nils-herrmann/clean_scirepeval huggingface dataset:
Semi-supervised Learning with Induced Word Senses for State of the Art Word Sense Disambiguation

Word Sense Disambiguation (WSD) aims to determine the meaning of a word in context, and successful approaches are known to benefit many applications in Natural Language Processing. Although, supervised learning has been shown to provide superior WSD performance, current sense-annotated corpora do not contain a sufficient number of instances per word type to train supervised systems for all words. While unsupervised techniques have been proposed to overcome this data sparsity problem, such techniques have not outperformed supervised methods. In this paper, we propose a new approach to building semi-supervised WSD systems that combines a small amount of sense-annotated data with information from Word Sense Induction, a fully-unsupervised technique that automatically learns the different senses of a word based on how it is used. In three experiments, we show how sense induction models may be effectively combined to ultimately produce high-performance semi-supervised WSD systems that exceed the performance of state-of-the-art supervised WSD techniques trained on the same sense-annotated data. We anticipate that our results and released software will also benefit evaluation practices for sense induction systems and those working in low-resource languages by demonstrating how to quickly produce accurate WSD systems with minimal annotation effort.
Mucus layers in inflammatory bowel disease.

The intestinal epithelium is covered with mucus with the main structural building block being the densely O-glycosylated MUC2 mucin. The intestinal epithelium is exposed to ingested material, our digestive machinery, and large amounts of microorganisms. Mucus is the first line of defense and aids to limit exposure to all these threats to the epithelium. In the small intestine, mucus acts as a matrix, which contains antimicrobial products, such as defensins and immunoglobulin A that limit epithelial exposure to the luminal bacteria. In the colon, the stratified inner mucus layer acts as a physical barrier excluding bacteria from the epithelium. Bacterial penetration of this normally restricted zone is observed in many colitis models and also in patients with ulcerative colitis. Mucus defects that allow bacteria to reach the epithelium and to stimulate an immune system response can lead to the development of intestinal inflammation. The current state of our knowledge concerning the function of the mucus layers and the main mucin component, MUC2, in inflammatory bowel disease is described in this review.
Classifying smoking urges via machine learning

Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states.



To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision.



The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications.



In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions.
Robust Statistical Frontalization of Human and Animal Faces

The unconstrained acquisition of facial data in real-world conditions may result in face images with significant pose variations, illumination changes, and occlusions, affecting the performance of facial landmark localization and recognition methods. In this paper, a novel method, robust to pose, illumination variations, and occlusions is proposed for joint face frontalization and landmark localization. Unlike the state-of-the-art methods for landmark localization and pose correction, where large amount of manually annotated images or 3D facial models are required, the proposed method relies on a small set of frontal images only. By observing that the frontal facial image of both humans and animals, is the one having the minimum rank of all different poses, a model which is able to jointly recover the frontalized version of the face as well as the facial landmarks is devised. To this end, a suitable optimization problem is solved, concerning minimization of the nuclear norm (convex surrogate of the rank function) and the matrix $$\ell _1$$ ℓ 1 norm accounting for occlusions. The proposed method is assessed in frontal view reconstruction of human and animal faces, landmark localization, pose-invariant face recognition, face verification in unconstrained conditions, and video inpainting by conducting experiment on 9 databases. The experimental results demonstrate the effectiveness of the proposed method in comparison to the state-of-the-art methods for the target problems.
Network Slicing Based 5G and Future Mobile Networks: Mobility, Resource Management, and Challenges

5G networks are expected to be able to satisfy users' different QoS requirements. Network slicing is a promising technology for 5G networks to provide services tailored for users' specific QoS demands. Driven by the increased massive wireless data traffic from different application scenarios, efficient resource allocation schemes should be exploited to improve the flexibility of network resource allocation and capacity of 5G networks based on network slicing. Due to the diversity of 5G application scenarios, new mobility management schemes are greatly needed to guarantee seamless handover in network-slicing-based 5G systems. In this article, we introduce a logical architecture for network-slicing-based 5G systems, and present a scheme for managing mobility between different access networks, as well as a joint power and subchannel allocation scheme in spectrum-sharing two-tier systems based on network slicing, where both the co-tier interference and cross-tier interference are taken into account. Simulation results demonstrate that the proposed resource allocation scheme can flexibly allocate network resources between different slices in 5G systems. Finally, several open issues and challenges in network-slicing-based 5G networks are discussed, including network reconstruction, network slicing management, and cooperation with other 5G technologies.
Overcoming Challenges in Fixed Point Training of Deep Convolutional Networks

