nils-herrmann/clean_scirepeval huggingface dataset:
Development of concepts for human labour accounting in Emergy Assessment and other Environmental Sustainability Assessment methods

Human labour is central to the functioning of any human influenced process. Nevertheless, Environmental Sustainability Assessments (ESAs) do not systematically include human labour as an input. Systematic omission of labour inputs in ESAs may constitute an unfortunate, significant bias in favour of labour intensive processes and a systematic underestimation of environmental impacts has implications for decision making. A brief review of the evaluation of human labour in ESAs reveals that only Emergy Assessment (EmA) accounts for labour as standard. Focussing on EmA, we find, however, that there is no agreement on the calculation method for labour. We formalise the calculation of human labour unit emergy values (UEVs) as being the ratio between the emergy resource basis of the labour system and a proxy for labour, with or without allocation to account for different qualities of labour. The formalised calculation approach is demonstrated using examples from the literature (USA, with allocation based on educational level; Ghana, with allocation based on income level; the World, with no allocation) We elaborate on how labour may be considered as endogenous or exogenous to the studied system, and how inputs can be categorised as direct labour taking place in the system under study and indirect labour occurring upstream in the supply chain associated with the studied system. With appropriate modifications, the formalised calculation approach and the distinction between direct and indirect labour may be transferred to other ESA methodologies. Concerning EmA, we recommend that product UEVs should systematically be calculated with and without labour, and that working hours rather than salary should be used when accounting for labour inputs. We recognise that there is a risk of double counting of environmental impacts when including labour. We conclude, however, that it can be ignored for most production systems, since only a negligible fraction of emergy already accounted for is likely to be included in the emergy flow from labour inputs.
Naphtha Production Assessment from the Perspective of the Emergy Accounting
Venue: APMS

Naphtha is the main petroleum derivative used as a feedstock for the steam cracking of olefins and aromatic petrochemical products. The environmental performance of the production of 1 kg of naphtha is evaluated using a Life Cycle Inventory based emergy accounting, considering the environmental load of labor and services. The biophysical flows, which reflect the work of nature in the creation of the resources required for the production and the monetary systems, are evaluated in terms of emergy. The information recorded in a life cycle inventory (LCI) is used for the estimation of the specific emergy of naphtha. The results show that labor and services flows correspond to 10.54% and 12.93% of the total system emergy, respectively. The LCI based emergy accounting, although being an unconventional measurement method, was found feasible bringing additional information that may help decision making.
The Cityscapes Dataset for Semantic Urban Scene Understanding
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large scale dataset to train and test approaches for pixel level and instance level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in depth analysis of the dataset characteristics, as well as a performance evaluation of several state of the art approaches based on our benchmark.
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Vision based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images, thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel level annotations are required. In this paper, we propose to use a virtual world to automatically generate realistic synthetic images with pixel level annotations. Then, we address the question of how useful such data can be for semantic segmentation in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations. We use SYNTHIA in combination with publicly available real world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show how the inclusion of SYNTHIA in the training stage significantly improves performance on the semantic segmentation task.
The emerging role of water footprint in supply chain management: A critical literature synthesis and a hierarchical decision making framework

Freshwater overexploitation and scarcity have led to extensive shifts in demand patterns for water friendly products. As several agricultural and industrial activities are closely intertwined with water consumption, the availability of sufficient freshwater resources constitutes a significant precondition for covering global consumer needs. In this context, the design and management of sustainable supply chains in terms of freshwater resources' preservation have emerged as major challenges in the corporate agenda. As such, the concept of water footprint as a key performance indicator of freshwater utilization has been introduced at national, corporate and product levels. In this manuscript, we first provide a critical literature synthesis concerning product water footprint assessment in order to map the state of the art research related to freshwater consumption and pollution in the agricultural and industrial sectors. Our analysis demonstrates that although water footprint assessment is a rapidly evolving research field, scientific publications focusing on a holistic approach concerning freshwater exploitation at a supply chain extent are rather limited. The findings further verify that the agrifood sector dominates global water use. In this respect, we analyse both corporate and academic literature in order to identify emerging issues on freshwater resources' management for agrifood products. Finally, we propose a first effort hierarchical decision making framework that includes water footprint mitigation policies for agrifood supply chains in order to support all stakeholders in developing a comprehensive water stewardship strategy.
Using System Dynamics Approach in Modeling the Integrated Farming Scenario to Increase Cassava Production in Indonesia

