nils-herrmann/clean_scirepeval huggingface dataset:
Joint Learning of Intrinsic Images and Semantic Segmentation

Semantic segmentation of outdoor scenes is problematic when there are variations in imaging conditions. It is known that albedo (reflectance) is invariant to all kinds of illumination effects. Thus, using reflectance images for semantic segmentation task can be favorable. Additionally, not only segmentation may benefit from reflectance, but also segmentation may be useful for reflectance computation. Therefore, in this paper, the tasks of semantic segmentation and intrinsic image decomposition are considered as a combined process by exploring their mutual relationship in a joint fashion. To that end, we propose a supervised end-to-end CNN architecture to jointly learn intrinsic image decomposition and semantic segmentation. We analyze the gains of addressing those two problems jointly. Moreover, new cascade CNN architectures for intrinsic-for-segmentation and segmentation-for-intrinsic are proposed as single tasks. Furthermore, a dataset of 35K synthetic images of natural environments is created with corresponding albedo and shading (intrinsics), as well as semantic labels (segmentation) assigned to each object/scene. The experiments show that joint learning of intrinsic image decomposition and semantic segmentation is beneficial for both tasks for natural scenes. Dataset and models are available at: https://ivi.fnwi.uva.nl/cv/intrinseg.
Which research in design creativity and innovation? Let us not forget the reality of companies

Studying design creativity and innovation from practical perspectives for companies requires both a good understanding of the company ecosystem and its inner processes contributing to delivered innovations and a rigorous design research methodology to provide effective design models, methods, platforms that are truly effective in the context of company. Working in an Industrial Engineering laboratory, we advocate a more systemic vision of design creativity and innovation in company ecosystems. We present in this paper an attempt to develop and make professional an innovation engineering. Our research works are illustrated along the different research topics of an innovation process. We start by a recent survey on innovation practice and organizational models led in 28 large companies. The lessons learned about this survey reinforce our belief that there is a need for a new method in agile management of radical innovation projects in company contexts. We currently develop, test and apply such a methodology named: Radical Innovation Design® (RID). Its effectiveness has been evaluated through a large scale evaluation of the project outcomes for the company. Two extensions of RID have been proposed and deployed in company contexts: a selection procedure for innovation clusters and a value-driven process for airplane development projects. Keyword: innovation process, value model, innovation engineering, proof of concept, Radical
IoT Applications on Secure Smart Shopping

We propose to design a smart shopping system based on RFID. The smart carts used in such a system can navigate the customers to the desired products, and they can calculate the billing information as the customer shops. Items that are put in a smart shopping cart can be automatically recognized and billed. In this way, customers can avoid long queuing time at the check-out points. The grocery management also becomes much easier: The sensors on the grocery shelf can keep updating the number of items to the central server so that the server is always aware of the items' status in the store. Furthermore, the inventory management also becomes much easier, because all the items can be automatically read by a RFID reader instead of manually scanned by a laborer. In this paper, we propose a smart shopping cart system with consideration of how this system can work functionally and securely.
Denoising image sequences does not require motion estimation

State of the art movie restoration methods either estimate motion and filter out the trajectories, or compensate the motion by an optical flow estimate and then filter out the compensated movie. Now, the motion estimation problem is ill posed. This fact is known as the aperture problem: trajectories are ambiguous since they could coincide with any promenade in the space-time isophote surface. In this paper, we try to show that, for denoising, the aperture problem can be taken advantage of. Indeed, by the aperture problem, many pixels in the neighboring frames are similar to the current pixel one wishes to denoise. Thus, denoising by an averaging process can use many more pixels than just the ones on a single trajectory. This observation leads to use for movies a recently introduced image denoising method, the NL-means algorithm. This static 3D algorithm outperforms motion compensated algorithms, as it does not lose movie details. It involves the whole movie isophote and not just a trajectory.
Lifted Inference Rules With Constraints

Lifted inference rules exploit symmetries for fast reasoning in statistical relational models. Computational complexity of these rules is highly dependent on the choice of the constraint language they operate on and therefore coming up with the right kind of representation is critical to the success of lifted inference. In this paper, we propose a new constraint language, called setineq, which allows subset, equality and inequality constraints, to represent substitutions over the variables in the theory. Our constraint formulation is strictly more expressive than existing representations, yet easy to operate on. We reformulate the three main lifting rules: decomposer, generalized binomial and the recently proposed single occurrence for MAP inference, to work with our constraint representation. Experiments on benchmark MLNs for exact and sampling based inference demonstrate the effectiveness of our approach over several other existing techniques.
Enhancing wifi-based localization with visual clues

