nils-herrmann/clean_scirepeval huggingface dataset:
Student pass rates prediction using optimized support vector machine and decision tree

Since student performance and pass rates in school reflect teaching level of the school and even all education system, it is critical to improve student pass rates and reduce dropout rates. Decision Tree (DT) algorithm and Support Vector Machine (SVM) algorithm in data mining, have been used by researchers to find important student features and predict the student pass rates, however they did not consider the coefficient of initialization, and whether there is a dependency between student features. Therefore, in this study, we propose a new concept: features dependencies, and use the grid search algorithm to optimize DT and SVM, in order to improve the accuracy of the algorithm. Furthermore, we added 10-fold cross-validation to DT and SVM algorithm. The results show the experiment can achieve better results in this work. The purpose of this study is providing assistance to students who have greater difficulties in their studies, and students who are at risk of graduating through data mining techniques.
MEMS-Based Power Generation Techniques for Implantable Biosensing Applications

Implantable biosensing is attractive for both medical monitoring and diagnostic applications. It is possible to monitor phenomena such as physical loads on joints or implants, vital signs, or osseointegration in vivo and in real time. Microelectromechanical (MEMS)-based generation techniques can allow for the autonomous operation of implantable biosensors by generating electrical power to replace or supplement existing battery-based power systems. By supplementing existing battery-based power systems for implantable biosensors, the operational lifetime of the sensor is increased. In addition, the potential for a greater amount of available power allows additional components to be added to the biosensing module, such as computational and wireless and components, improving functionality and performance of the biosensor. Photovoltaic, thermovoltaic, micro fuel cell, electrostatic, electromagnetic, and piezoelectric based generation schemes are evaluated in this paper for applicability for implantable biosensing. MEMS-based generation techniques that harvest ambient energy, such as vibration, are much better suited for implantable biosensing applications than fuel-based approaches, producing up to milliwatts of electrical power. High power density MEMS-based approaches, such as piezoelectric and electromagnetic schemes, allow for supplemental and replacement power schemes for biosensing applications to improve device capabilities and performance. In addition, this may allow for the biosensor to be further miniaturized, reducing the need for relatively large batteries with respect to device size. This would cause the implanted biosensor to be less invasive, increasing the quality of care received by the patient.
Michelangelo, the Last Judgment fresco, Saint Bartholomew and the Golden Ratio.

Art and anatomy were particularly closely intertwined during the Renaissance period and numerous painters and sculptors expressed themselves in both fields. Among them was Michelangelo Buonarroti (1475-1564), who is renowned for having produced some of the most famous of all works of art, the frescoes on the ceiling and on the wall behind the altar of the Sistine Chapel in Rome. Recently, a unique association was discovered between one of Michelangelo's most celebrated works (The Creation of Adam fresco) and the Divine Proportion/Golden Ratio (GR) (1.6). The GR can be found not only in natural phenomena but also in a variety of human-made objects and works of art. Here, using Image-Pro Plus 6.0 software, we present mathematical evidence that Michelangelo also used the GR when he painted Saint Bartholomew in the fresco of The Last Judgment, which is on the wall behind the altar. This discovery will add a new dimension to understanding the great works of Michelangelo Buonarroti.
A closed-form solution to uncalibrated photometric stereo via diffuse maxima

In this paper we propose a novel solution to uncalibrated photometric stereo. Our approach is to eliminate the so-called generalized bas relief (GBR) ambiguity by exploiting points where the Lambertian reflection is maximal. We demonstrate several noteworthy properties of these maxima: 1) Closed-form solution: A single diffuse maximum constrains the GBR ambiguity to a semi-circle in 3D space; 2) Efficiency: As few as two diffuse maxima in different images identify a unique solution; 3) GBR-invariance: The estimation error of the GBR parameters is completely independent of the true parameters. Furthermore, our algorithm is remarkably robust: It can obtain an accurate estimate of the GBR parameters even with extremely high levels of outliers in the detected maxima (up to 80% of the observations). The method is validated on real data and achieves state-of-the-art results.
Partially labeled topic models for interpretable text mining

Much of the world's electronic text is annotated with human-interpretable labels, such as tags on web pages and subject codes on academic publications. Effective text mining in this setting requires models that can flexibly account for the textual patterns that underlie the observed labels while still discovering unlabeled topics. Neither supervised classification, with its focus on label prediction, nor purely unsupervised learning, which does not model the labels explicitly, is appropriate. In this paper, we present two new partially supervised generative models of labeled text, Partially Labeled Dirichlet Allocation (PLDA) and the Partially Labeled Dirichlet Process (PLDP). These models make use of the unsupervised learning machinery of topic models to discover the hidden topics within each label, as well as unlabeled, corpus-wide latent topics. We explore applications with qualitative case studies of tagged web pages from del.icio.us and PhD dissertation abstracts, demonstrating improved model interpretability over traditional topic models. We use the many tags present in our del.icio.us dataset to quantitatively demonstrate the new models' higher correlation with human relatedness scores over several strong baselines.
Understanding Reduced-Voltage Operation in Modern DRAM Devices: Experimental Characterization, Analysis, and Mechanisms

