nils-herrmann/clean_scirepeval huggingface dataset:
Cost estimation for solid waste management in industrialising regions--precedents, problems and prospects.

The importance of cost planning for solid waste management (SWM) in industrialising regions (IR) is not well recognised. The approaches used to estimate costs of SWM can broadly be classified into three categories - the unit cost method, benchmarking techniques and developing cost models using sub-approaches such as cost and production function analysis. These methods have been developed into computer programmes with varying functionality and utility. IR mostly use the unit cost and benchmarking approach to estimate their SWM costs. The models for cost estimation, on the other hand, are used at times in industrialised countries, but not in IR. Taken together, these approaches could be viewed as precedents that can be modified appropriately to suit waste management systems in IR. The main challenges (or problems) one might face while attempting to do so are a lack of cost data, and a lack of quality for what data do exist. There are practical benefits to planners in IR where solid waste problems are critical and budgets are limited.
Semantic Road Layout Understanding by Generative Adversarial Inpainting

Autonomous driving is becoming a reality, yet vehicles still need to rely on complex sensor fusion to understand the scene they act in. The ability to discern static environment and dynamic entities provides a comprehension of the road layout that poses constraints to the reasoning process about moving objects. We pursue this through a GAN-based semantic segmentation inpainting model to remove all dynamic objects from the scene and focus on understanding its static components such as streets, sidewalks and buildings. We evaluate this task on the Cityscapes dataset and on a novel synthetically generated dataset obtained with the CARLA simulator and specifically designed to quantitatively evaluate semantic segmentation inpaintings. We compare our methods with a variety of baselines working both in the RGB and segmentation domains.
A Critical Discourse Analysis of Women ’ s Portrayal in News Reporting of Sexual Violence

This study explores and compares the portrayal of women in the news reporting of crimes of sexual violence against women between two newspapers from different cultures, the Jakarta Post and the Guardian. The Jakarta Post is an English quality newspaper published in Indonesia, and the Guardian is a quality broadsheet from Great Britain. To explore the representation of women, this study accounts the portrayal of men as well since the two entities are strongly inter-related. The analytical tool used in this study is naming analysis of social actors, which is a part of critical discourse analysis. This analysis is aimed at probing the representation through the choice of lexical items in representing the main news actors. The findings of the analysis indicate that the choices of the naming categories used by both newspapers are different. The Jakarta Post mostly functionalises both the victims and the perpetrators in terms of their legal status in the criminal cases. This suggests that the broadsheet tends to view them as part of the legal processes instead of as people. The Guardian typically classifies the victims in terms of their age and gender and refers to the perpetrators with their surnames instead of as parts of the criminal cases. The Guardian’s tendency to represent both perpetrators and victims as people instead of parts of legal processes indicates that the paper is attempting to focus the reports more on the crimes themselves rather than the participants involved in the cases.
Active and interactive learning online: a comparison of Web-based and conventional writing classes

This study examines how students enrolled in two web-based sections of a technical writing class performed compared to students enrolled in a conventional version of the class. Although no significant difference in student performance was found between the two learning conditions, our data reveal intriguing relationships between students’ prior knowledge, attitudes, and learning styles and our web-based writing environment. One finding that we focus on here was that reflective, global learners performed significantly better online than active, sequential learners, whereas there was no difference between them in the conventional class. Our study highlights the complexity of effective teaching and the difficulty of making comparisons between the online and the classroom environments. In particular, we maintain that the transfer of active learning strategies to the web is not straightforward and that interactivity as a goal of instructional website design requires significant elaboration.
Variable selection for generalized linear mixed models by L 1-penalized estimation

Generalized linear mixed models are a widely used tool for modeling longitudinal data. However , their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates. The presented approach to the fitting of generalized linear mixed models includes an L 1-penalty term that enforces variable selection and shrinkage simultaneously. A gradient ascent algorithm is proposed that allows to maximize the penalized log-likelihood yielding models with reduced complexity. In contrast to common procedures it can be used in high-dimensional settings where a large number of potentially influential explanatory variables is available. The method is investigated in simulation studies and illustrated by use of real data sets.
The Business Transformation Payoffs of Cloud Services at Mohawk

