nils-herrmann/clean_scirepeval huggingface dataset:
Predicting Performance on MOOC Assessments using Multi-Regression Models

The past few years has seen the rapid growth of data mining approaches for the analysis of data obtained from Massive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a student may achieve on a given grade-related assessment based on information, considered as prior performance or prior activity in the course. We develop a personalized linear multiple regression (PLMR) model to predict the grade for a student, prior to attempting the assessment activity. The developed model is real-time and tracks the participation of a student within a MOOC (via click-stream server logs) and predicts the performance of a student on the next assessment within the course offering. We perform a comprehensive set of experiments on data obtained from two openEdX MOOCs via a Stanford University initiative. Our experimental results show the promise of the proposed approach in comparison to baseline approaches and also helps in identification of key features that are associated with the study habits and learning behaviors of students.
Compact and wideband microstrip bandstop filter

A novel one-section bandstop filter (BSF), which possesses the characteristics of compact size, wide bandwidth, and low insertion loss is proposed and fabricated. This bandstop filter was constructed by using single quarter-wavelength resonator with one section of anti-coupled lines with short circuits at one end. The attenuation-pole characteristics of this type of bandstop filters are investigated through TEM transmission-line model. Design procedures are clearly presented. The 3-dB bandwidth of the first stopband and insertion loss of the first passband of this BSF is from 2.3 GHz to 9.5 GHz and below 0.3 dB, respectively. There is good agreement between the simulated and experimental results.
Holistic ship design optimization

Ship design is a complex endeavor requiring the successful coordination of many disciplines, of both technical and non-technical nature, and of individual experts to arrive at valuable design solutions. Inherently coupled with the design process is design optimization, namely the selection of the best solution out ofmany feasible ones on the basis of a criterion, or rather a set of criteria. A systemic approach to ship design may consider the ship as a complex system integrating a variety of subsystems and their components, for example, subsystems for cargo storage and handling, energy/power generation and ship propulsion, accommodation of crew/passengers and ship navigation. Independently, considering that ship design should actually address the whole ship’s life-cycle, it may be split into various stages that are traditionally composed of the concept/preliminary design, the contractual and detailed design, the ship construction/fabrication process, ship operation for an economic life and scrapping/recycling. It is evident that an optimal ship is the outcome of a holistic optimization of the entire, above-defined ship system over her whole life-cycle. But even the simplest component of the above-defined optimization problem, namely the first phase (conceptual/preliminary design), is complex enough to require to be simplified (reduced) in practice. Inherent to ship design optimization are also the conflicting requirements resulting from the design constraints and optimization criteria (merit or objective functions), reflecting the interests of the various ship design stake holders. The present paper provides a brief introduction to the holistic approach to ship design optimization, defines the generic ship design optimization problem and demonstrates its solution by use of advanced optimization techniques for the computer-aided generation, exploration and selection of optimal designs. It discusses proposed methods on the basis of some typical ship design optimization problems with multiple objectives, leading to improved and partly innovative designs with increased cargo carrying capacity, increased safety and survivability, reduced required powering and improved environmental protection. The application of the proposed methods to the integrated ship system for life-cycle optimization problem remains a challenging but straightforward task for the years to come.
High temperature (≫200°C) isolated gate drive topologies for Silicon Carbide (SiC) JFET

Volume and weight limitations for components in hybrid electrical vehicle (HEV) propulsion systems demand highly-compact and highly-efficient power electronics. The application of silicon carbide (SiC) semiconductor technology in conjunction with high temperature (HT) operation allows the power density of the DC-DC converters and inverters to be increased. Elevated ambient temperatures of above 200degC also affects the gate drives attached to the power semiconductors. This paper focuses on the selection of HT components and discusses different gate drive topologies for SiC JFETs with respect to HT operation capability, limitations, dynamic performance and circuit complexity. An experimental performance comparison of edge-triggered and phase-difference HT drivers with a conventional room temperature JFET gate driver is given. The proposed edge-triggered gate driver offers high switching speeds and a cost effective implementation. Switching tests at 200degC approve an excellent performance at high temperature and a low temperature drift of the driver output voltage.
Cyberbullying among young adults in Malaysia: The roles of gender, age and Internet frequency

