Miracle-dz/UniFAR-FTUs huggingface dataset:
Lightweight Web Services Migration Framework in Hybrid Clouds

Service-oriented architectures allow the deployment of loosely coupled services that are platform independent. An enterprise can take advantage of service-oriented architecture in two different directions. On one side, the abstraction of technology implementation allows the deployment of web services in disparate systems. On the other side, the flexibility and independence of services from each other makes scalability easier to achieve. This paper presents a migration solution of web services in hybrid clouds. The adoption of hybrid cloud solutions is valuable for dynamic workloads to maintain the availability of web services during periods of spikes in demand. The migration solution is a lightweight framework composed of web services to manage cloud instances and the migration task of web services deployed on Java-based web containers. The peak management process is based on Java Management Extensions (JMX) technology to monitor resources and deployed web services. In addition, the framework dynamically integrates a set of JMX metrics to synchronize enterprise demand for resources with the migration process. Finally, a design of the framework prototype is described and a real case of CPU intensive web service is presented to test the migration process and show an improvement of CPU usage and execution time.
A novel edge detection algorithm for remote sensing images based on the self-similarity of fractal character

A novel edge detection algorithm for remote sensing images, which combines the image gray level gradient and the fractal self-similarity character of image edges based on fractal theory, is introduced. The self-similarity coefficient between the element block and the local block, which are both centered at the current pixel being processed, in addition to gray level gradient decision-making criteria, are used to make the decision about the existence of image edges. A binary operator is then used to threshold its magnitude and produce the edge map of the image. The results of the experiment are presented which show the effectiveness and the noise resistance of the proposed new algorithm for remote sensing image edge detection.
Recent advances in imaging and understanding interstitial cystitis

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a debilitating condition associated with intense pelvic pain and bladder storage symptoms. Since diagnosis is difficult, prevalence estimates vary with the methodology used. There is also a lack of proven imaging tools and biomarkers to assist in differentiation of IC/BPS from other urinary disorders (overactive bladder, vulvodynia, endometriosis, and prostatitis). Current uncertainty regarding the etiology and pathology of IC/BPS ultimately impacts its timely and successful treatment, as well as hampers future drug development. This review will cover recent developments in imaging methods, such as magnetic resonance imaging, that advance the understanding of IC/BPS and guide drug development.
Adaptive Control by Regulation-Triggered Batch Least Squares

The paper extends a recently proposed indirect, certainty-equivalence, event-triggered adaptive control scheme to the case of nonobservable parameters. The extension is achieved by using a novel batch least-squares identifier (BaLSI), which is activated at the time of the events. BaLSI guarantees the finite-time asymptotic constancy of the parameter estimates and the fact that the trajectories of the closed-loop system follow the trajectories of the nominal closed-loop system (nominal in the sense of the asymptotic parameter estimate, not in the sense of the true unknown parameter). Thus, if the nominal feedback guarantees global asymptotic stability and local exponential stability, then unlike conventional adaptive control, the newly proposed event-triggered adaptive scheme guarantees global asymptotic regulation with a uniform exponential convergence rate. The developed adaptive scheme is tested to a well known control problem—the state regulation of the wing-rock model. Comparisons with other adaptive schemes are also provided for this particular problem.
Regression of Human Cirrhosis

Abstract Context. —Cirrhosis is widely regarded as being irreversible. Recent studies have demonstrated that fibrosis may decrease with time in humans and experimental animals if the disease activity becomes quiescent. The histologic appearance of regressing cirrhosis in the human has not been described in detail. Objectives. —To define histologic parameters that indicate regression of cirrhosis and to provide an interpretation of how regression occurs from a histologic point of view. Design. —A patient who underwent a series of biopsies that showed apparent regression of hepatitis B cirrhosis is presented. In addition, 52 livers removed at transplantation having cirrhosis or incomplete septal cirrhosis were graded for histologic parameters that suggest progression or regression of fibrosis. Progression parameters were steatohepatitis, inflammation, bridging necrosis, and piecemeal necrosis. The regression parameters (collectively called the hepatic repair complex) were delicate perforated septa, isolated th.
On the Design of Computation Offloading in Cache-Aided D2D Multicast Networks