Collectively, disruptive technologies and market forces have resulted in a significant shift in the structure of many industries, presenting a serious challenge to near-term profitability and long-term viability. Cloud capabilities continue to promise payoffs in reduced costs and increased efficiencies, but in this article, we show they can provide business model transformation opportunities as well. To date, the focus of much research on cloud computing and cloud services has been on understanding the technology challenges, business opportunities or applications for particular domains.3 Cloud services, however, also offer great new opportunities for small and mediumsized enterprises (SMEs) that lack large IT shops or internal capabilities, as well as larger firms. An early analysis of four SMEs4 found that cloud services can offer both economic and business operational value previously denied them. This distinction is important because it shows that cloud services can provide value beyond simple cost avoidance or reduction
Smart Cities: A Survey on Data Management, Security, and Enabling Technologies

Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that can be leveraged for safety, efficiency, and infotainment applications and services for city residents. The management of this voluminous data through its lifecycle is fundamental to the realization of smart cities. Therefore, in contrast to existing surveys on smart cities we provide a data-centric perspective, describing the fundamental data management techniques employed to ensure consistency, interoperability, granularity, and reusability of the data generated by the underlying IoT for smart cities. Essentially, the data lifecycle in a smart city is dependent on tightly coupled data management with cross-cutting layers of data security and privacy, and supporting infrastructure. Therefore, we further identify techniques employed for data security and privacy, and discuss the networking and computing technologies that enable smart cities. We highlight the achievements in realizing various aspects of smart cities, present the lessons learned, and identify limitations and research challenges.
Efficient Action Detection in Untrimmed Videos via Multi-task Learning

This paper studies the joint learning of action recognition and temporal localization in long, untrimmed videos. We employ a multi-task learning framework that performs the three highly related steps of action proposal, action recognition, and action localization refinement in parallel instead of the standard sequential pipeline that performs the steps in order. We develop a novel temporal actionness regression module that estimates what proportion of a clip contains action. We use it for temporal localization but it could have other applications like video retrieval, surveillance, summarization, etc. We also introduce random shear augmentation during training to simulate viewpoint change. We evaluate our framework on three popular video benchmarks. Results demonstrate that our joint model is efficient in terms of storage and computation in that we do not need to compute and cache dense trajectory features, and that it is several times faster than its sequential ConvNets counterpart. Yet, despite being more efficient, it outperforms stateof-the-art methods with respect to accuracy.
Deep exemplar-based colorization

We propose the first deep learning approach for exemplar-based local colorization. Given a reference color image, our convolutional neural network directly maps a grayscale image to an output colorized image. Rather than using hand-crafted rules as in traditional exemplar-based methods, our end-to-end colorization network learns how to select, propagate, and predict colors from the large-scale data. The approach performs robustly and generalizes well even when using reference images that are unrelated to the input grayscale image. More importantly, as opposed to other learning-based colorization methods, our network allows the user to achieve customizable results by simply feeding different references. In order to further reduce manual effort in selecting the references, the system automatically recommends references with our proposed image retrieval algorithm, which considers both semantic and luminance information. The colorization can be performed fully automatically by simply picking the top reference suggestion. Our approach is validated through a user study and favorable quantitative comparisons to the-state-of-the-art methods. Furthermore, our approach can be naturally extended to video colorization. Our code and models are freely available for public use.
COCA-COLA : International Business Strategy for Globalization

The purpose of this research was to analysis the efficiency of global strategies. This paper identified six key strategies necessary for firms to be successful when expanding globally. These strategies include differentiation, marketing, distribution, collaborative strategies, labor and management strategies, and diversification. Within this analysis, we chose to focus on the Coca-Cola Company because they have proven successful in their international operations and are one of the most recognized brands in the world. We performed an in-depth review of how effectively or ineffectively Coca-Cola has used each of the six strategies. The paper focused on Coca-Cola's operations in the United States, China, Belarus, Peru, and Morocco. The author used electronic journals from the various countries to determine how effective Coca-Cola was in these countries. The paper revealed that Coca-Cola was very successful in implementing strategies regardless of the country. However, the author learned that Coca-Cola did not effectively utilize all of the strategies in each country.
Dielectric properties of epoxy nanocomposites

