Miracle-dz/UniFAR-FTUs huggingface dataset:
THE ADDED VALUE OF WRITTEN KNOWLEDGE BUILDING IN A THREE-DIMENSIONAL VIRTUAL WORLD

Introduction The concept of knowledge building (Scardamalia & Bereiter, 1994) is closely related to the notion of constructivism (Papert, 1991). Both assume that learners construct knowledge by interpreting their perceptual experiences in terms of prior knowledge, current mental structures and existing beliefs (Jonassen & McAleese, 1993). Constructivism implies that learning is the personal interpretation of the world situated in a rich context. Learners' involvement in the process of knowledge construction, development and evaluation aims at the development of their reflective awareness. Collaboration is used to encourage the construction of an understanding from multiple viewpoints. In this project, it is assumed that learning occurs by building a virtual world both under the physical point of view, building virtual houses and objects, and under the cultural point of view, by exchanging, discussing and generating ideas, knowledge and specific content for each virtual house. The students' reflective activities are stimulated by a twofold need: to present and explain to other partners their own ideas and to take into account suggestions, comments and request coming from the distant partners. Visualisation (Edelson, Gordin & Pea, 1997) has become particularly relevant as a consequence of an increased emphasis on inquiry-based learning. Visualisation exploits the ability of the human visual system to identify patterns in images. In the fields of art, physics and science, inquiry is often difficult when only based on textual data. Through the presentation of visual data, a further and often easier manner of investigating and learning is offered. This option is very often considered as one of the most productive features of the new technology. The combination of different media (graphics, sound, animation, text) reflects the multiple representations humans are capable of. The visual thinking skills are stimulated when students have the possibility to build the objects to be visualized. Desktop virtual reality software makes concrete this opportunity by offering a set of tools through which is easy to create and visualize 3D objects. The creation of 3D objects within educational environment includes different stages where different thinking strategies are involved, such as: a) Deciding upon the general aim of why to build. This phase is strictly connected to the idea of students capacity to take decisions about their own curricula; b) Planning, pre-visualizing the objects by displaying essential elements on paper, with materials easy to be manipulated (Das, Lego), by collecting photos, pictures connected to the objects; c) Planning the educational value …
CONTROL FOR MULTI-TIME-DELAY UNCERTAIN DISCRETE SYSTEMS

This paper studies the problems of quadratic stabilization and H∞ control for a class of multi-time-delay uncertain discrete systems. Suppose that the time-varying uncertain parameters are norm-bounded, but not required to satisfy strict matched conditions, state and input for the system are both with multi-time-delay. New sufficient conditions of quadratic stabilization and H∞ control are given for the system by generalized Lyapunov function and linear matrix inequalities. In terms of linear matrix inequalities, H∞ controller can be obtained and a prescribed H∞ performance lever of the closed-loop system has an upper bound for all admissible parameters uncertainties. Furthermore, the optimal H∞ robust control law can be obtained by solving some convex optimization problems. A numerical example is offered to show the potential of the proposed techniques.
Monitoring execution of OWLS web services ?

In this paper we describe mechanisms for execution monitoring of OWL-S web services. The described mechanisms are implemented as extensions of the OWL-S Virtual Machine which is a component that controls interactions between a client and web services. All extensions are driven by practical needs that arose as part of two projects involving OWL-S web services. Specifically, an event-based model for monitoring and logging is described that allows a human or software agent to analyze, replay or debug the execution. Next, we describe mechanisms for error handling and reporting which is not addressed by the OWLS specifications. Finally, we describe the virtual machine introspection extensions motivated by needs of advanced software agents as brokers or process mediators that need to interact with web services dynamically.
No-reference Sharpness Index for Scanning Electron Microscopy Images Based on Dark Channel Prior

Scanning electron microscopy (SEM) image can link with the microscopic world through reflecting interaction between electrons and materials. The SEM images are easily subject to blurring distortions during the imaging process. Inspired by the fact that dark channel prior captures the changes to blurred SEM images caused by the blur process, we propose a method to evaluate the SEM images sharpness based on the dark channel prior. A SEM image database is first established with mean opinion score collected as ground truth. For the quality assessment of the SEM image, the dark channel map is generated. Since blurring is typically characterized by the spread of edge, edge of dark channel map is extracted. Then noise is removed by an edge-preserving filter. Finally, the maximum gradient and the average gradient of image are combined to generate the final sharpness score. The experimental results on the SEM blurred image database show that the proposed algorithm outperforms both the existing state-of-the-art image sharpness metrics and the general-purpose no-reference quality metrics.
Predictive validity of risk/needs assessment for young offenders under community supervision

