nils-herrmann/clean_scirepeval huggingface dataset:
Ionic Conductivity in Relation to Ternary Phase Diagram of Poly(ethylene oxide) Succinonitrile, and Lithium Bis(trifluoromethane)sulfonimide Blends
Year: 2012

In an effort to develop free standing lithium battery membrane, binary and ternary phase diagrams of poly(ethylene oxide) (PEO) bis(trifluoromethane)sulfonimide (LiTFSI) and succinonitrile (SCN) (i.e. solid plasticizer) mixtures have been established by means of differential scanning calorimetry and polarized optical microscopy. The occurrence of hydrogen bonds and/or coordination bonds in each binary pair (PEO/SCN, SCN/LiTFSI, and PEO/LiTFSI) was examined using Fourier transform infrared spectroscopy. The binary PEO/LiTFSI mixture exhibits a eutectic phase diagram with the liquid crystal coexistence region having various crystal forms of the lithium salt, whereas the SCN/LiTFSI blend shows a wide noncrystalline region, which is highly desirable for organic solvent free battery applications. The PEO/SCN blend shows a typical eutectic behavior, which is explicable in the framework of the Flory Huggins theory in conjunction with the phase field theory of crystal solidification. Various coexistence regi.
Going Deep: Using deep learning techniques with simplified mathematical models against XOR BR and TBR PUFs (Attacks and Countermeasures)
Venue: 2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)
Year: 2020

This paper contributes to the study of PUFs vulnerability against modeling attacks by evaluating the security of XOR BR PUFs, XOR TBR PUFs, and obfuscated architectures of XOR BR PUF using a simplified mathematical model and deep learning (DL) techniques. DL modeling attacks were invoked against PUFs with different stage sizes (e.g. 64, 128, 256) and all are implemented on FPGA chips. Obtained results show that DL modeling attacks could easily break the security of 4 input XOR BR PUFs and 4 input XOR TBR PUFs with modeling accuracy \sim 99$ Similar attacks were executed using single layer neural networks (NN) and support vector machines (SVM) with polynomial kernel and the obtained results showed that single NNs failed to break the PUF security. Furthermore, SVM results confirmed the same modeling accuracy reported in previous research \sim 50\ For the first time, this research empirically shows that DL networks can be used as powerful modeling techniques against these complex PUF architectures for which previous conventional machine learning techniques had failed. Furthermore, a detailed scalability analysis is conducted on the DL networks with respect to PUFs' stage size and complexity. The analysis shows that the number of layers and hidden neurons inside every layer has a linear relationship with PUFs' stage size, which agrees with the theoretical findings in deep learning. Consequently, A new obfuscated architecture is introduced as a first step to counter DL modeling attacks and it showed significant resistance against such attacks (16% 40% less accuracy) This research provides an important step towards prioritizing the efforts to introduce new PUF architectures that are more secure and invulnerable to modeling attacks. Moreover, it triggers future discussions on the removal of influential bits and the level of obfuscation needed to confirm that a specific PUF architecture is resistant against powerful DL modeling attacks.
Predicting Human Computation Game Scores with Player Rating Systems
Venue: ICEC
Year: 2017

Human computation games aim to apply human skill toward real world problems through gameplay. Such games may suffer from poor retention, potentially due to the constraints that using pre existing problems place on game design. Previous work has proposed using player rating systems and matchmaking to balance the difficulty of human computation games, and explored the use of rating systems to predict the outcomes of player attempts at levels. However, these predictions were win/loss, which required setting a score threshold to determine if a player won or lost. This may be undesirable in human computation games, where what scores are possible may be unknown. In this work, we examined the use of rating systems for predicting scores, rather than win/loss, of player attempts at levels. We found that, except in cases with a narrow range of scores and little prior information on player performance, Glicko 2 performs favorably to alternative methods.
Use of Geosynthetics in Railways Including Geocomposites and Vertical Drains
Year: 2011

Australia relies heavily on rail for the transportation of bulk commodities and passenger services, and has introduced faster and heavier trains in recent years due to a growing demand. Large cyclic loading from heavy haul and passenger trains often leads to progressive deterioration of the track. The excessive deformations and degradations of the ballast layer and unacceptable differential settlement or pumping of underlying soft and compressible subgrade soils necessitate frequent costly track maintenance works. A proper understanding of load transfer mechanisms and their effects on track deformations are essential prerequisites for minimising maintenance costs. The reinforcement of the track by means of geosynthetics leads to significant reduction in the downward propagation of stresses and assures more resilient longterm performance. The geocomposite serves the functions of reinforcement, drainage and separation, thereby reducing the vertical and lateral deformations. Stabilization of soft subgrade soils by using prefabricated vertical drains (PVDs) is also essential for improving the overall stability of track and to reduce the differential settlement during the operation of trains. The effectiveness of using geocomposite geosynthetic and PVDs has been observed through field measurements and finite element analyses. These have been the first fully instrumented, comprehensive field trials carried out in Australian Railways, and it was very encouraging to see the field observations matching the numerical predictions.
Computing the Schulze Method for Large Scale Preference Data Sets
Venue: IJCAI
Year: 2018

