nils-herrmann/clean_scirepeval huggingface dataset:
Quality, productivity and economic benefits of software reuse: a review of industrial studies

Systematic software reuse is proposed to increase productivity and software quality and lead to economic benefits. Reports of successful software reuse programs in industry have been published. However, there has been little effort to organize the evidence systematically and appraise it. This review aims to assess the effects of software reuse in industrial contexts. Journals and major conferences between 1994 and 2005 were searched to find observational studies and experiments conducted in industry, returning eleven papers of observational type. Systematic software reuse is significantly related to lower problem (defect, fault or error) density in five studies and to decreased effort spent on correcting problems in three studies. The review found evidence for significant gains in apparent productivity in three studies. Other significant benefits of software reuse were reported in single studies or the results were inconsistent. Evidence from industry is sparse and combining results was done by vote-counting. Researchers should pay more attention to using comparable metrics, performing longitudinal studies, and explaining the results and impact on industry. For industry, evaluating reuse of COTS or OSS components, integrating reuse activities in software processes, better data collection and evaluating return on investment are major challenges.
Strategical Argumentative Agent for Human Persuasion

Automated agents should be able to persuade people in the same way people persuade each other via dialogs. Today, automated persuasion modeling and research use unnatural assumptions regarding persuasive interaction, which creates doubt regarding their applicability for real-world deployment with people. In this work we present a novel methodology for persuading people through argumentative dialogs. Our methodology combines theoretical argumentation modeling, machine learning and Markovian optimization techniques that together result in an innovative agent named SPA. Two extensive field experiments, with more than 100 human subjects, show that SPA is able to persuade people significantly more often than a baseline agent and no worse than people are able to persuade each other.
An Integrated Reasoning Approach to Moral Decision-Making

We present a computational model, MoralDM, which integrates several AI techniques in order to model recent psychological findings on moral decision-making. Current theories of moral decision-making extend beyond pure utilitarian models by relying on contextual factors that vary with culture. MoralDM uses a natural language system to produce formal representations from psychological stimuli, to reduce tailorability. The impacts of secular versus sacred values are modeled via qualitative reasoning, using an order of magnitude representation. MoralDM uses a combination of first-principles reasoning and analogical reasoning to determine consequences and utilities when making moral judgments. We describe how MoralDM works and show that it can model psychological results and improve its performance via accumulating examples.
Multiscale ultrawide foveated video extrapolation

Video extrapolation is the task of extending a video beyond its original field of view. Extrapolating video in a manner that is consistent with the original video and visually pleasing is difficult. In this work we aim at very wide video extrapolation which increases the complexity of the task. Some video extrapolation methods simplify the task by using a rough color extrapolation. A recent approach focuses on artifact avoidance and run time reduction using foveated video extrapolation, but fails to preserve the structure of the scene. This paper introduces a multi-scale method which combines a coarse to fine approach with foveated video extrapolation. Foveated video extrapolation reduces the effective number of pixels that need to be extrapolated, making the extrapolation less time consuming and less prone to artifacts. The coarse to fine approach better preserves the structure of the scene while preserving finer details near the domain of the input video. The combined method gains improvement both visually and in processing time.
Curiously Modern DNA for a ``250 Million-Year-Old'' Bacterium

Studies of ancient DNA have attracted considerable attention in scientific journals and the popular press. Several of the more extreme claims for ancient DNA have been questioned on biochemical grounds (i.e., DNA surviving longer than expected) and evolutionary grounds (i.e., nucleotide substitution patterns not matching theoretical expectations for ancient DNA). A recent letter to Nature from Vreeland et al. (2000), however, tops all others with respect to age and condition of the specimen. These researchers extracted and cultured a bacterium from an inclusion body from what they claim is a 250 million-year (Myr)-old salt crystal. If substantiated, this observation could fundamentally alter views about bacterial physiology, ecology and evolution. Here we report on molecular evolutionary analyses of the 16S rDNA from this specimen. We find that 2-9-3 differs from a modern halophile, Salibacillus marismortui, by just 3 unambiguous bp in 16S rDNA, versus the ∼59 bp that would be expected if these bacteria evolved at the same rate as other bacteria. We show, using a Poisson distribution, that unless it can be shown that S. marismortui evolves 5 to 10 times more slowly than other bacteria for which 16S rDNA substitution rates have been established, Vreeland et al.'s claim would be rejected at the 0.05 level. Also, a molecular clock test and a relative rates test fail to substantiate Vreeland et al.'s claim that strain 2-9-3 is a 250-Myr-old bacterium. The report of Vreeland et al. thus falls into a long series of suspect ancient DNA studies.
Feature Extraction and Classification for Automatic Speaker Recognition System – A Review

