Segmentation-Aware Convolutional Networks Using Local Attention Masks

We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regions and increases their spatial precision. To obtain segmentation information, we set up a CNN to provide an embedding space where region co-membership can be estimated based on Euclidean distance. We use these embeddings to compute a local attention mask relative to every neuron position. We incorporate such masks in CNNs and replace the convolution operation with a "segmentation-aware" variant that allows a neuron to selectively attend to inputs coming from its own region. We call the resulting network a segmentation-aware CNN because it adapts its filters at each image point according to local segmentation cues. We demonstrate the merit of our method on two widely different dense prediction tasks, that involve classification (semantic segmentation) and regression (optical flow). Our results show that in semantic segmentation we can match the performance of DenseCRFs while being faster and simpler, and in optical flow we obtain clearly sharper responses than networks that do not use local attention masks. In both cases, segmentation-aware convolution yields systematic improvements over strong baselines. Source code for this work is available online at http://cs.cmu.edu/~aharley/segaware

DEVELOPMENT AND EVALUATION OF AN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR THE CALCULATION OF SOIL WATER RECHARGE IN A WATERSHED

Modeling of groundwater recharge is one of the most important topics in hydrology due to its essential application to water resources management. In this study, an Adaptive Neuro Fuzzy Inference System (ANFIS) method is used to simulate groundwater recharge for watersheds. In-situ observational datasets for temperature, precipitation, evapotranspiration, (ETo) and groundwater recharge of the Lake Karla, Thessaly, Greece watershed were taken into consideration for the present study. The datasets consisted of monthly average values of the last almost 50 years, where 70% of the values used for learning with the rest for the testing phase. The testing was performed under a set of different membership functions without expert’s knowledge acquisition and with the support of a five-layer neural network. Experimental verification shows that, the 3-3-3 combination under the trapezoid membership function with the hybrid neural network support and the 2-2-2 combination under the g-bell membership function with the same neural network support perform the best among all combinations with RMSE 4.78881 and 4.12944 giving on average 5% deviation from the observed values

MODELING OF HYDROLOGICAL AND ENVIRONMENTAL PROCESSES THROUGH OPENMI AND WEB SERVICES

Integrated collaborative modeling has been proven lately to be the most accurate computer methodology that allows modelers to scrutinize the environmental processes using a holistic approach. Due to the dynamic and interdependent nature, such processes involve the interlinking of hydrological, meteorological, environmental, ecosystems and socioeconomical characteristics. In this paper we deal with the development and the integration of a collaborative system of models devoted to the water quantity and quality monitoring, and also to the management of water resources in a watershed. The system is also tailored by a socio-economical study that highlights the impact of the aforementioned management to the local community of the region under study. Models that integrate the collaborative system need to be coupled so that to run simultaneously under the spatial and temporal synchronization condition. To achieve such a simultaneous synchronization, the Open Modeling Interface, (OpenMI) is invoked. The system has been applied and tested to the Lake Karla watershed in Thessaly region, Greece. However due to the loose integration methodology used for its development and to its open ended property, the system can be easily parametrized to offer such an analysis on other similar case studies. An extension to the OpenMI standard provides the remote simultaneous run of models using web services and allowing the development of a cloud repository of models for future use

