nils-herrmann/clean_scirepeval huggingface dataset:
Shades of gray: Opening up a software producing organization with the open software enterprise model

Software producing organizations are frequently judged by others for being ‘open’ or ‘closed’, where a more ‘closed’ organization is seen as being detrimental to its software ecosystem. These qualifications can harm the reputation of these companies, for they are deemed to promote vendor lock-in, use closed data formats, and are seen as using intellectual property laws to harm others. These judgements, however, are frequently based on speculation and the need arises for a method to establish openness of an organization, such that decisions are no longer based on prejudices, but on an objective assessment of the practices of a software producing organization. In this article the open software enterprise model is presented that roduct software vendors
Copper-graphene SPR-based biosensor for urea detection

Cu) is used as a plasmonic material whereas graphene is used to prevent Cu from oxidation and enhance the reflectance intensity. Numerical investigation is performed using finite-difference-time-domain (FDTD) method by comparing the sensing performance such as reflectance intensity that explains the sensor sensitivity and the full-width-at-half-maximum (FWHM) of the spectrum for detection accuracy. The measurements were observed with various Cu thin film thicknesses ranging from 20nm to 80nm with 785nm operating wavelength. The proposed sensor shows that the 40nm-thick Cu-graphene (1 layer) SPR-based sensor gave better performance with narrower plasmonic spectrum line width (reflectance intensity of 91.2%) and better FWHM of 3.08°. The measured results also indicate that the Cu-graphene SPR-based sensor is suitable for detecting urea with refractive index of 1.49 in dielectric medium.
Crowdsourcing Question-Answer Meaning Representations

We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We also develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions. A detailed qualitative analysis demonstrates that the crowd-generated question-answer pairs cover the vast majority of predicate-argument relationships in existing datasets (including PropBank, NomBank, QASRL, and AMR) along with many previously under-resourced ones, including implicit arguments and relations. The QAMR data and annotation code is made publicly available1 to enable future work on how best to model these complex phenomena.
Automatic Image Colorization Via Multimodal Predictions

We aim to color greyscale images automatically, without any manual intervention. The color proposition could then be interactively corrected by user-provided color landmarks if necessary. Automatic colorization is nontrivial since there is usually no one-to-one correspondence between color and local texture. The contribution of our framework is that we deal directly with multimodality and estimate, for each pixel of the image to be colored, the probability distribution of all possible colors, instead of choosing the most probable color at the local level. We also predict the expected variation of color at each pixel, thus defining a nonuniform spatial coherency criterion. We then use graph cuts to maximize the probability of the whole colored image at the global level. We work in the L-a-b color space in order to approximate the human perception of distances between colors, and we use machine learning tools to extract as much information as possible from a dataset of colored examples. The resulting algorithm is fast, designed to be more robust to texture noise, and is above all able to deal with ambiguity, in contrary to previous approaches.
A feather-weight virtual machine for windows applications

Many fault-tolerant and intrusion-tolerant systems require the ability to execute unsafe programs in a realistic environment without leaving permanent damages. Virtual machine technology meets this requirement perfectly because it provides an execution environment that is both realistic and isolated. In this paper, we introduce an OS level virtual machine architecture for Windows applications called Feather-weight Virtual Machine (FVM), under which virtual machines share as many resources of the host machine as possible while still isolated from one another and from the host machine. The key technique behind FVM is namespace virtualization, which isolates virtual machines by renaming resources at the OS system call interface. Through a copy-on-write scheme, FVM allows multiple virtual machines to physically share resources but logically isolate their resources from each other. A main technical challenge in FVM is how to achieve strong isolation among different virtual machines and the host machine, due to numerous namespaces and interprocess communication mechanisms on Windows. Experimental results demonstrate that FVM is more flexible and scalable, requires less system resource, incurs lower start-up and run-time performance overhead than existing hardware-level virtual machine technologies, and thus makes a compelling building block for security and fault-tolerant applications.
Maintaining connectivity in UAV swarm sensing

In many applications, Unmanned Aerial Vehicles (UAVs) provide an indispensable platform for gathering information about the situation on the ground. However, to maximise information gained about the environment, such platforms require increased autonomy to coordinate the actions of multiple UAVs. This has led to the development of flight planning and coordination algorithms designed to maximise information gain during sensing missions. However, these have so far neglected the need to maintain wireless network connectivity. In this paper, we address this limitation by enhancing an existing multi-UAV planning algorithm with two new features that together make a significant contribution to the state-of-the-art: (1) we incorporate an on-line learning procedure that enables UAVs to adapt to the radio propagation characteristics of their environment, and (2) we integrate flight path and network routing decisions, so that modelling uncertainty and the affect of UAV position on network performance is taken into account.
DetNet: A Backbone network for Object Detection