Indoor localization is of great importance to a wide range of applications in the era of mobile computing. Current mainstream solutions rely on Received Signal Strength (RSS) of wireless signals as fingerprints to distinguish and infer locations. However, those methods suffer from fingerprint ambiguity that roots in multipath fading and temporal dynamics of wireless signals. Though pioneer efforts have resorted to motion-assisted or peer-assisted localization, they neither work in real time nor work without the help of peer users, which introduces extra costs and constraints, and thus degrades their practicality. To get over these limitations, we propose Argus, an image-assisted localization system for mobile devices. The basic idea of Argus is to extract geometric constraints from crowdsourced photos, and to reduce fingerprint ambiguity by mapping the constraints jointly against the fingerprint space. We devise techniques for photo selection, geometric constraint extraction, joint location estimation, and build a prototype that runs on commodity phones. Extensive experiments show that Argus triples the localization accuracy of classic RSS-based method, in time no longer than normal WiFi scanning, with negligible energy consumption.
Slice-timing effects and their correction in functional MRI

Exact timing is essential for functional MRI data analysis. Datasets are commonly measured using repeated 2D imaging methods, resulting in a temporal offset between slices. To compensate for this timing difference, slice-timing correction (i.e. temporal data interpolation) has been used as an fMRI pre-processing step for more than fifteen years. However, there has been an ongoing debate about the effectiveness and applicability of this method. This paper presents the first elaborated analysis of the impact of the slice-timing effect on simulated data for different fMRI paradigms and measurement parameters, taking into account data noise and smoothing effects. Here we show, depending on repetition time and paradigm design, slice-timing effects can significantly impair fMRI results and slice-timing correction methods can successfully compensate for these effects and therefore increase the robustness of the data analysis. In addition, our results from simulated data were supported by empirical in vivo datasets. Our findings suggest that slice-timing correction should be included in the fMRI pre-processing pipeline.
Modeling and optimal control of automated trolleys

Automated trolleys have been developed to meet the needs of material handling in industries. The velocity of automated trolleys is regulated by an S-shaped (or trapezoid-shaped) acceleration and deceleration profile. In consequence of the velocity profile, the control system of automated trolleys is nonlinear and open-looped. In order to linearize the control system, we use a second order dynamic element to replace the acceleration and declaration curve in practice, and design an optimal controller under the quadratic cost function. Performance of the proposed approach is also compared to the conventional method. The simulation shows a better dynamic performance of the developed control system.
Detecting shadow for direct radiation using CityGML models for photovoltaic potentiality analysis

Photovoltaic method is very popular for generating electrical power. Its energy production depends on solar radiation on that location and orientation. Shadow rapidly decreases performance of the Photovoltaic system. In this research, it is being investigated that how exactly real-time shadow can be detected. In principle, 3D city models containing roof structure, vegetation, thematically differentiated surface and texture, are suitable to simulate exact real-time shadow. An automated procedure to measure exact shadow effect from the 3D city models and a long-term simulation model to determine the produced energy from the photovoltaic system is being developed here. In this paper, a method for detecting shadow for direct radiation has been discussed with its result using a 3D city model to perform a solar energy potentiality analysis. . Former military area Scharnhauser Park shown in figure 2 has been choosen as the case study area for this research. It is an urban conversion and development area of 150 hecta res in the community of Ostfildern on the southern border near Stuttgart with 7000 inhabitants. About 80% heating energy demand of the whole area is supplied by renewable energies and a small portion of electricity is delivered by existing roof top photovoltaic system (Tereci et al, 2009). This has been selected as the study area for this research because of availability CityGML and LIDAR data, building footprints and existing photovoltaic cells on roofs and façades. Land Survey Office Baden-Wüttemberg provides the laser scanning data with a density of 4 points per square meter at a high resolution of 0.2 meter. The paper has been organized with a brief introduction at the beginning explaining background of photovoltaic energy and motivation for this research in. Then the effect of shadow on photovoltaic cells and a methodology for detecting shadow from direct radiation. Then result has been shown applying the methodology and some brief idea about the future work of this research has been presented.
Neonatal Critical Care Communication (NC3): training NICU physicians and nurse practitioners

Communicating with families is a core skill for neonatal clinicians, yet formal communication training rarely occurs. This study examined the impact of an intensive interprofessional communication training for neonatology fellows and nurse practitioners.Evidence-based, interactive training for common communication challenges in neonatology incorporated didactic sessions, role-plays and reflective exercises. Participants completed surveys before, after, and one month following the training.Five neonatology fellows and eight nurse practitioners participated (n=13). Before the training, participants overall felt somewhat prepared (2.6 on 5 point Likert-type scale) to engage in core communication challenges; afterwards, participants overall felt very well prepared (4.5 on Likert-type scale) (P<0.05). One month later, participants reported frequently practicing the taught skills and felt quite willing to engage in difficult conversations.An intensive communication training program increased neonatology clinicians’ self-perceived competence to face communication challenges which commonly occur, but for which training is rarely provided.
Outlier Detection in Arbitrarily Oriented Subspaces