Automatic speaker recognition (ASR) has found immense applications in the industries like banking, security, forensics etc. for its advantages such as easy implementation, more secure, more user friendly. To have a good recognition rate is a pre-requisite for any ASR system which can be achieved by making an optimal choice among the available techniques for ASR. In this paper, different techniques for the system have been discussed such as MFCC, LPCC, LPC, Wavelet decomposition for feature extraction and VQ, GMM, SVM, DTW, HMM for feature classification. All these techniques are also compared with each other to find out best suitable candidate among them. On the basis of the comparison done, MFCC has upper edge over other techniques for feature extraction as it is more consistent with human hearing. GMM comes out to be the best among classification models due to its good classification accuracy and less memory usage.  Automatic Speaker Recognition, Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Cepstral Coefficients, Gaussian Mixture Model ( GMM), Vector Quantization ( VQ), Dynamic Time Warping (DTW), Hidden Markov Model ( HMM), Wavelet decomposition
Improved Winding Proposal for Wound Rotor Resolver Using Genetic Algorithm and Winding Function Approach

Among position sensors, resolvers are superior from reliability point of view. However, obtaining lower output voltage harmonics and simple manufacturing process is a challenge in the design and optimization of resolvers. In this paper, a metaheuristic optimization algorithm is used to minimize total harmonic distortion of the output signals, and consequently the estimated position error in concentrated coil wound field resolvers. Meanwhile, to minimize total coil numbers, manufacturing costs, and complexity of the winding process, modified objective function and constraints are proposed. In this way, a modified winding function method is employed for performance analysis of the axial flux resolver. Then, the merits of the optimized winding configuration with respect to fractional slot concentrated windings are shown. The results of the proposed configurations are verified with a three-dimensional time-stepping finite-element method. Finally, the prototype of the studied axial flux resolver is constructed and tested. Good agreement is obtained between simulation and experimental results, confirming the effectiveness of the optimization process.
Visibility of wavelet quantization noise

The discrete wavelet transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression, measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that we call DWT uniform quantization noise; it is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r2/sup -/spl lambda//, where r is the display visual resolution in pixels/degree, and /spl lambda/ is the wavelet level. Thresholds increase rapidly with wavelet spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from lowpass to horizontal/vertical to diagonal. We construct a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.
Visual servoing of UAV using cuboid model with simultaneous tracking of multiple planar faces

Pose estimation is a key component for robot navigation. An Unmanned Aerial Vehicle (UAV) that is instructed to reach certain location requires a way of measuring its pose. This article presents a method for UAV visual servoing that uses the 3D pose of the drone as controller feedback. A remote monocular camera observes the tracked UAV while it moves and rotates in a 3D space. Pose is obtained from a 3D tracking process based on a cuboid model. In particular, a simultaneous face tracking strategy where 3D pose estimations from different faces are combined is introduced. Face combination was validated using a robotic arm with a cuboid at the final joint. For the UAV control, hover and path following tasks were tested. Results show that the proposed method correctly handles changes in pose, even though no face is always visible. Also, the UAV maintained a low speed in order to satisfy the small inter-frame displacement constraint imposed by visual tracking algorithm.
Automated memory leak diagnosis by regression testing

