nils-herrmann/clean_scirepeval huggingface dataset:
The influence of personality traits on information seeking behaviour of students

The present study was undertaken with the objective to explore the influence of the five personality dimensions on the information seeking behaviour of the students in higher educational institutions. Information seeking behaviour is defined as the sum total of all those activities that are usually undertaken by the students of higher education to collect, utilize and process any kind of information needed for their studies. Data has been collected from 600 university students of the three broad disciplines of studies from the Universities of Eastern part of India (West Bengal). The tools used for the study were General Information schedule (GIS), Information Seeking Behaviour Inventory (ISBI) and NEO-FFI Personality Inventory. Product moment correlation has been worked out between the scores in ISBI and those in NEO-FFI Personality Inventory. The findings indicated that the five personality traits are significantly correlated to all the dimensions of information seeking behaviour of the university students.
Physics-of-Failure-Based Prognostics and Health Management for High-Power White Light-Emitting Diode Lighting

Recently, high-power white light-emitting diodes (LEDs) have attracted much attention due to their versatility in applications and to the increasing market demand for them. So great attention has been focused on producing highly reliable LED lighting. How to accurately predict the reliability of LED lighting is emerging as one of the key issues in this field. Physics-of-failure-based prognostics and health management (PoF-based PHM) is an approach that utilizes knowledge of a product's life cycle loading and failure mechanisms to design for and assess reliability. In this paper, after analyzing the materials and geometries for high-power white LED lighting at all levels, i.e., chips, packages and systems, failure modes, mechanisms and effects analysis (FMMEA) was used in the PoF-based PHM approach to identify and rank the potential failures emerging from the design process. The second step in this paper was to establish the appropriate PoF-based damage models for identified failure mechanisms that carry a high risk.
Fast Linearization of Tree Kernels over Large-Scale Data

Convolution tree kernels have been successfully applied to many language processing tasks for achieving state-of-the-art accuracy. Unfortunately, higher computational complexity of learning with kernels w.r.t. using explicit feature vectors makes them less attractive for large-scale data. In this paper, we study the latest approaches to solve such problems ranging from feature hashing to reverse kernel engineering and approximate cutting plane training with model compression. We derive a novel method that relies on reverse-kernel engineering together with an efficient kernel learning method. The approach gives the advantage of using tree kernels to automatically generate rich structured feature spaces and working in the linear space where learning and testing is fast. We experimented with training sets up to 4 million examples from Semantic Role Labeling. The results show that (i) the choice of correct structural features is essential and (ii) we can speed-up training from weeks to less than 20 minutes.
Multitask Learning with Deep Neural Networks for Community Question Answering

In this paper, we developed a deep neural network (DNN) that learns to solve simultaneously the three tasks of the cQA challenge proposed by the SemEval-2016 Task 3, i.e., question-comment similarity, question-question similarity and new question-comment similarity. The latter is the main task, which can exploit the previous two for achieving better results. Our DNN is trained jointly on all the three cQA tasks and learns to encode questions and comments into a single vector representation shared across the multiple tasks. The results on the official challenge test set show that our approach produces higher accuracy and faster convergence rates than the individual neural networks. Additionally, our method, which does not use any manual feature engineering, approaches the state of the art established with methods that make heavy use of it.
An Optimization-Based Method for Feature Ranking in Nonlinear Regression Problems

In this paper, we consider the feature ranking problem, where, given a set of training instances, the task is to associate a score with the features in order to assess their relevance. Feature ranking is a very important tool for decision support systems, and may be used as an auxiliary step of feature selection to reduce the high dimensionality of real-world data. We focus on regression problems by assuming that the process underlying the generated data can be approximated by a continuous function (for instance, a feedforward neural network). We formally state the notion of relevance of a feature by introducing a minimum zero-norm inversion problem of a neural network, which is a nonsmooth, constrained optimization problem. We employ a concave approximation of the zero-norm function, and we define a smooth, global optimization problem to be solved in order to assess the relevance of the features. We present the new feature ranking method based on the solution of instances of the global optimization problem depending on the available training data. Computational experiments on both artificial and real data sets are performed, and point out that the proposed feature ranking method is a valid alternative to existing methods in terms of effectiveness. The obtained results also show that the method is costly in terms of CPU time, and this may be a limitation in the solution of large-dimensional problems.
Incidence of Epstein-Barr virus in pediatric leukemia in the Sudan.

Results of several studies have suggested a probable etiologic association between Epstein-Barr virus (EBV) and leukemias; therefore, the aim of this study was to investigate the association of EBV in childhood leukemia.



A direct isothermal amplification method was developed for detection of the latent membrane protein 1 (LMP1) of EBV in the peripheral blood of 80 patients with leukemia (54 had lymphoid leukemia and 26 had myeloid leukemia) and of 20 hematologically healthy control subjects.



