Understanding the behaviors and intentions of humans are one of the main challenges autonomous ground vehicles still faced with. More specifically, when it comes to complex environments such as urban traffic scenes, inferring the intentions and actions of vulnerable road users such as pedestrians become even harder. In this paper, we address the problem of intent action prediction of pedestrians in urban traffic environments using only image sequences from a monocular RGB camera. We propose a real-time framework that can accurately detect, track and predict the intended actions of pedestrians based on a tracking-by-detection technique in conjunction with a novel spatio-temporal DenseNet model. We trained and evaluated our framework based on real data collected from urban traffic environments. Our framework has shown resilient and competitive results in comparison to other baseline approaches. Overall, we achieved an average precision score of 84.76% with a real-time performance at 20 FPS.

When you're running a modern data cluster, which are becoming increasingly commonplace and essential to businesses, you inevitably discover headaches. Typically a wide variety of workloads run on a single cluster, which can make it a nightmare to manage and operate - similar to managing traffic in a busy city. There's a real pain for the operations folks out there who have to manage Spark, Hive, impala and Kafka applications running on the same cluster where they have to worry about each app's resource requirements, the time distribution of the cluster workloads, the priority levels of each app or user, and then make sure everything runs like a predictable well-oiled machine. Anyone working in data ops will have a strong point of view here since you'll have no doubt spent countless hours, day in and day out, studying the behaviour of giant production clusters in the discovery of insights into how to improve performance, predictability and stability. Whether it is a thousand node Hadoop cluster running batch jobs, or a five hundred node Spark cluster running AI, ML or some type of advanced, real-time, analytics.

The $K$-means algorithm is extended to allow for partitioning of skewed groups. Our algorithm is called TiK-Means and contributes a $K$-means type algorithm that assigns observations to groups while estimating their skewness-transformation parameters. The resulting groups and transformation reveal general-structured clusters that can be explained by inverting the estimated transformation. Further, a modification of the jump statistic chooses the number of groups. Our algorithm is evaluated on simulated and real-life datasets and then applied to a long-standing astronomical dispute regarding the distinct kinds of gamma ray bursts.

Can neural networks learn to compare graphs without feature engineering? In this paper, we show that it is possible to learn representations for graph similarity with neither domain knowledge nor supervision (i.e.\ feature engineering or labeled graphs). We propose Deep Divergence Graph Kernels, an unsupervised method for learning representations over graphs that encodes a relaxed notion of graph isomorphism. Our method consists of three parts. First, we learn an encoder for each anchor graph to capture its structure. Second, for each pair of graphs, we train a cross-graph attention network which uses the node representations of an anchor graph to reconstruct another graph. This approach, which we call isomorphism attention, captures how well the representations of one graph can encode another. We use the attention-augmented encoder's predictions to define a divergence score for each pair of graphs. Finally, we construct an embedding space for all graphs using these pair-wise divergence scores. Unlike previous work, much of which relies on 1) supervision, 2) domain specific knowledge (e.g. a reliance on Weisfeiler-Lehman kernels), and 3) known node alignment, our unsupervised method jointly learns node representations, graph representations, and an attention-based alignment between graphs. Our experimental results show that Deep Divergence Graph Kernels can learn an unsupervised alignment between graphs, and that the learned representations achieve competitive results when used as features on a number of challenging graph classification tasks. Furthermore, we illustrate how the learned attention allows insight into the the alignment of sub-structures across graphs.

With the emergence of batteryless computing platforms, we are now able to execute computer programs on embedded systems that do not require a dedicated energy source. These platforms are typically used in sensing applications [30, 39, 70, 73, 79], and their hardware architecture consists primarily of a sensor-enabled microcontroller that is powered by some form of harvested energy such as solar, RF or piezoelectric [63]. Programs that run on these platforms follow the so-called intermittent computing paradigm [50, 52, 75, 77] where a system pauses and resumes its code execution based on the availability of harvested energy. Over the past decade, the efficiency of batteryless computing platforms has been improved by reducing their energy waste through hardware provisioning, through check-pointing [64] to avoid restarting code execution from the beginning at each power-up [8], and through discarding stale sensor data [34] which are no longer useful. Despite these advancements, the capability of batteryless computing platforms has remained limited to simple sensing applications only. In this paper, we introduce the concept of intermittent learning (Figure 1) which makes energy harvested embedded systems capable of executing lightweight machine learning tasks. Their ability to run machine learning tasks inside energy harvesting microcontrollers pushes the boundary of batteryless computing as these devices are able to sense, learn, infer, and evolve over a prolonged lifetime. The proposed intermittent learning paradigm enables a true lifelong learning experience in mobile and embedded systems and advances sensor systems from being smart to smarter. Once deployed in the field, an intermittent learner classifies sensor data as well as learns from them to update the classifier at run-time--without requiring any help from any external system.