Memory leaks are tedious to detect and require significant debugging effort to be reproduced and localized. In particular, many of such bugs escape classical testing processes used in software development. One of the reasons is that unit and integration tests run too short for leaks to manifest via memory bloat or degraded performance. Moreover, many of such defects are environment-sensitive and not triggered by a test suite. Consequently, leaks are frequently discovered in the production scenario, causing elevated costs. In this paper we propose an approach for automated diagnosis of memory leaks during the development phase. Our technique is based on regression testing and exploits existing test suites. The key idea is to compare object (de-)allocation statistics (collected during unit/integration test executions) between a previous and the current software version. By grouping these statistics according to object creation sites we can detect anomalies and pinpoint the potential root causes of memory leaks. Such diagnosis can be completed before a visible memory bloat occurs, and in time proportional to the execution of test suite. We evaluate our approach using real leaks found in 7 Java applications. Results show that our approach has sufficient detection accuracy and is effective in isolating the leaky allocation site: true defect locations rank relatively high in the lists of suspicious code locations if the tests trigger the leak pattern. Our prototypical system imposes an acceptable instrumentation and execution overhead for practical memory leak detection even in large software projects.
An Embedded Support Vector Machine

In this paper we work on the balance between hardware and software implementation of a machine learning algorithm, which belongs to the area of statistical learning theory. We use system-on-chip technology to demonstrate the potential usefulness of moving the critical sections of an algorithm into HW: the so-called hardware/software balance. Our experiments show that the approach can achieve speedups using a complex machine learning algorithm called a support vector machine. The experiments are conducted on a real-time Java virtual machine named Java optimized processor
Sentence-State LSTM for Text Representation

The baseline BiLSTM model consists of two LSTM components, which process the input in the forward left-to-right and the backward rightto-left directions, respectively. In each direction, the reading of input words is modelled as a recurrent process with a single hidden state. Given an initial value, the state changes its value recurrently, each time consuming an incoming word. Take the forward LSTM component for example. Denoting the initial state as h 0, which is a model parameter, the recurrent state transition step for calculating h 1, . . . , h n+1 is defined as follows (Graves and Schmidhuber, 2005):
Monolithic three-stage 6–18GHz high power amplifier with distributed interstage in GaN technology

This paper reports on a three-stage broadband amplifier architecture suitable for wide bandwidth applications such as electronic warfare systems. The proposed topology comprises a distributed active power splitter to function as an interstage driver amplifier, whereas the pre-driver and power amplifier stages are reactively-matched. As a result, purely resistive interstage impedances are obtained and therefore the proposed architecture allows wider bandwidth operation as compared to the conventional reactively-matched multistage topology. A 6GHz to 18GHz three-stage high gain and high power amplifier MMIC is designed and realized in order to demonstrate the capabilities of the concept. The MMIC is based on a 0.25μm gate length AlGaN/GaN HEMT microstrip transmission line technology. It shows a measured small-signal gain of (25 ± 3)over the entire frequency range. The saturated output power is higher than 25W at the center frequency in pulsed operation.
Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of Global Positioning System (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.
Enhanced belief propagation decoding of polar codes through concatenation

The bit-channels of finite-length polar codes are not fully polarized, and a proportion of such bit-channels are neither completely “noiseless” nor completely “noisy”. By using an outer low-density parity-check code for these intermediate channels, we show how the performance of belief propagation (BP) decoding of the overall concatenated polar code can be improved. A simple example reports an improvement in Eb /N0 of 0.3 dB with respect to the conventional BP decoder.
Heterogeneous Domain Adaptation for Multiple Classes

In this paper, we present an efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are represented by heterogeneous features of different dimensions. Specifically, we propose to reconstruct a sparse feature transformation matrix to map the weight vector of classifiers learned from the source domain to the target domain. We cast this learning task as a compressed sensing problem, where each binary classifier induced from multiple classes can be deemed as a measurement sensor. Based on the compressive sensing theory, the estimation error of the transformation matrix decreases with the increasing number of classifiers. Therefore, to guarantee reconstruction performance, we construct sufficiently many binary classifiers based on the error correcting output coding. Extensive experiments are conducted on both a toy dataset and three real-world datasets to verify the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy.
Processing of wood-based microfibrillated cellulose and nanofibrillated cellulose , and applications relating to papermaking : a review

As an emerging cellulosic nanomaterial, microfibrillated cellulose (MFC) and nanofibrillated cellulose (NFC) have shown enormous potential in the forest products industry. The forest products industry and academia are working together to realise the possibilities of commercializing MFC and NFC. However, there are still needs to improve the processing, characterisation and material properties of nanocellulose in order to realise its full potential. The annual number of research publications and patents on nanocellulose with respect to manufacturing, properties and applications is now up in the thousands, so it is of the utmost importance to review articles that endeavour to research on this explosive topic of cellulose nanomaterials. This review examines the past and current situation of wood-based MFC and NFC in relation to its processing and applications relating to papermaking.
Multimodal Gesture Recognition via Multiple Hypotheses Rescoring

