Model for Spatiotemporal Crime Prediction with Improved Deep Learning

Crime is hard to anticipate since it occurs at random and can occur anywhere at any moment, making it a difficult issue for any society to address. By analyzing and comparing eight known prediction models: Naive Bayes, Stacking, Random Forest, Lazy:IBK, Bagging, Support Vector Machine, Convolutional Neural Network, and Locally Weighted Learning – this study proposed an improved deep learning crime prediction model using convolutional neural networks and the xgboost algorithm to predict crime. The major goal of this research is to provide an improved crime prediction model based on previous criminal records. Using the Boston crime dataset, where our larceny crime dataset was extracted, exploratory data analysis (EDA) is used to uncover patterns and explain trends in crimes. The performance of the proposed model on the basis of accuracy, recall, and f-measure was 100% outperforming the other models used in this study. The analysis of the proposed model and prediction can aid security services in making better use of their resources, anticipating crime at a certain time, and serving the society better

Discovering the Common Good in Practice: The Catholicity of Catholic Charities

This research examines a group of UK Catholic charities working in the field of homelessness and social exclusion in order to understand how their Catholicity is constituted and how this impacts on their practice. I argue that their Catholicity is primarily found in how their practices enact, test and extend Catholic social vision rather than in institutional alignment. I demonstrate that the charities have an ecclesiological specificity which official Catholic texts fail to recognise. They operate across the porous boundaries of the visible Church, drawing into their work people who share elements of the social vision articulated in Catholic thought and tradition. Theologically, they enact the Catholic intuition about the meaning of social bonds and reciprocal human flourishing by working to counter social exclusion and vulnerability and point social realities towards the Kingdom. Their location on ecclesial boundaries, their inclusiveness, and their embeddedness in secular structures of social welfare and politics, are necessary conditions of social mission. I use the concept of the common good as a hermeneutic in order to read the charities as a case study testing how Catholic social teaching’s methodological strategies propose shared moral horizons. Using Thomas Bushlack’s concept of civic virtue in conversation with normative Catholic social teaching about the common good enables fresh insights into the practices which enact this principle. The charities discover the meaning of the common good by recognising and wrestling with the absence of the conditions that enable people to seek fulfilment. Their asymmetrical relational work, shaped by their narratives, renders an abstract and elusive concept as a real and practical task. Their communally held and inclusively enacted intuitions disclose pragmatic coherence with Conciliar ecclesiology and validate its orientations. The charities act as agents and inventors of mediated social mission, illuminating an expansive Catholicity

A multi-objective approach to estimate parameters of compartmental epidemiological models. Application to Ebola Virus Disease epidemics.

In this work, we propose a novel methodology to adjust parameters of compartmental epidemiological models. It is based on solving a multi-objective optimization problem that consists in fitting some of the model outputs to real observations. First, according to the available data of the considered epidemic, we define a multi-objective optimization problem where the model parameters are the optimization variables. Then, this problem is solved by considering a particular optimization algorithm called ParWASF-GA (ParallelWeighting Achievement Scalarizing Function Genetic Algorithm). Finally, the decision maker chooses, within the set of possible solutions, the values of parameters that better suit his/her preferences. In order to illustrate the benefit of using our approach, it is applied to estimate the parameters of a deterministic epidemiological model, called Be-CoDiS (Between-Countries Disease Spread), used to forecast the possible spread of human diseases within and between countries. We consider data from different Ebola outbreaks from 2014 up to 2019. In all cases, the proposed methodology helps to obtain reasonable predictions of the epidemic magnitudes with the considered model

MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation

The goal of sequential recommendation (SR) is to predict a user's potential interested items based on her/his historical interaction sequences. Most existing sequential recommenders are developed based on ID features, which, despite their widespread use, often underperform with sparse IDs and struggle with the cold-start problem. Besides, inconsistent ID mappings hinder the model's transferability, isolating similar recommendation domains that could have been co-optimized. This paper aims to address these issues by exploring the potential of multi-modal information in learning robust and generalizable sequence representations. We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal synergy while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation. On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation, providing more precise matching between users and items. We pre-train the model with contrastive learning objectives and fine-tune it in an efficient manner. Extensive experiments demonstrate the effectiveness and flexibility of MISSRec, promising an practical solution for real-world recommendation scenarios.Comment: Accepted to ACM MM 202

NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image

This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole- scene hyperspectral (HS) information from a 3-channel RGB image. As in the previous challenge, two tracks were provided: (i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB images are themselves calculated numerically using the ground-truth HS images and supplied spectral sensitivity functions (ii) a "Real World" track, simulating capture by an uncalibrated and unknown camera, where the HS images are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever, natural hyperspectral image data set is presented, containing a total of 510 HS images. The Clean and Real World tracks had 103 and 78 registered participants respectively, with 14 teams competing in the final testing phase. A description of the proposed methods, alongside their challenge scores and an extensive evaluation of top performing methods is also provided. They gauge the state-of-the-art in spectral reconstruction from an RGB image

Optical sensors for the determination of strong base and acid in harsh organic and saline environments

This dissertation focuses on the development of optical acid and base sensors and their uses in ternary systems. Chapter 1 provides an overview of the dissertation. Background on topics related to the optical sensors is discussed including: measurement of pH (mild and harsh conditions), optical pH sensing (aqueous and ternary systems), basic sol-gel processing, and a summary of instruments used in this research. Chapter 2 involves the use of optical acid and base sensors in concentrated NaOH-ROH-HZO (R = Me, Et, i-Pr) mixed solvent systems, in which a novel linear relationship is observed between (aA/aCalcohol) and Obese for high pKal indicators encapsulated in sol-gel (ZrSiy02(x+y))-organic polymer composites. This relationship leads to a dual transducer approach to give Chase and Calcoho. with high accuracy and precision. In addition, the selection of indicators with high pKas, large dynamic spectral ranges, and chemical stability to OH\u27 attack and oxidation is presented

RECENT CNN-BASED TECHNIQUES FOR BREAST CANCER HISTOLOGY IMAGE CLASSIFICATION

Histology images are extensively used by pathologists to assess abnormalities and detect malignancy in breast tissues. On the other hand, Convolutional Neural Networks (CNN) are by far, the privileged models for image classification and interpretation. Based on these two facts, we surveyed the recent CNN-based methods for breast cancer histology image analysis. The survey focuses on two major issues usually faced by CNN-based methods namely the design of an appropriate CNN architecture and the lack of a sufficient labelled dataset for training the model. Regarding the design of the CNN architecture, methods examining breast histology images adopt three main approaches: Designing manually from scratch the CNN architecture, using pre-trained models and adopting an automatic architecture design. Methods addressing the lack of labelled datasets are grouped into four categories: methods using pre-trained models, methods using data augmentation, methods adopting weakly supervised learning and those adopting feedforward filter learning. Research works from each category and reported performance are presented in this paper. We conclude the paper by indicating some future research directions related to the analysis of histology images

Sequential Recommendation with Relation-Aware Kernelized Self-Attention

Recent studies identified that sequential Recommendation is improved by the attention mechanism. By following this development, we propose Relation-Aware Kernelized Self-Attention (RKSA) adopting a self-attention mechanism of the Transformer with augmentation of a probabilistic model. The original self-attention of Transformer is a deterministic measure without relation-awareness. Therefore, we introduce a latent space to the self-attention, and the latent space models the recommendation context from relation as a multivariate skew-normal distribution with a kernelized covariance matrix from co-occurrences, item characteristics, and user information. This work merges the self-attention of the Transformer and the sequential recommendation by adding a probabilistic model of the recommendation task specifics. We experimented RKSA over the benchmark datasets, and RKSA shows significant improvements compared to the recent baseline models. Also, RKSA were able to produce a latent space model that answers the reasons for recommendation.Comment: 8 pages, 5 figures, AAA