The energy consumption of DRAM is a critical concern in modern computing systems. Improvements in manufacturing process technology have allowed DRAM vendors to lower the DRAM supply voltage conservatively, which reduces some of the DRAM energy consumption. We would like to reduce the DRAM supply voltage more aggressively, to further reduce energy. Aggressive supply voltage reduction requires a thorough understanding of the effect voltage scaling has on DRAM access latency and DRAM reliability.
 In this paper, we take a comprehensive approach to understanding and exploiting the latency and reliability characteristics of modern DRAM when the supply voltage is lowered below the nominal voltage level specified by DRAM standards. Using an FPGA-based testing platform, we perform an experimental study of 124 real DDR3L (low-voltage) DRAM chips manufactured recently by three major DRAM vendors. We find that reducing the supply voltage below a certain point introduces bit errors in the data, and we comprehensively characterize the behavior of these errors. We discover that these errors can be avoided by increasing the latency of three major DRAM operations (activation, restoration, and precharge). We perform detailed DRAM circuit simulations to validate and explain our experimental findings. We also characterize the various relationships between reduced supply voltage and error locations, stored data patterns, DRAM temperature, and data retention.
 Based on our observations, we propose a new DRAM energy reduction mechanism, called Voltron. The key idea of Voltron is to use a performance model to determine by how much we can reduce the supply voltage without introducing errors and without exceeding a user-specified threshold for performance loss. Our evaluations show that Voltron reduces the average DRAM and system energy consumption by 10.5% and 7.3%, respectively, while limiting the average system performance loss to only 1.8%, for a variety of memory-intensive quad-core workloads. We also show that Voltron significantly outperforms prior dynamic voltage and frequency scaling mechanisms for DRAM.
Attention-Based Super Resolution from Videos

A video from a moving camera produces different number of observations of different scene areas. We can construct an attention map of the scene by bringing the frames to a common reference and counting the number of frames that observed each scene point. Different representations can be constructed from this. The base of the attention map gives the scene mosaic. Super-resolved images of parts of the scene can be obtained using a subset of observations or video frames. We can combine mosaicing with super-resolution by using all observations, but the magnification factor will vary across the scene based on the attention received. The height of the attention map indicates the amount of super-resolution for that scene point. We modify the traditional super-resolution framework to generate a varying resolution image for panning cameras in this paper. The varying resolution image uses all useful data available in a video. We introduce the concept of attention-based super-resolution and give the modified framework for it. We also show its applicability on a few indoor and outdoor videos.
TransCut: Transparent Object Segmentation from a Light-Field Image

The segmentation of transparent objects can be very useful in computer vision applications. However, because they borrow texture from their background and have a similar appearance to their surroundings, transparent objects are not handled well by regular image segmentation methods. We propose a method that overcomes these problems using the consistency and distortion properties of a light-field image. Graph-cut optimization is applied for the pixel labeling problem. The light-field linearity is used to estimate the likelihood of a pixel belonging to the transparent object or Lambertian background, and the occlusion detector is used to find the occlusion boundary. We acquire a light field dataset for the transparent object, and use this dataset to evaluate our method. The results demonstrate that the proposed method successfully segments transparent objects from the background.
Increased sagittal plane segmental motion in the lower cervical spine in women with chronic whiplash-associated disorders, grades I-II: a case-control study using a new measurement protocol.

Case-control study comparing sagittal plane segmental motion in women (n = 34) with chronic whiplash-associated disorders, Grades I-II, with women (n = 35) with chronic insidious onset neck pain and with a normal database of sagittal plane rotational and translational motion.



To reveal whether women with chronic whiplash-associated disorders, Grades I-II, demonstrate evidence of abnormal segmental motions in the cervical spine.



It is hypothesized that unphysiological spinal motion experienced during an automobile accident may result in a persistent disturbance of segmental motion. It is not known whether patients with chronic whiplash-associated disorders differ from patients with chronic insidious onset neck pain with respect to segmental mobility.



Lateral radiographic views were taken in assisted maximal flexion and extension. A new measurement protocol determined rotational and translational motions of segments C3-C4 and C5-C6 with high precision. Segmental motion was compared with normal data as well as among groups.



