nils-herrmann/clean_scirepeval huggingface dataset:
The effect of plant growth promoting rhizobacteria ( PGPR ) and zinc fertilizer on forage yield of maize under water deficit stress conditions

Drought is one of the most important abiotic stresses affecting yield of the crops in arid and semiarid regions. Rhizobacterial populations of stressed soils are adapted and tolerant to stress, PGPR strains that can be used as inoculants for crops grown in stressed ecosystems. This study was carried out to study the effects of ACC-deaminase containing PGPR and zinc fertilizer on the yield and some agronomic traits of maize under water deficit stress in the mahvelat region of Iran in 2011. The experiment was conducted as a splitfactorial design with 3 replications. The main plots consisted of three water regimes achieved by performed irrigation every 6 (normal condition, I1), 9 (moderate water deficit stress, I2) and 12 days (severe water deficit stress, I3). The sub-plots were six different treatments, including: Grain inoculation with Pseudomonas fluorescens strain169 (T1), Grain inoculation with P. fluorescens and zinc sulfate fertilizer (T2), Grain inoculation with Pseudomonas putida strain 108 (T3), Grain inoculation with P. putida and zinc sulfate fertilizer (T4), Application of zinc sulfate fertilizer (T5) and Non-application of grain inoculation or zinc sulfate fertilizer, as a control (T6). The results showed that water deficit stress decreased leaf, stem and tassel dry weights, cobs weight, grain yield and the harvest index of ear in plant (HIEP). Under water deficit stress, grain inoculation significantly increased stem and tassel dry weights, cob weight and grain yield. The present study also indicated that under water deficit stress using zinc sulfate fertilizer improved tassel dry weights, cob weight and grain yield. Hence, bacterial inoculation of seeds by P. fluorescens strain 169 or P. putida strain108, and using zinc sulphate fertilizer are recommended for maize cultivation in semi-arid and arid regions.
Lightweight Encryption Algorithm Based on Modified XTEA for Low-Resource Embedded Devices

The number of resource-limited wireless devices utilized in many areas of Internet of Things is growing rapidly; there is a concern about privacy and security. Various lightweight block ciphers are proposed; this work presents a modified lightweight block cipher algorithm. A Linear Feedback Shift Register is used to replace the key generation function in the XTEA1 Algorithm. Using the same evaluation conditions, we analyzed the software implementation of the modified XTEA using FELICS (Fair Evaluation of Lightweight Cryptographic Systems) a benchmarking framework which calculates RAM footprint, ROM occupation and execution time on three largely used embedded devices: 8-bit AVR microcontroller, 16-bit MSP microcontroller and 32-bit ARM microcontroller. Implementation results show that it provides less software requirements compared to original XTEA. We enhanced the security level and the software performance.
File system trace replay methods through the lens of metrology

There are various methods to evaluate the performance of file systems through the replay of file system traces. Despite this diversity, little attention was given on comparing the alternatives, thus bringing some skepticism about the results attained using these methods. In this paper, to fill this understanding gap, we analyze two popular trace replay methods through the lens of metrology. This case study indicates that the evaluated methods provide similar, good precision but are biased in some scenarios. Our results identified limitations in the implementation of the replay tool as well as flaws in the established practices to experiment with trace replayers as the root causes of the measurement bias. After improving the implementation of the trace replayer and discarding inappropriate experimental practices, we were able to reduce the bias, leading to lower measurement uncertainty. Finally, our case study also shows that, in some cases, collecting only the file system activity is not enough to accurately replay the traces; in these cases, collecting resource consumption information, such as the amount of allocated memory, can improve the quality of trace replay methods.
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

We consider the class of iterative shrinkage-thresholding algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods, which can be viewed as an extension of the classical gradient algorithm, is attractive due to its simplicity and thus is adequate for solving large-scale problems even with dense matrix data. However, such methods are also known to converge quite slowly. In this paper we present a new fast iterative shrinkage-thresholding algorithm (FISTA) which preserves the computational simplicity of ISTA but with a global rate of convergence which is proven to be significantly better, both theoretically and practically. Initial promising numerical results for wavelet-based image deblurring demonstrate the capabilities of FISTA which is shown to be faster than ISTA by several orders of magnitude.
Benefits of social media in business-to-business customer interface in innovation

