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Blockchain networks have made inroads in the supply chain domains due to their transparency, security and immutable logs. For cloud providers, a typical supply chain consists of complex networks of suppliers, manufacturers, distributors, retailers, auditors, and consumers. Even though there is an increased focus on self-serve or cloud provider managed SaaS (Software-as-a-service), a portion of sales for an enterprise customer occurs the old-fashioned way with the sales department drawing up a purchase order to begin the procurement process. In many cases, there could be several disjoint suppliers who fulfill the order behind the scenes unbeknownst to the buyer. In this paper we propose C3HSB, a decentralized distributed application architecture using blockchain and smart contracts to build the supply chain. Motivated by challenges in the industry, this approach can be considered as a building block for future supply chain procurement and deployment of cloud assets. The design goal for our system was to facilitate efficient handling of enterprise-scale purchase orders and alternatives in real-time while maintaining data provenance. C3HSB streamlines complex non-repudiated transaction workflows, promotes trust, and achieves cost savings for the customer and suppliers.

Deepfakes have become a major public concern on the Internet as fake images and videos could be used to spread misleading information about a person or an organization. In this paper, we explore if deepfakes can be generated for histopathology images using advances in deep learning. This is because the field of digital pathology is gaining a lot of momentum since the Food and Drug Administration (FDA) approved a few digital pathology systems for primary diagnosis and consultation in the United States. Specifically, we investigate if state-of-the- art generative adversarial networks (GANs) can produce fake histopathology images that can trick an expert pathologist. For our investigation, we used whole slide images (WSIs) hosted by The Cancer Genome Atlas (TCGA). We selected 3 WSIs of colon cancer patients and produced 100,000 patches of 256×256 pixels in size. We trained three popular GANs to generate fake patches of the same size. We then constructed a set of images containing 30 real and 30 fake patches. An expert pathologist reviewed these images and marked them as either real or fake. We observed that the pathologist marked 10 fake patches as real and correctly identified 34 patches (as fake or real). Thirteen patches were incorrectly identified as fake. The pathologist was unsure of 3 fake patches. Interestingly, the fake patches that were correctly identified by the pathologist, had missing morphological features, abrupt background change, pleomorphism, and other incorrect artifacts. Our investigation shows that while certain parts of a histopathology image can be mimicked by existing GANs, the intricacies of the stained tissue and cells cannot be fully captured by them. Unlike radiology, where it is relatively easier to manipulate an image using a GAN, we argue that it is a harder challenge in digital pathology to generate an entire WSI that is fake.

Federated learning (FL) is a machine learning approach where the goal is to train a centralized model using a large number of clients that host private datasets. FL trains a smaller version of the model at each dataset site and then aggregates all the models at the server. In practice, clients (i.e., dataset holders) that participate in the learning process may possess corrupted or noisy datasets resulting in low accuracy models. Additionally, malicious clients may poison the data or carry out model discovery attacks.In this paper, we propose a refining algorithm called RefinedFed, to eliminate corrupted, low accuracy, and noisy models that can negatively impact the centralized model by reducing its accuracy or cause other malicious activities. Furthermore, RefinedFed reduces the uplink communication cost with the centralized server, which in return results in faster aggregation on the server side. Based on our preliminary experiments on the MNIST dataset, we observed that RefinedFed improved the global model accuracy from 84% to 91% while consuming less time for aggregation.

In this paper, we present a system for fast approximate score computation, a fundamental task for score-based structure learning of multinomial Bayesian networks. Our work is motivated by the fact that exact score computation on large datasets is very time consuming. Our system enables approximate score computation on large datasets in an efficient and scalable manner with probabilistic error bounds on the statistics required for score computation. Our system has several novel features including gossip-based decentralized computation of statistics, lower resource consumption via a probabilistic approach of maintaining statistics, and effective distribution of tasks for score computation using hashing techniques. The demo will provide a real-time and interactive experience to a user on how our system employs the principle of gossiping and hashing techniques in a novel way for fast approximate score computation. The user will be able to control different aspects of our system's execution on a cluster with up to 32 nodes. The approximate scores output by our system can be then used by existing score-based structure learning algorithms.

We propose a method for scalable first-order rule learning on large-scale Twitter data. By learning rules, probabilistic inference queries can be executed to reason over the data to ascertain its veracity. Our method employs a divide-and-conquer approach, graph-based modeling, and data parallel processing during rule learning using a commodity cluster to overcome the problem of slow structure learning on large-scale Twitter data. The first-order predicates (constructed on the posts) are first partitioned in a balanced way by pivoting around users to reduce the chance of missing relevant rules. By constructing a weighted graph and applying graph partitioning, balanced partitions of the ground predicates can be created. Each partition is then processed using an existing structure learning approach to get the set of rules for that partition. We report a preliminary evaluation of our method to show that it offers a promising solution for scalable first-order rule learning on Twitter data.

