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In our model, a venture seeks capital through an initial coin offering (ICO). The ICO enables the venture to collect demand information from decentralized investors. The venture makes a tradeoff between ensuring the project’s success and forecasting market demand through token size and token price. We find that the higher the demand uncertainty and production cost, the greater the venture’s incentive to learn from its investors. To identify when the venture has an incentive to raise funds through an ICO, we compare it with traditional bank financing and analyze the venture’s preference between the two financing options. The results show that when demand uncertainty is high, an ICO can provide both financing and information benefits, whereas when demand uncertainty is low, although the venture does not collect information through an ICO, it can still get financing benefit. Only when demand uncertainty is intermediate, the venture prefers bank financing. In addition, we find that although the revenue sharing effect of the ICO results in an underinvestment issue, it can also alleviate the loss caused by the increased cost of production. Therefore, with high information accuracy, as production cost goes up, ICOs become more attractive than bank financing for the venture.

Nonoccurring behavior (NOB) studies have attracted the growing attention of scholars as a crucial part of behavioral science. As an effective method to discover both NOB and occurring behaviors (OB), negative sequential pattern (NSP) mining is successfully used in analyzing medical treatment and abnormal behavior patterns. At this time, NSP mining is still an active and challenging research domain. Most of the algorithms are inefficient in practice. Briefly, the key weaknesses of NSP mining are: 1) an inefficient positive sequential pattern (PSP) mining process, 2) a strict constraint of negative containment, and 3) the lack of an effective Negative Sequential Candidate (NSC) generation method. To address these weaknesses, we propose a highly efficient algorithm with improved techniques, named sc-NSP, to mine NSP efficiently. We first propose an improved PrefixSpan algorithm in the PSP mining process, which connects to a bitmap storage structure instead of the original structure. Second, sc-NSP loosens the frequency constraint and exploits the NSC generation method of positive and negative sequential patterns mining (PNSP) (a classic NSP mining method). Furthermore, a novel pruning strategy is designed to reduce the computational complexity of sc-NSP. Finally, sc-NSP obtains the support of NSC by using the most efficient bitwise-based calculation operation. Theoretical analyses show that sc-NSP performs particularly well on data sets with a large number of elements and items in sequence. Comparison and extensive experiments along with case studies on health data show that sc-NSP is 10 times more efficient than other state-of-the-art methods, and the number of NSPs obtained is 5 times greater than other methods.

The future communication technologies like 6G are capable to provide higher mobility and better quality of service requirements to Internet of Things (IoT). To ensure mobility, the 6G technologies need more reliable and scalable solutions, which are capable to integrate large scale heterogeneous IoT networks. In a heterogeneous environment, seamless mobility along with the demands of IP addresses requires a proxy mobile IPv6 (PMIPv6) protocol that provides cost-effective solutions in next generation IoT networks. The PMIPv6 has been exploited for resource efficiency in IoT-enabled next generation networks. In this paper, we have proposed a demand-based resource-efficient location-aware PMIPv6 extension for seamless mobility in the next generation IoT networks. The proposed approach efficiently utilizes the network resources using location information and received signal strength (RSS). This solution enhances the performance of PMIPv6 protocol in terms of signaling cost, and load on network entities. Furthermore, mathematical models are derived in terms of signaling cost, load distribution. The proposed solution is compared with the existing RSS-based PMIPv6 extension protocols. The results show that the proposed scheme enhances the performance and is a resource-friendly for the next generation large scale IoT networks.

Searchable Symmetric Encryption (SSE), which enables users to search over encrypted data without decryption, has gained increasing attention from both academic and industrial fields. However, existing SSE schemes either have low search efficiency or cannot support multi-keyword search, dynamic updates, and result verification simultaneously. To solve these problems, we propose a Verifiable and Dynamic Multi-keyword Search (VDMS) scheme over encrypted data by using the bitmap and RSA accumulator, which provides multi-keyword search over encrypted data in an efficient, verifiable and updated way. The bitmap is used as a data structure to build the indexes, which improves the search efficiency and reduces the storage space of the indexes. The RSA accumulator and bitmap are combined to verify the correctness of results. Formal security analysis proves that our VDMS is adaptively secure against Chosen-Keyword Attacks (CKA), and empirical experiments using a real-world dataset demonstrate that our VDMS is efficient and feasible in practical applications.