The aim of this research is to explore social media and its benefits especially from business-to-business innovation and related customer interface perspective, and to create a more comprehensive picture of the possibilities of social media for the business-to-business sector. Business-to-business context was chosen because it is in many ways a very different environment for social media than business-to-consumer context, and is currently very little academically studied. A systematic literature review on B2B use of social media and achieved benefits in the inn ovation con text was performed to answer the questions above and achieve the research goals. The study clearly demonstrates that not merely B2C's, as commonly believed, but also B2B's can benefit from the use of social media in a variety of ways. Concerning the broader classes of innovation --related benefits, the reported benefits of social media use referred to increased customer focus and understanding, increased level of customer service, and decreased time-to-market. The study contributes to the existing social media --related literature, because there were no found earlier comprehensive academic studies on the use of social media in the innovation process in the context of B2B customer interface.
Psoriasis vulgaris with fibrokeratoma from pityriasis amiantacea

PsV: psoriasis vulgaris  Pityriasis amiantacea is a rare clinical condition characterized by masses of waxy and sticky scales that adhere to the scalp and tenaciously attach to hair bundles. Pityriasis amiantacea can be associated with psoriasis vulgaris (PsV).We examined a patient with pityriasis amiantacea caused by PsV who also had keratotic horns on the scalp, histopathologically fibrokeratomas. To the best of our knowledge, this is the first case of scalp fibrokeratoma stimulated by pityriasis amiantacea and PsV.
Fast Document Segmentation Using Contour and X-Y Cut Technique

This paper describes fast and efficient method for page segmentation of document containing nonrectangular block. The segmentation is based on edge following algorithm using small window of 16 by 32 pixels. This segmentation is very fast since only border pixels of paragraph are used without scanning the whole page. Still, the segmentation may contain error if the space between them is smaller than the window used in edge following. Consequently, this paper reduce this error by first identify the missed segmentation point using direction information in edge following then, using X-Y cut at the missed segmentation point to separate the connected columns. The advantage of the proposed method is the fast identification of missed segmentation point. This methodology is faster with fewer overheads than other algorithms that need to access much more pixel of a document.
Self-Defining Memories of Body Image Shame and Binge Eating in Men and Women : Body Image Shame and Self-Criticism in Adulthood as Mediating Mechanisms

Despite the growing evidence of the association between shame experiences and eating psychopathology, the specific effect of body image-focused shame memories on binge eating remains largely unexplored. The current study examined this association and considered current body image shame and self-criticism as mediators. A multi-group path analysis was conducted to examine gender differences in these relationships. The sample included 222 women and 109 men from the Portuguese general and college student populations who recalled an early body image-focused shame experience and completed measures of the centrality of the shame memory, current body image shame, binge eating symptoms, depressive symptoms, and self-criticism. For both men and women, the effect of the centrality of shame memories related to body image on binge eating symptoms was fully mediated by body image shame and self-criticism. In women, these effects were further mediated by self-criticism focused on a sense of inadequacy and also on self-hatred. In men, only the form of self-criticism focused on a sense of inadequacy mediated these associations. The present study has important implications for the conceptualization and treatment of binge eating symptoms. Findings suggest that, in both genders, body image-focused shame experiences are associated with binge eating symptoms via their effect on current body image shame and self-criticism.
The synthetic geo-ecological environmental evaluation of a coastal coal-mining city using spatiotemporal big data : A case study in Longkou , China

The geological and ecological (geo-ecological) environment of the coastal coal-mining city is a basic element for human subsistence, and it connects the regional economy with social sustainable development. Using Longkou, a coastal mining city, as an example, a geological and ecological environmental quality assessment was conducted based on spatiotemporal big data. Remote sensing images, a digital elevation model (DEM), and precipitation and interpolation processing were used to generate factor layers. A synthetic evaluation index system was set up, including physical geography, geological conditions, mining intensity, ecological environmental recovery and geological hazards associated with mining. Moreover, an analytical hierarchy process was used to calculate the factor weight of each evaluation factor, and a consistency check was performed to build an assessment model of the geoecological environment of Longkou. The results indicate that multi-factor spatiotemporal big data provide a scientific assessment of the geo-ecological environmental quality with indispensable data and methods. The spatial distribution of geo-ecological environmental quality presented clear specialization of zonality, showing poor quality in the coastal coal mine ore concentration area and good quality in the inland and mountainous areas of Nanshan Mountain. The geo-ecological environmental quality of Longkou was divided into 5 levels as worst, poor, middle, good and better districts. The good and better districts accounted for 76.763% of the total area of the assessment region, indicating that the geoecological environmental quality of the study area was in good condition. The mining intensity and ecological environment recovery were major factors in determining the regional variation of the geoecological environment of Longkou. The possible causes inducing uncertainties and limitations in evaluation of the geo-ecological environmental quality were discussed. The model combining AHP with GIS proposed in this paper is an effective means of evaluating regional geo-ecological environmental quality.
EMI filter design in motor drives with Common Mode voltage active compensation

