nils-herrmann/clean_scirepeval huggingface dataset:
Human Activity Recognition in Smart Environments

This paper presents a method for image based human activity recognition, in a smart environment. We use background subtraction and skeletisation as image processing techniques, combined with Artificial Neural Networks for human posture classification and Hidden Markov Models for activity interpretation. By this approach we successfully recognized basic human actions such as walking, rotating, sitting and bending up/down, lying and falling. The method can be applied in smart houses, for elderly people who live alone.
Image classification of fine-grained fashion image based on style using pre-trained convolutional neural network

Deep learning has emerged as a new methodology with continuous interests in artificial intelligence, and it can be applied in various business fields for better performance. In fashion business, deep learning, especially Convolutional Neural Network (CNN), is used in classification of apparel image. However, apparel classification can be difficult due to various apparel categories and lack of labeled image data for each category. Therefore, we propose to pre-train the GoogLeNet architecture on ImageNet dataset and fine-tune on our fine-grained fashion dataset based on design attributes. This will complement the small size of dataset and reduce the training time. After 10-fold experiments, the average final test accuracy results 62%.
Putting people in the map: anthropogenic biomes of the world

H have long distinguished themselves from other species by shaping ecosystem form and process using tools and technologies, such as fire, that are beyond the capacity of other organisms (Smith 2007). This exceptional ability for ecosystem engineering has helped to sustain unprecedented human population growth over the past half century, to such an extent that humans now consume about one-third of all terrestrial net primary production (NPP; Vitousek et al. 1986; Imhoff et al. 2004) and move more earth and produce more reactive nitrogen than all other terrestrial processes combined (Galloway 2005; Wilkinson and McElroy 2007). Humans are also causing global extinctions (Novacek and Cleland 2001) and changes in climate that are comparable to any observed in the natural record (Ruddiman 2003; IPCC 2007). Clearly, Homo sapiens has emerged as a force of nature rivaling climatic and geologic forces in shaping the terrestrial biosphere and its processes. Biomes are the most basic units that ecologists use to describe global patterns of ecosystem form, process, and biodiversity. Historically, biomes have been identified and mapped based on general differences in vegetation type associated with regional variations in climate (Udvardy 1975; Matthews 1983; Prentice et al. 1992; Olson et al. 2001; Bailey 2004). Now that humans have restructured the terrestrial biosphere for agriculture, forestry, and other uses, global patterns of species composition and abundance, primary productivity, land-surface hydrology, and the biogeochemical cycles of carbon, nitrogen, and phosphorus, have all been substantially altered (Matson et al. 1997; Vitousek et al. 1997; Foley et al. 2005). Indeed, recent studies indicate that human-dominated ecosystems now cover more of Earth’s land surface than do “wild” ecosystems (McCloskey and Spalding 1989; Vitousek et al. 1997; Sanderson et al. 2002, Mittermeier et al. 2003; Foley et al. 2005). It is therefore surprising that existing descriptions of biome systems either ignore human influence altogether or describe it using at most four anthropogenic ecosystem classes (urban/built-up, cropland, and one or two cropland/natural vegetation mosaic(s); classification systems include IGBP, Loveland et al. 2000; “Olson Biomes”, Olson et al. 2001; GLC 2000, Bartholome and Belward 2005; and GLOBCOVER, Defourny et al. 2006). Here, we present an alternate view of the terrestrial biosphere, based on an empirical analysis of global patterns of sustained direct human interaction with ecosystems, yielding a global map of “anthropogenic biomes”. We then examine the potential of anthropogenic biomes to serve as a new global framework for ecology, complete with CONCEPTS AND QUESTIONS
Face swapping: automatically replacing faces in photographs

In this paper, we present a complete system for automatic face replacement in images. Our system uses a large library of face images created automatically by downloading images from the internet, extracting faces using face detection software, and aligning each extracted face to a common coordinate system. This library is constructed off-line, once, and can be efficiently accessed during face replacement. Our replacement algorithm has three main stages. First, given an input image, we detect all faces that are present, align them to the coordinate system used by our face library, and select candidate face images from our face library that are similar to the input face in appearance and pose. Second, we adjust the pose, lighting, and color of the candidate face images to match the appearance of those in the input image, and seamlessly blend in the results. Third, we rank the blended candidate replacements by computing a match distance over the overlap region. Our approach requires no 3D model, is fully automatic, and generates highly plausible results across a wide range of skin tones, lighting conditions, and viewpoints. We show how our approach can be used for a variety of applications including face de-identification and the creation of appealing group photographs from a set of images. We conclude with a user study that validates the high quality of our replacement results, and a discussion on the current limitations of our system.
E-COMMERCE ENTREPRENEURSHIP : EMERGING PRACTICES , KEY CHALLENGES AND FUTURE DIRECTIONS

