nils-herrmann/clean_scirepeval huggingface dataset:
Re-embedding words

We present a fast method for re-purposing existing semantic word vectors to improve performance in a supervised task. Recently, with an increase in computing resources, it became possible to learn rich word embeddings from massive amounts of unlabeled data. However, some methods take days or weeks to learn good embeddings, and some are notoriously difficult to train. We propose a method that takes as input an existing embedding, some labeled data, and produces an embedding in the same space, but with a better predictive performance in the supervised task. We show improvement on the task of sentiment classification with respect to several baselines, and observe that the approach is most useful when the training set is sufficiently small.
An Energy-Efficient Architecture for the Internet of Things (IoT)

Internet of things (IoT) is a smart technology that connects anything anywhere at any time. Such ubiquitous nature of IoT is responsible for draining out energy from its resources. Therefore, the energy efficiency of IoT resources has emerged as a major research issue. In this paper, an energy-efficien t architecture for IoT has been proposed, which consists of three layers, namely, sensing and control, information processing, and presentation. The architectural design allows the system to predict the sleep interval of sensors based upon their remaining battery level, their previous usage history, and quality of information required for a particular application. The predicted value can be used to boost the utilization of cloud resources by reprovisioning the allocated resources when the corresponding sensory nodes are in sleep mode. This mechanism allows the energy-efficient utilization of all the IoT resources. The experimental results show a significant amount of energy saving in the case of sensor nodes and improved resource utilization of cloud resources.
A Low-Cost High-Performance Digital Radar Test Bed

This paper describes the design of a dual-channel S-band digital radar test bed. The test bed combines stretch processing with a novel and cost-effective hardware architecture that enables it to achieve an in-band dynamic range of 60 dB over 600 MHz of instantaneous bandwidth. The dual digital receiver channels allow for adaptive digital beamforming which can be used to mitigate a directional source of interference. Experimental test and verification results are presented to demonstrate system performance.
A classification and comparison of usability evaluation methods for interactive adaptive systems

Usability is a crucial factor for interactive adaptive system success. Several methods have been proposed to support the usability evaluation of interactive adaptive systems. However, the diversity of these methods leaves the evaluators wondering what would be the most appropriate usability evaluation methods for their specific goals. This paper examines the characteristics of usability evaluation methods for interactive adaptive systems and identifies the most relevant features for categorizing them. The comparison and classification of these methods will serve as a reference for researchers in the field of interactive adaptive systems and as a guide for the choice of suitable usability evaluation methods.
A Detection Statistic for Random-Valued Impulse Noise

This paper proposes an image statistic for detecting random-valued impulse noise. By this statistic, we can identify most of the noisy pixels in the corrupted images. Combining it with an edge-preserving regularization, we obtain a powerful two-stage method for denoising random-valued impulse noise, even for noise levels as high as 60%. Simulation results show that our method is significantly better than a number of existing techniques in terms of image restoration and noise detection
Internet of Things ( IoT ) Security : Current Status , Challenges and Countermeasures

This paper presents a survey and an analysis of the current status and concerns of Internet of things (IoT) security. The IoT framework aspires to connect anyone with anything, anywhere. As opposed to the traditional Internet, in addition to humans, an IoT connects a large number of machines, resourceconstrained devices and sensors using heterogeneous wired and wireless networks. An IoT typically has a three conceptual layers consisting of Perception, Network, and Application layers. This paper describes security issues within and across these layers. Many security principles that should be enforced at each layer are also presented. Previous work specific to enforcing security for each IoT layer and corresponding countermeasures are also discussed. Finally, the paper presents future directions for securing the IoT.
Automatic Source Code Summarization of Context for Java Methods

Source code summarization is the task of creating readable summaries that describe the functionality of software. Source code summarization is a critical component of documentation generation, for example as Javadocs formed from short paragraphs attached to each method in a Java program. At present, a majority of source code summarization is manual, in that the paragraphs are written by human experts. However, new automated technologies are becoming feasible. These automated techniques have been shown to be effective in select situations, though a key weakness is that they do not explain the source code's context. That is, they can describe the behavior of a Java method, but not why the method exists or what role it plays in the software. In this paper, we propose a source code summarization technique that writes English descriptions of Java methods by analyzing how those methods are invoked. We then performed two user studies to evaluate our approach. First, we compared our generated summaries to summaries written manually by experts. Then, we compared our summaries to summaries written by a state-of-the-art automatic summarization tool. We found that while our approach does not reach the quality of human-written summaries, we do improve over the state-of-the-art summarization tool in several dimensions by a statistically-significant margin.
Integrated force and distance sensing using elastomer-embedded commodity proximity sensors