In this paper, we propose a novel outlier detection model to find outliers that deviate from the generating mechanisms of normal instances by considering combinations of different subsets of attributes, as they occur when there are local correlations in the data set. Our model enables to search for outliers in arbitrarily oriented subspaces of the original feature space. We show how in addition to an outlier score, our model also derives an explanation of the outlierness that is useful in investigating the results. Our experiments suggest that our novel method can find different outliers than existing work and can be seen as a complement of those approaches.
DETECTING SIGNALS IN NOISY DATA-CAN ENSEMBLE CLASSIFIERS HELP IDENTIFY ADVERSE DRUG REACTION IN TWEETS ?

In this paper, we describe our system for detecting adverse reactions from tweets, a task organized as part of Pacific Symposium of Biocomputing Social Media Mining Shared Task. The shared task primarily involves a binary classification of the tweets, whether they contain description of adverse drug reaction or not. We propose an ensemble machine learning classifier to tackle the unbalanced distribution of the classes in the data provided for the task. A feature set containing unigrams, bigrams, and trigrams (a selected list, using mutual information), co-occurrence of drug and side effect, negation, sentiment score, etc. were used to train the classifiers in identifying the tweets with the description of adverse drug reactions. Our system obtained 0.4195 F-score and ranked first in the shared task.
RPCA: A Novel Preprocessing Method for PCA

We propose a preprocessing method to improve the performance of Principal Component Analysis (PCA) for classification problems composed of two steps; in the first step, the weight of each feature is calculated by using a feature weighting method. Then the features with weights larger than a predefined threshold are selected. The selected relevant features are then subject to the second step. In the second step, variances of features are changed until the variances of the features are corresponded to their importance. By taking the advantage of step 2 to reveal the class structure, we expect that the performance of PCA increases in classification problems. Results confirm the effectiveness of our proposed methods.
Validation of a Malay Version of the Smartphone Addiction Scale among Medical Students in Malaysia

This study was initiated to determine the psychometric properties of the Smart Phone Addiction Scale (SAS) by translating and validating this scale into the Malay language (SAS-M), which is the main language spoken in Malaysia. This study can distinguish smart phone and internet addiction among multi-ethnic Malaysian medical students. In addition, the reliability and validity of the SAS was also demonstrated.



A total of 228 participants were selected between August 2014 and September 2014 to complete a set of questionnaires, including the SAS and the modified Kimberly Young Internet addiction test (IAT) in the Malay language.



There were 99 males and 129 females with ages ranging from 19 to 22 years old (21.7±1.1) included in this study. Descriptive and factor analyses, intra-class coefficients, t-tests and correlation analyses were conducted to verify the reliability and validity of the SAS. Bartlett's test of sphericity was significant (p <0.01), and the Kaiser-Mayer-Olkin measure of sampling adequacy for the SAS-M was 0.92, indicating meritoriously that the factor analysis was appropriate. The internal consistency and concurrent validity of the SAS-M were verified (Cronbach's alpha = 0.94). All of the subscales of the SAS-M, except for positive anticipation, were significantly related to the Malay version of the IAT.



This study developed the first smart phone addiction scale among medical students. This scale was shown to be reliable and valid in the Malay language.
Potential Health Impact of Environmentally Released Micro- and Nanoplastics in the Human Food Production Chain: Experiences from Nanotoxicology.

High concentrations of plastic debris have been observed in the oceans. Much of the recent concern has focused on microplastics in the marine environment. Recent studies of the size distribution of the plastic debris suggested that continued fragmenting of microplastics into nanosized particles may occur. In this review we assess the current literature on the occurrence of environmentally released micro- and nanoplastics in the human food production chain and their potential health impact. The currently used analytical techniques introduce a great bias in the knowledge, since they are only able to detect plastic particles well above the nanorange. We discuss the potential use of the very sensitive analytical techniques that have been developed for the detection and quantification of engineered nanoparticles. We recognize three possible toxic effects of plastic particles: first due to the plastic particles themselves, second to the release of persistent organic pollutant adsorbed to the plastics, and third to the leaching of additives of the plastics. The limited data on microplastics in foods do not predict adverse effect of these pollutants or additives. Potential toxic effects of microplastic particles will be confined to the gut. The potential human toxicity of nanoplastics is poorly studied. Based on our experiences in nanotoxicology we prioritized future research questions.
A long memory property of stock market returns and a new model *