The use of the internet in this modern era is increased many fold. The communications between different peers take place in digital form. While sharing the messages between different recipients, the confidentiality of the messages is very important. For creating the high level of security while sharing the secret messages, the cryptographic algorithms are introduced along with steganography. Image Steganography is a methodology used to hide the messages inside of the cover image. Initially, the secret information is encrypted by using the RSA Algorithm. Then the encrypted secret information is hidden in the Least Significant Bit (LSB) of the different components of the color image in such a way that the original quality of the image to be minimized. The recipient of the message is able to retrieve the encrypted secret message from the LSB bit of stego_image and then the cipher text is converted into original plain text by using the RSA algorithm. The proposed algorithm verified and analysed its performance against the different combinations of key pairs.

VRML (Virtual Reality Modeling Language) is a nonproprietary language used for scientific simulation and visualization. VRML alone do not have the capability of holding numerical values but with the help of slider design, the numeric values can be dynamically varied. The VRML Graphic User Interface (GUI) presented in this paper demonstrates dynamic simulation of Seed Grader Machine Parameters and its effect on final output. This paper describes how VRML capabilities can be utilized for simulating Seed Grader Machine Parameters in virtual Environment. The concept can be applied for variety of applications involving similar requirements.

The W3C Generating RDF from Tabular Data on the Web Recommendation provides a mechanism for mapping CSV-formatted data to any RDF graph model. Since the Wikibase data model used by Wikidata can be expressed as RDF, this Recommendation can be used to document tabular snapshots of parts of the Wikidata knowledge graph in a simple form that is easy for humans and applications to read. Those snapshots can be used to document how subgraphs of Wikidata have changed over time and can be compared with the current state of Wikidata using its Query Service to detect vandalism and value added through community contributions.

With the rapid growth of Internet, plenty of applications has been implemented for users to exchange resources with each other over networks. As a result, it has been a critical challenge to efficiently and fairly allocate resource among participating agents. Motivated by the famous BitTorrent system, a BD Mechanism for resource exchange allocation is proposed, which is based on a combinatorial structure called bottleneck decomposition. The mechanism has been shown to lead to market equilibrium, a state optimizing each individual's utility, and to be truthful, that is robust against agents' strategic behaviors. However, the crux on how to compute a bottleneck decomposition of any graph remains untouched. In this paper, we focus on the computation of bottleneck decomposition to fill the blanks and prove that the bottleneck decomposition of a network can be computed in O(n^6\log(nU)) time, where n and U are the number of agents and the maximal amount of resource any agent has in the network. Based on the bottleneck decomposition, a fair allocation in a resource exchange network can be obtained in polynomial time. We further carefully discuss the fairness in resource exchange scenario, showing the derived allocation satisfies several common fairness notions simultaneously.

This paper details RDF/OWL storage and management in two popular Relational Database Management Systems (RDBMSs): Oracle and Virtuoso. Popularity, sustainability, and conformance with the SPARQL language are the main reasons for choosing these systems. This work combines empirical and analytical studies guided by a comparative framework developed and motivated in the paper. Several dimensions are considered, including RDF data type preservation, SPARQL query and update processing, reasoning capabilities, custom inferences, blank node management, and other functional and non-functional features. Furthermore, a review of the performance assessments reported in the literature has been conducted. This study’s results identify the advantages and shortcomings of these RDBMSs for storing and managing RDF/OWL. They can help improve these systems or serve as a guide in choosing an appropriate system to use in a project context. Opportunities for further research efforts are also suggested.

Singular value decomposition (SVD) is one of the most effective algorithms in recommender systems (RSs). Due to the iterative nature of SVD algorithms, one big challenge is initialization that has a major impact on the convergence and performance of RSs. Unfortunately, existing SVD algorithms in the literature typically initialize the user and item features in a random manner; thus, data information is not fully utilized. This work addresses the challenge of developing an efficient initialization method for SVD algorithms. We propose a general neural embedding initialization framework, where a low-complexity probabilistic autoencoder neural network initializes the features of user and item. This framework supports explicit and implicit feedback data sets. The design details of our proposed framework are elaborated and discussed. Experimental results show that RSs based on our proposed initialization framework outperform the state-of-the-art methods in rating prediction. Moreover, regarding item ranking, our proposed framework shows an improvement of at least 2.20% ~ 5.74% than existing SVD algorithms and other matrix factorization methods in the literature.

