Miracle-dz/UniFAR-FTUs huggingface dataset:
Migrating Scientific Workflows to the Cloud: Through Graph-Partitioning, Scheduling and Peer-to-Peer Data Sharing

In recent years cloud computing has raised significant interest in the scientific community. Running scientific experiments in the cloud has its advantages such as elasticity, scalability and software maintenance. However, the communication latencies added by the virtualization, the technology behind the cloud's service provisioning in general, is observed to be the major hindrance for migrating scientific computing applications to the cloud. The problem escalates further when we consider scientific workflows, where significant data is exchanged across different tasks. So to migrate scientific workflows to the cloud, we propose a way to reduce the data communication by partitioning and scheduling the workflow and adapting a peer-to-peer data sharing among the nodes. Different size Montage workflows were considered for the analysis of the approach. From the study, we observed that the partitioning along with the peer-to-peer file sharing reduced the data communication in cloud up to 80%.
The Complete Maternally and Paternally Inherited Mitochondrial Genomes of a Freshwater Mussel Potamilus alatus (Bivalvia: Unionidae)

Doubly uniparental inheritance (DUI) of mitochondrial DNA, found only in some bivalve families and characterized by the existence of gender-associated mtDNA lineages that are inherited through males (M-type) or females (F-type), is one of the very few exceptions to the general rule of strict maternal mtDNA inheritance in animals. M-type sequences are often undetected and hence still underrepresented in the GenBank, which hinders the progress of the understanding of the DUI phenomenon. We have sequenced and analyzed the complete M and F mitogenomes of a freshwater mussel, Potamilus alatus. The M-type was 493 bp longer (M = 16 560, F = 16 067 bp). Gene contents, order and the distribution of genes between L and H strands were typical for unionid mussels. Candidates for the two ORFan genes (forf and morf) were found in respective mitogenomes. Both mitogenomes had a very similar A+T bias: F = 61% and M = 62.2%. The M mitogenome-specific cox2 extension (144 bp) is much shorter than in other sequenced unionid mitogenomes (531–576 bp), which might be characteristic for the Potamilus genus. The overall topology of the phylogenetic tree is in very good agreement with the currently accepted phylogenetic relationships within the Unionidae: both studied sequences were placed within the Ambleminae subfamily clusters in the corresponding M and F clades.
Learning the hash code with generalised regression neural networks for handwritten signature biometric data retrieval

Handwritten signature recognition is one important component of biometric authentication. This is a central process in a broad range of areas requiring personal identification, such as security, legal contracts and bank transactions. Extensive efforts have been put into the research towards the verification of handwritten signatures, which contain biometric information. Although many successful methods have been used, they often disregard the size of databases, which can be very large, posing scalability problems to their application in real-world scenarios. To overcome this problem, in this paper, we use binary embeddings of high-dimensional data which is an efficient tool for indexing big datasets of biometric images. The rationale is to find a good hash function such that similar data points in Euclidean space preserve their similarities in the resulting Hamming space for fast data retrieval and state-of-the-art classification performance. In the settings of an handwritten signature retrieval system, an indexing hashing-based scheme is presented. We propose to learn k-bits hash code with a generalised regression neural network (GRNN), which yielded competitive results in the GPDS database.
IPv4 care-of address registration for IPv4 support on the NEMO basic support protocol

On the Internet, two different IP protocols are deployed such as IPv4 [7] and IPv6 [1]. The Mobile Router uses the basic NEMO protocol [2] which is IPv6 protocol specific. During the early period of time that IPv6 transition is occurring it is very likely that a Mobile Router will move to an IPv4 only access network. When this occurs the Mobile Router will no longer be able to operate using the basic NEMO protocol. There has already been some earlier work to provide IPv6 capability over an IPv4 access network for a Mobile Router [9] [10]. This paper provides a capability by to maintain IPv6 connectivity for the Mobile Router via its Home Agent with IPv4-in-IPv6 encapsulation with no special boxes to be deployed elsewhere in the network.
Functional analysis of polymorphisms in the COX-2 gene and risk of lung cancer

