There is a growing concern about the complete lifecycle security of data in Internet of Things (IoT). This may cause privacy and trust problems for users regarding data sources, data storage, and access control for data sharing. Blockchain is a valuable solution to the above problems through distributed ledger technology, and it has been widely applied in various fields such as public services, finance, and IoT. However, the data in IoT are characterized by a large quantity, large capacity, and timely response, and existing blockchain systems only partially resolve them for data security and performance. We propose DSChain for IoT data security to address the challenges mentioned above. Our system uses a certificateless signature to ensure a trusted data source and public auditing to ensure the integrity of stored data while using ciphertext-policy attribute-based encryption to control access to shared data. Moreover, we propose a packaging mechanism based on the Merkle Hash Tree that effectively improves system performance. We implement the DSChain and provide a detailed analysis of performance and security. The experimental results indicate that DSChain can achieve approximately 1,035 transactions per second on a single peer and is scalable.

In this big data era, knowledge becomes increasingly linked, along with the rapid growth in data volume. Connected knowledge is naturally represented and stored as knowledge graphs, which are of more and more importance for many frontier research areas such as machine intelligence. Effectively finding relations between entities in a large knowledge graph plays a key role in many knowledge graph applications, as the most valuable part of a knowledge graph is its rich connectedness, which captures rich information about the objects in the real world. However, due to the intrinsic complexity of real-world knowledge, finding semantically close relations by navigation in a large knowledge graph is very challenging. Canonical graph exploration methods inevitably result in combinatorial explosion especially when the paths connecting two entities are long: the search space is$O(d^l)$, where$d$is the average graph node degree and$l$is the length of the path. In this paper, we will systematically study the semantic navigation problem for large knowledge graphs. Inspired by AlphaGo, which was overwhelmingly successful in the game Go, we designed an efficient semantic navigation method based on a well-tailored Monte Carlo Tree Search algorithm with the unique characteristics of knowledge graphs considered. Extensive experiments on different real-life knowledge bases show that our method is not only effective but also very efficient.

Semantic understanding of programs is a fundamental problem for programming language processing (PLP). Recent works that learn representations of code based on pre-training techniques in NLP have pushed the frontiers in this direction. However, the semantics of PL and NL have essential differences. These being ignored, we believe it is difficult to build a model to better understand programs, by either directly applying off-the-shelf NLP pre-training techniques to the source code, or adding features to the model by the heuristic. In fact, the semantics of a program can be rigorously defined by formal semantics in PL theory. For example, the operational semantics, describes the meaning of a valid program as updating the environment (i.e., the memory address-value function) through fundamental operations, such as memory I/O and conditional branching. Inspired by this, we propose a novel program semantics learning paradigm, that the model should learn from information composed of (1) the representations which align well with the fundamental operations in operational semantics, and (2) the information of environment transition, which is indispensable for program understanding. To validate our proposal, we present a hierarchical Transformer-based pre-training model called OSCAR to better facilitate the understanding of programs. OSCAR learns from intermediate representation (IR) and an encoded representation derived from static analysis, which are used for representing the fundamental operations and approximating the environment transitions respectively. OSCAR empirically shows the outstanding capability of program semantics understanding on many practical software engineering tasks. Code and models are released at: \url{https://github.com/pdlan/OSCAR}.

Earlier-stage evaluations of a new AI architecture/system need affordable AI benchmarks. Only using a few AI component benchmarks like MLPerf alone in the other stages may lead to misleading conclusions. Moreover, the learning dynamics are not well understood, and the benchmarks' shelf-life is short. This paper proposes a balanced benchmarking methodology. We use real-world benchmarks to cover the factors space that impacts the learning dynamics to the most considerable extent. After performing an exhaustive survey on Internet service AI domains, we identify and implement nineteen representative AI tasks with state-of-the-art models. For repeatable performance ranking (RPR subset) and workload characterization (WC subset), we keep two subsets to a minimum for affordability. We contribute by far the most comprehensive AI training benchmark suite. The evaluations show: (1) AIBench Training (v1.1) outperforms MLPerf Training (v0.7) in terms of diversity and representativeness of model complexity, computational cost, convergent rate, computation, and memory access patterns, and hotspot functions; (2) Against the AIBench full benchmarks, its RPR subset shortens the benchmarking cost by 64%, while maintaining the primary workload characteristics; (3) The performance ranking shows the single-purpose AI accelerator like TPU with the optimized TensorFlow framework performs better than that of GPUs while losing the latter's general support for various AI models. The specification, source code, and performance numbers are available from the AIBench homepage https://www.benchcouncil.org/aibench-training/index.html.