In this paper the design issues of input electromagnetic interference (EMI) filters for inverter-fed motor drives including motor Common Mode (CM) voltage active compensation are studied. A coordinated design of motor CM-voltage active compensator and input EMI filter allows the drive system to comply with EMC standards and to yield an increased reliability at the same time. Two CM input EMI filters are built and compared. They are, designed, respectively, according to the conventional design procedure and considering the actual impedance mismatching between EMI source and receiver. In both design procedures, the presence of the active compensator is taken into account. The experimental evaluation of both filters' performance is given in terms of compliance of the system to standard limits.
Development of a pneumatic soft actuator with pleated inflatable structures

Many soft actuators have been studied for use in robots that come into contact with humans, including communication, entertainment, and medical/health care robots. One reason for this is that soft robots are expected to exhibit intrinsic safety in case an accident occurs. This paper proposes a plastic-film pneumatic actuator with a pleated structure that do not undergo the elastic deformation typical of rubber materials. By utilizing thin plastic films, the mass of an actuator can be significantly reduced, even if the actuators are the same size as a human arm. If the mass of the actuator is reduced, the kinetic energy when contacts with humans mechanically can be reduced considerably without reducing the working speed. More specifically, we propose a pleated structure made of plastic to achieve structural deformation generated from a two-dimensional pleated film. The pleated structure easily generates various bending motions. In this paper, a design method for determining the shape parameters of the pleated actuator structure using approximate models with considering measurement results of generating force is presented. We evaluated the adequacy of our approach in experiments using sample actuators. Furthermore, we show the constraints required to determine the necessary parameters. Thus, this paper provides an easy method for designing a lightweight and flexible plastic-film actuator.
A pruning based method to learn both weights and connections for LSTM

This project is one of the research topics in Professor William Dally’s group. In this project, we developed a pruning based method to learn both weights and connections for Long Short Term Memory (LSTM). In this method, we discard the unimportant connections in a pretrained LSTM, and make the weight matrix sparse. Then, we retrain the remaining model. After we remaining model is converge, we prune this model again and retrain the remaining model iteratively, until we achieve the desired size of model and performance. This method will save the size of the LSTM as well as prevent overfitting. Our results retrained on NeuralTalk shows that we can discard nearly 90% of the weights without hurting the performance too much. Part of the results in this project will be posted in NIPS 2015.
Genetic basis for individual variations in pain perception and the development of a chronic pain condition.

Pain sensitivity varies substantially among humans. A significant part of the human population develops chronic pain conditions that are characterized by heightened pain sensitivity. We identified three genetic variants (haplotypes) of the gene encoding catecholamine-O-methyltransferase (COMT) that we designated as low pain sensitivity (LPS), average pain sensitivity (APS) and high pain sensitivity (HPS). We show that these haplotypes encompass 96% of the human population, and five combinations of these haplotypes are strongly associated (P=0.0004) with variation in the sensitivity to experimental pain. The presence of even a single LPS haplotype diminishes, by as much as 2.3 times, the risk of developing myogenous temporomandibular joint disorder (TMD), a common musculoskeletal pain condition. The LPS haplotype produces much higher levels of COMT enzymatic activity when compared with the APS or HPS haplotypes. Inhibition of COMT in the rat results in a profound increase in pain sensitivity. Thus, COMT activity substantially influences pain sensitivity, and the three major haplotypes determine COMT activity in humans that inversely correlates with pain sensitivity and the risk of developing TMD.
Skype-Hunter: A real-time system for the detection and classification of Skype traffic

In the previous years, Skype has gained more and more popularity, since it is seen as the best VoIP software with good quality of sound, ease of use and one that works everywhere and with every OS. Because of its great diffusion, both the operators and the users are, for different reasons, interested in detecting Skype traffic. In this paper we propose a real-time algorithm (named Skype-Hunter) to detect and classify Skype traffic. In more detail, this novel method, by means of both signature-based and statistical procedures, is able to correctly reveal and classify the signaling traffic as well as the data traffic (calls and file transfers). To assess the effectiveness of the algorithm, experimental tests have been performed with several traffic data sets, collected in different network scenarios. Our system outperforms the ‘classical’ statistical traffic classifiers as well as the state-of-the-art ad hoc Skype classifier.
Phase control for resonant DC-DC converter with class-DE inverter and class-E rectifier