This study investigated the extent of young adults’ (N = 393; 17–30 years old) experience of cyberbullying, from the perspectives of cyberbullies and cyber-victims using an online questionnaire survey. The overall prevalence rate shows cyberbullying is still present after the schooling years. No significant gender differences were noted, however females outnumbered males as cyberbullies and cyber-victims. Overall no significant differences were noted for age, but younger participants were found to engage more in cyberbullying activities (i.e. victims and perpetrators) than the older participants. Significant differences were noted for Internet frequency with those spending 2–5 h online daily reported being more victimized and engage in cyberbullying than those who spend less than an hour daily. Internet frequency was also found to significantly predict cyber-victimization and cyberbullying, indicating that as the time spent on Internet increases, so does the chances to be bullied and to bully someone. Finally, a positive significant association was observed between cyber-victims and cyberbullies indicating that there is a tendency for cyber-victims to become cyberbullies, and vice versa. Overall it can be concluded that cyberbullying incidences are still taking place, even though they are not as rampant as observed among the younger users.
Predicting Student Academic Performance using Fuzzy ARTMAP Network

Predicting student academic performance plays an important role in academics. Classifying st udents using conventional techniques cannot give the desired lev l of accuracy, while doing it with the use of soft computing techniques may prove to be beneficial. A student can be classi fied into one of the available categories based on his behavioral and qualitative features. The paper presents a Neural N etwork model fused with Fuzzy Logic to model academi c profile of students. The model mimics teacher’s ability to deal with imprecise information representing student’s characteristics in linguistic form. The suggested model is developed in MATLAB which takes into consideration various features of students under study. The input to the model consists of dat of students studying in any faculty. A combination of Fuzzy Logic ARTMAP Neural Network results into a model useful for management of educational institutes for improving the quality of education. A good prediction of student’s success ione way to be in the competition in education sys tem. The use of Soft Computing methodology is justified for its real-time applicability in education system.
Risk factors associated with bus accident severity in the United States: a generalized ordered logit model.

Recent years have witnessed a growing interest in improving bus safety operations worldwide. While in the United States buses are considered relatively safe, the number of bus accidents is far from being negligible, triggering the introduction of the Motor-coach Enhanced Safety Act of 2011.



The current study investigates the underlying risk factors of bus accident severity in the United States by estimating a generalized ordered logit model. Data for the analysis are retrieved from the General Estimates System (GES) database for the years 2005-2009.



Results show that accident severity increases: (i) for young bus drivers under the age of 25; (ii) for drivers beyond the age of 55, and most prominently for drivers over 65 years old; (iii) for female drivers; (iv) for very high (over 65 mph) and very low (under 20 mph) speed limits; (v) at intersections; (vi) because of inattentive and risky driving.
Dynamic Time-Dependent Route Planning in Road Networks with User Preferences

There has been tremendous progress in algorithmic methods for computing driving directions on road networks. Most of that work focuses on time-independent route planning, where it is assumed that the cost on each arc is constant per query. In practice, the current traffic situation significantly influences the travel time on large parts of the road network, and it changes over the day. One can distinguish between traffic congestion that can be predicted using historical traffic data, and congestion due to unpredictable events, e. g., accidents. In this work, we study the dynamic and time-dependent route planning problem, which takes both prediction (based on historical data) and live traffic into account. To this end, we propose a practical algorithm that, while robust to user preferences, is able to integrate global changes of the time-dependent metric (e. g., due to traffic updates or user restrictions) faster than previous approaches, while allowing subsequent queries that enable interactive applications.
On the Security of Carrier Phase-based Ranging

Multicarrier phase-based ranging is fast emerging as a cost-optimized solution for a wide variety of proximitybased applications due to its low power requirement, low hardware complexity and compatibility with existing standards such as ZigBee and 6LoWPAN. Given potentially critical nature of the applications in which phasebased ranging can be deployed (e.g., access control, asset tracking), it is important to evaluate its security guarantees. Therefore, in this work, we investigate the security of multicarrier phase-based ranging systems and specifically focus on distance decreasing relay attacks that have proven detrimental to the security of proximity-based access control systems (e.g., vehicular passive keyless entry and start systems). We show that phase-based ranging, as well as its implementations, are vulnerable to a variety of distance reduction attacks. We describe different attack realizations and verify their feasibility by simulations and experiments on a commercial ranging system. Specifically, we successfully reduced the estimated range to less than 3m even though the devices were more than 50 m apart. We discuss possible countermeasures against such attacks and illustrate their limitations, therefore demonstrating that phase-based ranging cannot be fully secured against distance decreasing attacks.
Optimal Incremental Parsing