The enzyme cyclooxygenase 2 (COX-2) is known to be involved in tumorigenesis and metastasis in certain types of cancer. Nevertheless, the prognostic value of COX-2 overexpression and its polymorphisms in patients with non-small cell lung cancer (NSCLC) have yet to be fully elucidated. The aim of the present study was to investigate the association between the three most commonly studied COX-2 gene polymorphisms (−1195 G/A, −765 G/C and 8473 T/C) with COX-2 expression and lung cancer risk in a Brazilian cohort. In the present hospital based, case-control retrospective study, 104 patients with NSCLC and 202 cancer free control subjects were genotyped for −1195 G/A, −765 G/C and 8473 T/C polymorphisms using allelic discrimination with a reverse transcription quantitative polymerase chain reaction method. COX-2 mRNA expression was analyzed in surgically resected tumors from 34 patients with NSCLC. The results revealed that COX-2 expression levels were higher in tumor tissue compared with normal lung tissue. However, this overexpression of COX-2 was not associated with the patient outcome, and furthermore, none of the analyzed polymorphisms were associated with the risk of developing lung cancer, COX-2 overexpression, or the overall survival of the patients with NSCLC. Taken together, the findings described in the present study do not support a major role for COX-2 polymorphisms and COX-2 overexpression in lung carcinogenesis within the Brazilian population.
Rational Diversification of a Promoter Providing Fine-Tuned Expression and Orthogonal Regulation for Synthetic Biology

Yeast is an ideal organism for the development and application of synthetic biology, yet there remain relatively few well-characterised biological parts suitable for precise engineering of this chassis. In order to address this current need, we present here a strategy that takes a single biological part, a promoter, and re-engineers it to produce a fine-graded output range promoter library and new regulated promoters desirable for orthogonal synthetic biology applications. A highly constitutive Saccharomyces cerevisiae promoter, PFY1p, was identified by bioinformatic approaches, characterised in vivo and diversified at its core sequence to create a 36-member promoter library. TetR regulation was introduced into PFY1p to create a synthetic inducible promoter (iPFY1p) that functions in an inverter device. Orthogonal and scalable regulation of synthetic promoters was then demonstrated for the first time using customisable Transcription Activator-Like Effectors (TALEs) modified and designed to act as orthogonal repressors for specific PFY1-based promoters. The ability to diversify a promoter at its core sequences and then independently target Transcription Activator-Like Orthogonal Repressors (TALORs) to virtually any of these sequences shows great promise toward the design and construction of future synthetic gene networks that encode complex “multi-wire” logic functions.
A probabilistic approach for human everyday activities recognition using body motion from RGB-D images

In this work, we propose an approach that relies on cues from depth perception from RGB-D images, where features related to human body motion (3D skeleton features) are used on multiple learning classifiers in order to recognize human activities on a benchmark dataset. A Dynamic Bayesian Mixture Model (DBMM) is designed to combine multiple classifier likelihoods into a single form, assigning weights (by an uncertainty measure) to counterbalance the likelihoods as a posterior probability. Temporal information is incorporated in the DBMM by means of prior probabilities, taking into consideration previous probabilistic inference to reinforce current-frame classification. The publicly available Cornell Activity Dataset [1] with 12 different human activities was used to evaluate the proposed approach. Reported results on testing dataset show that our approach overcomes state of the art methods in terms of precision, recall and overall accuracy. The developed work allows the use of activities classification for applications where the human behaviour recognition is important, such as human-robot interaction, assisted living for elderly care, among others.
DeepDroid: Dynamically Enforcing Enterprise Policy on Android Devices

It is becoming a global trend for company employees equipped with mobile devices to access company’s assets. Besides enterprise apps, lots of personal apps from various untrusted app stores may also be installed on those devices. To secure the business environment, policy enforcement on what, how, and when certain apps can access system resources is required by enterprise IT. However, Android, the largest mobile platform with a market share of 81.9%, provides very restricted interfaces for enterprise policy enforcement. In this paper, we present DeepDroid, a dynamic enterprise security policy enforcement scheme on Android devices. Different from existing approaches, DeepDroid is implemented by dynamic memory instrumentation of a small number of critical system processes without any firmware modification. DeepDroid can be easily deployed on various smartphone platforms with a wide range of Android versions. Moreover, based on the context information extracted from Binder interception, a fine-grained policy can be enforced. We develop a prototype of DeepDroid and test it on various smartphones and Android versions. The experimental results show that DeepDroid can effectively enforce enterprise resource access policies with negligible performance overhead.
2016 Ieee International Conference on Big Data (big Data) Radii : Bridging the Divide between Data and Infrastructure Management to Support Data-driven Collaborations

—We have witnessed a dramatic increase in national cyberinfrastructure resources to support data-driven research. Orchestrating these resources to enable the creation of col-laborative infrastructure capable of supporting data intensive activities is challenging. In this work we present RADII, a novel architecture and system that enables the provisioning and configuration of collaborative infrastructure by orchestrating data and infrastructure management in an integrated manner. We also introduce a cross-layer data annotation mechanism that together with Software-Defined Networking (SDN) support allows the embedding of user-defined performance policies into file metadata which are translated into executable optimal network plans. We have deployed RADII on a worldwide production testbed and demonstrated through experimentation that RADII can improve network throughput of data transfers by 28.2% as compared to conventional approaches.
A Wideband 77-GHz, 17.5-dBm Fully Integrated Power Amplifier in Silicon