The dielectric properties of epoxy nanocomposites with insulating nano-fillers, viz., TiO2, ZnO and AI2O3 were investigated at low filler concentrations by weight. Epoxy nanocomposite samples with a good dispersion of nanoparticles in the epoxy matrix were prepared and experiments were performed to measure the dielectric permittivity and tan delta (400 Hz-1 MHz), dc volume resistivity and ac dielectric strength. At very low nanoparticle loadings, results demonstrate some interesting dielectric behaviors for nanocomposites and some of the electrical properties are found to be unique and advantageous for use in several existing and potential electrical systems. The nanocomposite dielectric properties are analyzed in detail with respect to different experimental parameters like frequency (for permittivity/tan delta), filler size, filler concentration and filler permittivity. In addition, epoxy microcomposites for the same systems were synthesized and their dielectric properties were compared to the results already obtained for nanocomposites. The interesting dielectric characteristics for epoxy based nanodielectric systems are attributed to the large volume fraction of interfaces in the bulk of the material and the ensuing interactions between the charged nanoparticle surface and the epoxy chains.
Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images

We perform fine-grained land use mapping at the city scale using ground-level images. Mapping land use is considerably more difficult than mapping land cover and is generally not possible using overhead imagery as it requires close-up views and seeing inside buildings. We postulate that the growing collections of georeferenced, ground-level images suggest an alternate approach to this geographic knowledge discovery problem. We develop a general framework that uses Flickr images to map 45 different land-use classes for the City of San Francisco. Individual images are classified using a novel convolutional neural network containing two streams, one for recognizing objects and another for recognizing scenes. This network is trained in an end-to-end manner directly on the labeled training images. We propose several strategies to overcome the noisiness of our user-generated data including search-based training set augmentation and online adaptive training. We derive a ground truth map of San Francisco in order to evaluate our method. We demonstrate the effectiveness of our approach through geo-visualization and quantitative analysis. Our framework achieves over 29% recall at the individual land parcel level which represents a strong baseline for the challenging 45-way land use classification problem especially given the noisiness of the image data.
Constraint programming for LNG ship scheduling and inventory management

We propose a constraint programming approach for the optimization of inventory routing in the liquefied natural gas industry. We present two constraint programming models that rely on a disjunctive scheduling representation of the problem. We also propose an iterative search heuristic to generate good feasible solutions for these models. Computational results on a set of largescale test instances demonstrate that our approach can find better solutions than existing approaches based on mixed integer programming, while being 4 to 10 times faster on average.
A stabilization control of two wheels driven wheelchair

The paper describes a stabilization control of two wheels driven wheelchair based on pitch angle disturbance observer (PADO). PADO makes it possible to stabilize the wheelchair motion and remove casters. This brings a sophisticated mobility of wheelchair because the casters are obstacle to realize step passage motion and so on. The proposed approach based on PADO is robust against disturbance of pitch angle direction and the more functional wheelchairs is expected in the developed system. The validity of the proposed method is confirmed by simulation and experiment.
Rectangling panoramic images via warping

Stitched panoramic images mostly have irregular boundaries. Artists and common users generally prefer rectangular boundaries, which can be obtained through cropping or image completion techniques. In this paper, we present a content-aware warping algorithm that generates rectangular images from stitched panoramic images. Our algorithm consists of two steps. The first local step is mesh-free and preliminarily warps the image into a rectangle. With a grid mesh placed on this rectangle, the second global step optimizes the mesh to preserve shapes and straight lines. In various experiments we demonstrate that the results of our approach are often visually plausible, and the introduced distortion is often unnoticeable.
From One Point to a Manifold: Knowledge Graph Embedding for Precise Link Prediction

Knowledge graph embedding aims at offering a numerical knowledge representation paradigm by transforming the entities and relations into continuous vector space. However, existing methods could not characterize the knowledge graph in a fine degree to make a precise link prediction. There are two reasons for this issue: being an ill-posed algebraic system and adopting an overstrict geometric form. As precise link prediction is critical for knowledge graph embedding, we propose a manifold-based embedding principle (ManifoldE) which could be treated as a well-posed algebraic system that expands point-wise modeling in current models to manifold-wise modeling. Extensive experiments show that the proposed models achieve substantial improvements against the state-of-the-art baselines, particularly for the precise prediction task, and yet maintain high efficiency. All of the related poster, slides, datasets and codes have been published in http://www. ibookman.net/conference.html.
Computational intelligence - concepts to implementations