EBV LMP1 gene transcripts were found in 29 (36.3%) of the 80 patients with leukemia but in none of the healthy controls (P < .0001). Of the 29 EBV(+) cases, 23 (79.3%), 5 (17.3%), and 1 (3.4%) were acute lymphoblastic leukemia, acute myeloid leukemia, and chronic myeloid leukemia, respectively.



EBV LMP1 gene transcriptional activity was observed in a significant proportion of patients with acute lymphoblastic leukemia. EBV infection in patients with lymphoid leukemia may be a factor involved in the high incidence of pediatric leukemia in the Sudan.
Reciprocal and Heterogeneous Link Prediction in Social Networks

Link prediction is a key technique in many applications in social networks, where potential links between entities need to be predicted. Conventional link prediction techniques deal with either homogeneous entities, e.g., people to people, item to item links, or non-reciprocal relationships, e.g., people to item links. However, a challenging problem in link prediction is that of heterogeneous and reciprocal link prediction, such as accurate prediction of matches on an online dating site, jobs or workers on employment websites, where the links are reciprocally determined by both entities that heterogeneously belong to disjoint groups. The nature and causes of interactions in these domains makes heterogeneous and reciprocal link prediction significantly different from the conventional version of the problem. In this work, we address these issues by proposing a novel learnable framework called ReHeLP, which learns heterogeneous and reciprocal knowledge from collaborative information and demonstrate its impact on link prediction. Evaluation on a large commercial online dating dataset shows the success of the proposed method and its promise for link prediction.
Optimal algorithms for evaluating rank joins in database systems

In the rank join problem, we are given a set of relations and a scoring function, and the goal is to return the join results with the top k scores. It is often the case in practice that the inputs may be accessed in ranked order and the scoring function is monotonic. These conditions allow for efficient algorithms that solve the rank join problem without reading all of the input. In this article, we present a thorough analysis of such rank join algorithms. A strong point of our analysis is that it is based on a more general problem statement than previous work, making it more relevant to the execution model that is employed by database systems. One of our results indicates that the well-known HRJN algorithm has shortcomings, because it does not stop reading its input as soon as possible. We find that it is NP-hard to overcome this weakness in the general case, but cases of limited query complexity are tractable. We prove the latter with an algorithm that infers provably tight bounds on the potential benefit of reading more input in order to stop as soon as possible. As a result, the algorithm achieves a cost that is within a constant factor of optimal.
Implementation of a low power decimation filter using 1/3-band IIR filter

This paper presents a unique design and implementation of a low power decimation filter. The designed decimation filter architecture shows how the 1/3band IIR filter and a poly-phase half-band FIR filter are used effectively for multirate multistage signal processing. The 1/3-band IIR filter is realized using six first order all-pass filters. Each filter stage is simulated using Matlab and, the complete architecture of the decimation filter is captured using Simulink and a DSP Blockset. The hardware realization of the decimation filter is obtained using FPGA Xilinx technology. The designed decimation filter reduces the hardware by 59% and the power dissipation by 34% compared to conventional decimation filters.
Duhem modeling of friction-induced hysteresis

In this article we recast the Dahl, LuGre, and Maxwell-slip models as extended, generalized, or semilinear Duhem models. We classified each model as either rate independent or rate dependent. Smoothness properties of the three friction models were also considered. We then studied the hysteresis induced by friction in a single-degree-of-freedom system. The resulting system was modeled as a linear system with Duhem feedback. For each friction model, we computed the corresponding hysteresis map. Next, we developed a DC servo motor testbed and performed motion experiments. We then modeled the testbed dynamics and simulated the system using all three friction models. By comparing the simulated and experimental results, it was found that the LuGre model provides the best model of the gearbox friction characteristics. A manual tuning approach was used to determine parameters that model the friction in the DC motor.
Regulatory network of lipid-sensing nuclear receptors: roles for CAR, PXR, LXR, and FXR.

Cloning and characterization of the orphan nuclear receptors constitutive androstane receptor (CAR, NR1I3) and pregnane X receptor (PXR, NR1I2) led to major breakthroughs in studying drug-mediated transcriptional induction of drug-metabolizing cytochromes P450 (CYPs). More recently, additional roles for CAR and PXR have been discovered. As examples, these xenosensors are involved in the homeostasis of cholesterol, bile acids, bilirubin, and other endogenous hydrophobic molecules in the liver: CAR and PXR thus form an intricate regulatory network with other members of the nuclear receptor superfamily, foremost the cholesterol-sensing liver X receptor (LXR, NR1H2/3) and the bile-acid-activated farnesoid X receptor (FXR, NR1H4). In this review, functional interactions between these nuclear receptors as well as the consequences on physiology and pathophysiology of the liver are discussed.
Specific ion effects on membrane potential and the permselectivity of ion exchange membranes.