In the whiplash-associated disorders group, the C3-C4 and C4-C5 segments showed significantly increased rotational motions. Translational motions within each segment revealed a significant deviation from normal at the C3-C4 segment in the whiplash-associated disorders and insidious onset neck pain groups and at the C5-C6 segment in the whiplash-associated disorders group. Significantly more women in the whiplash-associated disorders group (35.3%) had abnormal increased segmental motions compared to the insidious onset neck pain group (8.6%) when both the rotational and the translational parameters were analyzed. When the translational parameter was analyzed separately, no significant difference was found between groups, or 17.6% (whiplash-associated disorders group) and 8.6% (insidious onset neck pain group), respectively.



Hypermobility in the lower cervical spine segments in 12 out of 34 patients with chronic whiplash-associated disorders in this study point to injury caused by the accident. This subgroup, identified by the new radiographic protocol, might need a specific therapeutic intervention.
MRF based clutch with integrated electrical drive

Actuators based on magnetorheological fluids represent an innovative technology to optimize brakes and clutches. Compared to conventional electromechanical systems, MRF actuators show improved and advanced characteristics featuring a smooth adjustable torque, a fast response time, a noiseless operation and a reduced design space. In the present paper, the authors propose an actuator design for a MRF based clutch combined with an integrated electric drive. The actuator design is a further development with enhanced performances considering the torque capacity. Both functionalities of this actuator can be controlled widely independent due to a suitable adjustment of the magnetic field. Therefore a model and a new control strategy are developed. The capability of this concept is proven by measurements of a new proof-of-concept prototype that provides an appropriated ratio between coupling and motor torque of 5∶1. This highly integrated mechatronical actuator system allows the design of innovative applications whereby novel features are feasible.
WebShodh: A Code Mixed Factoid Question Answering System for Web

Code-Mixing (CM) is a natural phenomenon observed in many multilingual societies and is becoming the preferred medium of expression and communication in online and social media fora. In spite of this, current Question Answering (QA) systems do not support CM and are only designed to work with a single interaction language. This assumption makes it inconvenient for multi-lingual users to interact naturally with the QA system especially in scenarios where they do not know the right word in the target language. In this paper, we present WebShodh an end-end web-based Factoid QA system for CM languages. We demonstrate our system with two CM language pairs: Hinglish (Matrix language: Hindi,  English) and Tenglish (Matrix language: Telugu,  English). Lack of language resources such as annotated corpora, POS taggers or parsers for CM languages poses a huge challenge for automated processing and analysis. In view of this resource scarcity, we only assume the existence of bi-lingual dictionaries from the matrix languages to English and use it for lexically translating the question into English. Later, we use this loosely translated question for our downstream analysis such as Answer Type(AType) prediction, answer retrieval and ranking. Evaluation of our system reveals that we achieve an MRR of 0.37 and 0.32 for Hinglish and Tenglish respectively. We hosted this system online and plan to leverage it for collecting more CM questions and answers data for further improvement.
A UWB Aperture Coupled Microstrip Antenna for S and C Bands

This paper presents a multilayer aperture coupled microstrip antenna with a non symmetric U-shaped feed line. The antenna structure consists of a rectangular patch which is excited through two slots on the ground plane. A parametric study is presented on the effects of the position and dimensions of the slots. Results show that the antenna has VSWR < 2 from 2.6 GHz to 5.4 GHz (70%) and the gain of the structure is more than 7 dB from 2.7 GHz to 4.4 GHz (48%).
An efficient system for vehicle tracking in multi-camera networks

The recent deployment of very large-scale camera networks has led to a unique version of the tracking problem whose goal is to detect and track every vehicle within a large urban area. To address this problem we exploit constraints inherent in urban environments (i.e. while there are often many vehicles, they follow relatively consistent paths) to create novel visual processing tools that are highly efficient in detecting cars in a fixed scene and at connecting these detections into partial tracks.We derive extensions to a network flow based probabilistic data association model to connect these tracks between cameras. Our real time system is evaluated on a large set of ground-truthed traffic videos collected by a network of seven cameras in a dense urban scene.
Transparent object detection and reconstruction on a mobile platform

