nils-herrmann/clean_scirepeval huggingface dataset:
Brain Tumour Mr Image Segmentation and Classification Using by PCA and RBF Kernel Based Support Vector Machine

In this paper, we have fostered a new transpire for automatic dissection of brain neoplasms in MR images. The propositioned method entails of four stages explicitly preprocessing, dissection, feature extraction, feature reduction and classification. In preprocessing wiener filter is harnessed for noise reduction and to render the image apposite for extracting the features. In the second stage, amended region convalescing base dissection is used for apportioning the image into momentous regions. In the third stage, combined edge and Texture based features are extricated using Histogram and Co-occurrence Matrix then Principal Component Analysis (PCA) is depleted to demote the dimensionality of the feature space which results in a more efficient and accurate classification. Finally, in the classification stage, a kernel based SVM classifier is exploited to amalgamate the hesitant images into mundane and exotic. The espoused tentative are gauged using the metric similarity index (SI), overlap fraction (OF) and extra fraction (EF). For comparison, the enactment of the propositioned technique has colossally exalted the tumor divulging in scrupulousness with other neural network corroborated classifier.
Retinal blood vessel segmentation and bifurcation detection using combined filters

In this research, retinal blood vessel fundus images will be segmented. Moreover, bifurcation point of the segmented blood vessel images will be determined. The data used for this research was obtained from fundus image database of DRIVE (Digital Retinal Images for Vessel Extraction) dataset. The preprocessing phases contain of several steps such as green channel extraction, histogram equalization and optic disc elimination. Meanwhile, the segmentation phase was performed by combining two filtering methods. These methods are median and derivative of Gaussian filter. Median filter was used to reduce the noise on retinal fundus image such as hard and soft exudates as well as dot and blot hemorrhages. Combination of these two filtering methods were also employed to determine the bifurcation point of segmented blood vessel image. The bifurcation points and the result from blood vessel segmentation were among two of the unique parameters of retinal fundus image. These parameters will be used eventually as unique features of the advanced research which is a biometric system to identify an individual based on his/her unique retinal pattern.
Some Eecient Solutions to the Aane Scheduling Problem Part Ii Multidimensional Time

This paper extends the algorithms which were developed in Part I to cases in which there is no aane schedule, i.e. to problems whose parallel complexity is polynomial but not linear. The natural generalization is to multidimensional schedules with lexicographic ordering as temporal succession. Multidimensional aane schedules, are, in a sense, equivalent to polynomial schedules, and are much easier to handle automatically. Furthermore , there is a strong connection between multidimensional schedules and loop nests, which allows one to prove that a static control program always has a multidimensional schedule. Roughly, a larger dimension indicates less parallelism. In the algorithm which is presented here, this dimension is computed dynamically, and is just suucient for scheduling the source program. The algorithm lends itself to a \divide and conquer" strategy. The paper gives some experimental evidence for the applicability, performances and limitations of the algorithm. R esum e Dans cet article, les algorithmes qui ont et e propos es dans la premi ere partie sont etendus au cas o u le programme source n'a pas de base de temps aane, c'est-a-dire a des algorithmes dont la complexit e parall ele est polynomiale mais non lin eaire. La solution naturelle est l'emploi de bases de temps a plusieurs dimensions, l'ordre de succession temporelle etant l'ordre lexicographique. Les bases de temps multidimensionnelles sont, en un certain sens, equivalentes a des bases de temps polynomiales, et sont beaucoup plus faciles a manipuler algorithmiquement. De plus, il y a une connexion forte entre bases de temps multidimensionnelles et nids de boucles, ce qui permet de d emontrer qu'un programme a contr^ ole statique a toujours une base de temps multidimensionnelle. En gros, plus grande est la dimension et moins il y a de parall elisme. Dans l'algorithme ici pr esent e, cette dimension est d etermin ee dynamiquement; elle est juste suusante pour permettre l'ordonnancement du programme source. Ennn, cet algorithme se pr^ ete a l'application de la strat egie \diviser pour r egner". On pr esente en conclusion quelques r esultats exp erimentaux permettant de juger du domaine d'application, des performances et des limitations de l'algorithme.
Read disturb fault detection in STT-MRAM

Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM) has potential to become a universal memory technology because of its various advantageous features such as high density, non-volatility, scalability, high endurance and CMOS compatibility. However, read disturb is a major reliability issue in which a read operation can lead to a bitflip, because read and write current share the same path. This major reliability challenge is growing with technology scaling as read to write current ratio decreases. In this paper, we propose a circuit-level technique to detect read disturb by sensing the current during the read operation. Experimental results show that the proposed technique can effectively detect read disturb at the cost of negligible power and area overhead.
Fully integrated 98mV start up DC-DC converter for energy harvesting in batteryless IoT/Wearable devices