The Schulze method is a voting rule widely used in practice and enjoys many positive axiomatic properties. While it is computable in polynomial time, its straight forward implementation does not scale well for large elections. In this paper, we develop a highly optimised algorithm for computing the Schulze method with Pregel, a framework for massively parallel computation of graph problems, and demonstrate its applicability for large preference data sets. In addition, our theoretic analysis shows that the Schulze method is indeed particularly well suited for parallel computation, in stark contrast to the related ranked pairs method. More precisely we show that winner determination subject to the Schulze method is NL complete, whereas this problem is P complete for the ranked pairs method.
Technical economic assessment of a superconductor fault current limiter
Year: 2018

Modern societies depend on the continuous supply of electricity, which has become a key focus for power system engineers. It is critical to the economy that a high quality and an uninterruptable power supply is maintained to ensure there are no costly losses. This thesis looks at addressing two network issues: security and reliability, by incorporating the use of a Resistive Superconductor Fault Current Limiter (R SFCL) device. The technical analysis investigates the two scenarios independently. The first case assesses the installation of a peak lopping diesel generating facility with a series installed R SFCL to address the shortfall forecasted in the future. The second case evaluates the functionality of the R SFCL as a busbar coupler to improve the substation's reliability performance. Both applications of the R SFCL are to reduce the fault current levels on the network to within manageable levels. An economic assessment is handled to provide the most inexpensive and reliable solution for addressing the network drivers for a review time of 25 years. The fault analysis is conducted in a steady state assumption to evaluate the efficiency of reducing the fault current under contingency conditions. The simulations are conducted on two real 132/22 kV substation networks to investigate the fault current levels experienced on the network. The results from the simulations were then used for an economic assessment of the device against traditional network solutions. The device proved effective in limiting the fault current for the two scenarios, but the economic assessment impacted the application of the R SFCL. The economic assessment proved that the R SFCL is beneficial for improving the reliability performance of a substation that has outage difficulties, but to address network security, the application of a R SFCL is not the favourable option today.
Gender based violence and its effects on women's reproductive health: the case of Hatcliffe, Harare, Zimbabwe.
Venue: African journal of reproductive health
Year: 2014

Gender based violence (GBV) negatively impacts on women's reproductive health (R.H) and is contrary to human rights and RH statutory instruments. The study triangulates quantitative and qualitative research methods with women in the reproductive age group being the target group. The study noted that 95% of the respondents experienced physical violence, 31% rape by a stranger, 92% spousal rape and 65% forced marriages. Socio cultural, religious, economic and policy implementation factors underlie a culture of silence that prevails among the victims of GBV. The study recommends economic empowerment of women and information, education, counselling pertaining to the negative effects of GBV to both males and females. There is need for education about grievance procedures and scaling up of policy enforcement in order to curb the gruesome effects of GBV.
Entity query feature expansion using knowledge base links
Venue: TREC
Year: 2014

Recent advances in automatic entity linking and knowledge base construction have resulted in entity annotations for document and query collections. For example, annotations of entities from large general purpose knowledge bases, such as Freebase and the Google Knowledge Graph. Understanding how to leverage these entity annotations of text to improve ad hoc document retrieval is an open research area. Query expansion is a commonly used technique to improve retrieval effectiveness. Most previous query expansion approaches focus on text, mainly using unigram concepts. In this paper, we propose a new technique, called entity query feature expansion (EQFE) which enriches the query with features from entities and their links to knowledge bases, including structured attributes and text. We experiment using both explicit query entity annotations and latent entities. We evaluate our technique on TREC text collections automatically annotated with knowledge base entity links, including the Google Freebase Annotations (FACC1) data. We find that entity based feature expansion results in significant improvements in retrieval effectiveness over state of the art text expansion approaches.
Microwave Velocimeter Based on Cross Correlation for Dual Phase Flows
Venue: 2018 IEEE International RF and Microwave Conference (RFM)
Year: 2018

