In May of 2018, a "center of excellence" for artificial intelligence opened in Medellín, Colombia. According to an article by Jared Wade, the center comes from a partnership between US-based Institute for Robotic Process Automation and Artificial Intelligence (IRPA AI) and Medellín-based startup incubator, Ruta-N. The launch was facilitated by the Agency for Cooperation and Investment in Medellín (ACI) with the goal of fostering specialized skills in the local labor force, and is part of a larger plan to promote research, development, entrepreneurship, and innovation. This is good news, but I'm biased. You see, I visited Medellín in September of last year and fell in love.

With the recent advances of the Internet of Things, and the increasing accessibility of ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as relationship explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as Artificial Social Intelligence (ASI) that has the potential to tackle the social relationship explosion problem. This paper discusses the role of IoT in social relationships detection and management, the problem of social relationships explosion in IoT and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.

Your brain has been altered, neurologically rewired as you acquired a particular skill. This renovation has left you with a specialized area in your left ventral occipital temporal region, shifted facial recognition into your right hemisphere, reduced your inclination toward holistic visual processing, increased your verbal memory, and thickened your corpus callosum, which is the information highway that connects the left and right hemispheres of your brain. You are likely highly literate. As you learned to read, probably as a child, your brain reorganized itself to better accommodate your efforts, which had both functional and inadvertent consequences for your mind. So, to account for these changes to your brain--e.g,

In this paper, we investigate the constraint typology of mixed-integer linear programming MILP formulations. MILP is a commonly used mathematical programming technique for modelling and solving real-life scheduling, routing, planning, resource allocation, timetabling optimization problems, providing optimized business solutions for industry sectors such as: manufacturing, agriculture, defence, healthcare, medicine, energy, finance, and transportation. Despite the numerous real-life Combinatorial Optimization Problems found and solved, and millions yet to be discovered and formulated, the number of types of constraints, the building blocks of a MILP, is relatively much smaller. In the search of a suitable machine readable knowledge representation for MILPs, we propose an optimization modelling tree built based upon an MILP ontology that can be used as a guidance for automated systems to elicit an MILP model from end-users on their combinatorial business optimization problems.

The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale in terms of both the size of the graph and the number of model parameters. These limitations are in stark contrast to the increasing scale (in data size and model size) of experiments in deep learning research. In this work, we propose GIST, a novel distributed approach that enables efficient training of wide (overparameterized) GCNs on large graphs. GIST is a hybrid layer and graph sampling method, which disjointly partitions the global model into several, smaller sub-GCNs that are independently trained across multiple GPUs in parallel. This distributed framework improves model performance and significantly decreases wall-clock training time. GIST seeks to enable large-scale GCN experimentation with the goal of bridging the existing gap in scale between graph machine learning and deep learning.

The autonomous automotive industry is one of the largest and most conventional projects worldwide, with many technology companies effectively designing and orienting their products towards automobile safety and accuracy. These products are performing very well over the roads in developed countries. But can fail in the first minute in an underdeveloped country because there is much difference between a developed country environment and an underdeveloped country environment. The following study proposed to train these Artificial intelligence models in environment space in an underdeveloped country like Pakistan. The proposed approach on image classification uses convolutional neural networks for image classification for the model. For model pre-training German traffic signs data set was selected then fine-tuned on Pakistan's dataset. The experimental setup showed the best results and accuracy from the previously conducted experiments. In this work to increase the accuracy, more dataset was collected to increase the size of images in every class in the data set. In the future, a low number of classes are required to be further increased where more images for traffic signs are required to be collected to get more accuracy on the training of the model over traffic signs of Pakistan's most used and popular roads motorway and national highway, whose traffic signs color, size, and shapes are different from common traffic signs.

Vehicle Re-identification (re-id) over surveillance camera network with non-overlapping field of view is an exciting and challenging task in intelligent transportation systems (ITS). Due to its versatile applicability in metropolitan cities, it gained significant attention. Vehicle re-id matches targeted vehicle over non-overlapping views in multiple camera network. However, it becomes more difficult due to inter-class similarity, intra-class variability, viewpoint changes, and spatio-temporal uncertainty. In order to draw a detailed picture of vehicle re-id research, this paper gives a comprehensive description of the various vehicle re-id technologies, applicability, datasets, and a brief comparison of different methodologies. Our paper specifically focuses on vision-based vehicle re-id approaches, including vehicle appearance, license plate, and spatio-temporal characteristics. In addition, we explore the main challenges as well as a variety of applications in different domains. Lastly, a detailed comparison of current state-of-the-art methods performances over VeRi-776 and VehicleID datasets is summarized with future directions. We aim to facilitate future research by reviewing the work being done on vehicle re-id till to date.

The clustering methods have been used in a variety of fields such as image processing, data mining, pattern recognition, and statistical analysis. Generally, the clustering algorithms consider all variables equally relevant or not correlated for the clustering task. Nevertheless, in real situations, some variables can be correlated or may be more or less relevant or even irrelevant for this task. This paper proposes partitioning fuzzy clustering algorithms based on Euclidean, City-block and Mahalanobis distances and entropy regularization. These methods are an iterative three steps algorithms which provide a fuzzy partition, a representative for each fuzzy cluster, and the relevance weight of the variables or their correlation by minimizing a suitable objective function. Several experiments on synthetic and real datasets, including its application to noisy image texture segmentation, demonstrate the usefulness of these adaptive clustering methods.

Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson's disease using machine learning and the concept of BoVW. The proposed approach concerns a hierarchical-based learning technique to design visual dictionaries through the Deep Optimum-Path Forest classifier. The proposed method was evaluated in six datasets derived from data collected from individuals when performing handwriting exams. Experimental results showed the potential of the technique, with robust achievements.

Statistical learning theory provides the foundation to applied machine learning, and its various successful applications in computer vision, natural language processing and other scientific domains. The theory, however, does not take into account the unique challenges of performing statistical learning in geospatial settings. For instance, it is well known that model errors cannot be assumed to be independent and identically distributed in geospatial (a.k.a. regionalized) variables due to spatial correlation; and trends caused by geophysical processes lead to covariate shifts between the domain where the model was trained and the domain where it will be applied, which in turn harm the use of classical learning methodologies that rely on random samples of the data. In this work, we introduce the geostatistical (transfer) learning problem, and illustrate the challenges of learning from geospatial data by assessing widely-used methods for estimating generalization error of learning models, under covariate shift and spatial correlation. Experiments with synthetic Gaussian process data as well as with real data from geophysical surveys in New Zealand indicate that none of the methods are adequate for model selection in a geospatial context. We provide general guidelines regarding the choice of these methods in practice while new methods are being actively researched.