Recent CNN based object detectors, no matter one-stage methods like YOLO , SSD , and RetinaNet  or two-stage detectors like Faster R-CNN [5], R-FCN [6] and FPN [7] are usually trying to directly finetune from ImageNet pre-trained models designed for image classification. There has been little work discussing on the backbone feature extractor specifically designed for the object detection. More importantly, there are several differences between the tasks of image classification and object detection. (i) Recent object detectors like FPN and RetinaNet usually involve extra stages against the task of image classification to handle the objects with various scales. (ii) Object detection not only needs to recognize the category of the object instances but also spatially locate the position. Large downsampling factor brings large valid receptive field, which is good for image classification but compromises the object location ability. Due to the gap between the image classification and object detection, we propose DetNet in this paper, which is a novel backbone network specifically designed for object detection. Moreover, DetNet includes the extra stages against traditional backbone network for image classification, while maintains high spatial resolution in deeper layers. Without any bells and whistles, state-of-the-art results have been obtained for both object detection and instance segmentation on the MSCOCO benchmark based on our DetNet (4.8G FLOPs) backbone. The code will be released for the reproduction.
Are Men and Women Really That Different ? Examining Some of Sexual Strategies Theory ( SST ) ’ s Key Assumptions about Sex-Distinct Mating Mechanisms

Sexual Strategies Theory (SST; Buss and Schmitt 1993) suggests that, typically, men more so than women are more likely to spend proportionately more of their mating effort in short-term mating, lower their standards in short-term compared to long-term mating, feel reproductively constrained, and seek, but certainly not avoid, sex if pregnancy is likely in short-term relationships. A series of 4 survey studies each containing hundreds of college student participants from the western portion of the United States were conducted to test these hypotheses. The findings are inconsistent with SST but are consistent with Attachment Fertility Theory (AFT; Miller et al. 2005) that argues for relatively few evolved gender differences in mating strategies and preferences.
A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions

In recent years, there have been some interesting studies on predictive modeling in data streams. However, most such studies assume relatively balanced and stable data streams but cannot handle well rather skewed (e.g., few positives but lots of negatives) and stochastic distributions, which are typical in many data stream applications. In this paper, we propose a new approach to mine data streams by estimating reliable posterior probabilities using an ensemble of models to match the distribution over under-samples of negatives and repeated samples of positives. We formally show some interesting and important properties of the proposed framework, e.g., reliability of estimated probabilities on skewed positive class, accuracy of estimated probabilities, efficiency and scalability. Experiments are performed on several synthetic as well as real-world datasets with skewed distributions, and they demonstrate that our framework has substantial advantages over existing approaches in estimation reliability and predication accuracy.
A Performance Study on Different Cost Aggregation Approaches Used in Real-Time Stereo Matching

Many vision applications require high-accuracy dense disparity maps in real-time and online. Due to time constraint, most real-time stereo applications rely on local winner-takes-all optimization in the disparity computation process. These local approaches are generally outperformed by offline global optimization based algorithms. However, recent research shows that, through carefully selecting and aggregating the matching costs of neighboring pixels, the disparity maps produced by a local approach can be more accurate than those generated by many global optimization techniques. We are therefore motivated to investigate whether these cost aggregation approaches can be adopted in real-time stereo applications and, if so, how well they perform under the real-time constraint. The evaluation is conducted on a real-time stereo platform, which utilizes the processing power of programmable graphics hardware. Six recent cost aggregation approaches are implemented and optimized for graphics hardware so that real-time speed can be achieved. The performances of these aggregation approaches in terms of both processing speed and result quality are reported.
Optimizing schedules of retrieval practice for durable and efficient learning: how much is enough?