Aims: To assess whether Youth Level of Service/Case Management Inventory Australian Adaptation (YLS/CMI-AA) risk/needs data improve recidivism prediction for young offenders under community supervision, compared to static risk data from the Bureau's Reoffending Database (ROD). Method: The analysis included all 1,050 young offenders who commenced a supervised community order (other than bail or parole) in 2014 with a valid YLS/CMI-AA and ROD record. Recidivism was defined as a new proven offence within 12 months of order commencement. Logistic regression assessed the individual and collective relationships of static risk factors and YLS/CMI-AA scores to recidivism. Area Under the Curve (AUC), model fit indices and multiple cross-validation methods were used to evaluate the models. Results: Interactions between variables in models built with the full sample necessitated that separate models be built for Indigenous and non-Indigenous offenders. For non-Indigenous offenders, the AUC for the combined (ROD with YLS/CMI-AA) model (.767, 95% CI (.728, .807)) was within the acceptable range (0.7-0.8) but did not significantly outperform the ROD-only model (.740, (95% CI .698, .781)). For Indigenous offenders, AUCs were significantly lower than for non-Indigenous offenders, below the acceptable range, and also showed no significant benefit from combining YLS/CMI-AA and ROD data. Compared with AUCs for the combined model, cross-validated AUCs were lower, and corresponding AUCs for the 2013 cohort were inconsistent. Conclusion: YLS/CMI-AA data did not significantly improve the predictive accuracy of static risk-based models of recidivism for Indigenous or non-Indigenous offenders. Validation methods suggested that the results may not generalise beyond the current cohort.
Real Time Object Tracking and Identification Using a Camera

In this paper, we proposed a method for detecting and tracking of moving objects based on low resolution image employing a block matching technique and also proposed an identification method using a color and spatial information. Many tracking algorithms have better performance under static background but sometimes mistracking results are obtained under background with complex motions. Since a low resolution image has a nice property that it can remove the small size pixels, it is adopted to solve this problem due to the fact that most of the fake motions in the background have small region. In tracking the moving object, many applications have problems when objects occlude each other. The peripheral increment sign correlation is used to solve this problem. The identification object is performed using a color and spatial information of the tracked object. The experimental results prove the feasibility and usefulness of the proposed method.
Using customer input to drive change in user assistance

If designing and developing optimal products, services, and information requires clarity and audience awareness, then developing ways to acquire customer input is a critical piece in the best development workflow solutions [43]. Seeking direct input from customers and learning how they use a product are the most effective methods for generating ideas for enhancements and innovations to the technical content that supports a product, as well as to the product itself [21]. Taking the steps to create opportunities for customer feedback enables direct customer input and establishes the proactive best practice of demonstrating to the customer that they are a top priority in the product development focus [34]. While there may not be a fixed agenda for an optimal customer meeting, there may, in fact, be a set of topics for discussion that comprise a set of best practices for gaining customer input for the user assistance [45]. Customer input helps provide requests for change and thus aids in managing change, raising the importance of a change and requirements management system [34, 52]. A change and requirements management system is even more critical in the context of application lifecycle management and globally distributed development [27, 49, 50, 55]. Managing relationships with customers leads to a two-way path for collaboration, both for helping customers solve their problems and for a company to enhance its products and overall success [26, 28].
Is MTTR More Important Than MTTF For Improving User-Perceived Availability ?

High availability of internet systems can be achieved either through high mean-time-to-failure (MTTF) or low mean-time-to-recovery (MTTR). Traditionally, system designers have focused on maximizing MTTF as a way of providing high availability. However, recent work in recovery-oriented computing has emphasized recovery from failures, rather than failure avoidance [1]. In this paper, we investigate the impact of MTTR and MTTF, as well as user retry rates, on the user-perceived availability of internet systems through simulation experiments. Previous work suggests that between two systems with a given availability, the one with lower MTTR always yields higher userperceived availability [3]. Our results show that while this is true for certain ranges of system parameters, it is not always the case. This leads us to propose a method for determining whether improving MTTR or MTTF will yield greater user-visible benefits for a given system, allowing a system administrator to invest in system improvements that yield maximum benefits.
Survey on Human Activity Recognition Techniques For Video Surveillance

Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents’ actions and the environmental conditions. Vision-based activity recognition is a very important and challenging problem to track and understand the behavior of agents through videos taken by various cameras. The primary technique employed is computer vision. Vision-based activity recognition has found many applications such as human-computer interaction, user interface design, robot learning, and surveillance, among others. This report describes the literature served for developing human activity recognition algorithm and a novel approach to it.
Scalar Multiplication Algorithms of Elliptic Curve Cryptography over GF (2 m )