The rise of the World Wide Web and electronic commerce has created one of the most challenging environments for entrepreneurship in recent history. Market needs and the technology required to meet those needs can change even while the product or service is still being developed (Iansiti and MacCormak, 1997). This framework demands that founders of organizations strive for "relentless innovation," leading their firms through the continual infusion of new ideas, and emphasizing constant innovation, experimentation, and rapid change (Cohen and Jordan, 1999). The environment of e-commerce enables entrepreneurs to rapidly try new approaches, quickly share successes and failures, and monitor what is new and useful (Oliva, 1998). E-commerce permits managers to become quicker in how they gather, synthesize, utilize, and disseminate information. Those who are willing to experiment with new product and service offerings will be positioned to compete most effectively (Hodgetts, Luthans, and Slocum, 1999). The extent to which entrepreneurs capitalize on the conditions presented by e-commerce and engage in experimentation and innovation is of major interest in contemporary research. Because of the demand for innovative organizational behavior present in e-business, this is an important domain in which to study entrepreneurship. However, because it is so recent, very little is known about the role attributes or innovative activities of entrepreneurs in Internet organizations. What enables emerging organizations to achieve effective performance? What are the opportunities and constraints in operating within this environment? These and many others questions are arising as the field of Internet entrepreneurship grows and develops. The answers 3 will inform the development of the growing research base in this area and will facilitate the subsequent comparison of key variables and relationships found in firms engaging in e-commerce with those in traditional brick and mortar firms. This paper presents the emergent practices and processes of e-commerce entrepreneurship, explores the key challenges facing the new ventures, and offers a research agenda to guide future work in this area. The paper draws on recent research findings and focuses on founders and organizations whose activities encompass the Internet, conducting multiple transactions on the Web, rather than on organizations that use the Web only for customer advertising and information gathering purposes. It is the premise of this paper that the first major step in developing a model of e-commerce entrepreneurship is to examine the processes and behaviors found within the ventures. The examination is conducted along several dimensions and contributes …
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings

One-hot CNN (convolutional neural network) has been shown to be effective for text categorization (Johnson & Zhang, 2015a;b). We view it as a special case of a general framework which jointly trains a linear model with a non-linear feature generator consisting of ‘text region embedding + pooling’. Under this framework, we explore a more sophisticated region embedding method using Long Short-Term Memory (LSTM). LSTM can embed text regions of variable (and possibly large) sizes, whereas the region size needs to be fixed in a CNN. We seek effective and efficient use of LSTM for this purpose in the supervised and semi-supervised settings. The best results were obtained by combining region embeddings in the form of LSTM and convolution layers trained on unlabeled data. The results indicate that on this task, embeddings of text regions, which can convey complex concepts, are more useful than embeddings of single words in isolation. We report performances exceeding the previous best results on four benchmark datasets.
Speech and language processing: an introduction to natural language processing

For undergraduate or advanced undergraduate courses in Classical Natural Language Processing, Statistical Natural Language Processing, Speech Recognition, Computational Linguistics, and Human Language Processing. An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology at all levels and with all modern technologies this text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. The authors cover areas that traditionally are taught in different courses, to describe a unified vision of speech and language processing. Emphasis is on practical applications and scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web. The Second Edition offers a significant amount of new and extended material.  Click on the "Resources" tab to View Downloadable Files:Solutions Power Point Lecture Slides Chapters 1-5, 8-10, 12-13 and 24 Now Available! For additional resourcse visit the author website: http://www.cs.colorado.edu/~martin/slp.html
Adaptive Orientation Model Fitting for Latent Overlapped Fingerprints Separation

Overlapped fingerprints are commonly encountered in latent fingerprints lifted from crime scenes. Such overlapped fingerprints can hardly be processed by state-of-the-art fingerprint matchers. Several methods have been proposed to separate the overlapped fingerprints. However, these methods neither provide robust separation results, nor could be generalized for most overlapped fingerprints. In this paper, we propose a novel latent overlapped fingerprints separation algorithm based on adaptive orientation model fitting. Different from existing methods, our algorithm estimates the initial orientation fields in a more accurate way and then separates the orientation fields for component fingerprints through an iterative correction process. Global orientation field models are used to predict and correct the orientations in overlapped regions. Experimental results on the latent overlapped fingerprints database show that the proposed algorithm outperforms the state-of-the-art algorithm in terms of accuracy.
Economic Growth and Income Inequality