A 77-GHz, +17.5 dBm power amplifier (PA) with fully integrated 50-Omega input and output matching and fabricated in a 0.12-mum SiGe BiCMOS process is presented. The PA achieves a peak power gain of 17 dB and a maximum single-ended output power of 17.5 dBm with 12.8% of power-added efficiency (PAE). It has a 3-dB bandwidth of 15 GHz and draws 165 mA from a 1.8-V supply. Conductor-backed coplanar waveguide (CBCPW) is used as the transmission line structure resulting in large isolation between adjacent lines, enabling integration of the PA in an area of 0.6 mm2. By using a separate image-rejection filter incorporated before the PA, the rejection at IF frequency of 25 GHz is improved by 35 dB, helping to keep the PA design wideband
Search for β2 Adrenergic Receptor Ligands by Virtual Screening via Grid Computing and Investigation of Binding Modes by Docking and Molecular Dynamics Simulations

We designed a program called MolGridCal that can be used to screen small molecule database in grid computing on basis of JPPF grid environment. Based on MolGridCal program, we proposed an integrated strategy for virtual screening and binding mode investigation by combining molecular docking, molecular dynamics (MD) simulations and free energy calculations. To test the effectiveness of MolGridCal, we screened potential ligands for β2 adrenergic receptor (β2AR) from a database containing 50,000 small molecules. MolGridCal can not only send tasks to the grid server automatically, but also can distribute tasks using the screensaver function. As for the results of virtual screening, the known agonist BI-167107 of β2AR is ranked among the top 2% of the screened candidates, indicating MolGridCal program can give reasonable results. To further study the binding mode and refine the results of MolGridCal, more accurate docking and scoring methods are used to estimate the binding affinity for the top three molecules (agonist BI-167107, neutral antagonist alprenolol and inverse agonist ICI 118,551). The results indicate agonist BI-167107 has the best binding affinity. MD simulation and free energy calculation are employed to investigate the dynamic interaction mechanism between the ligands and β2AR. The results show that the agonist BI-167107 also has the lowest binding free energy. This study can provide a new way to perform virtual screening effectively through integrating molecular docking based on grid computing, MD simulations and free energy calculations. The source codes of MolGridCal are freely available at http://molgridcal.codeplex. com.
ELI-C : A Loop-level Workload Characterization Tool

Every processor manufacturing cycle includes a workload gathering step. The input to this step is a diverse set of workloads from processor-specific domains. Simulating the entire workload set is time consuming and resource intensive, and therefore is infeasible. To reduce the simulation time, some techniques in the literature have exploited the internal program repetitiveness to extract representative segments. Other approaches involve exploiting the program similarities and reducing the number of workloads. In this paper, we propose an orthogonal and complementary approach to reduce the simulation time by exploiting loop-level program characterization similarities across workloads in a set. We developed a loop-level analyzer, dubbed ELI-C, that extracts loop characterizations from workloads, and then applies a machine learning technique to group workloads based on similarities. Finally, we employ a novel heuristic to exclude workloads that are highly similar and arrive at a smaller representative subset that preserves the characteristics of the initial set. Simulation results demonstrate that applying this technique to a set of 20 workloads reduces the set size into half while preserving the main characterization of the initial set of workloads. Keywords—Loop-level analysis, performance, workload characterization.
State of Charge Estimation for Lion-Lithium Batteries Using Extended Kalman Theorem

For the hybrid and electric vehicles, a battery management system is required to supervise and optimize the electrical energy management. SOC and SOH are key parameters to supervise the good use and to contribute to enhance the lifetime of the battery. This paper presents the research for an intelligent state of charge estimation. The state of charge is a very important factor to characterize the state of the storage elements. This work consists in implementing a real time observer in order to supervise key parameters of the battery. An extended Kalman filter based on a lumped battery model is used to achieve this goal. A lumped model has been defined to consider the electrochemical phenomena, and has been linked with a thermal model to take into account the influence of the temperature. A strategy to set up the Kalman filter has been proposed to get a fast and accurate convergence of the state of charge observation despite bad initializations. Extensions Kalman have been implemented to take into account the influence of the aging on the battery's dynamic model. For the hybrid and electric vehicles, a battery management system is required to supervise and optimize the electrical energy management. All the simplified in order to be implementable on an embedded digital signal models have been processor. The real time state of charge observer has been validated on a lithium ion cell for different operating conditions.
Semiclassical quantization of superstrings: AdS_5 x S^5 and beyond

