions is the question of finding coefficients, which is the task of linear regression, which is solved by minimisation of the quadratic function of the loss built on the basis of the use of the least-squares method and representing the discrepancy of the empirical data and the estimates obtained on their basis. The results of the experimental studies show the error of the reconstruction one-dimensional function of probability density for the case of audio signals and acoustic interferences, given different kinds and orders of polynomial approximation.
KrishiMitr (Farmer’s Friend): Using Machine Learning to Identify Diseases in Plants

Automatic disease detection using visible symptoms on leaves is becoming more and more important. Here we describe an algorithm, which uses machine learning to detect diseases in a wide variety of plants and diseases. High accuracy (>93%) was obtained with very noisy images, different backgrounds and different disease coverage. The algorithm is able to train itself, which means that the accuracy can increase with usage. It can run on a variety of platforms including smartphones and can thus aid non-expert farmers manage diseases effectively.