nils-herrmann/clean_scirepeval huggingface dataset:
Offensive Language Detection Using Multi-level Classification

Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. In the same time, flames (such as rants, taunts, and squalid phrases) are offensive/abusive phrases which might attack or offend the users for a variety of reasons. An automatic discriminative software with a sensitivity parameter for flame or abusive language detection would be a useful tool. Although a human could recognize these sorts of useless annoying texts among the useful ones, it is not an easy task for computer programs. In this paper, we describe an automatic flame detection method which extracts features at different conceptual levels and applies multilevel classification for flame detection. While the system is taking advantage of a variety of statistical models and rule-based patterns, there is an auxiliary weighted pattern repository which improves accuracy by matching the text to its graded entries.
A Framework for Coordinated Surface Operations Planning at Dallas-Fort Worth International Airport

An Integer Programming formulation is developed for optimizing surface operations at Dallas-Fort Worth airport, with the goal of assessing the potential benefits of taxi route planning. The model is based on operations in the eastern half of the airport under the most frequently used configuration. The focus is on operational concepts that optimize taxi routes by utilizing different control points on the airport surface. The benefits of two different concepts for optimizing taxiway operations, namely controlled pushback and taxi reroutes are analyzed, for both current data and a projected data set with approximately twice the traffic density. The analysis estimates that: (1) for current traffic densities, controlled pushback would reduce the average departure taxi time by 17% without altering runway schedule conformance, while the benefits of taxi reroutes would be minimal; and (2) for high-density operations, controlled pushback would reduce the average departure taxi time by 18%, while incorporating taxi reroutes would reduce the average arrival taxi time by 14%. Other benefits analyzed for these control strategies include a decrease in the average time spent in runway crossing queues.
The Effects of Task Difficulty and Multitasking on Performance

Multitasking is prevalent during computer-mediated work. Users tend to switch between multiple ongoing computer-based tasks either due to a personal decision to break from the current task (self-interruption) or due to an external interruption, such as an electronic notification. To examine how different types of multitasking, along with subjective task difficulty, influence performance, we conducted a controlled experiment using a custom-developed multitasking environment. A total of 636 subjects were randomly assigned into one of the three conditions: discretionary, where they were allowed to decide when and how often to switch tasks; mandatory, where they were forced to switch tasks at specific times; and sequential, where they had to perform tasks in sequence, without switching. The experimental environment featured a primary problem-solving task and five secondary tasks. The results show that when the primary task was considered difficult, subjects forced to multitask had significantly lower performance compared with not only the subjects who did not multitask but also the subjects who were able to multitask at their discretion. Conversely, when the primary task was considered easy, subjects forced to multitask had significantly higher performance than both the subjects who did not multitask and the subjects who multitasked at their discretion.
Bundle Methods for Regularized Risk Minimization

A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different not ions of risk and different regularizers. Examples include linear Support Vector Machines (SVMs), Gaus si n Processes, Logistic Regression, Conditional Random Fields (CRFs), and Lasso amongst others . This paper describes the theory and implementation of a scalable and modular convex solver w hich solves all these estimation problems. It can be parallelized on a cluster of workstation s, allows for data-locality, and can deal with regularizers such as L1 andL2 penalties. In addition to the unified framework we present ti ght convergence bounds, which show that our algorithm converge s in O(1/ε) steps toε precision for general convex problems and in O(log(1/ε)) steps for continuously differentiable problems. We demonstrate the performance of our general purpose solver o n a variety of publicly available data sets.
Quantitative and qualitative research: beyond the debate.

Psychology has been a highly quantitative field since its conception as a science. However, a qualitative approach to psychological research has gained increasing importance in the last decades, and an enduring debate between quantitative and qualitative approaches has arisen. The recently developed Mixed Methods Research (MMR) addresses this debate by aiming to integrate quantitative and qualitative approaches. This article outlines and discusses quantitative, qualitative and mixed methods research approaches with specific reference to their (1) philosophical foundations (i.e. basic sets of beliefs that ground inquiry), (2) methodological assumptions (i.e. principles and formal conditions which guide scientific investigation), and (3) research methods (i.e. concrete procedures for data collection, analysis and interpretation). We conclude that MMR may reasonably overcome the limitation of purely quantitative and purely qualitative approaches at each of these levels, providing a fruitful context for a more comprehensive psychological research.
Semi-Supervised Learning via New Deep Network Inversion