We describe a combined force and distance sensor using a commodity infrared distance sensor embedded in a transparent elastomer with applications in robotic manipulation. Prior to contact, the sensor works as a distance sensor (0–10 cm), whereas after contact the material doubles as a spring, with force proportional to the compression of the elastomer (0– 5 N). We describe its principle of operation and design parameters, including polymer thickness, mixing ratio, and emitter current, and show that the sensor response has an inflection point at contact that is independent of an object’s surface properties. We then demonstrate how two arrays of eight sensors, each mounted on a standard Baxter gripper, can be used to (1) improve gripper alignment during grasping, (2) determine contact points with objects, and (3) obtain crude 3D models that can serve to determine possible grasp locations.
DeltaCFS: Boosting Delta Sync for Cloud Storage Services by Learning from NFS

Cloud storage services, such as Dropbox, iCloud Drive, Google Drive, and Microsoft OneDrive, have greatly facilitated users’ synchronizing files across heterogeneous devices. Among them, Dropbox-like services are particularly beneficial owing to the delta sync functionality that strives towards greater network-level efficiency. However, when delta sync trades computation overhead for network-traffic saving, the tradeoff could be highly unfavorable under some typical workloads. We refer to this problem as the abuse of delta sync. To address this problem, we propose DeltaCFS, a novel file sync framework for cloud storage services by learning from the design of conventional NFS (Network File System). Specifically, we combine delta sync with NFS-like file RPC in an adaptive manner, thus significantly cutting computation overhead on both the client and server sides while preserving the network-level efficiency. DeltaCFS also enables a neat design for guaranteeing causal consistency and fine-grained version control of files. In our FUSE-based prototype system (which is open-source), DeltaCFS outperforms Dropbox by generating up to 11x less data transfer and up to 100x less computation overhead under concerned workloads.
Supplemental Document for Deep Photo Style Transfer

This document contains: links to two user studies (section 1), comparison against Wu et al.  (section 2), results with only semantic segmentation or photorealism regularization (section 3), merging classes for DilatedNet  Segmentation (section 4), a solution for handling noisy (section 5) or high-resolution (section 6) input, an extension for CNNMRF in photographic transfer (section 7), and some ideas we came up with, but ultimately did not work well, before reaching the matting Laplacian solution (section 8).
Analysis of a laboratory scale three-phase FC-TCR-based static VAr compensator

This paper presents the design and steady-state analysis of a Fixed Capacitor-Thyristor Controlled Reactor (FC-TCR)-based Static VAr Compensator (SVC). Reactive power compensation is demonstrated through the fundamental frequency analysis of the samples acquired from the designed system. The performance of the SVC in the presence of line reactance is also discussed. National Instrument (NI) based data acquisition system is used to perform the steady-state analysis. Besides, a few transient responses are also captured using the data acquisition system.
Scar revision

Most surgical patients end up with a scar and most of these would want at least some improvement in the appearance of the scar. Using sound techniques for wound closure surgeons can, to a certain extent, prevent suboptimal scars. This article reviews the principles of prevention and treatment of suboptimal scars. Surgical techniques of scar revision, i.e., Z plasty, W plasty, and geometrical broken line closure are described. Post-operative care and other adjuvant therapies of scars are described. A short description of dermabrasion and lasers for management of scars is given. It is hoped that this review helps the surgeon to formulate a comprehensive plan for management of scars of these patients.
ESD Design Strategies for High-Speed Digital and RF Circuits in Deeply Scaled Silicon Technologies

Challenges of electrostatic discharge (ESD) protection in deeply scaled silicon technologies are addressed by improving design, characterization, and modeling of I/O MOSFETs, interconnect, ESD protection and power clamp devices. Recent progress on ESD protection design for both high-speed digital I/O and radiofrequency (RF) circuits are presented. Topological trade-offs are compared. High speed circuit protection techniques such as the T-coil based ESD design are reviewed in detail. Package- and wafer-level charged device model (CDM) correlation issues are discussed. I/O, ESD devices, and metal interconnect effects are examined using very fast transmission line pulses (VF-TLP) and TLP.
Smart music player integrating facial emotion recognition and music mood recommendation