The literature on testing effects is vast but supports surprisingly few prescriptive conclusions for how to schedule practice to achieve both durable and efficient learning. Key limitations are that few studies have examined the effects of initial learning criterion or the effects of relearning, and no prior research has examined the combined effects of these 2 factors. Across 3 experiments, 533 students learned conceptual material via retrieval practice with restudy. Items were practiced until they were correctly recalled from 1 to 4 times during an initial learning session and were then practiced again to 1 correct recall in 1-5 subsequent relearning sessions (across experiments, more than 100,000 short-answer recall responses were collected and hand-scored). Durability was measured by cued recall and rate of relearning 1-4 months after practice, and efficiency was measured by total practice trials across sessions. A consistent qualitative pattern emerged: The effects of initial learning criterion and relearning were subadditive, such that the effects of initial learning criterion were strong prior to relearning but then diminished as relearning increased. Relearning had pronounced effects on long-term retention with a relatively minimal cost in terms of additional practice trials. On the basis of the overall patterns of durability and efficiency, our prescriptive conclusion for students is to practice recalling concepts to an initial criterion of 3 correct recalls and then to relearn them 3 times at widely spaced intervals.
Small-Signal Stability Analysis of an Autonomous Hybrid Renewable Energy Power Generation/Energy Storage System Part I: Time-Domain Simulations

Small-signal stability analyzed results of an autonomous hybrid renewable energy power generation/energy storage system connected to isolated loads using time-domain simulations is presented in this paper. The companion paper presents frequency-domain analyzed results of the same hybrid system. The proposed renewable energy power generation subsystems include three wind turbine generators (WTGs), a diesel engine generator, two fuel cells (FCs), and a photovoltaic system (PV) while the energy storage subsystems consist of a battery energy storage system and a flywheel energy storage system. An aqua electrolyzer absorbs a part of generated energy from PV or WTGs to generate available hydrogen for FCs. A time-domain approach based on three mathematical models for three studied cases under various operating points and disturbance conditions is performed. It can be concluded from the simulation results that the proposed hybrid power generation/energy storage system feeding isolated loads can be properly operated to achieve system power-frequency balance condition.
Ciphertext-policy hierarchical attribute-based encryption with short ciphertexts

Attribute-based encryption (ABE) systems allow encrypting to uncertain receivers by means of an access policy specifying the attributes that the intended receivers should possess. ABE promises to deliver fine-grained access control of encrypted data. However, when data are encrypted using an ABE scheme, key management is difficult if there is a large number of users from various backgrounds. In this paper, we elaborate ABE and propose a new versatile cryptosystem referred to as ciphertext-policy hierarchical ABE (CPHABE). In a CP-HABE scheme, the attributes are organized in a matrix and the users having higher-level attributes can delegate their access rights to the users at a lower level. These features enable a CP-HABE system to host a large number of users from different organizations by delegating keys, e.g., enabling efficient data sharing among hierarchically organized large groups. We construct a CP-HABE scheme with short ciphertexts. The scheme is proven secure in the standard model under non-interactive assumptions.
Automated systolic array architecture synthesis for high throughput CNN inference on FPGAs

Convolutional neural networks (CNNs) have been widely applied in many deep learning applications. In recent years, the FPGA implementation for CNNs has attracted much attention because of its high performance and energy efficiency. However, existing implementations have difficulty to fully leverage the computation power of the latest FPGAs. In this paper we implement CNN on an FPGA using a systolic array architecture, which can achieve high clock frequency under high resource utilization. We provide an analytical model for performance and resource utilization and develop an automatic design space exploration framework, as well as source-to-source code transformation from a C program to a CNN implementation using systolic array. The experimental results show that our framework is able to generate the accelerator for real-life CNN models, achieving up to 461 GFlops for floating point data type and 1.2 Tops for 8-16 bit fixed point.
Smart phone based blood pressure indicator

In this paper, we propose a methodology to estimate the range of human blood pressure (BP) using Photoplethysmography (PPG). 12 time domain features and 7 frequency domain features are pointed out and extracted from the PPG signal. A feature selection algorithm based on Maximal Information Coefficient (MIC) is presented to reduce the dimensionality of the feature set to effective ones, thereby cutting down resource requirements. Support Vector Machine (SVM) is used to classify the BP values into separate bins. The proposed methodology is validated and tested on a standard benchmark clean dataset as well as phone captured noisy dataset to justify its robustness and efficiency. Apart from a commending performance improvement, BP estimation is achieved with minimal features and processing, making the algorithm light weight for porting on smart phones.
Intelligent prognostics tools and e-maintenance

In today’s global competitive marketplace, there is intense pressure for manufacturing industries to continuously reduce and eliminate costly, unscheduled downtime and unexpected breakdowns. With the advent of Internet and tether-free technologies, companies necessitate dramatic changes in transforming traditional ‘‘fail and fix (FAF)’’ maintenance practices to a ‘‘predict and prevent (PAP)’’ e-maintenance methodology. Emaintenance addresses the fundamental needs of predictive intelligence tools to monitor the degradation rather than detecting the faults in a networked environment and, ultimately to optimize asset utilization in the facility. This paper introduces the emerging field of e-maintenance and its critical elements. Furthermore, performance assessment and prediction tools are introduced for continuous assessment and prediction of a particular product’s performance, ultimately enable proactive maintenance to prevent machine from breakdowns. Recent advances on intelligent prognostic technologies and tools are discussed. Several case studies are introduced to validate these developed technologies and tools.
A fuzzy goal programming approach for vendor selection problem in a supply chain