Accurate velocity measurement of solid particulates in multi phase flows is critical for many industrial processes. This paper presents a non intrusive microwave velocimeter for solid particulates in gas/solid flows. The proposed velocimeter is founded upon the cross correlation principle while operating at 24 GHz. By having two sensors 5 cm apart, the velocity of the particulates is deduced from the relative delay in the reflected waves received by both sensors. The deployed sensor is a hermetically sealed circular waveguide with a custom made TEM to TE11 mode transition. The proposed system is tested experimentally on a 1 inch PVC process pipe. The measurements drawn from the proposed system are benchmarked against a high speed camera measurements acquired downstream of the microwave velocimeter. Experimental results demonstrate a promising performance that can be further enhanced for real time velocity measurement in industrial processes.
HIGH EXPRESSION OF OSMOTICALLY RESPONSIVE GENES1 Is Required for Circadian Periodicity through the Promotion of Nucleo Cytoplasmic mRNA Export in Arabidopsis[W][OPEN]
Venue: Plant Cell
Year: 2013

This work shows that HOS1, previously characterized as a nuclear pore associated E3 ubiquitin ligase, is required for nucleo cytoplasmic mRNA export. This study demonstrates that this reduction in nucleo cytoplasmic export by hos1, or mutations to other previously characterized nuclear pore associated proteins, leads to altered RNA levels and rhythms, circadian clock function, and cold signaling. Cold acclimation has been shown to be attenuated by the degradation of the INDUCER OF CBF EXPRESSION1 protein by the E3 ubiquitin ligase HIGH EXPRESSION OF OSMOTICALLY RESPONSIVE GENES1 (HOS1) However, recent work has suggested that HOS1 may have a wider range of roles in plants than previously appreciated. Here, we show that hos1 mutants are affected in circadian clock function, exhibiting a long period phenotype in a wide range of temperature and light environments. We demonstrate that hos1 mutants accumulate polyadenylated mRNA in the nucleus and that the circadian defect in hos1 is shared by multiple mutants with aberrant mRNA export, but not in a mutant attenuated in nucleo cytoplasmic transport of microRNAs. As revealed by RNA sequencing, hos1 exhibits gross changes to the transcriptome with genes in multiple functional categories being affected. In addition, we show that hos1 and other previously described mutants with altered mRNA export affect cold signaling in a similar manner. Our data support a model in which altered mRNA export is important for the manifestation of hos1 circadian clock defects and suggest that HOS1 may indirectly affect cold signaling through disruption of the circadian clock.
Investigating Students' Use of Lecture Videos in Online Courses: A Case Study for Understanding Learning Behaviors via Data Mining
Venue: ICWL
Year: 2015

This study investigated students' learning behaviors in a fully online psychology course which offered 76 streaming lecture videos and supplementary resources, as well as individual and group activities. This paper focuses on students' use of lecture videos. Data collection included students' real usage of data on Blackboard Learn 9.1, a course survey, and students' final grades. The analysis applied data mining techniques, including sequential patterns, decision trees, and clustering analysis, as well as inferential statistics using ANOVA and correlations. Based on students' use of lecture videos, their learning behaviors were defined into three groups adaptive viewer, self regulating viewer, and infrequent viewer. Statistically significant differences within groups were found in their learning satisfaction, final grades, etc. This case study has shown that students' learning behaviors were varied in the online environment and that their use of course videos affected their learning outcomes.
Removing the abyss between conservation science and policy decisions in Brazil
Venue: Biodiversity and Conservation
Year: 2017

The executive and legislative branches of Brazilian government have either proposed or taken a variety of initiatives that threaten biodiversity and ecosystems. Opposition by the scientific community has largely been ignored by decision makers. In this short essay, we present recent examples of harmful policies that have great potential to erode biodiversity, and we suggest ways to communicate scientific knowledge to decision makers. If the current gap between conservation science and policies is not filled, the country will threaten the maintenance of its natural capital and, consequently, the sustainability of essential societal activities in the long term.
LETRIST: Locally Encoded Transform Feature Histogram for Rotation Invariant Texture Classification
Venue: IEEE Transactions on Circuits and Systems for Video Technology
Year: 2018