PREFACE Several computational analytic tools have matured in the last 10 to 15 years that facilitate solving problems that were previously difficult or impossible to solve. These new analytical tools, known collectively as computational intelligence tools, include artificial neural networks, fuzzy systems, and evolutionary computation. They have recently been combined among themselves as well as with more traditional approaches, such as statistical analysis, to solve extremely challenging problems. Diagnostic systems, for example, are being developed that include Bayesian, neural network, and rule-based diagnostic modules, evolutionary algorithm-based explanation facilities, and expert system shells. All of these components work together in a " seamless " way that is transparent to the user, and they deliver results that significantly exceed what is available with any single approach. At a system prototype level, computational intelligence tools are capable of yielding results in a relatively short time. For instance, the implementation of a conventional expert system often takes one to three years and requires the active participation of a " knowledge engineer " to build the knowledge and rule bases. In contrast, computational intelligence (CI) system solutions can often be prototyped in a few weeks to a few months and are implemented using available engineering and computational resources. Indeed, computational intelligence tools are capable of being applied in many instances by " domain experts " rather than solely by " computer gurus. " This means that biomedical engineers, for example, can solve problems in biomedical engineering without relying on outside computer science expertise such as is required to build knowledge bases for classical expert systems. Furthermore, innovative ways to combine CI tools are cropping up every day. For example, tools have been developed that incorporate knowledge elements with neural networks, fuzzy logic, and evolutionary computing theory. Such tools are quickly able to solve classification and clustering problems that would otherwise be extremely time consuming using other techniques. The concepts, paradigms, algorithms, and implementation of computational intelligence and its constituent methodologies—evolutionary computation, neural networks, and fuzzy logic—are the focus of this book. In addition, we emphasize practical applications throughout; that is, how to apply the concepts, paradigms, algorithms, and implementations discussed to practical problems in engineering and computer science. This emphasis culminates in the real-world case studies in a final chapter available on the Web. Computational intelligence is closely related to the field called "soft computing." There is, in fact, a significant overlap. According to Lotfi …
Moving towards maturity: challenges to successful e-government implementation and diffusion

The implementation, diffusion and adoption of e-government in the public sector has been a topic that has been debated by the research community for some time. In particular, the limited adoption of e-government services is attributed to factors such as the heterogeneity of users, lack of user-orientation, the limited transformation of public sector and the mismatch between expectations and supply. In this editorial, we review theories and factors impacting implementation, diffusion and adoption of e-government. Most theories used in prior research follow mainstream information systems concepts, which can be criticized for not taking into account e-government specific characteristics. The authors argue that there is a need for e-government specific theories and methodologies that address the idiosyncratic nature of e-government as the well-known information systems concepts that are primarily developed for business contexts are not equipped to encapsulate the complexities surrounding e-government. Aspects like accountability, digital divide, legislation, public governance, institutional complexity and citizens' needs are challenging issues that have to be taken into account in e-government theory and practices. As such, in this editorial we argue that e-government should develop as an own strand of research, while information systems theories and concepts should not be neglected.
Brain imaging of language plasticity in adopted adults: can a second language replace the first?

Do the neural circuits that subserve language acquisition lose plasticity as they become tuned to the maternal language? We tested adult subjects born in Korea and adopted by French families in childhood; they have become fluent in their second language and report no conscious recollection of their native language. In behavioral tests assessing their memory for Korean, we found that they do not perform better than a control group of native French subjects who have never been exposed to Korean. We also used event-related functional magnetic resonance imaging to monitor cortical activations while the Korean adoptees and native French listened to sentences spoken in Korean, French and other, unknown, foreign languages. The adopted subjects did not show any specific activations to Korean stimuli relative to unknown languages. The areas activated more by French stimuli than by foreign stimuli were similar in the Korean adoptees and in the French native subjects, but with relatively larger extents of activation in the latter group. We discuss these data in light of the critical period hypothesis for language acquisition.
Node Classification in Social Networks

When dealing with large graphs, such as those that arise in the context of online social networks, a subset of nodes may be labeled. These labels can indicate demographic values, interest, beliefs or other characteristics of the nodes (users). A core problem is to use this information to extend the labeling so that all nodes are assigned a label (or labels). In this chapter, we survey classification techniques that have been proposed for this problem. We consider two broad categories: methods based on iterative application of traditional classifiers using graph information as features, and methods which propagate the existing labels via random walks. We adopt a common perspective on these methods to highlight the similarities between different approaches within and across the two categories. We also describe some extensions and related directions to the central problem of node classification.