Abstract- Since the inception of elliptic curve cryptography by Koblitz [1] and Miller [2] for implementing public-key protocols as the Diffie-Hellman key agreement, elliptic curve cryptography has become one of the most researched area for providing one stop reliable and secure solution in the field of cryptography. The ECC covers all relevant asymmetric cryptographic primitives like digital signature (ECDSA), key exchange and agreement protocols. Point multiplication serves as the basic building block in all ECC primitives and is the computationally most expensive operation and our analysis revolves around this concept. This paper gives an introduction to Elliptic Curve Cryptography and deals with evaluation of fast scalar multiplication with parallelization of field operation and point addition/multiplication. Elliptic curve cryptography offers best optimized solution with minimum resources like Low memory, High Throughput, low power consumption and minimum key length for the same level of security as compared to its counterpart like RSA, DSA etc. in public key cryptography domain. The work is based on the extensive research work done by Julio Lopez, Ricardo Dahab, Montgomery and other pioneer scientists and academicians in the field of elliptic curve cryptography. Given the importance of Scalar multiplication , we focused ourselves on the Fast Multiplication on Elliptic Curves over finite Binary field GF(2m) without Pre-computation whose background is set by Julio Lopez et al. in [1], because the finite field operations can be implemented very efficiently in hardware and software.
Evidence and future scenarios of a low-carbon energy transition in Central America: a case study in Nicaragua

The global carbon emissions budget over the next decades depends critically on the choices made by fast-growing emerging economies. Few studies exist, however, that develop country-specific energy system integration insights that can inform emerging economies in this decision-making process. High spatial- and temporal-resolution power system planning is central to evaluating decarbonization scenarios, but obtaining the required data and models can be cost prohibitive, especially for researchers in low, lower-middle income economies. Here, we use Nicaragua as a case study to highlight the importance of high-resolution open access data and modeling platforms to evaluate fuel-switching strategies and their resulting cost of power under realistic technology, policy, and cost scenarios (2014–2030). Our results suggest that Nicaragua could cost-effectively achieve a low-carbon grid (≥80%, based on non-large hydro renewable energy generation) by 2030 while also pursuing multiple development objectives. Regional cooperation (balancing) enables the highest wind and solar generation (18% and 3% by 2030, respectively), at the least cost (US$127 MWh−1). Potentially risky resources (geothermal and hydropower) raise system costs but do not significantly hinder decarbonization. Oil price sensitivity scenarios suggest renewable energy to be a more cost-effective long-term investment than fuel oil, even under the assumption of prevailing cheap oil prices. Nicaragua’s options illustrate the opportunities and challenges of power system decarbonization for emerging economies, and the key role that open access data and modeling platforms can play in helping develop low-carbon transition pathways.
Dynamic Intercept and Manipulation of Objects under Semi-Structured Motion for an Industrial Robot Workcell with Visual Guidance

Allowing dynamic motions for payloads within a workcell is a fundamentally novel idea for practical industrial robots. The ability to handle payloads moving in semi-structured ways would significantly increase the potential markets for industrial robotics. In this paper, we propose a generic theory to model the dynamic intercept and manipulation capability of vision based industrial robot systems. In order to verify the theory, we present experiments using our industrial workcell prototype to dynamically intercept and manipulate semi-randomly moving objects. We conduct experiments over 1000 runs with two different kinds of dynamic tasks, “scoop” and “trap. ” The experimental results validate our theory.
Design of non-volatile 4T-2 magnetic tunnel junction based random access memory cell

In this report, we presented an NV Random Access Memory cell using a novel easy and proficient model of Spin Transfer Torque Magnetic Tunnel Junction (STT-MTJ). Magnetic tunnel junction (MTJ) devices are CMOS well suited with high steadiness, high dependability and non-volatility. The combination of magnetic tunnel junction with CMOS circuits in magnetic RAM (MRAM) or Magnetic FPGA can get the digital circuits to major advantages related with non-volatile facility like immediate on/off, Zero standby power use of goods and services. MTJ (Magnetic Tunnel Junction) devices have various advantages over other magneto-resistive devices for use in MRAM cells, like MRAM produces a big signal for the read operation and a varying resistance that can make the circuit. Due to these attributes, MTJ-MRAM can operate at high velocity. A completed simulation model for the 4T and 2MTJ SRAM design is shown in this report, which is grounded on the recently confirmed STT (Spin-Transfer Torque) writing technique which promises to take down the switching current losing to ~120μA and the STT RAM cache reduces total power consumption from 13. 6μW -8. 2μW. This model has been confirmed in Verilog A language and the whole work carried out and ran out on cadence virtuoso platform at 45nm.
Information tracking approach to segmentation of ultrasound imagery of the prostate