Ethno-cultural background, bullying and victimization among adolescents

Σκοπός της παρούσας μελέτης ήταν να διερευνήσει το κατά πόσο συνδέεται η εθνο-πολιτισμική προέλευση με τα φαινόμενα του εκφοβισμού και της θυματοποίησης σε έφηβους μαθητές. Ειδικότερα, η έρευνα εξέτασε το πώς το φύλο, η εθνο-πολιτισμική προέλευση των συμμετεχόντων και η εθνο-πολιτισμική σύνθεση των σχολείων σχετίζονται με τον εκφοβισμό και τη θυματοποίηση λόγω-εθνο πολιτισμικής προέλευσης, με τις επιμέρους μορφές τους, καθώς και με την κατανομή των ρόλων των συμμετεχόντων στα φαινόμενα αυτά. Εκατό εξήντα τρεις (163) μαθητές από 5 εθνο-πολιτισμικές ομάδες, οιοποίοι φοιτούσαν σε 2 γυμνάσια του νομού Ροδόπης, ένα υψηλής εθνο-πολιτισμικής ετερότητας και ένα χαμηλής, συμπλήρωσαν ένα ερωτηματολόγιο αυτο-αναφοράς. Τα αποτελέσματα έδειξαν πως τα αγόρια-μέλη της κυρίαρχης εθνο-πολιτισμικής ομάδας παρουσίασαν υψηλότερες συνολικές βαθμολογίες στον εκφοβισμό και στη θυματοποίηση σε σχέση με τα κορίτσια της ίδιας ομάδας. Επίσης, οι χριστιανοί μαθητές ελληνικής εθνοπολιτισμικής προέλευσης σημείωσαν τη χαμηλότερη βαθμολογία σχετικά με τη θυματοποίηση, ενώ οι μουσουλμάνοι τουρκικής εθνο-πολιτισμικής προέλευσης την υψηλότερη. Μεταξύ των 2 σχολείων, στο σχολείο με τη μεγαλύτερη εθνο-πολιτισμικήετερότητα σημειώθηκε υψηλότερη βαθμολογία στον εκφοβισμό και στη θυματοποίηση λόγω εθνο-πολιτισμικής προέλευσης. Αναφορικά με τις επιμέρους μορφές των φαινομένων, οι μαθητές σημείωσαν υψηλή βαθμολογία στη λεκτική και στην έμμεση/κοινωνική μορφή των φαινομένων, με τα αγόρια να εμφανίζουν υψηλότερες βαθμολογίες και στις 3 μορφές του εκφοβισμού. Επίσης, στο σχολείο με τη μεγαλύτερηεθνο-πολιτισμική ετερότητα σημειώθηκε υψηλότερη βαθμολογία σε όλες τις μορφές των φαινομένων. Ως προς τους ρόλους των συμμετεχόντων, στον ρόλο του θύματος τα αγόρια εκπροσωπούνται χαμηλότερα, ενώ στον ρόλο του θύτη-θύματος υψηλότερα εκπροσωπούνται τα κορίτσια. Οι χριστιανοί μαθητές ελληνικής εθνο-πολιτισμικής προέλευσης παρουσίασαν τη μεγαλύτερη εκπροσώπηση στον ρόλο του θύτη, οιμουσουλμάνοι μαθητές τουρκικής εθνο-πολιτισμικής προέλευσης στον ρόλο των θυτώνθυμάτων και οι μουσουλμάνοι πομακικής, ρομανή και απροσδιόριστης εθνοπολιτισμικής προέλευσης στον ρόλο των θυμάτων. Τα ευρήματα συζητούνται ως προς τις παιδαγωγικές τους εφαρμογές.The purpose of this study was to investigate the relationship between ethnic-cultural background and bullying/victimization among adolescent students. In particular, the study examined how gender, ethnic-cultural background and ethnic-cultural school composition were related to bullying/victimization, as well as to its forms and the role assumed by the participants. 163 students from five ethnic-cultural groups attending two junior high schools, one with high and the other with low ethnic diversity, completed a self-report questionnaire. Data analysis revealed that boys, members of the dominant ethnic-cultural group, scored higher in bullying/victimization than girls in the same group. Furthermore, Orthodox Greek students scored lower in victimization, while Muslim Turkish students scored higher. Between the two schools, students attending the school with higher ethnic-cultural diversity scored higher in bullying and victimization. Regarding the forms of bullying/victimization, all students scored higher in verbal and indirect/social bullying, whileboys scored higher in physical bullying as well. Also, students attending the school with higher ethnic-cultural diversity had high scores in all forms of bullying/victimization. Regarding participants' role distribution, boys were less likely to be victims than girls. Orthodox Greek students were more likely to be bullies, Muslim Turkish students were both bullies and victims, while Muslim Pomak, Romany and undefined ethnic-cultural background students were more likely to be victims. Findings are discussed in terms of their pedagogical implication