research was focused on integrated farming system and usage of system dynamic approach. This research was done in Trenggalek District, East Java Province, Indonesia. It used secondary data, and supported by primary one. Modeling and making policy scenario used system dynamics approach. The model fit was examined by Theil test. Modeling with system dynamics approach is effective to create a complex model of IFS, because the model behavior is similar with those of the real world. The model also could be used to simulate policy scenarios and estimate the future performance of the model. The scenario done was a policy of technology innovation namely optimal production with the environmental awareness. This scenario focuses on using organic fertilizer in cassava goats integrated farming; it is quite effective and can decrease farming cost as much as 27.75% up to 34.36% The scenario needs introduction of composting technology and availability of the facilities and infrastructure. Therefore, the implementation needs supports from stakeholder, i.e. government and society, as well as public and private sectors.
Seasonal and regional variations of source contributions for PM10 and PM2.5 in urban environment.
Venue: The Science of the total environment

To characterize the sources of to PM10 and PM2.5, a long term, speciate and simultaneous dataset was sampled in a megacity in China during the period of 2006 2014. The PM concentrations and PM2.5/PM10 were higher in the winter. Higher percentages of Al, Si, Ca and Fe were observed in the summer, and higher concentrations of OC, NO3( and SO4(2 occurred in the winter. Then, the sources were quantified by an advanced three way model (defined as an ABB three way model) which estimates different profiles for different sizes. A higher percentage of cement and crustal dust was present in the summer; higher fractions of coal combustion and nitrate+SOC were observed in the winter. Crustal and cement contributed larger portion to coarse part of PM10, whereas vehicular and secondary source categories were enriched in PM2.5. Finally, potential source contribution function (PSCF) and source regional apportionment (SRA) methods were combined with the three way model to estimate geographical origins. During the sampling period, the southeast region (R4) was an important region for most source categories (0.6% 11.5% the R1 (centre region) also played a vital role (0.3 6.9%
Nano and Micro Scale Oxidative Patterning of Titanium Implant Surfaces for Improved Surface Wettability.
Venue: Journal of nanoscience and nanotechnology

A simple and scalable surface modification treatment is demonstrated, in which nano and microscale features are introduced into the surface of titanium (Ti) substrates by means of a novel and eco friendly oxidative aqueous solution composed of hydrogen peroxide (H202) and sodium bicarbonate (NaHCO3) By immersing mirror polished Ti discs in an aqueous mixture of 30 wt% H2O2/5 wt% NaHCO3 at 23 3 degrees C for 4 h, it was confirmed that this mixture is capable of generating microscale topographies on Ti surfaces. It also simultaneously formed nanochannels that were regularly arranged in a comb like pattern on the Ti surface, thus forming a hierarchical surface structure. Further, these nano/micro textured Ti surfaces showed great surface roughness and excellent wettability when compared with control Ti surfaces. This study demonstrates that a H2O2/NaHCO3 mixture can be effectively utilized to create reproducible nano/microscale topographies on Ti implant surfaces, thus providing an economical new oxidative solution that may be used effectively and safely as a Ti surface modification treatment.
Techno Economic Study on Non Capture CO 2 Utilization Technology