We investigate whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997), with negative results. We then investigate the evolution of income inequality over the same period and its correlation with growth. The dominating feature is inequality convergence across countries. This convergence has been significantly faster amongst developed countries. Growth does not appear to influence the evolution of inequality over time.
Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data

We present hidden fluid mechanics (HFM), a physics informed deep learning framework capable of encoding an important class of physical laws governing fluid motions, namely the Navier-Stokes equations. In particular, we seek to leverage the underlying conservation laws (i.e., for mass, momentum, and energy) to infer hidden quantities of interest such as velocity and pressure fields merely from spatio-temporal visualizations of a passive scaler (e.g., dye or smoke), transported in arbitrarily complex domains (e.g., in human arteries or brain aneurysms). Our approach towards solving the aforementioned data assimilation problem is unique as we design an algorithm that is agnostic to the geometry or the initial and boundary conditions. This makes HFM highly flexible in choosing the spatio-temporal domain of interest for data acquisition as well as subsequent training and predictions. Consequently, the predictions made by HFM are among those cases where a pure machine learning strategy or a mere scientific computing approach simply cannot reproduce. The proposed algorithm achieves accurate predictions of the pressure and velocity fields in both two and three dimensional flows for several benchmark problems motivated by real-world applications. Our results demonstrate that this relatively simple methodology can be used in physical and biomedical problems to extract valuable quantitative information (e.g., lift and drag forces or wall shear stresses in arteries) for which direct measurements may not be possible.
Using CloudSim to learn cloud computing architecture/system concepts in a graduate course

The recent expansion of the cloud computing paradigm has motivated educators to include cloud-related topics in computer science and computer engineering curricula. While programming and algorithm topics have been covered in different undergraduate and graduate courses, cloud architecture/system topics are still not usually studied in academic contexts. But design, deployment and management of datacenters, virtualization technologies for cloud, cloud management tools and similar issues should be addressed in current computer science and computer engineering programs. This work presents our approach and experiences in designing and implementing a curricular module covering all these topics. In this approach the utilization of a simulation tool, CloudSim, is essential to allow the students a practical approximation to the course contents.
On the Mechanics Underlying the Reservoir-Excess Separation in Systemic Arteries and their Implications for Pulse Wave Analysis

Several works have separated the pressure waveform p in systemic arteries into reservoir p(r) and excess p(exc) components, p = p(r) + p(exc), to improve pulse wave analysis, using windkessel models to calculate the reservoir pressure. However, the mechanics underlying this separation and the physical meaning of p(r) and p(exc) have not yet been established. They are studied here using the time-domain, inviscid and linear one-dimensional (1-D) equations of blood flow in elastic vessels. Solution of these equations in a distributed model of the 55 larger human arteries shows that p(r) calculated using a two-element windkessel model is space-independent and well approximated by the compliance-weighted space-average pressure of the arterial network. When arterial junctions are well-matched for the propagation of forward-travelling waves, p(r) calculated using a three-element windkessel model is space-dependent in systole and early diastole and is made of all the reflected waves originated at the terminal (peripheral) reflection sites, whereas p(exc) is the sum of the rest of the waves, which are obtained by propagating the left ventricular flow ejection without any peripheral reflection. In addition, new definitions of the reservoir and excess pressures from simultaneous pressure and flow measurements at an arbitrary location are proposed here. They provide valuable information for pulse wave analysis and overcome the limitations of the current two- and three-element windkessel models to calculate p(r).
Sentiment Classification of Chinese Microblogging Texts with Global RNN

Microblogging websites such as twitter and Sina Weibo have attracted many users to share their experiences and express their opinions on a variety of topics. Sentiment classification of microblogging texts is of great significance in analyzing users' opinion on products, persons and hot topics. However, conventional bag-of-words-based sentiment classification methods may meet some problems in processing Chinese microblogging texts because they does not consider semantic meanings of texts. In this paper, we proposed a global RNN-based sentiment method, which use the outputs of all the time-steps as features to extract the global information of texts, for sentiment classification of Chinese microblogging texts and explored different RNN-models. The experiments on two Chinese microblogging datasets show that the proposed method achieves better performance than conventional bag-of-words-based methods.
Delphi Technique Theoretical Framework in Qualitative Research