The data processing of iris detection which was done by computer system were able to get better and eficient information. One of method that was used in digital image processing is edge detection. The function of this method is to identify the line or edge of the image so that the information of border line of image that can be highlighted and applied to identify the iris digital image pattern. In this research, the researcher has been tried an alternative to segment the system in order to identify the iris focused to image processing by edge detection and iris fitur extraction used Sobel detection. Iris image was from Chinese Academy of Science Institute of Automation (CASIA) version 1.0 database. The images were gray level images in BMP format and 320 x 280 pixel. It was consists of 756 images. As the results, 20 selected iris digital images randomly could not be identified by Sobel detection.

For many years, data quality is among the most commonly discussed issue in Linked Open Data (LOD) due to the huge volume of integrated datasets that are usually heterogeneous. Several ontology-based approaches dealing with quality problems have been proposed. However, when datasets lack a well-defined schema, these approaches become ineffective because of the lack of metadata. Moreover, the detection of quality problems based on an analysis between RDF (Resource Description Framework) triples without requiring ontology statistical and semantical information is not addressed. Keeping in mind that ontologies are not always available and they may be incomplete or misused. In this paper, a novel free-ontology process called LODQuMa is proposed to assess and improve the quality of LOD. It is mainly based on profiling statistics, synonym relationships between predicates, QVCs (Quality Verification Cases), and SPARQL (SPARQL Protocol and RDF Query Language) query templates. Experiments on the DBpedia dataset demonstrate that the proposed process is effective for increasing the intrinsic quality dimensions, resulting in correct and compact datasets.

Software updates are highly significant in autonomous vehicles. These updates are utilized to provide enhanced features and updated security mechanisms. In order to ensure scalability and smooth roll-out Over-the-air (OTA) mechanism is a preferred option to propagate a software update. However, this approach is vulnerable to security attacks because of existence of wireless communication channel between the vehicle and the manufacturer. In that, an attacker can replace the legitimate software with a malicious software with an intent to get control over the vehicle. In this work, we are motivated to address this problem. We develop an enhanced uptane framework for detection of malicious OTA software updates in autonomous vehicles. For enhancing security, we incorporate convolutional neural network (CNN) in the uptane framework. The proposed framework is able to distinguish between malicious and benign software executables with high accuracy. For training and testing, we create two datasets by collecting executables of Windows and Linux operating system. We encourage the use of transfer learning by exploiting the developed CNN models in order to detect malicious executable designed for autonomous vehicles. We also benchmark the CNN models against state-of-the art models. Our work is highly beneficial for the community in providing a secure mechanism for software updates.

We propose a new video vectorization approach for converting videos in the raster format to vector representation with the benefits of resolution independence and compact storage. Through classifying extracted curves in each video frame into salient ones and non-salient ones, we introduce a novel bipartite diffusion curves (BDCs) representation in order to preserve both important image features such as sharp boundaries and regions with smooth color variation. This bipartite representation allows us to propagate non-salient curves across frames such that the propagation, in conjunction with geometry optimization and color optimization of salient curves, ensures the preservation of fine details within each frame and across different frames, and meanwhile, achieves good spatial-temporal coherence. Thorough experiments on a variety of videos show that our method is capable of converting videos to the vector representation with low reconstruction errors, low computational cost, and fine details, demonstrating our superior performance over the state of the art. We also show that, when used for video upsampling, our method produces results comparable to video super-resolution.