As the demand of data-hungry and computing intensive tasks grows dramatically, cache-aided device-to-device multicast (D2MD) networks are introduced, which can offload traffic from the base stations to D2D users (DUEs) directly to alleviate the heavy burden on backhaul links and improve the energy and spectrum efficiency. However, most previous works ignored the limitation of battery power and scarce computing capabilities of DUEs. In this paper, we study the computation and traffic offloading in cache-aided D2MD networks for the content delivery and delay sensitive task offloading services. Firstly, in order to provide stable multicast links and enhanced computing resources, a D2D cluster head (DCH) selection strategy is proposed that jointly considers the social attributes, available energy, and transfer rate of DUEs. Secondly, to improve the efficiency of content distribution and optimize the energy consumption of content delivery, we propose a novel multicast-aware coded and cooperative caching scheme, which may increase the opportunity for D2D multicasting to obtain the desired contents. Thirdly, considering the DUEs association, uplink full duplex DCH transmission power allocation, and mobile edge computing computation resource scheduling, an optimization computation offloading model is formulated. On this basis, we model the computation offloading and resource allocation optimization problem. Furthermore, we transform this problem into user allocation optimization problem and resource allocation optimization problem (RAOP), and RAOP is proved as a convex problem, and the optimal resource allocation solution is found. Finally, the simulation results show that our proposed schemes can effectively decrease the energy consumption and computing costs.
Low-Loss Regrowth-Free Long Wavelength Quantum Cascade Lasers

Optical power output is the most sought-after quantity in laser engineering. This is also true for quantum cascade lasers operating especially at long wavelengths. Buried heterostructure cascade lasers with epitaxial regrowth have typically shown the lowest loss due to high current confinement as well as superior lateral thermal conductivity at the expense of complexity and cost. Among the many factors affecting optical output are the widely used passivating materials such as Si<sub>3</sub>N<sub>4</sub> and SiO<sub>2</sub>. These materials have substantial optical absorption in the long wavelength infrared, which results in optical loss reducing the output of the laser. In this letter, we report on quantum cascade lasers with various waveguide widths and cavity lengths using both PECVD grown Si<sub>3</sub>N<sub>4</sub> and e-beam evaporated HfO<sub>2</sub> as passivating material on the same structure. Their slope efficiency was measured, and the cavity losses for the two lasers were calculated. We show that HfO<sub>2</sub> passivated lasers have approximately 5.5 cm<sup>−1</sup> lower cavity loss compared to Si<sub>3</sub>N<sub>4</sub> passivated lasers. We observe up to 38% reduction in lasing threshold current, for lasers with HfO<sub>2</sub> passivation. We model the losses of the cavity due to both insulator and metal contacts of the lasers using Comsol Multiphysics for various widths. We find that the loss due to absorption in the dielectric is a significant effect for Si<sub>3</sub>N<sub>4</sub> passivated lasers and lasers in the 8–12-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> range may benefit from low loss passivation materials such as HfO<sub>2</sub>. Our results suggest that low-loss long wavelength quantum cascade lasers can be realized without epitaxial overgrowth.
A Review of Computational Intelligence Techniques in Wireless Sensor and Actuator Networks

