nils-herrmann/clean_scirepeval huggingface dataset:
A Wide-Band CMOS to Waveguide Transition at mm-Wave Frequencies With Wire-Bonds

In this paper a two-step transition between a CMOS chip and a WR-10 waveguide is investigated. The transition between a coplanar waveguide (CPW) on Duroid to W-band aluminum waveguide is studied and measured results show a bandwidth of 49% (67-110 GHz), an average insertion loss of 0.35 dB, and return loss below -10 dB throughout the entire band. Wire-bonding is then investigated as a connection between on-chip CMOS ground-signal-ground (GSG) pads to the Duroid CPW where the CMOS to printed circuit board transition measured results show an average 1-dB loss (including the CMOS GSG launcher) in the W-band. The full transition is also used to demonstrate packaging of a 100-GHz voltage-controlled oscillator in 90-nm CMOS where the RF signal was transmitted successfully with an average 2.5-dB loss (which includes a 2-cm Duroid CPW line) compared to on-chip probing measurement results.
Gender differences in online and offline self-disclosure in pre-adolescence and adolescence.

Although there is developmental research on the prevalence of offline self-disclosure in pre-adolescence and adolescence, it is still unknown (a) how boys' and girls'online self-disclosure develops in this period and (b) how online and offline self-disclosure interact with each other. We formulated three hypotheses to explain the possible interaction between online and offline self-disclosure: the displacement, the rich-get-richer, and the rehearsal hypothesis. We surveyed 690 pre-adolescents and adolescents (10-17 years) at three time points with half-year intervals in between. We found significant gender differences in the developmental trajectories of self-disclosure. For girls, both online and offline self-disclosure increased sharply during pre- (10-11 years) and early adolescence (12-13 years), and then stabilized in middle and late adolescence. For boys, the same trajectory was found although the increase in self-disclosure started 2 years later. We found most support for the rehearsal hypothesis: Both boys and girls seemed to use online self-disclosure to rehearse offline self-disclosure skills. This particularly held for boys in early adolescence who typically have difficulty disclosing themselves offline.
Characterizing Machines and Workloads on a Google Cluster

Cloud computing offers high scalability, flexibility and cost-effectiveness to meet emerging computing requirements. Understanding the characteristics of real workloads on a large production cloud cluster benefits not only cloud service providers but also researchers and daily users. This paper studies a large-scale Google cluster usage trace dataset and characterizes how the machines in the cluster are managed and the workloads submitted during a 29-day period behave. We focus on the frequency and pattern of machine maintenance events, job- and task-level workload behavior, and how the overall cluster resources are utilized.
Learning to fly by crashing

How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid obstacles? One approach is to use a small dataset collected by human experts: however, high capacity learning algorithms tend to overfit when trained with little data. An alternative is to use simulation. But the gap between simulation and real world remains large especially for perception problems. The reason most research avoids using large-scale real data is the fear of crashes! In this paper, we propose to bite the bullet and collect a dataset of crashes itself! We build a drone whose sole purpose is to crash into objects: it samples naive trajectories and crashes into random objects. We crash our drone 11,500 times to create one of the biggest UAV crash dataset. This dataset captures the different ways in which a UAV can crash. We use all this negative flying data in conjunction with positive data sampled from the same trajectories to learn a simple yet powerful policy for UAV navigation. We show that this simple self-supervised model is quite effective in navigating the UAV even in extremely cluttered environments with dynamic obstacles including humans. For supplementary video see:
Automatic Driver Assistance System-Glare Free High Beam Control

At present automobiles upgrades with different technologies for assisting and providing safety for the occupant. Due to this a system known as Automatic Driver Assistance System (ADAS) proposed many systems which aim to help the driver in controlling the vehicle and to drive safely during night time. One of the systems of ADAS is Glare Free High Beam (GFHB) control. This provides the information of the oncoming vehicles and preceding vehicles based on camera, which slices out the high beam and shades spot when it detects the object by avoiding the glare and provides the driver with greater visibility. It makes use of the various sensors, actuator, image processor and microcontroller to detect an object. In this paper, unit level testing is done for different cases in Embedded C, simulated in Visual Studio and results are displayed using GUI.
End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies

