Miracle-dz/UniFAR-FTUs huggingface dataset:
The Development and Usage of a Relational Database Design Tool for Educational Purposes

Most universities where students study informational technologies and computer science have an introductory course dealing with the development and design of databases. These courses often include usage of database design tools. In this paper, the #EER tool is presented, the task of which is to make the process of relational databases design easier for the students and partially automatize it. The tool evolved due to the experience in using similar tools for educational purposes. It enables fast and efficient development of the relational database conceptual model and its automatized compilation into a relational model and further to data definition language (DDL) commands. #EER tool is based on the extended entity-relationship (EER) model for conceptual modeling of relational databases. Modular architecture of the tool, the development of which is based on the usage of the design patterns, along with the benefits that its usage brings, is also presented.
Object Tracking Based on Fragment Template and Multi-Feature Adaptive Fusion

Object tracking under complex circumstances is a challenging task because of background interference, object deformation, obstacle occlusion, etc. Given such conditions, robustly detecting through single-feature representation are difficult tasks. For these problems, this paper presents object tracking based on a fragment and a multi-feature adaptive fusion. Through importing the concept of fragments, we distinguish the different types of occlusions, then adopt different the strategies of combining methods. Through importing the color, HOG and corner features, this paper also proposes a self-adaptive multi-feature fusion strategy based on their contributions. Experimental results show this algorithm can track moving objects robustly and accurately.
Behavioral variation in Drosophila melanogaster: no evidence for common alleles of large-effect at the foraging gene in a population from North Carolina, USA

It has been postulated that natural populations of Drosophila melanogaster are comprised of two behavioral morphs termed "rover" and "sitter", and that this variation is caused mainly by large-effect alleles at a single locus. Contrary to common assertions, however, published support for the existence of common large effect alleles in nature is quite limited. To further investigate, we quantified the foraging behavior of 36 sequenced strains from a natural population, performed an association study, and described patterns of molecular evolution at the foraging locus. Though there was significant variation in foraging behavior among genotypes, this variation was continuously distributed and not significantly associated with genetic variation at the foraging gene. Patterns of molecular population genetic variation at this gene also provide no support for the hypothesis that for is a target of recent balancing selection. Though our data only apply to this specific population, we propose that additional data is required to support a hypothesis of common alleles of large effect on foraging behavior in nature.
Commutators with Reisz potentials in one and several parameters

Let M b be the operator of pointwise multiplication by b, that is M b f = bf. Set [A, B] = AB-BA. The Reisz potentials are the operators R α f(x) = ∫f(x-y)dy |y| α , 0 < α < 1. They map L p → L q , for 1 - α + 1/q = 1/p, a fact we shall take for granted in this paper. A Theorem of Chanillo [6] states that one has the equivalence ∥[M b , R α ]∥ p→q ≃∥b∥BMO with the later norm being that of the space of functions of bounded mean oscillation. We discuss a proof of this result in a discrete setting, and extend part of the equivalence above to the higher parameter setting.
Analyses and Experiments of Compact Spherical Tokamak-Stellarator "TOKASTAR"

A hybrid configuration called TOKASTAR combined with a spherical tokamak and a compact stellarator has been proposed to make an easy start-up of tokamak plasma current operation and to reduce the probability of plasma current disruptions by external helical field. In this configuration a natural built-in divertor is provided. According to the finite beta equilibrium analysis, the higher equilibrium beta value can be attained by adding plasma current and increasing average rotational transform. The particle orbit analysis denotes that the high energy particle confinement is improved by increasing inboard-side rotational transform created by the central conductor modification from tokamak-like straight central post to helical winding post. The experimental preparation is performed for this TOKASTAR concept demonstration.
Research on Military Equipment Fault Diagnosis Based on ANN and ES

There are some shortages of knowledge acquisition and inefficency in ES. So, combines ES with ANN to construst military equipment fault diagosis expert system. Introduces the neural network learning system, the knowledge base and the reasoning mechanism of the expert system. After introducing ANN and ES, utilizing the adapting, self-learning abilities of ANN, methods of knowledge acquirement and representation are studied, ways of solving the bottleneck problem of knowledge acquirement in Intelligence Fault Diagnosis Expert system (IFDES) are discussed, and knowledge base of ES founded on ANN is put forward. In the end, the feasibility and validity is testified by an instance.
In Vivo Assessment of Stem Cells for Treating Neurodegenerative Disease: Current Approaches and Future Prospects