In this paper we propose a novel approach to detect and reconstruct transparent objects. This approach makes use of the fact that many transparent objects, especially the ones consisting of usual glass, absorb light in certain wavelengths . Given a controlled illumination, this absorption is measurable in the intensity response by comparison to the background. We show the usage of a standard infrared emitter and the intensity sensor of a time of flight (ToF) camera to reconstruct the structure given we have a second view point. The structure can not be measured by the usual 3D measurements of the ToF camera. We take advantage of this fact by deriving this internal sensory contradiction from two ToF images and reconstruct an approximated surface of the original transparent object. Therefor we are using a perspectively invariant matching in the intensity channels from the first to the second view of initially acquired candidates. For each matched pixel in the first view a 3D movement can be predicted given their original 3D measurement and the known distance to the second camera position. If their line of sight did not pass a transparent object or suffered any other major defect, this prediction will highly correspond to the actual measured 3D points of the second view. Otherwise, if a detectable error occurs, we approximate a more exact point to point matching and reconstruct the original shape by triangulating the points in the stereo setup. We tested our approach using a mobile platform with one Swissranger SR4k. As this platform is mobile, we were able to create a stereo setup by moving it. Our results show a detection of transparent objects on tables while simultaneously identifying opaque objects that also existed in the test setup. The viability of our results is demonstrated by a successful automated manipulation of the respective transparent object.
The Bayesian Structural EM Algorithm

In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a belief network from incomplete data-that is, in the presence of missing values or hidden variables. In a re­ cent paper, I introduced an algorithm _ called �tr�ctu_ral EM that combines the standard Expectatton Maxtmtzatton (EM) algorithm, which optimizes parameters, with struc­ ture search for model selection. That algorithm learns networks based on penalized likelihood scores, which in­ clude the BIC/MDL score and various approximations to the Bayesian score. In this paper, I extend. Structural EM to deal directly with Bayesian model selectwn. I prove the convergence of the resulting algorithm and. �h�w how to apply it for learning a large class of proba�tltsttc models, including Bayesian networks and some vanants thereof.
The Good, the Bad, and the Ugly: An Empirical Study of Implicit Type Conversions in JavaScript

Most popular programming languages support situations where a value of one type is converted into a value of another type without any explicit cast. Such implicit type conversions, or type coercions, are a highly controversial language feature. Proponents argue that type coercions enable writing concise code. Opponents argue that type coercions are error-prone and that they reduce the understandability of programs. This paper studies the use of type coercions in JavaScript, a language notorious for its widespread use of coercions. We dynamically analyze hundreds of programs, including real-world web applications and popular benchmark programs. We find that coercions are widely used (in 80.42% of all function executions) and that most coercions are likely to be harmless (98.85%). Furthermore, we identify a set of rarely occurring and potentially harmful coercions that safer subsets of JavaScript or future language designs may want to disallow. Our results suggest that type coercions are significantly less evil than commonly assumed and that analyses targeted at real-world JavaScript programs must consider coercions.
Maternal uniparental disomy 7 and Silver-Russell syndrome - clinical update and comparison with other subgroups.

Maternal uniparental disomy (UPD) 7 is found in approximately 5% of patients with Silver-Russell syndrome. By a descriptive and comparative clinical analysis of all published cases (more than 60 to date) their phenotype is updated and compared with the clinical findings in patients with Sliver-Russell syndrome (SRS) of either unexplained etiology or epimutations of the imprinting center region 1 (ICR1) on 11p15. The higher frequency of relative macrocephaly and high forehead/frontal bossing makes the face of patients with epimutations of the ICR1 on 11p15 more distinctive than the face of cases with SRS of unexplained etiology or maternal UPD 7. Because of the distinct micrognathia in the latter, their triangular facial gestalt is more pronounced than in the other groups. However, solely by clinical findings patients with maternal UPD 7 cannot be discriminated unambiguously from patients with epimutations of the ICR1 on 11p15 or SRS of unexplained etiology. Therefore, both loss of methylation of the ICR1 on 11p15 and maternal UPD 7 should be investigated for if SRS is suspected.
Addressing Deafness and Hidden Terninal Problem in Directional Antenna based Wireless Multi-Hop Networks

We address deafness and directional hidden terminal problem that occur when MAC protocols are designed for directional antenna based wireless multi-hop networks. Deafness occurs when the transmitter fails to communicate to its intended receiver, because the receiver's antenna is oriented in a different direction. The directional hidden terminal problem occurs when the transmitter fails to hear a prior RTS/CTS exchange between another pair of nodes and cause collision by initiating a transmission to the receiver of the ongoing communication. Though directional antennas offer better spatial reuse, these problems can have a serious impact on network performance. In this paper, we study various scenarios in which these problems can occur and design a MAC protocol that solves them comprehensively using only a single channel and single radio interface. Current solutions in literature either do not address these issues comprehensively or use more than one radio/channel to solve them. We evaluate our protocol using detailed simulation studies. Simulation results indicate that our protocol can effectively address deafness and directional hidden terminal problem and increase network performance.