Fuzzy Cognitive Map-Based Modeling of Social Acceptance to Overcome Uncertainties in Establishing Waste Biorefinery Facilities

Sustainable Waste Biorefinery Facilities (WBFs) represent multifactorial systems that necessitate the organization, cooperation and the acceptance of different social stakeholders. However, these attempts have become targets of environmental, social and legal oppositions despite their obvious economic benefits. The variety of ambivalent and heterogeneous external effects of such projects result in either local support or opposition to the facility, which in turn becomes a critical factor affecting facility location decisions, and subsequent success of a WBF. Research has shown that simple surveys do not sufficiently measure social acceptance of such endeavors, and in most cases, local community factors dominate other external valuable impacts. In the current study, a novel Fuzzy Cognitive Map (FCM) modeling approach is proposed in order to analyze the socio-economic implications and to overcome multiple uncertainties occurring in sustainable WBF development and implementation. The primary investigation relates to the factors that influence the development of organic or chemical treatment of waste by the local communities and the competent authorities. The determination of concepts involved in the FCM modeling depends on a hybrid approach where both experts' opinion and statistical results from questionnaires distributed to stakeholders participate in the concept circumscription, thus identifying the centrality of each node in the model. Several steady state and dynamic analysis scenarios show the influence of driver concepts to receiver concepts on the social aspect FCM constructed

Supportiveness of Low-Carbon Energy Technology Policy Using Fuzzy Multicriteria Decision-Making Methodologies

The deployment of low-carbon energy (LCE) technologies and management of installations represents an imperative to face climate change. LCE planning is an interminable process affected by a multitude of social, economic, environmental, and health factors. A major challenge for policy makers is to select a future clean energy strategy that maximizes sustainability. Thus, policy formulation and evaluation need to be addressed in an analytical manner including multidisciplinary knowledge emanating from diverse social stakeholders. In the current work, a comparative analysis of LCE planning is provided, evaluating different multicriteria decision-making (MCDM) methodologies. Initially, by applying strengths, weaknesses, opportunities, and threats (SWOT) analysis, the available energy alternative technologies are prioritized. A variety of stakeholders is surveyed for that reason. To deal with the ambiguity that occurred in their judgements, fuzzy goal programming (FGP) is used for the translation into fuzzy numbers. Then, the stochastic fuzzy analytic hierarchical process (SF-AHP) and fuzzy technique for order performance by similarity to ideal solution (F-TOPSIS) are applied to evaluate a repertoire of energy alternative forms including biofuel, solar, hydro, and wind power. The methodologies are estimated based on the same set of tangible and intangible criteria for the case study of Thessaly Region, Greece. The application of FGP ranked the four energy types in terms of feasibility and positioned solar-generated energy as first, with a membership function of 0.99. Among the criteria repertoire used by the stakeholders, the SF-AHP evaluated all the criteria categories separately and selected the most significant category representative. Finally, F-TOPSIS assessed these criteria ordering the energy forms, in terms of descending order of ideal solution, as follows: solar, biofuel, hydro, and wind

Breaking Ties of Plurality Voting in Ensembles of Distributed Neural Network Classifiers Using Soft Max Accumulations

Part 2: Learning-Ensemble LearningInternational audienceAn ensemble of distributed neural network classifiers is composed when several different individual neural networks are trained based on their local training data. These classifiers can provide either a single class label prediction, or the normalized via the soft max real value class-outputs that represent posterior probabilities which give the confidence levels. To form the ensemble decision the individual classifier decisions can be combined via the well known majority (or plurality) voting that sums the votes for each class and selects the class that receives most of the votes. While the majority voting is the most popular combination rule many ties in votes can occur, especially in multi-class problems. Ties are usually broken either randomly where the unknown instance is assigned randomly to one of the tied classes or using the class proportions where the tied class with the largest proportion wins. We present a tie breaking strategy that uses soft max confidence accumulations. Every class accumulates a vote and a confidence for this vote. If a tie occurs then the tied class with the maximum confidence sum wins. The proposed tie breaking in the voting process performs very well in all cases of different data distributions on various benchmark datasets