In recent years, stem cell-related therapies have been widely applied for treating neurodegenerative disease. Despite their potential, stem cell tracking and imaging techniques for the evaluation of in vivo proof-of-concept (PoC) therapies have not been sufficiently represented in the research area. This review summarizes the recent approaches that have been used for tracking and imaging engrafted stem cells in vivo. Furthermore, we introduce tissue clearing technology that can be applied to develop three-dimensional in vivo experiments. Monitoring stem cell survival and migration and graft-host relationships is a useful strategy to evaluate the therapeutic efficacy of regenerative medicine approaches in neurodegenerative disease.
Multiple land mines localization using a wireless sensor network

This paper considers the problem of localization of multiple land mines using information collected by a network of wireless sensors deployed in the area of interest. These sensors detect the concentration of explosive vapours, emanating from buried land mines, in the air and have a wireless transmission system for exchanging information with a treatment or fusion centre. One of the key contribution of this paper is to locate and estimate the emission rate of multiple land mines. Using a model for the transport of the explosive chemicals in the air, we formulate the inverse problem consisting in determining sequentially the positions and emission rates of the land mines knowing concentration measurements provided by the sensors. To solve the inverse problem, we present a first solution based on a Least Squares optimization approach and a second solution based on probabilistic Bayesian techniques using a Markov Chain Monte Carlo sampling scheme. These two approaches are tested and compared on simulated data.
Which Tweets Will Be Headlines? A Hierarchical Bayesian Model for Bridging Social Media and Traditional Media

Microblogging platforms such as Twitter provide a convenient channel for people to express their feelings, report news, and communicate with friends. Most existing work on social media analysis has been focused on predicting users' behaviors, analyzing the corresponding social networks, tracking the popular topics, etc. However, there is limited research effort on uncovering the relationships between social media (e.g. Twitter) and traditional media (e.g., Washington Post and New York Times), which has a big impact in our daily lives and our society. This paper targets on a novel and important research problem as which and whose tweets are favored by the traditional media. The basic intuition is that whether a tweet could be picked up or not by traditional media depends not only on whether its content matches traditional media's interests towards this specific user but also the writer's personal influence, reflected by factors such as the number of followers. Based on this intuition, this paper proposes a Twitter Pick-Up Relational (TPUR) model to simultaneously integrate these factors. In particular, the dependence between the traditional media's interests towards a user and the content of each tweet, and the influence of each user are integrated in a hierarchical bayesian model. An extensive set of experiments are conducted on two datasets from two popular microblogging platforms, i.e., Twitter and Sina Weibo (Chinese version Twitter), to demonstrate the advantages of our algorithm against baseline methods on the proposed problem.
Wavelet based retinal recognition

Retina is a new biometric method to recognize a person. The fact that blood vessels have vessels with different thickness and width motivate us to analyze the retina using multi-resolution analysis method. A novel retina feature, named wavelet energy feature (WEF) is defined in this paper, employing wavelet, which is a powerful tool of multi-resolution analysis. WEF can reflect the wavelet energy distribution of the vessels with different thickness and width in several directions at different wavelet decomposition levels (scales), so its ability to discriminate retinas is very strong. Easiness to compute is another virtue of WEF. Using semiconductors and various environmental temperatures in electronic imaging systems cause noisy images, so in this article noisy retinal images are used in recognition. In existence of 5 db to 20 db noise, the proposed method can achieve %100 recognition rates.
Supporting the creation of semantic RESTful service descriptions

Research on semantic Web services (SWS) has been devoted to reduce the extensive manual eort required for manipulating Web services by enhancing them with semantic information. Recently, the world around services on the Web, thus far limited to "classical" Web services based on SOAP and WSDL, has significantly evolved with the proliferation of Web applications and APIs, often referred to as RESTful Web services. However, despite their success, RESTful services are currently facing similar limitations to those identified for traditional Web service technologies and present even further difficulties, such as the lack of machine-processable service descriptions. In order to address these challenges and to enable the wider adoption of RESTful service technologies, we advocate an integrated lightweight approach for formally describing semantic RESTful services. The approach is based on the use of the hRESTS (HTML for RESTful Services) and MicroWSMO microformats, which enable the creation of machine-readable service descriptions and the addition of semantic annotations, correspondingly. Finally, we present SWEET - Semantic Web sErvices Editing Tool - which effectively supports users in creating semantic descriptions of RESTful services based on the aforementioned technologies.
Let ’ s talk about news Supporting the editorial process at a radio station

The paper reports from a study at a local Swedish radio station informing the design of new IT support. We suggest a prototype application – the NewsMate – with the aim of supporting knowledge sharing and expertise location among the journalists in both office and field situations. The guiding design principles for the NewsMate are evaluated against existing systems and approaches.
Bootstrapping Phylogenies: Large Deviations and Dispersion Eeects Bootstrapping Phylogenies: Large Deviations and Dispersion Eeects