We discuss semiclassical quantization of closed superstrings in AdS_5 x S^5. We consider two basic examples: point-like string boosted along large circle of S^5 and folded string rotating in AdS_5. In the first case we clarify the general structure of the sigma model perturbation theory for the energy of string states beyond the leading 1-loop order (related to the plane-wave limit). In the second case we argue that the large spin limit of the expression for the ground-state energy (i.e. for the dimension of the corresponding minimal twist gauge theory operator) has the form S + f(\lambda) \ln S to all orders in \alpha' ~ \lambda^{-1/2} expansion, in agreement with the AdS/CFT duality. We also suggest an extension of the semiclassical approach to near-conformal (near-AdS) cases on the example of the fractional D3-brane on conifold background. Expanded version of talks at the Third International Sakharov Conference on Physics, Moscow, June 24-29, 2002 and at Strings 2002, Cambridge, July 15-20,2002
Women and leadership: challenges and opportunities in Saudi higher education

Purpose The purpose of this paper is to examine women managers’ perceptions of forces influencing their career progression to leadership positions in Saudi Arabian higher education (HE). Design/methodology/approach A qualitative, interpretive approach is adopted for analysing in-depth interviews with women managers in Saudi HE. Findings The findings of this study provide an in-depth understanding of Saudi women’s experience in management in HE. This study shows that women face a number of challenges preventing them from achieving equitable representation in leadership positions; conversely, they do have some opportunities which help them in their careers. Additionally, it provides more explanation of how the macro factors trickle down to the organizational level to shape organizational process and practices, and have an impact on women accessing leadership positions. Furthermore, this study suggests that socio-cultural background does matter when studying women in management in the Saudi context. Research limitations/implications The paper, mainly focussing on women managers in Saudi HE, gives a clear picture of women’s careers and influencing factors. Practical implications The research has implications for government and other related institutions to formulate effective policies and appropriate strategies to help Saudi women access leadership posts. Originality/value The novelty of this paper is in its focus on the under-researched topic of careers of Saudi women managers. The paper is also valuable as it emphasizes the societal context for researchers wishing to study women’s careers in the Arab world.
Effects of Digested Onion Extracts on Intestinal Gene Expression: An Interspecies Comparison Using Different Intestine Models

Human intestinal tissue samples are barely accessible to study potential health benefits of nutritional compounds. Numbers of animals used in animal trials, however, need to be minimalized. Therefore, we explored the applicability of in vitro (human Caco-2 cells) and ex vivo intestine models (rat precision cut intestine slices and the pig in-situ small intestinal segment perfusion (SISP) technique) to study the effect of food compounds. In vitro digested yellow (YOd) and white onion extracts (WOd) were used as model food compounds and transcriptomics was applied to obtain more insight into which extent mode of actions depend on the model. The three intestine models shared 9,140 genes which were used to compare the responses to digested onions between the models. Unsupervised clustering analysis showed that genes up- or down-regulated by WOd in human Caco-2 cells and rat intestine slices were similarly regulated by YOd, indicating comparable modes of action for the two onion species. Highly variable responses to onion were found in the pig SISP model. By focussing only on genes with significant differential expression, in combination with a fold change > 1.5, 15 genes showed similar onion-induced expression in human Caco-2 cells and rat intestine slices and 2 overlapping genes were found between the human Caco-2 and pig SISP model. Pathway analyses revealed that mainly processes related to oxidative stress, and especially the Keap1-Nrf2 pathway, were affected by onions in all three models. Our data fit with previous in vivo studies showing that the beneficial effects of onions are mostly linked to their antioxidant properties. Taken together, our data indicate that each of the in vitro and ex vivo intestine models used in this study, taking into account their limitations, can be used to determine modes of action of nutritional compounds and can thereby reduce the number of animals used in conventional nutritional intervention studies.
Inhalation Exposure to Nickel Hydroxide Nanoparticles Induces Systemic Acute Phase Response in Mice

It has been proposed that acute phase response can be a mechanism by which inhaled particles exert adverse effects on the cardiovascular system. Although some of the human acute phase proteins have been widely studied as biomarkers of systemic inflammation or cardiovascular diseases, there are only a few studies that investigated the role of serum amyloid P (SAP), a major acute phase protein in mice. In this study, we investigated the changes in SAP, following inhalation exposure to nickel hydroxide nanoparticles (nano-NH). We conducted 1) acute (4h) exposure to nano-NH at 100, 500, and 1000 μg/m^3 and 2) sub-acute (4h/d for 3d) exposure at 1000 μg/m3, then measured serum SAP protein levels along with hepatic Sap mRNA levels. The results show that inhaled nano-NH can induce systemic acute phase response indicated by increased serum SAP levels and hepatic Sap mRNA levels. To the best of our knowledge, this is the first study showing induction of SAP in response to repeated particle exposure, and the results suggest that SAP can be used as a biomarker for systemic inflammation induced by inhaled particles.