Energy harvesting is an important pillar for large scale exploitation of IoT smart nodes and wearable devices. To this end, DC-DC converters with ultra-low start-up voltage, full integration and high efficiency in a wide input voltage range are required. In this paper a novel one-input, two-output DC-DC converter circuit is introduced. Low-power output provides 0.8–3.5 V with maximum load current of 3.5 μA starting up from 98 mV input. High-power output is turned on by an ultra-low-power voltage detector when input voltage reaches 150 mV, to provide 0.8–2.4 V with maximum load current of 200 μA. The circuit has been implemented in UMC 180-nm CMOS technology. Maximum simulated system efficiency is 34% at 0.2 V input. Low-power output meets the real time power demand of sensing, data processing and data storage blocks of IoT/Wearable devices, while high-power output provides sufficient power for data transmission, enabling batteryless operation.
Development and Validation of the Transgender Attitudes and Beliefs Scale.

In recent years, issues surrounding transgender have garnered media and legal attention, contributing to rapidly shifting views on gender in the U.S. Yet, there is a paucity of data-driven studies on the public's views of transgender identity. This study reports the development and validation of the Transgender Attitudes and Beliefs Scale (TABS). After constructing an initial 96-item pool from consulting experts and existing scales, Phase 1 of the study was launched, involving an exploratory factor analysis of 48 items. The initial factor analysis with 295 participants revealed three factors across 33 items-16 items on interpersonal comfort, 11 on sex/gender beliefs, and 6 on human value. The internal consistency of each factor was high-α = .97 for Factor 1, α = .95 for Factor 2, and α = .94 for Factor 3. A confirmatory factor analysis was conducted in the second phase with an independent sample consisting of 238 participants. The Attitudes Toward Transgender Individual Scale and the Genderism and Transphobia Scale were also included to test for convergent validity, and the Rosenberg Self-Esteem Scale and the short form of the Marlowe-Crowne Social Desirability Scale were utilized to test discriminant validity. Both of the data collection phases employed MTurk, a form of online sampling with increased diversity compared to college student samples and more generalizability to the general U.S.



TABS represents an addition to the literature in its ability to capture a more nuanced conceptualization of transgender attitude not found in previous scales.
DISCO: A Multilingual Database of Distributionally Similar Words

This paper1 presents DISCO, a tool for retrieving the distributional similarity between two given words, and for retrieving the distributionally most similar words for a given word. Pre-computed word spaces are freely available for a number of languages including English, German, French and Italian, so DISCO can be used off the shelf. The tool is implemented in Java, provides a Java API, and can also be called from the command line. The performance of DISCO is evaluated by measuring the correlation with WordNet-based semantic similarities and with human relatedness judgements. The evaluations show that DISCO has a higher correlation with semantic similarities derived from WordNet than latent semantic analysis (LSA) and the web-based PMI-IR.
Growth rates of modern science: A bibliometric analysis

Many studies in information science have looked at the growth of science. In this study, we re-examine the question of the growth of science. To do this we (i) use current data up to publication year 2012 and (ii) analyse it across all disciplines and also separately for the natural sciences and for the medical and health sciences. Furthermore, the data are analysed with an advanced statistical technique - segmented regression analysis - which can identify specific segments with similar growth rates in the history of science. The study is based on two different sets of bibliometric data: (1) The number of publications held as source items in the Web of Science (WoS, Thomson Reuters) per publication year and (2) the number of cited references in the publications of the source items per cited reference year. We have looked at the rate at which science has grown since the mid-1600s. In our analysis of cited references we identified three growth phases in the development of science, which each led to growth rates tripling in comparison with the previous phase: from less than 1% up to the middle of the 18th century, to 2 to 3% up to the period between the two world wars and 8 to 9% to 2012.
Attachment theory and intergroup bias: evidence that priming the secure base schema attenuates negative reactions to out-groups.