Gaussian processes (GP) for machine learning have been studied systematically over the past two decades and they are by now widely used in a number of diverse applications. However, GP kernel design and the associated hyper-parameter optimization are still hard and to a large extend open problems. In this paper, we consider the task of GP regression for time series modeling and analysis. The underlying stationary kernel can be approximated arbitrarily close by a new proposed grid spectral mixture (GSM) kernel, which turns out to be a linear combination of low-rank sub-kernels. In the case where a large number of the sub-kernels are used, either the Nystr\"{o}m or the random Fourier feature approximations can be adopted to deal efficiently with the computational demands. The unknown GP hyper-parameters consist of the non-negative weights of all sub-kernels as well as the noise variance; their estimation is performed via the maximum-likelihood (ML) estimation framework. Two efficient numerical optimization methods for solving the unknown hyper-parameters are derived, including a sequential majorization-minimization (MM) method and a non-linearly constrained alternating direction of multiplier method (ADMM). The MM matches perfectly with the proven low-rank property of the proposed GSM sub-kernels and turns out to be a part of efficiency, stable, and efficient solver, while the ADMM has the potential to generate better local minimum in terms of the test MSE. Experimental results, based on various classic time series data sets, corroborate that the proposed GSM kernel-based GP regression model outperforms several salient competitors of similar kind in terms of prediction mean-squared-error and numerical stability.

Research has provided evidence that associative classification produces more accurate results compared to other classification models. The Classification Based on Association (CBA) is one of the famous Associative Classification algorithms that generates accurate classifiers. However, current association classification algorithms reside external to databases, which reduces the flexibility of enterprise analytics systems. This paper implements the CBA in Oracle database using two variant models: hardcoding the CBA in Oracle Data Mining (ODM) package and Integrating Oracle Apriori model with the Oracle Decision tree model. We compared the proposed model performance with Naive Bayes, Support Vector Machine, Random Forests, and Decision Tree over 18 datasets from UCI. Results showed that our models outperformed the original CBA model with 1 percent and is competitive to chosen classification models over benchmark datasets.

LIBS2ML is a library based on scalable second order learning algorithms for solving large-scale problems, i.e., big data problems in machine learning. LIBS2ML has been developed using MEX files, i.e., C++ with MATLAB/Octave interface to take the advantage of both the worlds, i.e., faster learning using C++ and easy I/O using MATLAB. Most of the available libraries are either in MATLAB/Python/R which are very slow and not suitable for large-scale learning, or are in C/C++ which does not have easy ways to take input and display results. So LIBS2ML is completely unique due to its focus on the scalable second order methods, the hot research topic, and being based on MEX files. Thus it provides researchers a comprehensive environment to evaluate their ideas and it also provides machine learning practitioners an effective tool to deal with the large-scale learning problems. LIBS2ML is an open-source, highly efficient, extensible, scalable, readable, portable and easy to use library. The library can be downloaded from the URL: \url{https://github.com/jmdvinodjmd/LIBS2ML}.

Web Ontology Language (OWL) reasoners are used to infer new logical relations from ontologies. While inferring new facts, these reasoners can be further optimized, e.g., by properly ordering disjuncts in disjunction expressions of ontologies for satisfiability testing of concepts. Different expansion-ordering heuristics have been developed for this purpose. The built-in heuristics in these reasoners determine the order for branches in search trees while each heuristic choice causes different effects for various ontologies depending on the ontologies' syntactic structure and probably other features as well. A learning-based approach that takes into account the features aims to select an appropriate expansion-ordering heuristic for each ontology. The proper choice is expected to accelerate the reasoning process for the reasoners. In this paper, the effect of our methodology is investigated on a well-known reasoner that is JFact. Our experiments show the average speedup by a factor of one to two orders of magnitude for satisfiability testing after applying learning methodology for selecting the right expansion-ordering heuristics.

Applied Intelligence manuscript No. (will be inserted by the editor) Abstract The widespread adoption of smartphones dramaticallygenerated by our attacks models when used to harden increases the risk of attacks and the spread the developed anti-malware system improves the detection of mobile malware, especially on the Android platform. Machine learning based solutions have been already Keywords Adversarial machine learning · malware used as a tool to supersede signature based anti-malware detection · poison attacks · adversarial example · systems. However, malware authors leverage attributes jacobian algorithm. Hence, to evaluate the vulnerability of machine 1 Introduction learning algorithms in malware detection, we propose five different attack scenarios to perturb malicious applications Nowadays using the Android application is very popular (apps). Every Android application inappropriately fits discriminant function on has a Jar-like APK format and is an archive file which the set of data points, eventually yielding a higher misclassification contains Android manifest and Classes.dex Further, to distinguish the adversarial manifest file holds information about the application examples from benign samples, we propose two defense structure and each part responsible for certain actions. To validate our For instance, the requested permissions must be accepted attacks and solutions, we test our model on three different by the users for successful installation of applications. We also test our methods The manifest file contains a list of hardware using various classifier algorithms and compare them components and permissions required by each application. Promising results show that generated the manifest file that are useful for running applications. Additionally, evasive variants is saved as the classes.dex In a nutshell, the by presenting some adversary-aware approaches?generated malware sample is statistically identical to a Do we require retraining of the current ML model to designbenign sample. To do so, adversaries adopt adversarial adversary-aware learning algorithms? How to properlymachine learning algorithms (AML) to design an example test and validate the countermeasure solutions inset called poison data which is used to fool machine a real-world network? The goal of this paper is to shedlearning models.