Wireless sensor and actuator networks (WSANs) are heterogeneous networks composed of many different nodes that can cooperatively sense the environment, determine an appropriate action to take, then change the environment’s state after acting on it. As a natural extension of wireless sensor networks (WSNs), WSANs inherit from them a variety of research challenges and bring forth many new ones. These challenges are related to dealing with imprecise and vague information, solving complicated optimization problems or collecting and processing data from multiple sources. Computational intelligence (CI) is an overarching term denoting a conglomerate of biologically and linguistically inspired techniques that provide robust solutions to NP-hard problems, reason in imprecise terms and yield high-quality yet computationally tractable approximate solutions to real-world problems. Many researchers have consequently turned to CI in hope of finding answers to a plethora of WSAN-related challenges. This paper reviews the application of several methodologies under the CI umbrella to the WSAN field. We describe and categorize existing works leaning on fuzzy systems, neural networks, evolutionary computation, swarm intelligence, learning systems, and their hybridizations to well-known or emerging WSAN problems along five major axes: 1) actuation; 2) communication; 3) sink mobility; 4) topology control; and 5) localization. The survey offers informative discussions to help reason through all the studies under consideration. Finally, we point to future research avenues by: 1) suggesting suitable CI techniques to specific problems; 2) borrowing concepts from WSNs that have yet to be applied to WSANs; or 3) describing the shortcomings of current methods in order to spark interest on the development of more refined models.
USO DO QUESTIONÁRIO MSLQ NA AVALIAÇÃO DA MOTIVAÇÃO E ESTRATÉGIAS DE APRENDIZAGEM DE ESTUDANTES DO ENSINO MÉDIO DE BIOLOGIA, FÍSICA E MATEMÁTICA

A motivacao e o desenvolvimento de estrategias de aprendizagem sao os principais aspectos associados a aprendizagem autorregulada, contribuindo de forma direta para o desempenho academico dos estudantes. Este trabalho apresenta os resultados obtidos na avaliacao da motivacao e estrategias de aprendizagem de estudantes do ensino medio de Biologia, Fisica e Matematica em uma escola publica do municipio de Sao Goncalo, Rio de Janeiro. O trabalho apresenta, tambem, a versao traduzida e adaptada para uso na educacao basica do questionario de estrategias de motivacao para aprendizagem (MSLQ), um dos principais instrumentos validados e utilizados na literatura. Sao apresentados os escores medios obtidos para os 15 constructos do questionario para estudantes de ensino medio de Biologia, Fisica e Matematica, a fim de servir como valor inicial de referencia para avaliacoes de motivacao e estrategias de aprendizagem. Observou-se diferencas significativas principalmente para os constructos da secao de motivacao entre as turmas de estudantes de uma mesma escola e disciplina. O instrumento apresenta bom potencial para uso em pesquisas, avaliacoes e intervencoes no contexto da educacao basica brasileira.
ALFA: a language for programming reactive robotic control systems

The author introduces ALFA, a language for programming reactive control mechanisms for autonomous mobile robots. ALFA programs consist of networks of computational modules connected by communications channels. These channels are themselves computational entities which perform mediation tasks. This makes it possible to design modular behaviors which may be inserted and removed without having to restructure the interconnections among modules and allows the system to interface cleanly with higher-level reasoning processes. ALFA supports a dataflow computation model as well as a state-machine model. The results of two experiments using ALFA to control real robots are briefly discussed. <<ETX>>
An Improvised Algorithm For Computer Vision Based Cashew Grading System Using Deep CNN

Computer vision is becoming popular at the present days in agricultural area with an assortment of technological improvements for grading, sorting, classification. Though there are much technical advancement, cashew grading and sorting are still difficult task in daily market. In this research work we discussed about the computer vision based grading system and also an overview of deep Convolution Neural Network (CNN). The CNN with deep layer has tremendous achievement in many image classification applications. The deep CNN which itself extract the features of the image for classification was the added advantage. In this work the various parameters are considered for optimization of CNN. This work portrays the importance grading cashew nut and also proposed a framework for computer vision based grading system for cashew nut using deep CNN.
On the Proximity of Markets with Integral Equilibria