--------------------------------------------------------------ABSTRACT------------------------------------------------------Using specialized knowledge and perspectives of a set in decision-makings about issues that are qualitative is very helpful. Delphi technique is a group knowledge acquisition method, which is also used for qualitative issue decision-makings. Delphi technique can be used for qualitative research that is exploratory and identifying the nature and fundamental elements of a phenomenon is a basis for study. It is a structured process for collecting data during the successive rounds and group consensus. Despite over a half century of using Delphi in scientific and academic studies, there are still several ambiguities about it. The main problem in using the Delphi technique is lack of a clear theoretical framework for using this technique. Therefore, this study aimed to present a comprehensive theoretical framework for the application of Delphi technique in qualitative research. In this theoretical framework, the application and consensus principles of Delphi technique in qualitative research were clearly explained.
Towards Intrusion Detection in Wireless Sensor Networks

In this work we study the problem of Intrusion Detection is sensor networks and we propose a lightweight scheme that can be applied to such networks. Its basic characteristic is that nodes monitor their neighborhood and collaborate with their nearest neighbors to bring the network back to its normal operational condition. We emphasize in a distributed approach in which, even though nodes don’t have a global view, they can still detect an intrusion and produce an alert. We apply our design principles for the blackhole and selective forwarding attacks by defining appropriate rules that characterize malicious behavior. We also experimentally evaluate our scheme to demonstrate its effectiveness in detecting the afore-mentioned attacks.
Personal and situational variables associated with wind turbine noise annoyance.

The possibility that wind turbine noise (WTN) affects human health remains controversial. The current analysis presents results related to WTN annoyance reported by randomly selected participants (606 males, 632 females), aged 18-79, living between 0.25 and 11.22 km from wind turbines. WTN levels reached 46 dB, and for each 5 dB increase in WTN levels, the odds of reporting to be either very or extremely (i.e., highly) annoyed increased by 2.60 [95% confidence interval: (1.92, 3.58), p < 0.0001]. Multiple regression models had R(2)'s up to 58%, with approximately 9% attributed to WTN level. Variables associated with WTN annoyance included, but were not limited to, other wind turbine-related annoyances, personal benefit, noise sensitivity, physical safety concerns, property ownership, and province. Annoyance was related to several reported measures of health and well-being, although these associations were statistically weak (R(2 )< 9%), independent of WTN levels, and not retained in multiple regression models. The role of community tolerance level as a complement and/or an alternative to multiple regression in predicting the prevalence of WTN annoyance is also provided. The analysis suggests that communities are between 11 and 26 dB less tolerant of WTN than of other transportation noise sources.
Progressive Web Apps: the Definite Approach to Cross-Platform Development?

Although development practices for apps have matured, cross-platform development remains a prominent topic. Typically, apps should always support both Android and iOS devices. They ought to run smoothly on various hardware, and be compatible with a host of platform versions. Additionally, device categories beyond smartphone and tablets have emerged, which makes multi-platform support even trickier. Truly developing an app once and serving the multitude of possible targets remains an issue despite having crossplatform frameworks that are acknowledged by practice and research. The technology unifier remains to be found, but Progressive Web Apps (PWA) might be a step towards it. In this paper, we analyse the foundations of PWAs in cross-platform development and scrutinize the status quo of current possibilities. Based on our observations, we investigate unified development, and discuss open questions. We seek to stimulate interest and narrow the immense gap that has arisen since industry started to embrace PWAs.
Author ' s personal copy A scaling mitigation approach during direct contact membrane distillation

Membrane scaling during the treatment of aqueous solutions containing sparingly soluble salts by direct contact membrane distillation (DCMD) was investigated. The results reveal that membrane scaling caused by CaSO4 was more severe than that by CaCO3 or silicate. However, under the experimental condition used in this study and at feed and distillate temperature of 20 C and 40 C, respectively, CaSO4 scaling occurred only after a sufficiently long induction time of up to 25 h (corresponding to a saturation index of up to 1.5). The induction period decreased and the size of the CaSO4 crystals increased as the feed temperature increased. SEM analysis reveals that prior to the onset of CaSO4 scaling, the membrane surface was relatively clean and was completely free of any large crystals. Subsequently, a simple operational regime involving regular membrane flushing to reset the induction period was developed and was proven to be effective in controlling CaSO4 scaling. At a low system recovery, the permeate flux was constant despite the fact that the feed solution was always at a super saturation condition. Results reported here also confirm the interplay between induction time and the saturation index.