Steering a car through traffic is a complex task that is difficult to cast into algorithms. Therefore, researchers turn to training artificial neural networks from front-facing camera data stream along with the associated steering angles. Nevertheless, most existing solutions consider only the visual camera frames as input, thus ignoring the temporal relationship between frames. In this work, we propose a Convolutional Long Short-Term Memory Recurrent Neural Network (C-LSTM), that is end-to-end trainable, to learn both visual and dynamic temporal dependencies of driving. Additionally, We introduce posing the steering angle regression problem as classification while imposing a spatial relationship between the output layer neurons. Such method is based on learning a sinusoidal function that encodes steering angles. To train and validate our proposed methods, we used the publicly available Comma.ai dataset. Our solution improved steering root mean square error by 35% over recent methods, and led to a more stable steering by 87%.
A cross-program investigation of students' perceptions of agile methods

Research was conducted on using agile methods in software engineering education. This paper explores the perceptions of students from five different academic levels of agile practices. Information has been gathered through the collection of quantitative and qualitative data over three academic years, and analysis reveals student experiences, mainly positive but also some negative. Student opinions indicate the preference to continue to use agile practices at the workplace if allowed. A way these findings may potentially be extrapolated to the industrial settings is discussed. Finally, this report should encourage other academics considering adoption of agile methods in their computer science or software engineering curricula.
Loughborough University Institutional Repository Automated sizing of coarse-grained sediments : image-processing procedures

This is the first in a pair of papers in which we present image-processing-based procedures for the measurement of fluvial gravels. The spatial and temporal resolution of surface grain-size characterization is constrained by the time-consuming and costly nature of traditional measurement techniques. Several groups have developed image-processing-based procedures, but none have demonstrated the transferability of these techniques between sites with different lithological, clast form and textural characteristics. Here we focus on image-processing procedures for identifying and measuring image objects (i.e. grains); the second paper examines the application of such procedures to the measurement of fluvially deposited gravels. Four image-segmentation procedures are developed, each having several internal parameters, giving a total of 416 permutations. These are executed on 39 images from three field sites at which the clasts have contrasting physical properties. The performance of each procedure is evaluated against a sample of manually digitized grains in the same images, by comparing three derived statistics. The results demonstrate that it is relatively straightforward to develop procedures that satisfactorily identify objects in any single image or a set of images with similar sedimentary characteristics. However, the optimal procedure is that which gives consistently good results across sites with dissimilar sedimentary characteristics. We show that neighborhood-based operations are the most powerful, and a morphological bottom-hat transform with a double threshold is optimal. It is demonstrated that its performance approaches that of the procedures giving the best results for individual sites. Overall, it out-performs previously published, or improvements to previously published, methods.
EmEx, a Tool for Automated Emotive Face Recognition Using Convolutional Neural Networks

The work described in this paper represents the study and the attempt to make a contribution to one of the most stimulating and promising sectors in the field of emotion recognition, which is health care management. Multidisciplinary studies in artificial intelligence, augmented reality and psychology stressed out the importance of emotions in communication and awareness. The intent is to recognize human emotions, processing images streamed in real-time from a mobile device. The adopted techniques involve the use of open source libraries of visual recognition and machine learning approaches based on convolutional neural networks (CNN).
Automated Detection of Logical Errors in Programs

Research and industrial experience reveal that code reviews as a part of software inspection might be the most cost-effective technique a team can use to reduce defects. Tools that automate code inspection mostly focus on the detection of a priori known defect patterns and security vulnerabilities. Automated detection of logical errors, due to a faulty implementation of applications’ functionality is a relatively uncharted territory. Automation can be based on profiling the intended behavior behind the source code. In this paper, we present a new code profiling method that combines an information flow analysis, the crosschecking of dynamic invariants with symbolic execution, and the use of fuzzy logic. Our goal is to detect logical errors and exploitable vulnerabilities. The theoretical underpinnings and the practical implementation of our approach are discussed. We test the APP_LogGIC tool that implements the proposed analysis on two realworld applications. The results show that profiling the intended program behavior is feasible in diverse applications. We discuss the heuristics used to overcome the problem of state space explosion and of the large data sets. Code metrics and test results are provided to demonstrate the effectiveness of the approach.
Clusters, language models, and ad hoc information retrieval

The language-modeling approach to information retrieval provides an effective statistical framework for tackling various problems and often achieves impressive empirical performance. However, most previous work on language models for information retrieval focused on document-specific characteristics, and therefore did not take into account the structure of the surrounding corpus, a potentially rich source of additional information. We propose a novel algorithmic framework in which information provided by document-based language models is enhanced by the incorporation of information drawn from clusters of similar documents. Using this framework, we develop a suite of new algorithms. Even the simplest typically outperforms the standard language-modeling approach in terms of mean average precision (MAP) and recall, and our new interpolation algorithm posts statistically significant performance improvements for both metrics over all six corpora tested. An important aspect of our work is the way we model corpus structure. In contrast to most previous work on cluster-based retrieval that partitions the corpus, we demonstrate the effectiveness of a simple strategy based on a nearest-neighbors approach that produces overlapping clusters.
Certifying algorithms for recognizing interval graphs and permutation graphs