We study Fisher markets that admit equilibria wherein each good is integrally assigned to some agent. While strong existence and computational guarantees are known for equilibria of Fisher markets with additive valuations (Eisenberg and Gale 1959; Orlin 2010), such equilibria, in general, assign goods fractionally to agents. Hence, Fisher markets are not directly applicable in the context of indivisible goods. In this work we show that one can always bypass this hurdle and, up to a bounded change in agents’ budgets, obtain markets that admit an integral equilibrium. We refer to such markets as pure markets and show that, for any given Fisher market (with additive valuations), one can efficiently compute a “near-by,” pure market with an accompanying integral equilibrium.Our work on pure markets leads to novel algorithmic results for fair division of indivisible goods. Prior work in discrete fair division has shown that, under additive valuations, there always exist allocations that simultaneously achieve the seemingly incompatible properties of fairness and efficiency (Caragiannis et al. 2016); here fairness refers to envyfreeness up to one good (EF1) and efficiency corresponds to Pareto efficiency. However, polynomial-time algorithms are not known for finding such allocations. Considering relaxations of proportionality and EF1, respectively, as our notions of fairness, we show that fair and Pareto efficient allocations can be computed in strongly polynomial time.
Novel application of Influenza A virus-inoculated chorioallantoic membrane to characterize a NP-specific monoclonal antibody for immunohistochemistry assaying

Monoclonal antibodies (MAbs) are widely applied in disease diagnoses. Herein, we report a MAb, WF-4, against Influenza A virus nucleoprotein (NP), its broad response with Influenza A virus, and its application in an immunohistochemistry (IHC) assay. WF-4 was screened by immunofluorescence assay (IFA). The results showed that its reactivity with baculovirus-expressed full-length recombinant NP (rNP) in Western blot (WB), indicating its IHC applicability. Fifteen Influenza A virus (reference subtypes H1 to H15) infected chicken embryonated chorioallantoic membranes (CAM), fixed by formalin, were all detectable in the WF-4-based IHC assay. Also, the reactivity of the IHC test with NP from experimentally inoculated H6N1 and from all recent outbreaks of H5 subtype avian Influenza A virus (AIV) field cases in Taiwan showed positive results. Our data indicate that CAM, a by-product of Influenza A virus preparation, is helpful for Influenza A virus-specific MAb characterization, and that the WF-4 MAb recognizes conserved and linear epitopes of Influenza A virus NP. Therefore, WF-4 is capable of detecting NP antigens via IHC and may be suitable for developing various tests for diagnosis of Influenza A virus and, especially, AIV infection.
Mitigating the effects of implicit constraints in verbal insight problem solving through training

The main focus of this thesis was to design training to mitigate the effects of constraints underlying verbal insight problem solving. Concurrent verbal protocols were collected. Experiments 1 and 2 tested training that was specific to solving problems that were exemplars of trained categories. In Experiment 1, heuristic training was provided for two categories of constraint: those with ambiguous words and those with human names that should be associated with animals. Transfer was positive for novel problems within the two trained categories but not for problems outside. Experiment 2 improved performance on problems with ambiguous words once shortcomings in Experiment 1 were addressed. Experiment 3 tested training that was specific to solving functional fixedness verbal insight problems. An iterative process of considering many functions of individual items was successful in facilitating performance but not for problems outside. Experiments 4 and 5 investigated the effectiveness of different types of generic training in facilitating solution of novel verbal insight problems. In Experiment 4, participants were trained to identify assumptions, which they made during problem solving and were inconsistent with the problem statement. In Experiment 5, participants were trained to iteratively consider different parts of the problem specification and to identify inconsistencies as in Experiment 4. Solution rate was improved in both experiments although instruction to explain and justify oneself during problem solving was also sufficient in facilitating performance in Experiment 5. Finally, Experiment 6 tested a novel method for identifying when a participant was constrained by an incorrect representation during verbal insight problem solving. The results supported that there is variability in the nature of stereotypical constraints involved and demonstrated how training can be designed to induce restructuring or a shift in representation.
Reduplicative Economy