We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and problems. The approach outperforms current state-of-the-art methods on MNIST reaching 99.14% of test set accuracy while using 5 labeled examples per class. Experiments with one-dimensional signals highlight the generality of the method. Importantly, our approach is simple, efficient, and requires no change in the deep network architecture. 1 ar X iv :1 71 1. 04 31 3v 1 [ st at .M L ] 1 2 N ov 2 01 7
Software Architectures for Robotics Systems: A Systematic Mapping Study

Software architecture related issues are important for robotic systems. Architecture-centric development and evolution of software for robotic systems has been attracting researchers’ attention for more than two decades.  The objective of this work is to systematically identify, taxonomically classify and holistically map the existing solutions, research progress and trends that influence architecture-driven modeling, development and evolution of robotic software.  We carried out a Systematic Mapping Study (SMS) to identify and analyze the relevant literature based on 56 peer-reviewed papers. We extract and synthesize the data from selected papers to (i) taxonomically classify the existing research and (ii) systematically map the solutions, frameworks, notations and evaluation methods to highlight the role of software architecture in robotic systems.  We have identified eight distinct research themes that support architectural solutions to enable (i) operations, (ii) evolution and (iii) development specific activities of robotic software. The research in this area has progressed from object-oriented to component-based and now to servicedriven robotics representing different architectural generations. The reported solutions have exploited modeldriven, service-oriented and reverse engineering techniques since 2005. An emerging trend is cloud robotics that exploits the foundations of service-driven architectures to support an interconnected web of robots. The results of this SMS facilitate knowledge transfer – benefiting researchers and practitioners – focused on exploiting software architecture to model, develop and evolve robotic systems.
A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising

Modelling and analyzing noise in images is a fundamental task in many computer vision systems. Traditionally, noise has been modelled per color channel assuming that the color channels are independent. Although the color channels can be considered as mutually independent in camera RAW images, signals from different color channels get mixed during the imaging process inside the camera due to gamut mapping, tone-mapping, and compression. We show the influence of the in-camera imaging pipeline on noise and propose a new noise model in the 3D RGB space to accounts for the color channel mix-ups. A data-driven approach for determining the parameters of the new noise model is introduced as well as its application to image denoising. The experiments show that our noise model represents the noise in regular JPEG images more accurately compared to the previous models and is advantageous in image denoising.
Wideband Offset Slot-Coupled Patch Antenna Array for X/Ku-Band Multimode Radars

A high-gain, wideband antenna element that covers most portions of X- and Ku-bands is essential for multimode radar applications. This letter presents an aperture-coupled stacked patch antenna for 8.75-12.25-GHz frequency band. Fixed beam tilt up to 30 ° in scan range can be obtained by offsetting the aperture relative to a driven patch. When there is no offset, a single antenna has broadside gain greater than 10 dBi. An antenna array (4 × 4) with fixed beam tilt is designed and validated with measurements. The prototype antenna exhibits 44% fractional bandwidth and maximum gain 20.6 dBi in the target frequency band.
Enhanced image algorithm at night of improved retinex based on HIS space

A kind of enhanced algorithm of improved Retinex based on HIS color space was proposed aiming at the situation in which low image contrast and unclear details were caused in environment short of light such as night. Firstly the original image was converted to HIS color space, and irradiation image was obtained after non-partial bilateral filter algorithm was conducted on luminance component I. Then reflection image was achieved according to Retinex algorithm, and the image was converted back to RGB space. Finally the result of subtracting the image of original image after Laplace filtering with obtained image was the image after handling. It indicates after simulated experiment verification that definition and contrast of low-light images were greatly increased and completeness of color information was retained, and there was major improvement for the result of the algorithm in this thesis compared with traditional algorithms.
A Review of Classic Edge Detectors

In this paper some of the classic alternatives for edge detection in digital images are studied. The main idea of edge detection algorithms is to find where abrupt changes in the intensity of an image have occurred. The first family of algorithms reviewed in this work uses the first derivative to find the changes of intensity, such as Sobel, Prewitt and Roberts. In the second reviewed family, second derivatives are used, for example in algorithms like Marr-Hildreth and Haralick. The obtained results are analyzed from a qualitative point of view (perceptual) and from a quantitative point of view (number of operations, execution time), considering different ways to convolve an image with a kernel (step required in some of the algorithms). Source Code For all the reviewed algorithms, an open source C implementation is provided which can be downloaded from the IPOL web page of this article1. An online demonstration is also available, where the user can test and reproduce our results.
SURREAL: SUbgraph Robust REpresentAtion Learning