Techno economic evaluation of Non Capture CO2 Utilization (NCCU) technology for the production of high value added products using greenhouse gas (CO2) was performed. The general scheme of NCCU process is composed of CO2 carbonation and brine electrolysis process. Through a carbonation reaction with sodium hydroxide that is generated from brine electrolysis and CO2 of the flue gas, it is possible to get high value added products such as sodium bicarbonate, sodium hydroxide, hydrogen chloride and also to reduce the CO2 emission simultaneously. For the techno economic study on NCCU technology, continuous operation of bench scale facility which could treat 2 kgCO2/day was performed. and based on the key performance data evaluated, the economic evaluation analysis targeted on the commercial chemical plant, which could treat 6 tons CO2 per day, was performed using the net present value (NPV) metrics. The results showed that the net profit obtained during the whole plant operation was about 7,890 mKRW (million Korean Won) on NPV metrics and annual CO2 reduction was estimated as about 2,000 tCO2. Also it was found that the energy consumption of brine electrolysis is one of the key factors which affect the plant operation cost (ex. electricity consumption) and the net profit of the plant. Based on these results, it could be deduced that NCCU technology of this study could be one of the cost effective CO2 utilization technology options.
A Shallow Convolutional Neural Network for Accurate Handwritten Digits Classification
Venue: ICPR 2016

At present the deep neural network is the hottest topic in the domain of machine learning and can accomplish a deep hierarchical representation of the input data. Due to deep architecture the large convolutional neural networks can reach very small test error rates below 0.4% using the MNIST database. In this work we have shown, that high accuracy can be achieved using reduced shallow convolutional neural network without adding distortions for digits. The main contribution of this paper is to point out how using simplified convolutional neural network is to obtain test error rate 0.71% on the MNIST handwritten digit benchmark. It permits to reduce computational resources in order to model convolutional neural network.
Real Time Fatigue Driver Detection from Electroencephalography Using Emotiv EPOC+

The fatigue driving detection has been developed with many kinds of approaches, such as video using face expressions and Electroencephalography (EEG) that uses the brainwave signals of the driver. This paper proposes a method to implement the driving fatigue detection in real time using Python and Emotiv EPOC+ with 14 channels. The EEG recorded database will extract their features per 30 seconds. The prediction process gets the EEG recorded data from the driver doing the driving simulation and trains it using the extracted features data from the database. The results print as Fit and Alert, or Fatigue and Sleepy. The contributions of the authors in this paper are as follows: i) the reduction of the processing time, such as reading input and output files and communicating among different programming languages; ii) the analysis and comparison of the dynamics of prediction results and significant channels from the results of the previous research, and iii) the development of the system from semi real time to real time forecasting.
Real Time Electroencephalography Based Emotion Recognition System

This paper proposes parametric, general and effectively automatic real time classification method of electroencephalography (EEG) signals based on emotions. The specific characteristics of the high frequency signals (alpha, beta, gamma) are observed, and then Fourier Transform, Features Extraction (mean, standard deviation, power) and the K Nearest Neighbors (KNN) are employed for signal processing, analysis and classification. The proposed method consists of two stages for a multi class classification and it can be considered as the framework of multi emotions based on Brain Computer Interface (BCI) The first stage, the calibration, is off line and it computes the signal processing, determines the features and trains the classification. The second stage, the real time, is the test on new data. The FFT is applied to avoid redundancy in the selected features; then the classification is carried out using the KNN. The results show that the average accuracy results are 82.33% (valence) and 87.32% (arousal)
A Prototype SSVEP Based Real Time BCI Gaming System
Venue: Comput. Intell. Neurosci.

Although brain computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram based gaming systems. We have developed a prototype three class brain computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.
Convolutional Neural Networks for Text Categorization: Shallow Word level vs. Deep Character level
Venue: ArXiv

This paper reports the performances of shallow word level convolutional neural networks (CNN) our earlier work (2015) on the eight datasets with relatively large training data that were used for testing the very deep character level CNN in Conneau et al. (2016) Our findings are as follows. The shallow word level CNNs achieve better error rates than the error rates reported in Conneau et al. though the results should be interpreted with some consideration due to the unique pre processing of Conneau et al. The shallow word level CNN uses more parameters and therefore requires more storage than the deep character level CNN; however, the shallow word level CNN computes much faster.
Dijkstra monads for free
Venue: POPL 2017