A certifying algorithm for a decision problem is an algorithm that provides a certificate with each answer that it produces. The certificate is a piece of evidence that proves that the answer has not been compromised by a bug in the implementation. We give linear-time certifying algorithms for recognition of interval graphs and permutation graphs. Previous algorithms fail to provide supporting evidence when they claim that the input graph is not a member of the class. We show that our certificates of non-membership can be authenticated in O(|V|) time.
Self-Adaptive Skin Segmentation in Color Images

In this paper, we present a new method for skin detection and segmentation, relying on spatial analysis of skin-tone pixels. Our contribution lies in introducing self-adaptive seeds, from which the skin probability is propagated using the distance transform. The seeds are determined from a local skin color model that is learned on-line from a presented image, without requiring any additional information. This is in contrast to the existing methods that need a skin sample for the adaptation, e.g., acquired using a face detector. In our experimental study, we obtained F-score of over 0.85 for the ECU benchmark, and this is highly competitive compared with several state-of-the-art methods.
Improving Document Clustering for Short Texts by Long Documents via a Dirichlet Multinomial Allocation Model

Document clustering for short texts has received considerable interest. Traditional document clustering approaches are designed for long documents and perform poorly for short texts due to the their sparseness representation. To better understand short texts, we observe that words that appear in long documents can enrich short text context and improve the clustering performance for short texts. In this paper, we propose a novel model, namely DDMAfs, which 1) improves the clustering performance of short texts by sharing structural knowledge of long documents to short texts; 2) automatically identifies the number of clusters; 3) separates discriminative words from irrelevant words for long documents to obtain high quality structural knowledge. Our experiments indicate that the DDMAfs model performs well on the synthetic dataset and real datasets. Comparisons between the DDMAfs model and state-of-the-art short text clustering approaches show that the DDMAfs model is effective.
Percutaneous left atrial appendage closure for stroke prophylaxis in patients with atrial fibrillation: 2.3-Year Follow-up of the PROTECT AF (Watchman Left Atrial Appendage System for Embolic Protection in Patients with Atrial Fibrillation) Trial.

The multicenter PROTECT AF study (Watchman Left Atrial Appendage System for Embolic Protection in Patients With Atrial Fibrillation) was conducted to determine whether percutaneous left atrial appendage closure with a filter device (Watchman) was noninferior to warfarin for stroke prevention in atrial fibrillation.



Patients (n=707) with nonvalvular atrial fibrillation and at least 1 risk factor (age >75 years, hypertension, heart failure, diabetes, or prior stroke/transient ischemic attack) were randomized to either the Watchman device (n=463) or continued warfarin (n=244) in a 2:1 ratio. After device implantation, warfarin was continued for ≈45 days, followed by clopidogrel for 4.5 months and lifelong aspirin. Study discontinuation rates were 15.3% (71/463) and 22.5% (55/244) for the Watchman and warfarin groups, respectively. The time in therapeutic range for the warfarin group was 66%. The composite primary efficacy end point included stroke, systemic embolism, and cardiovascular death, and the primary analysis was by intention to treat. After 1588 patient-years of follow-up (mean 2.3±1.1 years), the primary efficacy event rates were 3.0% and 4.3% (percent per 100 patient-years) in the Watchman and warfarin groups, respectively (relative risk, 0.71; 95% confidence interval, 0.44%-1.30% per year), which met the criteria for noninferiority (probability of noninferiority >0.999). There were more primary safety events in the Watchman group (5.5% per year; 95% confidence interval, 4.2%-7.1% per year) than in the control group (3.6% per year; 95% confidence interval, 2.2%-5.3% per year; relative risk, 1.53; 95% confidence interval, 0.95-2.70).