A ‘long memory’ property of stock market returns is investigated in this paper. It is found that not only there is substantially more correlation between absolute returns than returns themselves, but the power transformation of the absolute return lrfl” also has quite high autocorrelation for long lags. It is possible to characterize lrfld to be ‘long memory’ and this property is strongest when d is around 1. This result appears to argue against ARCH type specifications based upon squared returns. But our Monte-Carlo study shows that both ARCH type models based on squared returns and those based on absolute return can produce this property. A new general class of models is proposed which allows the power 6 of the heteroskedasticity equation to be estimated from the data.
The association between social support and physical activity in older adults: a systematic review

The promotion of active and healthy ageing is becoming increasingly important as the population ages. Physical activity (PA) significantly reduces all-cause mortality and contributes to the prevention of many chronic illnesses. However, the proportion of people globally who are active enough to gain these health benefits is low and decreases with age. Social support (SS) is a social determinant of health that may improve PA in older adults, but the association has not been systematically reviewed. This review had three aims: 1) Systematically review and summarise studies examining the association between SS, or loneliness, and PA in older adults; 2) clarify if specific types of SS are positively associated with PA; and 3) investigate whether the association between SS and PA differs between PA domains.



Quantitative studies examining a relationship between SS, or loneliness, and PA levels in healthy, older adults over 60 were identified using MEDLINE, PSYCInfo, SportDiscus, CINAHL and PubMed, and through reference lists of included studies. Quality of these studies was rated.



This review included 27 papers, of which 22 were cross sectional studies, three were prospective/longitudinal and two were intervention studies. Overall, the study quality was moderate. Four articles examined the relation of PA with general SS, 17 with SS specific to PA (SSPA), and six with loneliness. The results suggest that there is a positive association between SSPA and PA levels in older adults, especially when it comes from family members. No clear associations were identified between general SS, SSPA from friends, or loneliness and PA levels. When measured separately, leisure time PA (LTPA) was associated with SS in a greater percentage of studies than when a number of PA domains were measured together.



The evidence surrounding the relationship between SS, or loneliness, and PA in older adults suggests that people with greater SS for PA are more likely to do LTPA, especially when the SS comes from family members. However, high variability in measurement methods used to assess both SS and PA in included studies made it difficult to compare studies.
Macrophage plasticity and polarization: in vivo veritas.

Diversity and plasticity are hallmarks of cells of the monocyte-macrophage lineage. In response to IFNs, Toll-like receptor engagement, or IL-4/IL-13 signaling, macrophages undergo M1 (classical) or M2 (alternative) activation, which represent extremes of a continuum in a universe of activation states. Progress has now been made in defining the signaling pathways, transcriptional networks, and epigenetic mechanisms underlying M1-M2 or M2-like polarized activation. Functional skewing of mononuclear phagocytes occurs in vivo under physiological conditions (e.g., ontogenesis and pregnancy) and in pathology (allergic and chronic inflammation, tissue repair, infection, and cancer). However, in selected preclinical and clinical conditions, coexistence of cells in different activation states and unique or mixed phenotypes have been observed, a reflection of dynamic changes and complex tissue-derived signals. The identification of mechanisms and molecules associated with macrophage plasticity and polarized activation provides a basis for macrophage-centered diagnostic and therapeutic strategies.
Semantic Photo Retargeting Under Noisy Image Labels

With the popularity of mobile devices, photo retargeting has become a useful technique that adapts a high-resolution photo onto a low-resolution screen. Conventional approaches are limited in two aspects. The first factor is the de-emphasized role of semantic content that is many times more important than low-level features in photo aesthetics. Second is the importance of image spatial modeling: toward a semantically reasonable retargeted photo, the spatial distribution of objects within an image should be accurately learned. To solve these two problems, we propose a new semantically aware photo retargeting that shrinks a photo according to region semantics. The key technique is a mechanism transferring semantics of noisy image labels (inaccurate labels predicted by a learner like an SVM) into different image regions. In particular, we first project the local aesthetic features (graphlets in this work) onto a semantic space, wherein image labels are selectively encoded according to their noise level. Then, a category-sharing model is proposed to robustly discover the semantics of each image region. The model is motivated by the observation that the semantic distribution of graphlets from images tagged by a common label remains stable in the presence of noisy labels. Thereafter, a spatial pyramid is constructed to hierarchically encode the spatial layout of graphlet semantics. Based on this, a probabilistic model is proposed to enforce the spatial layout of a retargeted photo to be maximally similar to those from the training photos. Experimental results show that (1) noisy image labels predicted by different learners can improve the retargeting performance, according to both qualitative and quantitative analysis, and (2) the category-sharing model stays stable even when 32.36% of image labels are incorrectly predicted.