This communication sets the problem of incremental parsing in the context of a complete incremental compiling system. It turns out that, according to the incrementally paradigm of the attribute evaluator and data-flow analyzer to be used, two definitions of optimal incrementality in a parser are possible. Algorithms for achieving both forms of optimality are given, both of them based on ordinary LALR(1) parse tables. Optimality and correctness proofs, which are merely outlined in this communication, are made intuitive thanks to the concept of a well-formed list of threaded trees, a natural extension of the concept of threaded tree found in earlier works on incremental parsing.
Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition

Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word "embeddings".



(i) To create a highly accurate DNR and CCE system that avoids conventional, time-consuming feature engineering. (ii) To create richer, more specialized word embeddings by using health domain datasets such as MIMIC-III. (iii) To evaluate our systems over three contemporary datasets.



Two deep learning methods, namely the Bidirectional LSTM and the Bidirectional LSTM-CRF, are evaluated. A CRF model is set as the baseline to compare the deep learning systems to a traditional machine learning approach. The same features are used for all the models.



We have obtained the best results with the Bidirectional LSTM-CRF model, which has outperformed all previously proposed systems. The specialized embeddings have helped to cover unusual words in DrugBank and MedLine, but not in the i2b2/VA dataset.



We present a state-of-the-art system for DNR and CCE. Automated word embeddings has allowed us to avoid costly feature engineering and achieve higher accuracy. Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary.
Low Cost Indoor Positioning System

This report describes a low cost indoor position sensing system utilising a combination of radio frequency and ultrasonics. Using a single rf transmitter and four ceiling mounted ultrasonic transmitters it provides coverage in a typical room in an area greater than 8m by 8m. As well as finding position within a room, it uses data encoded into the rf signal to determine the relevant web server for a building, and which floor and room the user is in. It is intended to be used primarily by wearable/mobile computers, though it has also been extended for use as a tracking system.
Nanoparticle therapeutics: a personal perspective.

Nanotechnology offers many potential benefits to cancer research through passive and active targeting, increased solubility/bioavailablility, and novel therapies. However, preclinical characterization of nanoparticles is complicated by the variety of materials, their unique surface properties, reactivity, and the task of tracking the individual components of multicomponent, multifunctional nanoparticle therapeutics in in vivo studies. There are also regulatory considerations and scale-up challenges that must be addressed. Despite these hurdles, cancer research has seen appreciable improvements in efficacy and quite a decrease in the toxicity of chemotherapeutics because of 'nanotech' formulations, and several engineered nanoparticle clinical trials are well underway. This article reviews some of the challenges and benefits of nanomedicine for cancer therapeutics and diagnostics.
The biology of cancer stem cells.

Cancers originally develop from normal cells that gain the ability to proliferate aberrantly and eventually turn malignant. These cancerous cells then grow clonally into tumors and eventually have the potential to metastasize. A central question in cancer biology is, which cells can be transformed to form tumors? Recent studies elucidated the presence of cancer stem cells that have the exclusive ability to regenerate tumors. These cancer stem cells share many characteristics with normal stem cells, including self-renewal and differentiation. With the growing evidence that cancer stem cells exist in a wide array of tumors, it is becoming increasingly important to understand the molecular mechanisms that regulate self-renewal and differentiation because corruption of genes involved in these pathways likely participates in tumor growth. This new paradigm of oncogenesis has been validated in a growing list of tumors. Studies of normal and cancer stem cells from the same tissue have shed light on the ontogeny of tumors. That signaling pathways such as Bmi1 and Wnt have similar effects in normal and cancer stem cell self-renewal suggests that common molecular pathways regulate both populations. Understanding the biology of cancer stem cells will contribute to the identification of molecular targets important for future therapies.
Applying machine learning techniques to predict the properties of energetic materials