The quality of knowledge graphs can be assessed by a validation against specified constraints, typically use-case specific and modeled by human users in a manual fashion. Visualizations can improve the modeling process as they are specifically designed for human information processing, possibly leading to more accurate constraints, and in turn higher quality knowledge graphs. However, it is currently unknown how such visualizations support users when viewing RDF constraints as no scientific evidence for the visualizations’ effectiveness is provided. Furthermore, some of the existing tools are likely suboptimal, as they lack support for edit operations or common constraints types. To establish a baseline, we have defined visual notations to represent RDF constraints and implemented them in UnSHACLed, a tool that is independent of a concrete RDF constraint language. In this paper, we (i) present two visual notations that support all SHACL core constraints, built upon the commonly used visualizations VOWL and UML, (ii) analyze both notations based on cognitive effective design principles, (iii) perform a comparative user study between both visual notations, and (iv) present our open source tool UnSHACLed incorporating our efforts. Users were presented RDF constraints in both visual notations and had to answer questions based on visualization task taxonomies. Although no statistical significant difference in mean error rates was observed, all study participants preferred Sha- peVOWL in a self assessment to answer RDF constraint-related questions. Furthermore, ShapeVOWL adheres to more cognitive effective design principles according to our performed comparison. Study participants argued that the increased visual features of ShapeVOWL made it easier to spot constraints, but a list of constraints – as in ShapeUML – is easier to read. However, also that more deviations from the strict UML specification and introduction of more visual features can improve ShapeUML. From these findings we conclude that ShapeVOWL has a higher potential to represent RDF constraints more effective compared to ShapeUML. But also that the clear and efficient text encoding of ShapeUML can be improved with visual features. A one-size- fits-all approach to RDF constraint visualization and editing will be insufficient. Therefore, to support different audiences and use cases, user interfaces of RDF constraint editors need to support different visual notations. In the future, we plan to incorporate different editing approaches, informed by visualization task taxonomies, and non-linear workflows into UnSHACLed to improve its editing capabilities. Further research can built upon our findings and evaluate a ShapeUML variant with more visual features or investigate a mapping from both visual notations to ShEx constraints. ispartof: Semantic Web vol:13 issue:5 pages:757-792 status: published

Intelligent decision making and efficient trajectory planning are closely related in autonomous driving technology, especially in highway environment full of dynamic interactive traffic participants. This work integrates them into a unified hierarchical framework with long-term behavior planning (LTBP) and short-term dynamic planning (STDP) running in two parallel threads with different horizon, consequently forming a closed-loop maneuver and trajectory planning system that can react to the dynamic environment effectively and efficiently. In LTBP, a novel voxel structure and the `voxel expansion' algorithm are proposed for the generation of driving corridors in 3D configuration, which involves the prediction states of surrounding vehicles. By using Dijkstra search, the maneuver with minimal cost is determined in form of voxel sequences, then a quadratic programming (QP) problem is constructed for solving the optimal trajectory. And in STDP, another small-scaled QP problem is performed to track or adjust the reference trajectory from LTBP in response to the dynamic obstacles. Meanwhile, a Responsibility-Sensitive Safety (RSS) Checker keeps running at high frequency for real-time feedback to ensure security. Experiments on real data collected in different highway scenarios demonstrate the effectiveness and efficiency of our work.

An emergency response process outlines the workflow of different activities that need to be performed in response to an emergency. Effective emergency response requires communication and coordination with the operational systems belonging to different collaborating organizations. Therefore, it is necessary to establish information sharing and system-level interoperability among the diverse operational systems. Unlike typical e-government processes that are well structured and have a well-defined outcome, emergency response processes are knowledge-centric and their workflow structure and execution may evolve as the incident unfolds. It is impractical to define static plans and response process workflows for every possible situation. Instead, a dynamic response should be adaptable to the changing situation. We present an integrated approach that facilitates the dynamic composition of an executable response process. The proposed approach employs ontology-based reasoning to determine the default actions and resource requirements for the given incident and to identify relevant response organizations based on their jurisdictional and mutual aid agreement rules. The Web service APIs of the identified response organizations are then used to generate an executable response process that evolves dynamically. The proposed approach is implemented and experimentally validated using an example scenario derived from the FEMA Hazardous Materials Tabletop Exercises Manual.