A large deviation result is established for the bootstrap empirical distribution in a nite sample space, thereby validating both nonparametric and parametric bootstrapping in certain phylogenetic inference problems. The bias previously observed in the bootstrap distribution of the estimated tree topology is shown to stem from dispersion eeects in the joint distribution of sample and bootstrap empirical distributions. Both results are examined for maximum likelihood estimation in a three-taxon model having particularly simple geometry. They are also applicable to discrete parameter problems outside of phylogenetic inference.
Delay differentiation and adaptation in core stateless networks

We present a core-stateless quality of service architecture for achieving delay differentiation between flows. There are two key components in our approach: (1) per-class per-hop relative average delay-the average queueing delay perceived by the packets in a delay class at a link is inversely proportional to the delay weight of the class; (2) per-flow end-to-end delay class adaptation-the delay class of a flow is dynamically adjusted based on its perceived end-to-end delay in order to maintain the desired end-to-end average delay requirement of the flow. We show through simulations and analysis that these two components can, in concert, support end-to-end delay differentiation between flows using only simple mechanisms at the core routers in the network.
Stochastic Mortality, Macroeconomic Risks and Life Insurer Solvency

Motivated by a recent demographic study establishing a link between macroeconomic fluctuations and the mortality index kt in the Lee–Carter model, we develop a dynamic asset-liability model to assess the impact of macroeconomic fluctuations on the solvency of a life insurance company. Liabilities in this stochastic simulation framework are driven by a GDP-linked variant of the Lee–Carter mortality model. Furthermore, interest rates and stock prices react to changes in GDP, which itself is modelled as a stochastic process. Our simulation results show that insolvency probabilities are significantly higher when the reaction of mortality rates to changes in GDP is incorporated.
Energy-efficient resource management of cloud datacenters under fault tolerance constraints

The cloud computing as a ubiquitous paradigm could provide different services for internet users and Information Technology (IT) companies through datacenters that located around the world. However, cloud provider faces several problems such as high energy consumption and fault occurrence issues in cloud datacenters. Hence, cloud provider has to make a trade-off between energy and fault to gain more profit. In this paper, a migration method for the virtual machines to handle fault problem and prevent Service Level Agreement (SLA) violation is proposed by considering energy consumption constraints. The approach considers the lowest increasing in energy consumption and the minimal deadline miss ratio as the most significant factors for migration of each Virtual Machine (VM). The results show that low SLA violation is achieved with the least amount of energy consumption compared to other methods. It is also shown that the energy increasing by migration has an exponential relationship with the failure rate increasing.
Batch codes and their applications

A batch code encodes a string x into an m-tuple of strings, called buckets, such that each batch of k bits from x can be decoded by reading at most one (more generally, t) bits from each bucket. Batch codes can be viewed as relaxing several combinatorial objects, including expanders and locally decodable codes. We initiate the study of these codes by presenting some constructions, connections with other problems, and lower bounds. We also demonstrate the usefulness of batch codes by presenting two types of applications: trading maximal load for storage in certain load-balancing scenarios, and amortizing the computational cost of private information retrieval (PIR) and related cryptographic protocols.
HIV infection and antiretroviral therapy have divergent effects on mitochondria in adipose tissue.

BACKGROUND Although human immunodeficiency virus (HIV) infection and antiretroviral therapy (ART) affect mitochondrial DNA (mtDNA) content and function, comprehensive evaluations of their effects on mitochondria in muscle, adipose tissue, and blood cells are limited. METHODS Mitochondrial DNA quantification, mitochondrial genome sequencing, and gene expression analysis were performed on muscle, adipose tissue, and peripheral blood mononuclear cell (PBMC) samples from untreated HIV-positive patients, HIV-positive patients receiving nucleoside reverse transcriptase inhibitor (NRTI)-based ART, and HIV-negative controls. RESULTS The adipose tissue mtDNA/nuclear DNA (nDNA) ratio was increased in untreated HIV-infected patients (ratio, 353) and decreased in those receiving ART (ratio, 162) compared with controls (ratio, 255; P < .05 for both comparisons); the difference between the 2 HIV-infected groups was also significant (P = .002). In HIV-infected participants, mtDNA/nDNA in adipose tissue correlated with the level of activation (CD38+ /HLA-DR+) for CD4+ and CD8+ lymphocytes. No significant differences in mtDNA content were noted in muscle or PMBCs among groups. Exploratory DNA microarray analysis identified differential gene expression between patient groups, including a subset of adipose tissue genes. CONCLUSIONS HIV infection and ART have opposing effects on mtDNA content in adipose tissue; immune activation may mediate the effects of HIV, whereas NRTIs likely mediate the effects of ART.
Video provenance by motion vector analysis: A feasibility study