A fuzzy goal programming approach is applied in this paper for solving the vendor selection problem with multiple objectives, in which some of the parameters are fuzzy in nature. A vendor selection problem has been formulated as a fuzzy mixed integer goal programming vendor selection problem that includes three primary goals: minimizing the net cost, minimizing the net rejections, and minimizing the net late deliveries subject to realistic constraints regarding buyer's demand, vendors' capacity, vendors' quota flexibility, purchase value of items, budget allocation to individual vendor, etc. An illustration with a data set from a realistic situation is included to demonstrate the effectiveness of the proposed model. The proposed approach has the capability to handle realistic situations in a fuzzy environment and provides a better decision tool for the vendor selection decision in a supply chain.
A general solution to the P4P problem for camera with unknown focal length

This paper presents a general solution to the determination of the pose of a perspective camera with unknown focal length from images of four 3D reference points. Our problem is a generalization of the P3P and P4P problems previously developed for fully calibrated cameras. Given four 2D-to-3D correspondences, we estimate camera position, orientation and recover the camera focal length. We formulate the problem and provide a minimal solution from four points by solving a system of algebraic equations. We compare the Hidden variable resultant and Grobner basis techniques for solving the algebraic equations of our problem. By evaluating them on synthetic and on real-data, we show that the Grobner basis technique provides stable results.
Processing Social Media Images by Combining Human and Machine Computing during Crises

The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness as disaster unfolds. In addition to textual content, people post overwhelming amounts of imagery content on social networks within minutes of a disaster hit. Studies point to the importance of this online imagery content for emergency response. Despite recent advances in computer vision research, making sense of the imagery content in real-time during disasters remains a challenging task. One of the important challenges is that a large proportion of images shared on social media is redundant or irrelevant, which requires robust filtering mechanisms. Another important challenge is that images acquired after major disasters do not share the same characteristics as those in large-scale image collections with clean annotations of well-defined object categories such as house, car, airplane, cat, dog, etc., used traditionally in computer vision research. To tackle these challenges, we present a social media image processing pipeline that combines human and machine intelligence to perform two important tasks: (i) capturing and filtering of social media imagery content (i.e., real-time image streaming, de-duplication, and relevancy filtering); and (ii) actionable information extraction (i.e., damage severity assessment) as a core situational awareness task during an on-going crisis event. Results obtained from extensive experiments on real-world crisis datasets demonstrate the significance of the proposed pipeline for optimal utilization of both human and machine computing resources.
Technology-assisted training of arm-hand skills in stroke: concepts on reacquisition of motor control and therapist guidelines for rehabilitation technology design

It is the purpose of this article to identify and review criteria that rehabilitation technology should meet in order to offer arm-hand training to stroke patients, based on recent principles of motor learning. A literature search was conducted in PubMed, MEDLINE, CINAHL, and EMBASE (1997–2007). One hundred and eighty seven scientific papers/book references were identified as being relevant. Rehabilitation approaches for upper limb training after stroke show to have shifted in the last decade from being analytical towards being focussed on environmentally contextual skill training (task-oriented training). Training programmes for enhancing motor skills use patient and goal-tailored exercise schedules and individual feedback on exercise performance. Therapist criteria for upper limb rehabilitation technology are suggested which are used to evaluate the strengths and weaknesses of a number of current technological systems. This review shows that technology for supporting upper limb training after stroke needs to align with the evolution in rehabilitation training approaches of the last decade. A major challenge for related technological developments is to provide engaging patient-tailored task oriented arm-hand training in natural environments with patient-tailored feedback to support (re) learning of motor skills.
Structure-Infused Copy Mechanisms for Abstractive Summarization

Seq2seq learning has produced promising results on summarization. However, in many cases, system summaries still struggle to keep the meaning of the original intact. They may miss out important words or relations that play critical roles in the syntactic structure of source sentences. In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence. The approach naturally combines source dependency structure with the copy mechanism of an abstractive sentence summarizer. Experimental results demonstrate the effectiveness of incorporating source-side syntactic information in the system, and our proposed approach compares favorably to state-of-the-art methods.