It is known that training deep neural networks, in particular, deep convolutional networks, with aggressively reduced numerical precision is challenging. The stochastic gradient descent algorithm becomes unstable in the presence of noisy gradient updates resulting from arithmetic with limited numeric precision. One of the wellaccepted solutions facilitating the training of low precision fixed point networks is stochastic rounding. However, to the best of our knowledge, the source of the instability in training neural networks with noisy gradient updates has not been well investigated. This work is an attempt to draw a theoretical connection between low numerical precision and training algorithm stability. In doing so, we will also propose and verify through experiments methods that are able to improve the training performance of deep convolutional networks in fixed point.
Tank Water Level Monitoring System using GSM Network

Unexpected shortage of water supply is common phenomena especially in dense population such as in hostels. Water supply at the students’ hostels is usually drawn from tank at the roof top of the building. Apparently there is no early warning system to monitor the tank water level when it has reached the critical level. The situation worsened when there is no personnel or technician in-charge to do the maintenance at the time it is needed. It becomes worst especially at the week ends and public holidays. Students have to wait for couples of days for the water supply to resume. This paper presents the development of water level monitoring system with an integration of GSM module to alert the person-in-charge through Short Message Service (SMS). The water level is monitored and its data sent through SMS to the intended technician mobile’s phone upon reaching the critical level. The prototype was tested and functioned properly as a mean to reduce the risk of unexpected shortage of water supply.
Federated Learning with Non-IID Data

Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides privacy, security, regulatory and economic benefits. In this work, we focus on the statistical challenge of federated learning when local data is non-IID. We first show that the accuracy of federated learning reduces significantly, by up to ~55% for neural networks trained for highly skewed non-IID data, where each client device trains only on a single class of data. We further show that this accuracy reduction can be explained by the weight divergence, which can be quantified by the earth mover’s distance (EMD) between the distribution over classes on each device and the population distribution. As a solution, we propose a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices. Experiments show that accuracy can be increased by ~30% for the CIFAR-10 dataset with only 5% globally shared data.
A Review of Digital Image Steganography using LSB and LSB Array

Information exchange in the just about any cluster of computer has to be more secure in the era of cloud computing and big data. Steganography helps to prevent illegal attention through covering the secret message in a number of digitally electronically representative media, without hurting the accessibility of secret message. Image steganography methods are recently been helpful to send any secret message in the protected image carrier to prevent threats and attacks whereas it does not give any kind of opportunity to hackers to find out the secret concept. Inside a steganographic system secrets information is embedded inside of a cover file, to ensure that no one will suspect that anything perhaps there is inside carrier. The cover file could be image, audio or video. To really make it safer, the secrets information might be encrypted embedded then, it will be decrypted in the receiver. In this paper, we are reviewing some digital image steganographic techniques depending on LSB (least significant bit) & LSB array concept.
A 3.2-Gb/s transceiver with a quarter-rate linear phase detector reducing the phase offset

In this paper, the transceiver which incorporates a PLL using a ring voltage-controlled oscillator (VCO), a phase interpolator (PI), the quarter-rate linear phase detector (PD) and an output driver with pre-emphasis is presented. The phase detector which uses a clock whose frequency is a quarter of the data rate and reduces the phase offset is proposed. The transceiver, implemented in a 0.18-mum CMOS technology, operates at 3.2-Gb/s over a 10-cm PCB line with the bit error rate (BER) of less than 10-12. The chip area is 3.7 times 2.5 mm2 and the core without I/O consumes 45-mA and I/O buffers consume 80-mA from a 1.8-V supply.
An Optimal Fuzzy Logic Guidance Law using Particle Swarm Optimization

An Optimal fuzzy logic guidance (OFLG) law for a surface to air homing missile is introduced. The introduced approach is based on the well-known proportional navigation guidance (PNG) law. Particle Swarm Optimization (PSO) is used to optimize the of the membership functions&apos; (MFs) parameters of the proposed design. The distribution of the MFs is obtained by minimizing a nonlinear constrained multi-objective optimization problem where; control effort and miss distance are treated as competing objectives. The performance of the introduced guidance law is compared with classical fuzzy logic guidance (FLG) law as well as PNG one. The simulation results show that OFLG performs better than other guidance laws. Moreover, the introduced design is shown to perform well with the existence of noisy measurements.
Domain Adaptation with Adversarial Training and Graph Embeddings