Songs, as a medium of expression, have always been a popular choice to depict and understand human emotions. Reliable emotion based classification systems can go a long way in helping us parse their meaning. However, research in the field of emotion-based music classification has not yielded optimal results. In this paper, we present an affective cross-platform music player, EMP, which recommends music based on the real-time mood of the user. EMP provides smart mood based music recommendation by incorporating the capabilities of emotion context reasoning within our adaptive music recommendation system. Our music player contains three modules: Emotion Module, Music Classification Module and Recommendation Module. The Emotion Module takes an image of the user's face as an input and makes use of deep learning algorithms to identify their mood with an accuracy of 90.23%. The Music Classification Module makes use of audio features to achieve a remarkable result of 97.69% while classifying songs into 4 different mood classes. The Recommendation Module suggests songs to the user by mapping their emotions to the mood type of the song, taking into consideration the preferences of the user.
Isogeometric Analysis using T-splines

We explore T-splines, a generalization of NURBS enabling lo cal refinement, as a basis for isogeometric analysis. We review T-splines as a surface design methodology and then develop it for engineering analysis applications. We test T plines on some elementary twodimensional and three-dimensional fluid and structural ana ysis problems and attain good results in all cases. We summarize current limitations and f uture opportunities.
A Survey on Resource Management in IoT Operating Systems

Recently, the Internet of Things (IoT) concept has attracted a lot of attention due to its capability to translate our physical world into a digital cyber world with meaningful information. The IoT devices are smaller in size, sheer in number, contain less memory, use less energy, and have more computational capabilities. These scarce resources for IoT devices are powered by small operating systems (OSs) that are specially designed to support the IoT devices’ diverse applications and operational requirements. These IoT OSs are responsible for managing the constrained resources of IoT devices efficiently and in a timely manner. In this paper, discussions on IoT devices and OS resource management are provided. In detail, the resource management mechanisms of the state-of-the-art IoT OSs, such as Contiki, TinyOS, and FreeRTOS, are investigated. The different dimensions of their resource management approaches (including process management, memory management, energy management, communication management, and file management) are studied, and their advantages and limitations are highlighted.
BIM-based Integrated Framework for Detailed Cost Estimation and Schedule Planning of Construction Projects

With the increasing popularity of Building Information Modeling (BIM), several research studies have focused on its application in the construction industry, and advancements such as developments of BIM-based tools for construction scheduling and cost estimating have been carried out. Nevertheless, these tools are each developed only for one particular aspect of construction management, such as scheduling or cost estimating. As such, most are still limited to the product element level, rather than the construction operation level. This paper thus presents a BIM-based integrated framework for detailed cost estimation and schedule planning of construction projects. In the proposed framework, a BIM product model developed in Autodesk Revit, is integrated with a construction process model retrieved from RS Means, with the objective of generating detailed cost estimation and a workface construction schedule simultaneously. In this paper, the literature pertaining to BIM, cost estimating and scheduling is surveyed; then, the proposed framework is presented, and a simple building project is used as a case study to facilitate understanding of and verify the applicability of the integrated framework. Finally, findings from the implementation of the proposed framework are summarized.
Scholarly use of information: graduate students' information seeking behaviour

This study explored graduate students' information behaviour related to their process of inquiry and scholarly activities.  In depth, semi-structured interviews were conducted with one hundred graduate students representing all disciplines and departments from Carnegie Mellon University.  Working in pairs, we coded transcripts of interviews into meaningful categories using ATLAS.ti software. The combined use of quantitative and qualitative analysis aimed to reduce subjectivity.  Graduate students often begin with a meeting with professors who provide direction, recommend and provide resources. Other students help to shape graduate students' research activities, and university library personnel provide guidance in finding resources. The Internet plays a major role, although students continue to use print resources. Convenience, lack of sophistication in finding and using resources and course requirements affect their information behaviour. Findings vary across disciplines and between programmes.  Libraries can influence students' information behaviour by re-evaluating their instructional programmes and provision of resources and services. They can take a lead by working with academic staff to guide students.
Eye movement analysis for activity recognition

In this work we investigate eye movement analysis as a new modality for recognising human activity. We devise 90 different features based on the main eye movement characteristics: saccades, fixations and blinks. The features are derived from eye movement data recorded using a wearable electrooculographic (EOG) system. We describe a recognition methodology that combines minimum redundancy maximum relevance feature selection (mRMR) with a support vector machine (SVM) classifier. We validate the method in an eight participant study in an office environment using five activity classes: copying a text, reading a printed paper, taking hand-written notes, watching a video and browsing the web. In addition, we include periods with no specific activity. Using a person-independent (leave-one-out) training scheme, we obtain an average precision of 76.1% and recall of 70.5% over all classes and participants. We discuss the most relevant features and show that eye movement analysis is a rich and thus promising modality for activity recognition.
Classification and adulteration detection of vegetable oils based on fatty acid profiles.