Membrane potential and permselectivity are critical parameters for a variety of electrochemically-driven separation and energy technologies. An electric potential is developed when a membrane separates electrolyte solutions of different concentrations, and a permselective membrane allows specific species to be transported while restricting the passage of other species. Ion exchange membranes are commonly used in applications that require advanced ionic electrolytes and span technologies such as alkaline batteries to ammonium bicarbonate reverse electrodialysis, but membranes are often only characterized in sodium chloride solutions. Our goal in this work was to better understand membrane behaviour in aqueous ammonium bicarbonate, which is of interest for closed-loop energy generation processes. Here we characterized the permselectivity of four commercial ion exchange membranes in aqueous solutions of sodium chloride, ammonium chloride, sodium bicarbonate, and ammonium bicarbonate. This stepwise approach, using four different ions in aqueous solution, was used to better understand how these specific ions affect ion transport in ion exchange membranes. Characterization of cation and anion exchange membrane permselectivity, using these ions, is discussed from the perspective of the difference in the physical chemistry of the hydrated ions, along with an accompanying re-derivation and examination of the basic equations that describe membrane potential. In general, permselectivity was highest in sodium chloride and lowest in ammonium bicarbonate solutions, and the nature of both the counter- and co-ions appeared to influence measured permselectivity. The counter-ion type influences the binding affinity between counter-ions and polymer fixed charge groups, and higher binding affinity between fixed charge sites and counter-ions within the membrane decreases the effective membrane charge density. As a result permselectivity decreases. The charge density and polarizability of the co-ions also appeared to influence permselectivity leading to ion-specific effects; co-ions that are charge dense and have low polarizability tended to result in high membrane permselectivity.
Conflict management based on belief function entropy in sensor fusion

Wireless sensor network plays an important role in intelligent navigation. It incorporates a group of sensors to overcome the limitation of single detection system. Shafer evidence theory can combine the sensor data of the wireless sensor network by data fusion, which contributes to the improvement of accuracy and reliability of the detection system. However, due to different sources of sensors, there may be conflict among the sensor data under uncertain environment. Thus, this paper proposes a new method combining Deng entropy and evidence distance to address the issue. First, Deng entropy is adopted to measure the uncertain information. Then, evidence distance is applied to measure the conflict degree. The new method can cope with conflict effectually and improve the accuracy and reliability of the detection system. An example is illustrated to show the efficiency of the new method and the result is compared with that of the existing methods.
A contemporary view of organizational safety: variability and interactions of organizational processes

Studies of qualitative assessment of organizational processes (e.g., safety audits and performance indicators) and their incorporation into risk models have been based on a ‘normative view’ that decomposes organizations into separate processes that are likely to fail and lead to accidents. This paper discusses a control theoretic framework of organizational safety that views accidents as a result of performance variability of human behaviors and organizational processes whose complex interactions and coincidences lead to adverse events. Safety-related tasks managed by organizational processes are examined from the perspective of complexity and coupling. This allows safety analysts to look deeper into the complex interactions of organizational processes and how these may remain hidden or migrate toward unsafe boundaries. A taxonomy of variability of organizational processes is proposed and challenges in managing adaptability are discussed. The proposed framework can be used for studying interactions between organizational processes, changes of priorities over time, delays in effects, reinforcing influences, and long-term changes of processes. These dynamic organizational interactions are visualized with the use of system dynamics. The framework can provide a new basis for modeling organizational factors in risk analysis, analyzing accidents and designing safety reporting systems.
Digital Government and Administrative Burden Reduction

Administrative burden represents the costs to businesses, citizens and the administration itself of complying with government regulations and procedures. The burden tends to increase with new forms of public governance that rely less on direct decisions and actions undertaken by traditional government bureaucracies, and more on government creating and regulating the environment for other, non-state actors to jointly address public needs. Based on the reviews of research and policy literature, this paper explores administrative burden as a policy problem, presents how Digital Government (DG) could be applied to address this problem, and identifies societal adoption, organizational readiness and other conditions under which DG can be an effective tool for Administrative Burden Reduction (ABR). Finally, the paper tracks ABR to the latest Contextualization stage in the DG evolution, and discusses possible development approaches and technological potential of pursuing ABR through DG.
Non-Fragile Exponential $H_\infty $ Control for a Class of Nonlinear Networked Control Systems With Short Time-Varying Delay via Output Feedback Controller