We present a new framework for multimodal gesture recognition that is based on a multiple hypotheses rescoring fusion scheme. We specifically deal with a demanding Kinect-based multimodal data set, introduced in a recent gesture recognition challenge (ChaLearn 2013), where multiple subjects freely perform multimodal gestures. We employ multiple modalities, that is, visual cues, such as skeleton data, color and depth images, as well as audio, and we extract feature descriptors of the hands’ movement, handshape, and audio spectral properties. Using a common hidden Markov model framework we build single-stream gesture models based on which we can generate multiple single stream-based hypotheses for an unknown gesture sequence. By multimodally rescoring these hypotheses via constrained decoding and a weighted combination scheme, we end up with a multimodally-selected best hypothesis. This is further refined by means of parallel fusion of the monomodal gesture models applied at a segmental level. In this setup, accurate gesture modeling is proven to be critical and is facilitated by an activity detection system that is also presented. The overall approach achieves 93.3% gesture recognition accuracy in the ChaLearn Kinect-based multimodal data set, significantly outperforming all recently published approaches on the same challenging multimodal gesture recognition task, providing a relative error rate reduction of at least 47.6%.
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Bargaining with reading habit is no need. Reading is not kind of something sold that you can take or not. It is a thing that will change your life to life better. It is the thing that will give you many things around the world and this universe, in the real world and here after. As what will be given by this an introduction to support vector machines and other kernel based learning methods, how can you bargain with the thing that has many benefits for you?
LOMIT: LOCAL MASK-BASED IMAGE-TO-IMAGE TRANSLATION VIA PIXEL-WISE HIGHWAY ADAPTIVE INSTANCE NORMALIZATION

Recently, image-to-image translation has seen a significant success. Among many approaches, image translation based on an exemplar image, which contains the target style information, has been popular, owing to its capability to handle multimodality as well as its suitability for practical use. However, most of the existing methods extract the style information from an entire exemplar and apply it to the entire input image, which introduces excessive image translation in irrelevant image regions. In response, this paper proposes a novel approach that jointly extracts out the local masks of the input image and the exemplar as targeted regions to be involved for image translation. In particular, the main novelty of our model lies in (1) co-segmentation networks for local mask generation and (2) the local mask-based highway adaptive instance normalization technique. We demonstrate the quantitative and the qualitative evaluation results to show the advantages of our proposed approach. Finally, the code is available at https://github.com/AnonymousIclrAuthor/ Highway-Adaptive-Instance-Normalization.
Factors contributing to the facial aging of identical twins.

The purpose of this study was to identify the environmental factors that contribute to facial aging in identical twins.



During the Twins Day Festival in Twinsburg, Ohio, 186 pairs of identical twins completed a comprehensive questionnaire, and digital images were obtained. A panel reviewed the images independently and recorded the differences in the perceived twins' ages and their facial features. The perceived age differences were then correlated with multiple factors.



Four-point higher body mass index was associated with an older appearance in twins younger than age 40 but resulted in a younger appearance after age 40 (p = 0.0001). Eight-point higher body mass index was associated with an older appearance in twins younger than age 55 but was associated with a younger appearance after age 55 (p = 0.0001). The longer the twins smoked, the older they appeared (p < 0.0001). Increased sun exposure was associated with an older appearance and accelerated with age (p = 0.015), as was a history of outdoor activities and lack of sunscreen use. Twins who used hormone replacement had a younger appearance (p = 0.002). Facial rhytids were more evident in twins with a history of skin cancer (p = 0.05) and in those who smoked (p = 0.005). Dark and patchy skin discoloration was less prevalent in twins with a higher body mass index (p = 0.01) and more common in twins with a history of smoking (p = 0.005) and those with sun exposure (p = 0.005). Hair quantity was better with a higher body mass index (p = 0.01) although worse with a history of skin cancer (p = 0.005) and better with the use of hormones (p = 0.05).



This study offers strong statistical evidence to support the role of some of the known factors that govern facial aging.