The success of graph embeddings or node representation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent years. Representation learning algorithms aim to preserve local and global network structure by identifying node neighborhood notions. However, many existing algorithms generate embeddings that fail to properly preserve the network structure, or lead to unstable representations due to random processes (e.g., random walks to generate context) and, thus, cannot generate to multi-graph problems. In this paper, we propose a robust graph embedding using connection subgraphs algorithm, entitled “SURREAL,” a novel, stable graph embedding algorithmic framework. SURREAL learns graph representations using connection subgraphs by employing the analogy of graphs with electrical circuits. It preserves both local and global connectivity patterns, and addresses the issue of high-degree nodes. Further, it exploits the strength of weak ties and meta-data that have been neglected by baselines. The experiments show that SURREAL outperforms state-of-the-art algorithms by up to 36.85% on multi-label classification problem. Further, in contrast to baselines, SURREAL, being deterministic, is completely stable.
Geometric and Illumination Invariants for Object Recognition

We propose invariant formulations that can potentially be combined into a single system. In particular, we describe a framework for computing invariant features which are insensitive to rigid motion, a ne transform, changes of parameterization and scene illumination, perspective transform, and view point change. This is unlike most current research on image invariants which concentrates on either geometric or illumination invariants exclusively. The formulations are widely applicable to many popular basis representations, such as wavelets [3, 4, 24, 25], short-time Fourier analysis [13, 35], and splines [2, 5, 37]. Exploiting formulations that examine information about shape and color at di erent resolution levels, the new approach is neither strictly global nor local. It enables a quasi-localized, hierarchical shape analysis which is rarely found in other known invariant techniques, such as global invariants. Furthermore, it does not require estimating high-order derivatives in computing invariants (unlike local invariants), whence is more robust. We provide results of numerous experiments on both synthetic and real data to demonstrate the validity and exibility of the proposed framework.
Moving ERP Systems to the Cloud-Data Security Issues

This paper brings to light data security issues and concerns for organizations by moving their Enterprise Resource Planning (ERP) systems to the cloud. Cloud computing has become the new trend of how organizations conduct business and has enabled them to innovate and compete in a dynamic environment through new and innovative business models. The growing popularity and success of the cloud has led to the emergence of cloud-based Software-as-a-Service (SaaS) ERP systems, a new alternative approach to traditional on-premise ERP systems. Cloud-based ERP has a myriad of benefits for organizations. However, infrastructure engineers need to address data security issues before moving their enterprise applications to the cloud. Cloud-based ERP raises specific concerns about the confidentiality and integrity of the data stored in the cloud. Such concerns that affect the adoption of cloud-based ERP are based on the size of the organization. Small to medium enterprises (SMEs) gain the maximum benefits from cloud-based ERP as many of the concerns around data security are not relevant to them. On the contrary, larger organizations are more cautious in moving their mission critical enterprise applications to the cloud. A hybrid solution where organizations can choose to keep their sensitive applications on-premise while leveraging the benefits of the cloud is proposed in this paper as an effective solution that is gaining momentum and popularity for large organizations.
A Generalized Blahut-Arimoto Algorithm

Kavčić proposed in  an algorithm that optimizes the parameters of a Markov source at the input to a finite-state machine channel in order to maximize the mutual information rate. Numerical results for several channels indicated that his algorithm gives capacity-achieving input distributions. In this paper we prove that the stationary points of this algorithm indeed correspond one-to-one to the critical points of the information rate curve. Kavčić’s algorithm can be considered as a generalized Blahut-Arimoto algorithm, as it includes as special cases the classical Blahut-Arimoto algorithm for discrete memoryless channels and the solution to finding the capacity-achieving input distribution for finite-state channels with no noise.
Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning

We investigate a novel approach for image restoration by reinforcement learning. Unlike existing studies that mostly train a single large network for a specialized task, we prepare a toolbox consisting of small-scale convolutional networks of different complexities and specialized in different tasks. Our method, RL-Restore, then learns a policy to select appropriate tools from the toolbox to progressively restore the quality of a corrupted image. We formulate a stepwise reward function proportional to how well the image is restored at each step to learn the action policy. We also devise a joint learning scheme to train the agent and tools for better performance in handling uncertainty. In comparison to conventional human-designed networks, RL-Restore is capable of restoring images corrupted with complex and unknown distortions in a more parameter-efficient manner using the dynamically formed toolchain1.
A huge, undescribed soil ciliate (Protozoa: Ciliophora) diversity in natural forest stands of Central Europe