The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data. However, obtaining labeled data is a big challenge in many real-world problems. In such scenarios, a DNN model can leverage labeled and unlabeled data from a related domain, but it has to deal with the shift in data distributions between the source and the target domains. In this paper, we study the problem of classifying social media posts during a crisis event (e.g., Earthquake). For that, we use labeled and unlabeled data from past similar events (e.g., Flood) and unlabeled data for the current event. We propose a novel model that performs adversarial learning based domain adaptation to deal with distribution drifts and graph based semi-supervised learning to leverage unlabeled data within a single unified deep learning framework. Our experiments with two real-world crisis datasets collected from Twitter demonstrate significant improvements over several baselines.
Gradient-based adaptive interpolation in super-resolution image restoration

This paper presents a super-resolution method based on gradient-based adaptive interpolation. In this method, in addition to considering the distance between the interpolated pixel and the neighboring valid pixel, the interpolation coefficients take the local gradient of the original image into account. The smaller the local gradient of a pixel is, the more influence it should have on the interpolated pixel. And the interpolated high resolution image is finally deblurred by the application of Wiener filter. Experimental results show that our proposed method not only substantially improves the subjective and objective quality of restored images, especially enhances edges, but also is robust to the registration error and has low computational complexity.
System modeling using the Parallel DEVS formalism and the Modelica language

1569-190X/$ see front matter 2010 Elsevier B.V doi:10.1016/j.simpat.2010.03.004 * Corresponding author.  E-mail addresses: vsanz@dia.uned.es (V. Sanz) (S. Dormido). The analysis and identification of the requirements needed to describe P-DEVS models using the Modelica language are discussed in this manuscript. A new free Modelica package, named DEVSLib, is presented. It facilitates the description of discrete-event models according to the Parallel DEVS formalism and provides components to interface with continuous-time models, which can be composed using other Modelica libraries. In addition, DEVSLib contains models implementing Quantized State System (QSS) integration methods. The model definition capabilities provided by DEVSLib are similar to the ones in the simulation environments specifically designed for supporting the DEVS formalism. The main additional advantage of DEVSLib is that it can be used together with other Modelica libraries in order to compose multi-domain and multi-formalism hybrid models. DEVSLib is included in the DESLib Modelica library, which is freely available for download at http:// www.euclides.dia.uned.es.
Effects of Case Western Reserve University's transverse analysis on the quality of orthodontic treatment.

The purpose of this study was to evaluate the effect of using the transverse analysis developed at Case Western Reserve University (CWRU) in Cleveland, Ohio. The hypotheses were based on the following: (1) Does following CWRU's transverse analysis improve the orthodontic results? (2) Does following CWRU's transverse analysis minimize the active treatment duration?



A retrospective cohort research study was conducted on a randomly selected sample of 100 subjects. The sample had CWRU's analysis performed retrospectively, and the sample was divided according to whether the subjects followed what CWRU's transverse analysis would have suggested. The American Board of Orthodontics discrepancy index was used to assess the pretreatment records, and quality of the result was evaluated using the American Board of Orthodontics cast/radiograph evaluation. The Mann-Whitney test was used for the comparison.



CWRU's transverse analysis significantly improved the total cast/radiograph evaluation scores (P = 0.041), especially the buccolingual inclination component (P = 0.001). However, it did not significantly affect treatment duration (P = 0.106).



CWRU's transverse analysis significantly improves the orthodontic results but does not have significant effects on treatment duration.
Dipyridamole prevents triple-negative breast-cancer progression