RDF is one of the most commonly used knowledge representation forms. Many highly influential knowledge bases, such as Freebase and PubChemRDF, are in RDF format. An RDF data set is usually represented as a collection of subject-predicate-object triples. Despite the flexibility of RDF triples, it is challenging to serve SPARQL queries on RDF data efficiently by directly managing triples due to the following two reasons. First, heavy joins on a large number of triples are needed for query processing, resulting in a large number of data scans and large redundant intermediate results; Second, weakly-typed triple representation provides suboptimal random access - typically with logarithmic complexity. This data access challenge, unfortunately, cannot be easily met by a better query optimizer as large graph processing is extremely I/O-intensive. In this paper, we argue that strongly-typed graph representation is the key to high-performance RDF query processing. We propose Stylus - a strongly-typed store for serving massive RDF data. Stylus exploits a strongly-typed storage scheme to boost the performance of RDF query processing. The storage scheme is essentially a materialized join view on entities, it thus can eliminate a large number of unnecessary joins on triples. Moreover, it is equipped with a compact representation for intermediate results and an efficient graph-decomposition based query planner. Experimental results on both synthetic and real-life RDF data sets confirm that the proposed approach can dramatically boost the performance of SPARQL query processing.

Indoor localization is an important primitive that can enable many ubiquitous computing applications. This paper improves the scheme of landmark and inertial navigation through crowd sensing to address reliable and accurate indoor localization. The location of landmark can calibrate the location of user and inertial navigation can optimize the location of landmark in turn. In this work, we define the landmark as certain characteristic structure with Beacon. To tackle the challenges of misjudgment of landmarks and variability in user walking profiles, we have developed algorithms for reliable detection of landmarks and personalization of step length with the aid of crowd sensing. We have built an indoor localization system integrating these modules and an indoor floor map, which can be further improved with more users using our system. We demonstrate for the first time a meter-level indoor localization system that is self-improving, user adaptive, and easy to deploy. Extensive experiments on users with smartphone devices, with over 37 subjects walking over an aggregate distance of over 20 kilometers were carried out. Evaluation results show that our system can achieve a mean accuracy of 2m initially and 1m with the calibration of landmarks in a 39m × 21m testing area.

Indoor localization is an important primitive that can enable many ubiquitous computing applications. In this paper, we choose the iBeacon as landmark and improve the localization scheme of iBeacon and inertial navigation through crowd sensing. Specifically, we use crowd sensing to design a parameter learning algorithm for device diversity. Furthermore, we take advantage of crowd sensing to collect the correction information of iBeacon about inertial navigation, which can optimize the step length estimation and direction inference. We demonstrate for the first time a meter-level indoor localization system that is self-improving, user adaptive, and inclusive to diversity. Extensive experiments on users with mobile devices, with over 37 subjects walking over an aggregate distance of over 10 kilometers were carried out. Evaluation results show that the accuracy is within 2m in a 39m×21m testing area.

Multi-version data is often one of the most concerned information on the Web since this type of data is usually updated frequently. Even though there exist some Web information integration systems that try to maintain the latest update version, the maintained multi-version data usually includes inaccurate and invalid information due to the data integration or update delay errors. In this demo, we present CrowdCleaner, a smart data cleaning system for cleaning multi-version data on the Web, which utilizes crowdsourcing-based approaches for detecting and repairing errors that usually cannot be solved by traditional data integration and cleaning techniques. In particular, CrowdCleaner blends active and passive crowdsourcing methods together for rectifying errors for multi-version data. We demonstrate the following four facilities provided by CrowdCleaner: (1) an error-monitor to find out which items (e.g., submission date, price of real estate, etc.) are wrong versions according to the reports from the crowds, which belongs to a passive crowdsourcing strategy; (2) a task-manager to allocate the tasks to human workers intelligently; (3) a smart-decision-maker to identify which answer from the crowds is correct with active crowdsourcing methods; and (4) a whom-to-ask-finder to discover which users (or human workers) should be the most credible according to their answer records.

Computations performed by graph algorithms are data driven, and require a high degree of random data access. Despite the great progresses made in disk technology, it still cannot provide the level of efficient random access required by graph computation. On the other hand, memory-based approaches usually do not scale due to the capacity limit of single machines. In this paper, we introduce Trinity, a general purpose graph engine over a distributed memory cloud. Through optimized memory management and network communication, Trinity supports fast graph exploration as well as efficient parallel computing. In particular, Trinity leverages graph access patterns in both online and offline computation to optimize memory and communication for best performance. These enable Trinity to support efficient online query processing and offline analytics on large graphs with just a few commodity machines. Furthermore, Trinity provides a high level specification language called TSL for users to declare data schema and communication protocols, which brings great ease-of-use for general purpose graph management and computing. Our experiments show Trinity's performance in both low latency graph queries as well as high throughput graph analytics on web-scale, billion-node graphs.