The volume of the prostate is known to be a pivotal quantity used by clinicians to assess the condition of the gland during prostate cancer screening. As an alternative to palpation, an increasing number of methods for estimation of the volume of the prostate are based on using imagery data. The necessity to process large volumes of such data creates a need for automatic segmentation tools which would allow the estimation to be carried out with maximum accuracy and efficiency. In particular, the use of transrectal ultrasound (TRUS) imaging in prostate cancer screening seems to be becoming a standard clinical practice because of the high benefit-to-cost ratio of this imaging modality. Unfortunately, the segmentation of TRUS images is still hampered by relatively low contrast and reduced SNR of the images, thereby requiring the segmentation algorithms to incorporate prior knowledge about the geometry of the gland. In this paper, a novel approach to the problem of segmenting the TRUS images is described. The proposed approach is based on the concept of distribution tracking, which provides a unified framework for modeling and fusing image-related and morphological features of the prostate. Moreover, the same framework allows the segmentation to be regularized by using a new type of weak shape priors, which minimally bias the estimation procedure, while rendering the procedure stable and robust. The value of the proposed methodology is demonstrated in a series of in silico and in vivo experiments.
Parvovirus B19 infection in pregnancy and subsequent morbidity and mortality in offspring.

BACKGROUND Because parvovirus B19 infection in pregnancy has been associated with infant morbidity and mortality in case reports and after intrauterine transfusion, we tested the population-based association using serum and hospital data of high quality. METHODS We established a cohort of 113 228 children born to women tested for parvovirus B19 infection during pregnancy in a major diagnostic laboratory in Denmark, from 1994 to 2009. Information on 20 selected morbidity diagnoses and on mortality was obtained from the Danish National Patient Register, the Danish Cancer Register and the Danish Civil Registration System. Incidence rate ratios (IRR) were estimated by log-linear Poisson regression with adjustment for age and sex of the child, maternal age and year of maternal parvovirus B19 test. RESULTS A total of 1095 (1.0%) children were born to mothers who were infected with parvovirus B19 during pregnancy. During 1 million person-years of follow-up, 10 856 children experienced morbidity and 590 children died. Overall, maternal infection status was neither associated with morbidity during infancy (IRR 0.64; 95% CI: 0.40 to 1.02) or childhood (IRR 0.93; 95% CI: 0.77 to 1.14), nor with infant mortality (IRR 0.98; 95% CI: 0.44 to 2.20). Specifically, there was no association with 19 of 20 morbidities. An excess risk of cancer in the central nervous system was observed (IRR 5.88; 95% CI: 1.41 to 24.6); however, the number of exposed cases was very small (n = 2). CONCLUSIONS Parvovirus B19 infection during pregnancy was not associated with overall morbidity or mortality in infancy and childhood.
Ergosterol biosynthesis in Aspergillus fumigatus: its relevance as an antifungal target and role in antifungal drug resistance

Ergosterol, the major sterol of fungal membranes, is essential for developmental growth and the main target of antifungals that are currently used to treat fatal fungal infections. Emergence of resistance to existing antifungals is a current problem and several secondary resistance mechanisms have been described in Aspergillus fumigatus clinical isolates. A full understanding of ergosterol biosynthetic control therefore appears to be essential for improvement of antifungal efficacy and to prevent antifungal resistance. An ergosterol biosynthesis pathway in A. fumigatus has been proposed with 14 sterol intermediates resulting in ergosterol and another secondary final compound C-24 ethyl sterol. Transcriptomic analysis of the A. fumigatus response to host-imposed stresses or antifungal agents is expanding our understanding of both sterol biosynthesis and the modes of action of antifungal drugs. Ultimately, the identification of new targets for novel drug design, or the study of combinatorial effects of targeting sterol biosynthesis together with other metabolic pathways, is warranted.
Going Beyond Behavior-Based Intrusion Detection

Today's Intrusion Detection (ID) ideas focus on two solutions: detecting intrusions based on known vulnerabilities and detecting anomalies in the normal " behavior " in a particular system, be it the network or the host. Although not immune from false-positives or true-negatives, these systems do a lot for ID, but the damage from unknown attacks is more substantial as people deploy these systems and let their guards down. This paper attempts to see if a guarantee of security can be asserted in two popular ID areas: Host-Based ID and Denial of Service ID on the network. First, we explore what capability systems can do for Hosts and then explore the newer methods of Denial of Service source identification. These ideas have the potential to do much more than unreliable dependencies on behavior based Intrusion Detection.