Dipyridamole is a widely prescribed drug in ischemic disorders, and it is here investigated for potential clinical use as a new treatment for breast cancer. Xenograft mice bearing triple-negative breast cancer 4T1-Luc or MDA-MB-231T cells were generated. In these in vivo models, dipyridamole effects were investigated for primary tumor growth, metastasis formation, cell cycle, apoptosis, signaling pathways, immune cell infiltration, and serum inflammatory cytokines levels. Dipyridamole significantly reduced primary tumor growth and metastasis formation by intraperitoneal administration. Treatment with 15 mg/kg/day dipyridamole reduced mean primary tumor size by 67.5 % (p = 0.0433), while treatment with 30 mg/kg/day dipyridamole resulted in an almost a total reduction in primary tumors (p = 0.0182). Experimental metastasis assays show dipyridamole reduces metastasis formation by 47.5 % in the MDA-MB-231T xenograft model (p = 0.0122), and by 50.26 % in the 4T1-Luc xenograft model (p = 0.0292). In vivo dipyridamole decreased activated β-catenin by 38.64 % (p < 0.0001), phospho-ERK1/2 by 25.05 % (p = 0.0129), phospho-p65 by 67.82 % (p < 0.0001) and doubled the expression of IkBα (p = 0.0019), thus revealing significant effects on Wnt, ERK1/2-MAPK and NF-kB pathways in both animal models. Moreover dipyridamole significantly decreased the infiltration of tumor-associated macrophages and myeloid-derived suppressor cells in primary tumors (p < 0.005), and the inflammatory cytokines levels in the sera of the treated mice. We suggest that when used at appropriate doses and with the correct mode of administration, dipyridamole is a promising agent for breast-cancer treatment, thus also implying its potential use in other cancers that show those highly activated pathways.
Endogeneity and the dynamics of internal corporate governance $

We use a well-developed dynamic panel generalized method of moments (GMM) estimator to alleviate endogeneity concerns in two aspects of corporate governance research: the effect of board structure on firm performance and the determinants of board structure. The estimator incorporates the dynamic nature of internal governance choices to provide valid and powerful instruments that address unobserved heterogeneity and simultaneity. We re-examine the relation between board structure and performance using the GMM estimator in a panel of 6,000 firms over a period from 1991 to 2003, and find no causal relation between board structure and current firm performance. We illustrate why other commonly used estimators that ignore the dynamic relationship between current governance and past firm performance may be biased. We discuss where it may be appropriate to consider the dynamic panel GMM estimator in corporate governance research, as well as caveats to its use. & 2012 Elsevier B.V. All rights reserved.
The impact of project managers ’ leadership style on employees ’ job satisfaction , performance and turnover

Behavioral theory of leadership, divides leaders into two groups of “task-oriented” and “relationship-oriented” leaders. According to this theory, task-oriented leaders’ priority is achieving projected results for current tasks, whereas relationship-oriented leaders focus on employees and try to improve them and take into consideration their ideas and suggestions. The current research intends to discover the extent to which employees’ level of job satisfaction, quality of performance and rate turnover are affected by the leader’s style. The employees of Iranian oil&gas projects were selected as the target population for this study. An online survey was selected as the method of data collection. 100 employees from different projects participated in the survey. The collected data were analyzed using independent sample t-test. The results of the analysis showed that in all the three research areas there was a significant difference between the employees of task-oriented project managers and those who work with relationship-oriented project managers, while the latter group scored higher.
Human Reasoning and Cognitive Science

ion, 277, 283, 303, 305 Achourioti, Theodora, 9 adaptation, 39, 140, 142, 145–147, 153, 164, 167, 170, 171, 177, 282, 296, 365, 366 adaptationism, 141, 145, 146, 296 affect, 241, 243, 276, 277 affirmation of the consequent, 181, 183, 189, 192, 196, 200, 201, 210–212, 231, 232, 266, 269, 271 agent, 4, 40, 185, 192, 250, 251 algorithm, 24, 46, 120, 177, 217, 222, 231, 283, 304, 308, 321, 322, 325, 332, 333, 337, 348, 350, 351, 354, 365 Darwinian, 120, 139, 148, 153 fast and frugal, 7, 361, 365 altriciality, 152, 164, 169, 280, 288, 290, 296, 365 altruism, 146, 153, 156, 157 analogy, 9, 244, 267, 277, 301 anaphora, 53, 75, 82, 91, 98, 111, 115, 329 Aristotle, 9, 11, 81, 306, 320, 321, 323 Aspberger, see developmental syndrome, Asperger’s astrocyte, 151, 285 attention deficit hyperactivity disorder (ADHD), see developmental syndrome authority, 22, 45, 60, 75, 85, 111, 128, 132, 167 autism, see developmental syndrome, autism backpropagation, 219, 231 Bayes, 6, 66, 95, 102, 105, 114, 213, 215, 216 Biston betularia, 158 bottom–up, 126, 244, 277, 284, 317, 341 cell death, 285 cerebellum, 168, 284, 285, 296 cerebral lateralisation, 119, 134, 339 cheater detection, 15, 120, 139, 152, 153, 177 chimpanzee, 151, 161, 165, 167, 170, 241, 242, 248, 294, 296 circumscription, 189, 193 COBUILD, 83 communication adversarial, 85, 97, 107, 115, 128, 130, 310, 328