The "local" strategy of left atrial appendage closure is noninferior to "systemic" anticoagulation with warfarin. PROTECT AF has, for the first time, implicated the left atrial appendage in the pathogenesis of stroke in atrial fibrillation.
A 120 dB Dynamic Range 400 mW Class-D Speaker Driver With Fourth-Order PWM Modulator

High efficiency Class-D speaker drivers have become ubiquitous in battery driven mobile audio applications. Analog switching errors and timing jitter are known limitations to the dynamic range in these types of switching audio drivers. To overcome these limitations the Class-D architecture chosen in this paper provides a low 667 kHz pulse rate. This architecture is based on a 4th-order digital modulator with pulse-width modulation (PWM) feedback. The modulator loop processes the PWM signals at a high 24 MHz clock rate, and provides pulse edge timing resolution within a single clock period of the high-rate clock. Timing distortion errors due to uniform sampling of the low-rate PWM signals are reduced with the high forward loop gain of the digital modulator feedback loop. The driver receives a 22-bit digital audio input and combines the functionality of a high-fidelity digital-to-analog converter (DAC) with a speaker driver. It achieves 120 dB dynamic range over a 20 kHz bandwidth, provides 88% power efficiency while driving an 8 Ω speaker load, and consumes a total quiescent current of 2.4 mA.
Cultural Differences in Collaborative Authoring of Wikipedia

This article explores the relationship between national culture and computer-mediated communication (CMC) in Wikipedia. The articles on the topic game from the French, German, Japanese, and Dutch Wikipedia websites were studied using content analysis methods. Correlations were investigated between patterns of contributions and the four dimensions of cultural influences proposed by Hofstede (Power Distance, Collectivism versus Individualism, Femininity versus Masculinity, and Uncertainty Avoidance). The analysis revealed cultural differences in the style of contributions across the cultures investigated, some of which are correlated with the dimensions identified by Hofstede. These findings suggest that cultural differences that are observed in the physical world also exist in the virtual world. doi:10.1111/j.1083-6101.2006.00316.x
Decision Support Systems Using Intelligent Paradigms

Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of Soft Computing (SC) technologies that underlie the conception, design and utilization of intelligent systems. In this paper, we present different SC paradigms involving an artificial neural network trained using the scaled conjugate gradient algorithm, two different fuzzy inference methods optimised using neural network learning/evolutionary algorithms and regression trees for developing intelligent decision support systems. We demonstrate the efficiency of the different algorithms by developing a decision support system for a Tactical Air Combat Environment (TACE). Some empirical comparisons between the different algorithms are also provided.
Accelerating Apache Hive with MPI for Data Warehouse Systems

Data warehouse systems, like Apache Hive, have been widely used in the distributed computing field. However, current generation data warehouse systems have not fully embraced High Performance Computing (HPC) technologies even though the trend of converging Big Data and HPC is emerging. For example, in traditional HPC field, Message Passing Interface (MPI) libraries have been optimized for HPC applications during last decades to deliver ultra-high data movement performance. Recent studies, like DataMPI, are extending MPI for Big Data applications to bridge these two fields. This trend motivates us to explore whether MPI can benefit data warehouse systems, such as Apache Hive. In this paper, we propose a novel design to accelerate Apache Hive by utilizing DataMPI. We further optimize the DataMPI engine by introducing enhanced non-blocking communication and parallelism mechanisms for typical Hive workloads based on their communication characteristics. Our design can fully and transparently support Hive workloads like Intel HiBench and TPC-H with high productivity. Performance evaluation with Intel HiBench shows that with the help of light-weight DataMPI library design, efficient job start up and data movement mechanisms, Hive on DataMPI performs 30% faster than Hive on Hadoop averagely. And the experiments on TPC-H with ORCFile show that the performance of Hive on DataMPI can improve 32% averagely and 53% at most more than that of Hive on Hadoop. To the best of our knowledge, Hive on DataMPI is the first attempt to propose a general design for fully supporting and accelerating data warehouse systems with MPI.
PostBL: Post-mesh Boundary Layer Mesh Generation Tool

A boundary layer mesh is a mesh with dense element distribution in the normal direction along specific boundaries. PostBL is a utility to generate boundary layers elements on an already existing mesh model. PostBL supports creation of hexahedral, prism, quad and tri boundary layer elements. It is a part of MeshKit, which is an open-source library for mesh generation functionalities. Generally, boundary layer mesh generation is a pre-meshing process; in this effort, we start from a model that has already been meshed. Boundary layers elements can be generated along the entire skin, selected exterior or internal surface boundaries. MeshKit uses graph-based approach for representing meshing operations; PostBL is one such meshing operation. It can be coupled to other meshing operations like Jaal, NetGen, TetGen, CAMAL and custom meshing tools like RGG. Simple examples demonstrating generation of boundary layers on different mesh types and OECD Vattenfall T-Junction benchmark hexahedral-mesh are presented.