Classifying texture images, especially those with significant rotation, illumination, scale, and viewpoint changes, is a fundamental and challenging problem in computer vision. This paper proposes a simple yet effective image descriptor, called Locally Encoded TRansform feature hISTogram (LETRIST) for texture classification. LETRIST is a histogram representation that explicitly encodes the joint information within an image across feature and scale spaces. The proposed representation is training free, low dimensional, yet discriminative and robust for texture description. It consists of the following major steps. First, a set of transform features is constructed to characterize local texture structures and their correlation by applying linear and non linear operators on the extremum responses of directional Gaussian derivative filters in scale space. Established on the basis of steerable filters, the constructed transform features are exactly rotationally invariant as well as computationally efficient. Second, the scalar quantization via binary or multi level thresholding is adopted to quantize these transform features into texture codes. Two quantization schemes are designed, both of which are robust to image rotation and illumination changes. Third, the cross scale joint coding is explored to aggregate the discrete texture codes into a compact histogram representation, i.e. LETRIST. Experimental results on the Outex, CUReT, KTH TIPS, and UIUC texture data sets show that LETRIST consistently produces better or comparable classification results than the state of the art approaches. Impressively, recognition rates of 100.00% and 99.00% have been achieved on the Outex and KTH TIPS data sets, respectively. In addition, the noise robustness is evaluated on the Outex and CUReT data sets. The source code is publicly available at https:/github.com/stc cqupt/letrist.
Adaptive Deep Kernel Learning
Venue: ArXiv
Year: 2019

Deep kernel learning provides an elegant and principled framework for combining the structural properties of deep learning algorithms with the flexibility of kernel methods. By means of a deep neural network, it consists of learning a kernel operator which is combined with a differentiable kernel algorithm for inference. While previous work within this framework has mostly explored learning a single kernel for large datasets, we focus herein on learning a kernel family for a variety of tasks in few shot regression settings. Compared to single deep kernel learning, our novel algorithm permits finding the appropriate kernel for each task during inference, rather than using the same for all tasks. As such, our algorithm performs more effectively with complex task distributions in few shot learning, which we demonstrate by benchmarking against existing state of the art algorithms using real world, few shot regression tasks related to drug discovery.
Design Strategies for Bivalent Ligands Targeting GPCRs
Venue: ChemMedChem
Year: 2011

Specifically designed bivalent ligands targeting G protein coupled receptor (GPCR) dimeric structures have become increasingly popular in recent literature. The advantages of the bivalent approach are numerous, including enhanced potency and receptor subtype specificity. However, the use of bivalent ligands as potential pharmacotherapeutics is limited by problematic molecular properties, such as high molecular weight and lipophilicity. This Minireview focuses on the design of bivalent ligands recently described in the literature; discussing the choice of lead pharmacophore, the position and nature of the attachment point for linking the two pharmacophore units, and the length and composition of the spacer group. Furthermore, this Minireview distils the molecular descriptors of the bivalent ligands that exhibit in vivo activity, as well as highlights their ability to access the central nervous system.
The impact of a supportive leadership program in a policing organisation from the participants' perspective.
Venue: Work
Year: 2009

The aim of this study is to explore the implementation of an organisational level intervention, focussing on Supportive Leadership (SL) in an Australian police organisation from the perspective of supervisors and managers.  The impact of the intervention was explored using a qualitative methodology using semi structured telephone interviews with 44 participants who had attended the Supportive Leadership Workshop, designed to improve awareness of good management practices. Data was subjected to thematic analysis using a social constructivist theoretical orientation.  Findings showed that SL as a concept was generally accepted by a majority of participants and that they had integrated a number of SL strategies into their work practices. The participants also identified the importance of senior personnel role modelling SL and the negative impact of non role modelling.  The major limitation of the study was the non random sample of voluntary participants. However, the nature of conducting applied studies in police organisations is inherently difficult due to confidentiality and their paramilitary nature. This study highlights the need for future studies in police leadership and occupational stress that directly explore issues from the perspective of the supervisors and managers.  Interventions such as SL need support and role modelling from senior management to enhance their credibility.  This paper reports on an applied intervention that received major support and funding within a police organisation. It is of value to other organizations considering similar interventions because it highlights issues that could be addressed to further enhance the program.
The Nigeria Inter Bank and Monetary Policy Rates Nexus: Any Discernable Long Run Relationship?
Year: 2010

Inter bank markets are among the most important in the financial system. They are the focus of central banks" implementation of monetary policy and have a significant effect on the economy. Transactions in the inter bank funds market provide signal of what obtains in the open credit market. To provide for stability in short term interest rates, the Monetary Policy Rate (MPR) as the ,operating instrument" serves as an indicative rate for transactions in the inter bank money market as well as other deposit money banks' (DMBs) interest rates. It is this relationship between the inter bank and the monetary policy rates that this paper examined. The findings indicate that the rates are cointegrated and have a long run relationship, judging from the significance of the unit root test for the residual and the coefficient of the error correction variable in the error correction model. It is, however, the submission of this paper that much more investigation is needed on a number of issues such as the effect of liquidity hoarding in the inter bank market spread, and the impact of possible lending relationships between banks in the market on flow of funds and the inter bank rate, among others. These issues may provide significant insight on the behaviour of the Nigerian inter bank rate.