The unexpected interactions between reduplication and phonological processes were the direct motivation for the invention of Correspondence Theory (CT; McCarthy and Prince , a , etc.) as a component of Optimality Theory (OT; Prince and Smolensky , McCarthy and Prince , etc.). As uncovered in the pioneering study of reduplication, Wilbur (),  phonological processes frequently do not apply normally when combined with reduplication. Rather, what is observed is surface overor underapplication of the phonological processes. McCarthy and Prince analyze these departures from normal process application as instances of identity (faithfulness) between Base and Reduplicant. Extending such analyses to other areas of phonology, McCarthy and Prince (a) expand Correspondence Theory to cover input-output relations generally. As Correspondence Theory has evolved, it has gained a whole panoply of additional uses, such as: InputReduplicant relations (McCarthy and Prince a), Output-Output relations within a paradigm (Benua ) and Base-Epenthesis relations (Kitto and de  Lacy ). The advent of Correspondence Theory introduces new constraint families (M, D, I) but also significantly enriches the representations allowed within the theory. The original Containment model of Prince and Smolensky () handled deletion through underparsing, and epenthesis through empty prosodic positions (symbolized there by an empty box), which were to be filled in at phonetic interpretation. For a variety of empirical and conceptual reasons, the Containment model was superseded by Correspondence Theory, which allows a much richer conception of deviation from
Trichloroacetic acid UV-Cont Plants irradiated with UV but not treated with S-methylmethionine UV-SMM Plants irradiated with UV and treated with S-methylmethionine Communicated

We examined the interactions of an exogenously added non-proteinogenic amino acid, S-methylmethionine (SMM) and UV-B radiation in young maize plants. We observed that exposure to UV-B light caused a substantial increase in both the phenolics and anthocyanin contents. Pretreatment with SMM also induced a slight, but statistically significant increase in the total phenol content, and was also able to accelerate the rise in the UV-B-induced anthocyanin level. Gene expression patterns indicated that the general phenylpropanoid pathway was most strongly induced by the combined effect of SMM and UV, while the anthocyanin synthesis by the sole UV-B treatment. SMM treatment and UV-B light led to a substantial increase in the activities of the antioxidant enzymes catalase (CAT), 1,*
Telemedicine in response to challenges of modern diabetology

Due to the steadily increasing number of patients with diabetes all over the world, access to specialist diabetes care is becoming difficult, especially in rural areas. Approximately 30% of diabetic patients do not achieve the target blood glucose values, which is certainly an important problem of modern diabetology, because it translates into late vascular complications of diabetes, lower quality of life and premature death. In response to the raising demand for diabetes care, technological solutions increasing the accessibility to healthcare professionals with a concomitant reduction of costs of such care may be very useful. In a view of the above, the contribution of continuously developing widely understood information and communication technologies (ICT) to an improvement of blood glucose control in diabetic patients has been recently evaluated in research studies. Phone consultations, remote blood glucose levels monitoring and video consultations are the most commonly studied telemedical interventions. Each of them leads to an increased accessibility to specialist diabetes care. Besides clinical trials, in real patients’ life there are many mobile health applications (apps) available which may be helpful in daily life diabetes management however raise concerns about regulation and approval, privacy, accuracy and safety. Contrary to cardiology, telemedical solutions in diabetology are not popular for commercial use yet, which is why patients and their physicians still have to wait for standardisation and appropriate legal regulations for telemedical interventions in diabetes care. Although as evidenced by clinical trials, such interventions translate into improved blood glucose control, lower costs of diabetes care and even a reduction of hypoglycaemia risk.
Using Grounded Theory Coding Mechanisms to Analyze Case Study and Focus Group Data in the Context of Software Process Research

The primary aim of this chapter is to outline a potentially powerful framework for the combination of research approaches utilizing the Grounded Theory coding mechanism for Case Study, and Focus Groups data analysis. A secondary aim of this chapter is to provide a roadmap for such a usage by way of an example research project. The context for this project is the need to study and evaluate the actual practice of software development processes in real world commercial settings of software companies, which utilized both case study and focus group techniques. This research found that grounded theory coding strategies are a suitable and powerful data analysis mechanism to explore case study and focus group data. DOI: 10.4018/978-1-4666-0179-6.ch013