We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with ≈300 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.
Reconstruction in extensive axillary Hidradenitis suppurativa with local fasciocutaneous V-Y advancement flaps

We present our experience with the use of local fasciocutaneous V-Y advancement flaps in the reconstruction of 10 axillae in 6 patients for large defects following wide excision of long-standing Hidradenitis suppurativa of the axilla. The defects were closed with local V-Y subcutaneous island flaps. A single flap from the chest wall was sufficient for moderate defects. However, for larger defects, an additional flap was taken from the medial side of the ipsilateral arm. The donor defects could be closed primarily in all the patients. The local areas of the lateral chest wall and the medial side of the arm have a plentiful supply of cutaneous perforators and the flaps can be designed in a V-Y fashion without resorting to preoperative marking of the perforator. The flaps were freed sufficiently to allow adequate movement for closure of the defects. Although no attempt was made to identify the perforators specifically, many perforators were seen entering the flap. Some perforators can be safely divided to increase reach of the flap. All the flaps survived completely. A follow up of 2.5 years is presented.
Automatic calibration of RGBD and thermal cameras

Reasoning about a scene's thermal signature, in addition to its visual appearance and spatial configuration, would facilitate significant advances in perceptual systems. Applications involving the segmentation and tracking of persons, vehicles, and other heat-emitting objects, for example, could benefit tremendously from even coarsely accurate relative temperatures. With the increasing affordability of commercially available thermal cameras, as well as the imminent introduction of new, mobile form factors, such data will be readily and widely accessible. However, in order for thermal processing to complement existing methods in RGBD, there must be an effective procedure for calibrating RGBD and thermal cameras to create RGBDT (red, green, blue, depth, and thermal) data. In this paper, we present an automatic method for the synchronization and calibration of RGBD and thermal cameras in arbitrary environments. While traditional calibration methods fail in our multimodal setting, we leverage invariant features visible by both camera types. We first synchronize the streams with a simple optimization procedure that aligns their motion statistic time series. We then find the relative poses of the cameras by minimizing an objective that measures the alignment between edge maps from the two streams. In contrast to existing methods that use special calibration targets with key points visible to both cameras, our method requires nothing more than some edges visible to both cameras, such as those arising from humans. We evaluate our method and demonstrate that it consistently converges to the correct transform and that it results in high-quality RGBDT data.
A Syntactic Approach for Handwritten Mathematical Formula Recognition

Mathematical formulas are good examples of two-dimensional patterns as well as pictures or graphics. The use of syntactic methods is useful for interpreting such complex patterns. In this paper we propose a system for the interpretation of 2-D mathematic formulas based on a syntactic parser. This system is able to recognize a large class of 2-D mathematic formulas written on a graphic tablet. It starts the parsing by localization of the ``principal'' operator in the formula and attempts to partition it into subexpressions which are similarly analyzed by looking for a starting character. The generalized parser used in the system has been developed in our group for continuous speech recognition and picture interpretation.
Complexity leadership: a healthcare imperative.

The healthcare system is plagued with increasing cost and poor quality outcomes. A major contributing factor for these issues is that outdated leadership practices, such as leader-centricity, linear thinking, and poor readiness for innovation, are being used in healthcare organizations.



Complexity leadership theory provides a new framework with which healthcare leaders may practice leadership. Complexity leadership theory conceptualizes leadership as a continual process that stems from collaboration, complex systems thinking, and innovation mindsets.



Compared to transactional and transformational leadership concepts, complexity leadership practices hold promise to improve cost and quality in health care.
Acoustic target impact point identification system

This paper proposes a new target impact point estimation system using acoustic sensors. The proposed system estimates projectile trajectory where it hits a target plane by detecting shock wave created by the passage of a supersonic projectile near the target. The method first measures TDOA (Time Delay Of Arrival) of the shock wave from the two sets of acoustic sensors of the equilateral triangular shape arranged horizontally under the target. Then the acoustic hit coordinate on the target is calculated using triangulation method. The performance of the proposed algorithm was confirmed by comparing the actual impact point with the estimated coordinates of the impact point calculated by proposed algorithm through the actual shooting experiments.