Dijkstra monads enable a dependent type theory to be enhanced with support for specifying and verifying effectful code via weakest preconditions. Together with their closely related counterparts, Hoare monads, they provide the basis on which verification tools like F* Hoare Type Theory (HTT) and Ynot are built. We show that Dijkstra monads can be derived "for free" by applying a continuation passing style (CPS) translation to the standard monadic definitions of the underlying computational effects. Automatically deriving Dijkstra monads in this way provides a correct by construction and efficient way of reasoning about user defined effects in dependent type theories. We demonstrate these ideas in EMF* a new dependently typed calculus, validating it via both formal proof and a prototype implementation within F* Besides equipping F* with a more uniform and extensible effect system, EMF* enables a novel mixture of intrinsic and extrinsic proofs within F
Study and Analysis of MYCIN expert system

MYCIN was the one of the initial expert systems to perform with the level of expertise of a human expert and to provide users with complete explanation of its logical reasoning. MYCIN played a benchmark of most of the expert systems which were developed after MYCIN. Moreover, the techniques which were developed for MYCIN expert system have become most widely available design in the various small expert system building tools. It was designed to aid doctors in proper diagnosis and treatment of bacteremia infections. MYCIN was strictly a research system. The present paper provides summary of rule application concepts of MYCIN expert system.  MYCIN is an expert system. By that we mean that it is an AI program designed (a) to provide expert level solutions to complex problems, (b) to be understandable, and (c) to be flexible enough to accommodate new knowledge easily. Because we have designed MYCIN to provide advice through a consultative dialogue, we sometimes refer to it as a consultation system[10][11] There are two main parts to an expert system like MYCIN: a knowledge base and an inference mechanism, or engine (Figure 1 1) In addition there are often subprograms designed to facilitate interaction with users, to help build a knowledge base, to explain a line of reasoning, and so forth. Figure 1.1 Major Parts of an expert system. Arrows indicate information flow. The knowledge base is the program's store of facts and associations it "knows" about a subject area such as medicine. A critical design decision is how such knowledge is to be represented within the program. There are many choices, in general. For MYCIN, we chose to represent knowledge mostly as conditional statements, or rules, of the following form: IF: There is evidence that A and B are true, THEN: Conclude there is evidence that C is true. This form is often abbreviated to one of the following:
Recurrent Neural Networks for Customer Purchase Prediction on Twitter
Venue: CBRecSys@RecSys

The abundance of data posted to Twitter enables companies to extract useful information, such as Twitter users who are dissatisfied with a product. We endeavor to determine which Twitter users are potential customers for companies and would be receptive to product recommendations through the language they use in tweets after mentioning a product of interest. With Twitter's API, we collected tweets from users who tweeted about mobile devices or cameras. An expert annotator determined whether each tweet was relevant to customer purchase behavior and whether a user, based on their tweets, eventually bought the product. For the relevance task, among four models, a feed forward neural network yielded the best cross validation accuracy of over 80% per product. For customer purchase prediction of a product, we observed improved performance with the use of sequential input of tweets to recurrent models, with an LSTM model being best; we also observed the use of relevance predictions in our model to be more effective with less powerful RNNs and on more difficult tasks.
Modelling Contextual Information in Session Aware Recommender Systems with Neural Networks
Venue: RecSys

Preparing recommendations for unknown users or such that correctly respond to the short term needs of a particular user is one of the fundamental problems for e commerce. Most of the common Recommender Systems assume that user identification must be explicit. In this paper a Session Aware Recommender System approach is presented where no straightforward user information is required. The recommendation process is based only on user activity within a single session, defined as a sequence of events. This information is incorporated in the recommendation process by explicit context modeling with factorization methods and a novel approach with Recurrent Neural Network (RNN) Compared to the session modeling approach, RNN directly models the dependency of user observed sequential behavior throughout its recurrent structure. The evaluation discusses the results based on sessions from real life system with ephemeral items (identified only by the set of their attributes) for the task of top n best recommendations.