Today parallel computing is essential for the success of many real-world applications and software systems. Nonetheless, most computer science undergraduate courses teach students how to think and program sequentially. Further, software professionals have complained about the computer science curriculum's lag behind industry in their failing to cover modern programming technologies such as parallel programming. The emphasis on parallel programming has become even more important due to the increasing adoption of horizontal scaling approaches to cope with massive datasets. In order to help students coming from a serial curriculum comprehend parallel concepts, we used an innovative approach that utilized active learning, visualizations, examples, discussions, and practical exercises. Further, we conducted an experiment to examine the effect of active learning on students' understanding of parallel programming. Results indicate that the students that were actively engaged with the material performed better in terms of understanding parallel programming concepts than other students.

While the power of social media on the Internet is undeniable, it has become a major weapon for launching cyberattacks against an organization and its people. Today, there is a growing number of cyberattacks being launched through social media such as posting of false content from hacked accounts, posting malicious URLs to spread malware, and others. In this paper, we present a simple and flexible unified framework called SocialKB for modeling social media posts and reasoning about them to ascertain their veracity, a first step towards discovering emerging cyber threats. SocialKB is based on Markov Logic Networks (MLNs), a popular representation in statistical relational learning. It learns a knowledge base (KB) on the social media posts and users' behavior in a unified manner. By conducting probabilistic inference on the KB, SocialKB can identify suspicious users and malicious content. In this work, we specifically focus on tweets posted by users on Twitter. Finally, we report an evaluation of SocialKB on 20,000 tweets and discuss our early inference results.

Urban informatics is emerging as a new discipline for cities and governments to improve the lives of citizens using information technology. In this advanced seminar, we introduce the key challenges and opportunities in urban informatics, discuss topics in mobile computing, Internet of Things (IoT) and big data analytics, to advance the state-of-the-art in urban informatics and provide interesting use cases. This seminar is designed for academicians, researchers, city administrators/planners, application developers, and research students with background in mobile computing and database systems.

We present a novel tool called XGossip for Internet-scale cardinality estimation of XPath queries over distributed XML data. XGossip relies on the principle of gossip, is scalable, decentralized, and can cope with network churn and failures. It employs a novel divide-and-conquer strategy for load balancing and reducing the overall network bandwidth consumption. It has a strong theoretical underpinning and provides provable guarantees on the accuracy of cardinality estimates, the number of messages exchanged, and the total bandwidth usage. In this demonstration, users will experience three engaging scenarios: In the first scenario, they can set up, configure, and deploy XGossip on Amazon Elastic Compute Cloud (EC2). In the second scenario, they can execute XGossip, pose XPath queries, observe in real-time the convergence speed of XGossip, the accuracy of cardinality estimates, the bandwidth usage, and the number of messages exchanged. In the third scenario, they can introduce network churn and failures during the execution of XGossip and observe how these impact the behavior of XGossip.

Immunization saves millions of lives against vaccine-preventable diseases. Yet, 24 million children born every year do not receive proper immunization during their first year. UNICEF and WHO have emphasized the need to strengthen the immunization surveillance and monitoring in developing countries to reduce childhood deaths. In this regard, we present a software application called Jeev to track the vaccination coverage of children in rural communities. Jeev synergistically combines the power of smart phones and the ubiquity of cellular infrastructure, QR codes, and national identification cards. We present the design of Jeev and highlight its unique features along with a preliminary evaluation of its performance. We plan to pilot test Jeev in a rural population to study its effectiveness and identify socio-cultural issues that may arise in a large-scale deployment.

In recent years, there has been a growing interest for peer-to-peer (P2P) based computing and applications. One of the most important challenges in P2P environments is to quickly locate relevant data across many participating peers. In this demonstration, we present psiX, which is an Internet-scale service for publishing and locating XML documents. This service runs on several PlanetLab nodes geographically spread across the globe. The psiX system adopts a suite of new techniques for XML indexing and pattern matching in a P2P network, namely, (a) representing XML documents and XPath queries compactly via algebraic signatures, (b) searching signatures of documents and value summaries indexed using distributed hierarchical indexesbuilt over a Distributed Hash Table (DHT), and (c) gracefully adapting to failures while running on the Internet, where failures are a norm rather than an exception.

In recent years, there has been a rising interest in developing online approximation algorithms for data streams. Some of the key challenges are posed by the fact that streaming data can be read only once in a fixed order of arrival and only a limited amount of memory is available for storage. In this paper, we address the problem of approximately counting tree patterns over a stream of labeled trees (e.g., XML documents). We propose a new approximation algorithm called SketchTree that computes a synopsis of the stream in a single pass by processing each tree only once. Using a limited amount of memory, SketchTree provides approximate answers for both ordered and unordered tree pattern counts. Furthermore, we discuss a class of count queries that can be handled by SketchTree and their utility. We provide theoretical analyses to show that our algorithm has provably strong guarantees on the error bounds. Experiments on real datasets demonstrate that SketchTree can indeed estimate tree pattern counts within 10-15% relative error with high confidence under various situations.