Five studies examined the effects of priming the secure base schema on intergroup bias. In addition, Studies 1-2 examined the effects of dispositional attachment style, Studies 2-5 examined a mood interpretation, Study 3 examined the mediating role of threat appraisal, and Studies 4-5 examined the effects of secure base priming while inducing a threat to self-esteem or cultural worldview. Secure base priming led to less negative evaluative reactions toward out-groups than positive affect and neutral control conditions. In addition, whereas the effects of secure base priming did not depend on attachment style and were not explained by mood induction, they were mediated by threat appraisal and occurred even when self-esteem or cultural worldview was threatened. The discussion emphasizes the relevance of attachment theory for understanding intergroup attitudes.
Survey on Skin Tone Detection using Color Spaces

Skin is arguably the most widely used primitive in human image processing research and computer vision with application ranging from face detection and person tracking to pornography filtering. It has proven to be useful and robust cue for detecting human parts in images since (i) it is invariant to orientation and size (ii) it gives extra dimension compared to gray scale methods and (iii) it is fast to process. The main problems with the robustness of skin color detection are however depends on illumination condition, it varies between individuals, many everyday life objects are skin color like and skin color is not unique. Environments comprehensive survey in this topic is missing. The work presented in this paper is a survey of the most frequently used methods and techniques and their numerical evaluation results. General Terms Image Processing, Face Detection.
AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation

In this work, we focus on key topics related to underwater Simultaneous Localization and Mapping (SLAM) applications. Moreover, a detailed review of major studies in the literature and our proposed solutions for addressing the problem are presented. The main goal of this paper is the enhancement of the accuracy and robustness of the SLAM-based navigation problem for underwater robotics with low computational costs. Therefore, we present a new method called AEKF-SLAM that employs an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-based SLAM approach stores the robot poses and map landmarks in a single state vector, while estimating the state parameters via a recursive and iterative estimation-update process. Hereby, the prediction and update state (which exist as well in the conventional EKF) are complemented by a newly proposed augmentation stage. Applied to underwater robot navigation, the AEKF-SLAM has been compared with the classic and popular FastSLAM 2.0 algorithm. Concerning the dense loop mapping and line mapping experiments, it shows much better performances in map management with respect to landmark addition and removal, which avoid the long-term accumulation of errors and clutters in the created map. Additionally, the underwater robot achieves more precise and efficient self-localization and a mapping of the surrounding landmarks with much lower processing times. Altogether, the presented AEKF-SLAM method achieves reliably map revisiting, and consistent map upgrading on loop closure.
Smart home garden irrigation system using Raspberry Pi

Irrigation system is a method of allowing water to drip slowly to the roots of plants, either onto the soil surface or directly onto the root zone, through solenoid valve. However, it is found that the market price of the system is expensive for small area coverage. Thus, this paper proposes a design for smart home garden irrigation system that implements ready-to-use, energy-efficient, and cost effective devices. Raspberry Pi, which is implemented in this system is integrated with multi-sensors such as soil moisture sensors, ultrasonic sensors, and light sensors. This proposed system managed to reduce cost, minimize waste water, and reduce physical human interface. In this paper, the relay is utilized to control the switching of solenoid valve. The system also managed to measure moisture of the soil and control the solenoid valve according to human's requirements. It is conducted with Graphical User Interface (GUI) using Android application to activate watering activity. Email notification is also sent to the home user for alert purposes either for normal or critical operations. An experimental setup has been tested and it is proven that the system can intelligently control and monitor the soil moisture levels in the experiment field.
On Oblique Random Forests

In his original paper on random forests, Breiman proposed two different decision tree ensembles: one generated from “orthogonal” trees with thresholds on individual features in every split, and one from “oblique” trees separating the feature space by randomly oriented hyperplanes. In spite of a rising interest in the random forest framework, however, ensembles built from orthogonal trees (RF) have gained most, if not all, attention so far. In the present work we propose to employ “oblique” random forests (oRF) built from multivariate trees which explicitly learn optimal split directions at internal nodes using linear discriminative models, rather than using random coefficients as the original oRF. This oRF outperforms RF, as well as other classifiers, on nearly all data sets but those with discrete factorial features. Learned node models perform distinctively better than random splits. An oRF feature importance score shows to be preferable over standard RF feature importance scores such as Gini or permutation importance. The topology of the oRF decision space appears to be smoother and better adapted to the data, resulting in improved generalization performance. Overall, the oRF propose here may be preferred over standard RF on most learning tasks involving numerical and spectral data.
Sketch2normal: deep networks for normal map generation

Normal maps are of great importance for many 2D graphics applications such as surface editing, re-lighting, texture mapping and 2D shading etc. Automatically inferring normal map is highly desirable for graphics designers. Many researchers have investigated the inference of normal map from intuitive and flexiable line drawing based on traditional geometric methods while our proposed deep networks-based method shows more robustness and provides more plausible results.
Kangaroo care is effective in diminishing pain response in preterm neonates.