We investigated 12 natural forest stands in eastern Austria for soil ciliate diversity, viz., eight beech forests and two lowland and Pinus nigra forests each. The stands span a wide range of climatic (e.g., 543–1759 mm precipitation, 160–1035 m above sea-level) and abiotic (e.g., pH 4–7.4) factors. Samples were taken twice in autumn and late spring and analysed with the non-flooded Petri dish method. Species were identified in vivo, in silver preparations, and in the scanning electron microscope. A total of 233 species were found, of which 30 were undescribed, a surprising number showing our ignorance of soil ciliate diversity, even in Central Europe. Species number varied highly from 45 (acidic beech on silicate) to 120 (floodplain forest) and was strongly correlated with pH and overall habitat quality, as measured by climate, the C/P quotient (ratio of r-selected colpodean and k-selected polyhymenophorean ciliates), and the proportion of mycophagous ciliate species; multivariate analysis showed further important variables, viz., the general nutrient status (glucose, nitrogen, C/ N ratio) and microbial (urease) activity. The highest species number occurred in one of the two floodplain soils, supporting the intermediate disturbance hypothesis. The three main forest types could be clearly distinguished by their ciliate communities, using similarity indices and multidimensional scaling. Individual numbers varied highly from 135−1 (lowland forest) to 10,925 ml−1 (beech on silicate) soil percolate and showed, interestingly, a weak correlation with soil protozoan phospholipid fatty acids. Eight of the 30 new species found and a forgotten species, Arcuospathidium coemeterii (Kahl 1943) nov. comb., are described in detail, as examples of how species were recognized and soil protozoan diversity should be analyzed: Latispathidium truncatum bimicronucleatum, Protospathidium fusioplites, Erimophrya sylvatica, E. quadrinucleata, Paragonostomum simplex, Periholosticha paucicirrata, P. sylvatica, and Australocirrus zechmeisterae.
Judgments of genuine, suppressed, and faked facial expressions of pain.

The process of discriminating among genuine, suppressed, and faked expressions of pain was examined. Untrained judges estimated the severity of pain being experienced when viewing videotaped facial expressions of chronic pain patients undergoing a painful diagnostic test or dissimulating reactions. Verbal feedback as to whether pain was experienced also was provided, so as to be either consistent or inconsistent with the facial expression. Judges were able to distinguish genuine pain faces from baseline expressions but, relative to genuine pain faces, attributed more pain to faked faces and less pain to suppressed ones. Advance warning of deception did not improve discrimination but led to a more conservative or nonempathic judging style. Verbal feedback increased or decreased judgments, as appropriate, but facial information consistently was assigned greater weight. An augmenting model of the judgment process that attaches considerable importance to the context in which information is provided was supported.
Real Time Automated Fabric Defect Detection System using Microcontroller

In a Least Developed Country (LDC) like Bangladesh where the textile is the main core of our economy; still there is a major drawback in this sector which is the defect detection of the fabric. In the manual fault detection system with highly trained inspectors, very less percentage of the defects are being detected in upon fabrics in the textile industries. But a real time automatic system can increase this percentage in a maximum number. This research implements a textile defect detector which uses computer vision methodology with the combination of multi-layer neural networks to identify the classification of textile defects and detect it with a real time configured mechanical system containing a microcontroller. The recognizer, suitable for LDC countries, specially for Bangladesh where textile exports earns the maximum for the country’s economy.
User Based Aggregation for Biterm Topic Model

Biterm Topic Model (BTM) is designed to model the generative process of the word co-occurrence patterns in short texts such as tweets. However, two aspects of BTM may restrict its performance: 1) user individualities are ignored to obtain the corpus level words co-occurrence patterns; and 2) the strong assumptions that two co-occurring words will be assigned the same topic label could not distinguish background words from topical words. In this paper, we propose Twitter-BTM model to address those issues by considering user level personalization in BTM. Firstly, we use user based biterms aggregation to learn user specific topic distribution. Secondly, each user’s preference between background words and topical words is estimated by incorporating a background topic. Experiments on a large-scale real-world Twitter dataset show that Twitter-BTM outperforms several stateof-the-art baselines.