In this paper, a phase control scheme for Class-DE-E dc-dc converter is proposed and its performance is clarified. The proposed circuit is composed of phase-controlled Class-DE inverter and Class-E rectifier. The proposed circuit achieves the fixed frequency control without frequency harmonics lower than the switching frequency. Moreover, it is possible to achieve the continuous control in a wide range of the line and load variations. The output voltage decreases in proportion to the increase of the phase shift. The proposed converter keeps the advantages of Class-DE-E dc-dc converter, namely, a high power conversion efficiency under a high-frequency operation and low switch-voltage stress. Especially, high power conversion efficiency can be kept for narrow range control. We present numerical calculations for the design and the numerical analyses to clarify the characteristics of the proposed control. By carrying out circuit experiments, we show a quantitative similarity between the numerical predictions and the experimental results. In our experiments, the measured efficiency is over 84% with 2.5 W output power for 1.0-MHz operating frequency at the nominal operation. Moreover, the output voltage is regulated from 100% to 39%, keeping over 57% power conversion efficiency by using the proposed control scheme.
A new iterative position finding algorithm based on Taylor series expansion

This paper deals with the problem of estimating the position of emitters using only direction of arrival information. We propose an improvement of newly developed algorithm for position finding of a stationary emitter called sensitivity analysis. The proposed method uses Taylor series expansion iteratively to enhance the estimation of the emitter location and reduce position finding error. Simulation results show that our proposed method makes a great improvement on accuracy of position finding with respect to sensitivity analysis method.
Big Data in Telco

As businesses in every area face intense competition, advanced analytics will help improving their profitability and gain a competitive advantage by enhancing customer experience. Traditionally the analysis process has been done in off-line mode, by using Data Warehouse Technologies combined with BI tools. That is not enough anymore. Today, big data is becoming a business imperative. The benefits of big data have been deeply analyzed in many articles and reports during the past years. And it is evident that such increased value is something tangible in every area. Just to mention a few, this includes the energy sector, the financial services, the telecommunication, the transport, healthcare and education, etc. In this article, we intend to understand better what Big Data Technology is all about, the benefits that they bring to the society, focusing in particular to the telecom industry. The experimental environment is set up. Installation and configuration of the platform for data management and analytics will allow us to access all the sample data possessed by a telecom operator in a single platform. Big Data technology will be used to a telecom operator to find out pattern and reasons of call drop in real time and send customers’ apology text message and also refund money for dropped calls resulting in improved customer satisfaction and brand value.
Reliability characterization of 32nm high-K and Metal-Gate logic transistor technology

High-K (HK) and Metal-Gate (MG) transistor reliability is very challenging both from the standpoint of introduction of new materials and requirement of higher field of operation for higher performance. In this paper, key and unique HK+MG intrinsic transistor reliability mechanisms observed on 32nm logic technology generation is presented. We'll present intrinsic reliability similar to or better than 45nm generation.
Linear classifier design under heteroscedasticity in Linear Discriminant Analysis

Under normality and homoscedasticity assumptions, Linear Discriminant Analysis (LDA) is known to be optimal in terms of minimising the Bayes error for binary classification. In the heteroscedastic case, LDA is not guaranteed to minimise this error. Assuming heteroscedasticity, we derive a linear classifier, the Gaussian Linear Discriminant (GLD), that directly minimises the Bayes error for binary classification. In addition, we also propose a local neighbourhood search (LNS) algorithm to obtain a more robust classifier if the data is known to have a non-normal distribution. We evaluate the proposed classifiers on two artificial and ten real-world datasets that cut across a wide range of application areas including handwriting recognition, medical diagnosis and remote sensing, and then compare our algorithm against existing LDA approaches and other linear classifiers. The GLD is shown to outperform the original LDA procedure in terms of the classification accuracy under heteroscedasticity. While it compares favourably with other existing heteroscedastic LDA approaches, the GLD requires as much as 60 times lower training time on some datasets. Our comparison with the support vector machine (SVM) also shows that, the GLD, together with the LNS, requires as much as 150 times lower training time to achieve an equivalent classification accuracy on some of the datasets. Thus, our algorithms can provide a cheap and reliable option for classification in a lot of expert systems.
Hysteresis and phase transitions for 1 D and 3 D models in shape memory alloys

By means of the Ginzburg-Landau theory of phase transitions, we study a non-isothermal model to characterize the austenite-martensite transition in shape memory alloys. In the first part of this paper, the onedimensional model proposed in  is modified by varying the expression of the free energy. In this way, the description of the phenomenon of hysteresis, typical of these materials, is improved and the related stressstrain curves are recovered. Then, a generalization of this model to the three dimensional case is proposed and its consistency with the principles of thermodynamics is proved. Unlike other three dimensional models, the transition is characterized by a scalar valued order parameter φ and the Ginzburg-Landau equation, ruling the evolution of φ, allows us to prove a maximum principle, ensuring the boundedness of φ itself.