Motion vectors are used in digital video compression for the purpose of reducing file size, primarily by providing an estimate of a frame of interest as a motion-offset version of a reference frame or frames. Despite the name, a motion vector is not in and of itself a trace of object motion, but rather an index to a similar image block. The computational complexity of searching for a good motion vector means that many sub-optimal but computationally feasible approaches have been developed. It is conjectured that proprietary approaches to motion estimation lead to a provenance fingerprint. If the approach can be identified, then the identity of the source firmware or software can be inferred. This has further applications in edit and forgery detection. This paper describes the results of an early feasibility study in which the ability to distinguish between six motion vector algorithms is considered.
Identification and Characterization of B-Cell Epitopes in the DBL4ε Domain of VAR2CSA

Malaria during pregnancy in Plasmodium falciparum endemic regions is a major cause of mortality and severe morbidity. VAR2CSA is the parasite ligand responsible for sequestration of Plasmodium falciparum infected erythrocytes to the receptor chondroitin sulfate A (CSA) in the placenta and is the leading candidate for a placental malaria vaccine. Antibodies induced in rats against the recombinant DBL4ε domain of VAR2CSA inhibit the binding of a number of laboratory and field parasite isolates to CSA. In this study, we used a DBL4ε peptide-array to identify epitopes targeted by DBL4ε-specific antibodies that inhibit CSA-binding of infected erythrocytes. We identified three regions of overlapping peptides which were highly antigenic. One peptide region distinguished itself particularly by showing a clear difference in the binding profile of highly parasite blocking IgG compared to the IgG with low capacity to inhibit parasite adhesion to CSA. This region was further characterized and together these results suggest that even though antibodies against the synthetic peptides which cover this region did not recognize native protein, the results using the mutant domain suggest that this linear epitope might be involved in the induction of inhibitory antibodies induced by the recombinant DBL4ε domain.
Towards a Resource-Aware Component Model for Embedded Systems

Component-based development has shown to be a promising approach supporting reusability and managing complexity, but to date has not been extensively used for handling requirements from the embedded systems domain(real-time constraints, resource limitations, dependability, life-cycle properties, etc.). It is based on component models i.e. specification of components and their interaction. Most of the existing component models tailored for embedded systems only cover a small set of the embedded systems requirements. In our research we propose a two-layered component model - ProCom for design and development of embedded systems, and a modeling framework - REMES, for modeling of and reasoning about component- and system behavior, which includes relevant resource types for embedded systems.
Theta-burst transcranial magnetic stimulation to the prefrontal cortex impairs metacognitive visual awareness

We used a recently developed protocol of transcranial magnetic stimulation (TMS), theta-burst stimulation, to bilaterally depress activity in the dorsolateral prefrontal cortex as subjects performed a visual discrimination task. We found that TMS impaired subjects' ability to discriminate between correct and incorrect stimulus judgments. Specifically, after TMS subjects reported lower visibility levels for correctly identified stimuli, as if they were less fully aware of the quality of their visual information processing. A signal detection theory analysis confirmed that the results reflect a change in metacognitive sensitivity, not just response bias. The effect was specific to metacognition; TMS did not change stimulus discrimination performance, ruling out alternative explanations such as TMS impairing visual attention. Together these results suggest that activations in the prefrontal cortex in brain imaging experiments on visual awareness are not epiphenomena, but rather may reflect a critical metacognitive process.
A Balanced Method for Budgeted Influence Maximization

With the flourish of Web-based large Online Social Networks (OSNs), people on OSNs can easily yield influence on others. Finding how the influence spreads and maximizing influence spread within OSNs have been extensively studied. State-of-the-art researches suffer two defects: (a) need to acquire the topological structure of the network, which is impractical for the continuously changing networks in real life and thus can not balance very well between influence spread and running time; (b) assign the same cost for every node in OSN which cannot reflect the reality. To solve these problems we firstly propose PageRank Based Cost (PRBC) model to assess the cost of nodes in OSN according to their importance (influence); secondly we present Budgeted Random Maximal Degree Neighbor (BRMDN) algorithm by exploiting the scale free property. Results from extensive experiments show that BRMDN can well balance influence spread and running time. Keywords-social network; cost model; influence maximiza- tion;