The detection of adulteration of high priced oils is a particular concern in food quality and safety. Therefore, it is necessary to develop authenticity detection method for protecting the health of customers. In this study, fatty acid profiles of five edible oils were established by gas chromatography coupled with mass spectrometry (GC/MS) in selected ion monitoring mode. Using mass spectral characteristics of selected ions and equivalent chain length (ECL), 28 fatty acids were identified and employed to classify five kinds of edible oils by using unsupervised (principal component analysis and hierarchical clustering analysis), supervised (random forests) multivariate statistical methods. The results indicated that fatty acid profiles of these edible oils could classify five kinds of edible vegetable oils into five groups and are therefore employed to authenticity assessment. Moreover, adulterated oils were simulated by Monte Carlo method to establish simultaneous adulteration detection model for five kinds of edible oils by random forests. As a result, this model could identify five kinds of edible oils and sensitively detect adulteration of edible oil with other vegetable oils about the level of 10%.
A Mathematical Model and Calibration Procedure for Galvanometric Laser Scanning Systems

Laser galvanometric scanning systems are commonly used in various fields such as three dimensional scanning, medical imaging, material processing, measurement devices and laser display systems. The systems of such kind suffer from distortions. On top of that they do not have a center of projection, which makes it impossible to use common projector calibration procedures. The paper presents a novel mathematical model to predict the image distortions caused by galvanometric mirror scanning systems. In addition, we describe a calibration procedure for recovering its intrinsic and extrinsic parameters.
Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network

To develop a computer-aided method that reduces the variability of Cobb angle measurement for scoliosis assessment.



A deep neural network (DNN) was trained with vertebral patches extracted from spinal model radiographs. The Cobb angle of the spinal curve was calculated automatically from the vertebral slopes predicted by the DNN. Sixty-five in vivo radiographs and 40 model radiographs were analyzed. An experienced surgeon performed manual measurements on the aforementioned radiographs. Two examiners used both the proposed and the manual measurement methods to analyze the aforementioned radiographs.



For model radiographs, the intraclass correlation coefficients were greater than 0.98, and the mean absolute differences were less than 3°. This indicates that the proposed system showed high repeatability for measurements of model radiographs. For the in vivo radiographs, the reliabilities were lower than those from the model radiographs, and the differences between the computer-aided measurement and the manual measurement by the surgeon were higher than 5°.



The variability of Cobb angle measurements can be reduced if the DNN system is trained with enough vertebral patches. Training data of in vivo radiographs must be included to improve the performance of DNN.



Vertebral slopes can be predicted by DNN. The computer-aided system can be used to perform automatic measurements of Cobb angle, which is used to make reliable and objective assessments of scoliosis.
Robust Matrix Elastic Net based Canonical Correlation Analysis: An Effective Algorithm for Multi-View Unsupervised Learning

This paper presents a robust matrix elastic net based canonical correlation analysis (RMEN-CCA) for multiple view unsupervised learning problems, which emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN to distill naturally meaningful features without any prior assumption and to measure effectively correlations between different ’views’. We can further employ directly the kernel trick to extend the RMEN-CCA to the kernel scenario with theoretical guarantees, which takes advantage of the kernel trick for highly complicated nonlinear feature learning. Rather than simply incorporating existing regularization minimization terms into CCA, this paper provides a new learning paradigm for CCA and is the first to derive a coupled feature selection based CCA algorithm that guarantees convergence. More significantly, for CCA, the newly-derived RMEN-CCA bridges the gap between measurement of relevance and coupled feature selection. Moreover, it is nontrivial to tackle directly the RMEN-CCA by previous optimization approaches derived from its sophisticated model architecture. Therefore, this paper further offers a bridge between a new optimization problem and an existing efficient iterative approach. As a consequence, the RMEN-CCA can overcome the limitation of CCA and address large-scale and streaming data problems. Experimental results on four popular competing datasets illustrate that the RMEN-CCA performs more effectively and efficiently than do state-of-the-art approaches.
Designing inflatable structures

We propose an interactive, optimization-in-the-loop tool for designing inflatable structures. Given a target shape, the user draws a network of seams defining desired segment boundaries in 3D. Our method computes optimally-shaped flat panels for the segments, such that the inflated structure is as close as possible to the target while satisfying the desired seam positions. Our approach is underpinned by physics-based pattern optimization, accurate coarse-scale simulation using tension field theory, and a specialized constraint-optimization method. Our system is fast enough to warrant interactive exploration of different seam layouts, including internal connections, and their effects on the inflated shape. We demonstrate the resulting design process on a varied set of simulation examples, some of which we have fabricated, demonstrating excellent agreement with the design intent.