This paper investigates non-fragile exponential <inline-formula> <tex-math notation="LaTeX">$ {H_\infty }$ </tex-math></inline-formula> control problems for a class of uncertain nonlinear networked control systems (NCSs) with randomly occurring information, such as the controller gain fluctuation and the uncertain nonlinearity, and short time-varying delay via output feedback controller. Using the nominal point technique, the NCS is converted into a novel time-varying discrete time model with norm-bounded uncertain parameters for reducing the conservativeness. Based on linear matrix inequality framework and output feedback control strategy, design methods for general and optimal non-fragile exponential <inline-formula> <tex-math notation="LaTeX">$ {H_\infty }$ </tex-math></inline-formula> controllers are presented. Meanwhile, these control laws can still be applied to linear NCSs and general fragile control NCSs while introducing random variables. Finally, three examples verify the correctness of the presented scheme.
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Rotenone, is a biopesticide which can be isolated from Derris species roots. However, procuring significant amount of rotenone using green alternative solvent rather than harmful organic solvents for commercialization is a challenge to be faced. Therefore, an approach using imidazolium-based ionic liquids (ILs) as an extraction medium was employed in this study. Five different types of binary solvent systems comprising a combination of acetone and five respective ionic liquids (ILs) of (1) [BMIM] Cl; (2) [BMIM] OAc; (3) [BMIM] NTf2; (4) [BMIM] OTf; and (5) [BMPy] Cl were used in the normal soaking extraction (NSE) of rotenone for a 24-hour extraction. The yield of the rotenone, % (w/w), and its concentration (mg/mL) in the dried roots was quantitatively determined by means of the reversed-phase high-performance liquid chromatography (RP-HPLC) and thin-layer chromatography (TLC). The results showed that a binary solvent system of [BMIM] OTf:acetone was the best solvent system combination compared to other solvent systems (p < 0.05). It contributed to the highest rotenone content of 2.69 ± 0.21% (w/w) (4.04 ± 0.34 mg/ml) at the 14th hour of the exhaustive extraction time. In conclusion, a combination of certain ILs with a selective organic solvent has been proven to be able to increase a significant amount of bioactive constituents in the phytochemical extraction process.
BetrFS: A Right-Optimized Write-Optimized File System

FS chmod mkdir open read stat unlink write BetrFS 4913 ± 0.27 67072 ± 25.68 1697 ± 0.12 561 ± 0.01 1076 ± 0.01 47873 ± 7.7 32142 ± 4.35 btrfs 4574 ± 0.27 24805 ± 13.92 1812 ± 0.12 561 ± 0.01 1258 ± 0.01 26131 ± 0.73 3891 ± 0.08 ext4 4970 ± 0.14 41478 ± 18.99 1886 ± 0.13 556 ± 0.01 1167 ± 0.05 16209 ± 0.2 3359 ± 0.04 XFS 5342 ± 0.21 73782 ± 19.27 1757 ± 0.12 1384 ± 0.07 1134 ± 0.02 19124 ± 0.32 9192 ± 0.28 zfs 36449 ± 118.37 171080 ± 307.73 2681 ± 0.08 6467 ± 0.06 1913 ± 0.04 78946 ± 7.37 18382 ± 0.42
A modular manipulator for industrial applications: Design and implement

In order to solve the problem that the long cycle and the repetitive work in the process of designing the industrial robot, a modular manipulator system developed for general industrial applications is introduced in this paper. When the application scene is changed, the corresponding robotic modules can be selected to assemble a new robot configuration that meets the requirements. The modules can be divided into two categories: joint modules and link modules. Joint modules consist of three types of revolute joint modules with different torque, and link modules mainly contain T link module and L link module. By connection of different types of modules, various of configurations can be achieved. Considering the traditional 6-DoF manipulators are difficult to meet the needs of the unstructured industrial applications, a 7-DoF redundant manipulator prototype is designed on the basis of the robotic modules.
Constructing Structured Information Networks from Massive Text Corpora

In today’s computerized and information-based society, text data is rich but messy. People are soaked with vast amounts of natural-language text data, ranging from news articles, social media post, advertisements, to a wide range of textual information from various domains (medical records, corporate reports). To turn such massive unstructured text data into actionable knowledge, one of the grand challenges is to gain an understanding of the factual information (e.g., entities, attributes, relations, events) in the text. In this tutorial, we introduce data-driven methods to construct structured information networks (where nodes are different types of entities attached with attributes, and edges are different relations between entities) for text corpora of different kinds (especially for massive, domain-specific text corpora) to represent their factual information. We focus on methods that are minimally-supervised, domain-independent, and languageindependent for fast network construction across various application domains (news, web, biomedical, reviews). We demonstrate on real datasets including news articles, scientific publications, tweets and reviews how these constructed networks aid in text analytics and knowledge discovery at a large scale.