To test the efficacy of maternal skin-to-skin contact, or kangaroo care (KC), on diminishing the pain response of preterm neonates to heel lancing.



A crossover design was used, in which the neonates served as their own controls. Subjects Preterm neonates (n = 74), between 32 and 36 weeks' postmenstrual age and within 10 days of birth, who were breathing without assistance and who were not receiving sedatives or analgesics in 3 level II to III neonatal intensive care units in Canada.



In the experimental condition, the neonate was held in KC for 30 minutes before the heel-lancing procedure and remained in KC for the duration of the procedure. In the control condition, the neonate was in the prone position in the isolette. The ordering of conditions was random.



The primary outcome was the Premature Infant Pain Profile, which is composed of 3 facial actions, maximum heart rate, and minimum oxygen saturation changes from baseline in 30-second blocks. Videotapes, taken with the camera positioned on the neonate's face so that an observer could not tell whether the neonate was being held or was in the isolette, were coded by research assistants who were naïve to the purpose of the study. Heart rate and oxygen levels were continuously monitored into a computer for later analysis. A repeated-measures analysis of covariance was used, with order of condition and site as factors and severity of illness as a covariate.



Premature Infant Pain Profile scores across the first 90 seconds from the heel-lancing procedure were significantly (.002<P<.04) lower by 2 points in the KC condition.



For preterm neonates who are 32 weeks' postmenstrual age or older, KC seems to effectively decrease pain from heel lancing. Further study is needed to determine if younger neonates or those requiring assistance in breathing, or older infants or toddlers, would benefit from KC, or if it would remain effective over several procedures. Given its effectiveness, and that parents of neonates in critical care units want to participate more in comforting their children, KC is a potentially beneficial strategy for promoting family health.
How do I communicate my emotions on SNS and IMs?

With influx of new devices and applications over the past few years, computer mediated communication has developed as an alternate to face-to-face communication. Through our study we attempt to understand how individuals communicate in mediated settings in emotion-laden situations. We explore their preferred medium of communication in different situations along with analysis of the content of communication. Spatial arrays, lexical surrogates, vocal spellings and grammatical markers were used as strategies by individuals for communicating non-verbal cues in text based communication. We also look at how messages sent on IMs or posts on social networks are interpreted by readers in terms of the emotional state of the sender. We found that while valence of the sender gets easily and accurately communicated, arousal is misinterpreted in most situations. In this paper, we present findings from our study which can be valuable for technology companies looking to better the current communication experience across different media.
Sequential pattern recognition for medical records analysis

Automatic recognition of patterns is an important problem in a variety of engineering and scientific disciplines. The most common application in medicine relates to medical imaging for diagnostics' purpose. This paper presents another field of pattern recognition application - mining medical records. Analysis of treatment pathways (in which diagnostic and therapeutic procedures are sequenced on a timeline) as a mean of providing and standardized patient care of high quality, is one of the issues gaining in importance in the last decade in the field of medical informatics. As there is a need for scalable computational tools for pattern exploration, we propose a conceptual framework, adapted to Poland's context, for expressing data mining tasks to discover knowledge about patients and providers similarities involving multiple time and procedure granularities.
Difficulty in Video Games: Understanding the Effects of Dynamic Difficulty Adjustment in Video Games on Player Experience

This thesis investigates the impact of difficulty in the context of Dynamic Difficulty Adjustment (DDA) in video games on the player experience (PX). A series of quantitative and qualitative studies were employed to compare the effects of DDA systems on the PX and to gain an understanding of the lived PX of players engaging with such systems. This research contributes to game design knowledge and the current limited understanding of the impact of DDA systems on the PX. The findings are of relevance to practitioners and researchers in the domain of entertainment games as well as other related domains such as games for education and health care.
Recognition of Car Makes and Models From a Single Traffic-Camera Image

This paper proposes the recognition framework of car makes and models from a single image captured by a traffic camera. Due to various configurations of traffic cameras, a traffic image may be captured in different viewpoints and lighting conditions, and the image quality varies in resolution and color depth. In the framework, cars are first detected using a part-based detector, and license plates and headlamps are detected as cardinal anchor points to rectify projective distortion. Car features are extracted, normalized, and classified using an ensemble of neural-network classifiers. In the experiment, the performance of the proposed method is evaluated on a data set of practical traffic images. The results prove the effectiveness of the proposed method in vehicle detection and model recognition.