The assurance of secret data transmitted over the Internet and confining its access to explicit grouped information has become a significant security and protection issue. To hide the presence of such information, a robust and blind color image steganography algorithm, using fractal cover images, Singular Value Decomposition (SVD), Integer Wavelet Transform (IWT), and Discrete Wavelet Transform (DWT) is proposed in this paper. The proposed algorithm conceals the color secret image intelligently inside a fractal cover image thus creating a unique stego-image that is virtually a replica of the original cover image. The novelty of this proposed blind image hiding method lies in the fact that a small file size of a unique fractal image can be used to conceal a large amount of information payload. The fractal cover images can be implemented without debasing its quality to make the shrouded information sufficiently strong against image processing attacks. The algorithm possesses features like robustness, imperceptibility, security, and greater hiding capacity due to the application of fractal compression, DWT-SVD, and IWT-SVD hybrid transform schemes. The secret image is inserted in U and V components of the fractal cover with a scaling factor based on the HVS model and a stego-image with sufficiently large keyspace is created. Furthermore, the proposed work compares the performance analysis and security offered by IWT- SVD and DWT- SVD hybrid combinations for different fractal covers using certain metrics like PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), SSIM (Structural Similarity Index), BRISQUE (Blind/ Reference less Image Spatial Quality Evaluator) score and Bits per pixel (BPP). The results prove that image degradation is very less (less than 0.6%) in the proposed algorithm even when the secret image file size is more than double the file size of the fractal cover image which also saves the bandwidth and storage requirements.

RDF graph has been increasingly used to store and represent information shared over the Web, including social graphs and knowledge bases. With the increasing scale of RDF graphs and the concurrency level of SPARQL queries, current RDF systems are confronted with inefficient concurrent query processing on massive data parallelism. The situation becomes more severe in the face of data-intensive queries (aka heavy query), which usually lead to suboptimal response time (latency) as well as throughput collapse. In this article, we present Wukong+G, the first graph-based distributed RDF query processing system that efficiently exploits the hybrid parallelism of CPU and GPU. Wukong+G is made fast and concurrent with four key designs. First, Wukong+G tames massive random memory accesses in graph exploration by efficiently mapping data between CPU and GPU for latency hiding, including a set of techniques like query-aware prefetching, pattern-aware pipelining and fine-grained swapping. Second, Wukong+G scales up by introducing a GPU-friendly RDF store to support RDF graphs exceeding GPU memory size, by using techniques like predicate-based grouping, pairwise caching and look-ahead replacing to narrow the gap between host and device memory scale. Third, Wukong+G scales out through a communication layer that decouples the transferring process for query metadata and intermediate results, and further leverages both native and GPUDirect RDMA to enable efficient communication on a CPU/GPU cluster. Finally, Wukong+G simultaneously runs multiple queries on a single GPU to improve overall throughput and fully exploits hardware heterogeneity (CPU/GPU) by scheduling a single query on CPU and GPU adaptively. We have implemented Wukong+G by extending a state-of-the-art distributed RDF store (i.e., Wukong) with distributed GPU support. Evaluation on a heterogeneous CPU/GPU cluster with RDMA-capable network shows that Wukong+G outperforms Wukong by up to 9.0× (from 2.3×) and scales well on 10 GPU cards for heavy queries. Wukong+G can also improve both latency and throughput by more than one order of magnitude when facing hybrid workloads.

In image forensics, quantization step estimation plays a crucial role in revealing the JPEG compression history. This study focuses on the estimation for images that have been JPEG compressed and re-saved in lossless formats. Several effective methods have been developed, whereas the performance on small sized images still needs to be improved. In this paper, a novel JPEG quantization step estimation method with low complexity and high efficiency is proposed. First, for a given decompressed image, as the special shape of its DCT coefficients distribution is highly related to the quantization step, a function of the candidate step taking a similar shape is designed. Then, the quantization step is determined as the candidate leading to the maximum response of the designed function on the probability density function of DCT coefficients. The relation between the maximum response and the quantization step is verified mathematically, which provides a theoretical basis for the proposed method. In addition, to further improve the estimation performance, two fine adjustment procedures are adopted. Experimental results demonstrate that the proposed method outperforms some state-of-the-art works, especially on small sized images.

Different web services can be composed to perform increasingly complex tasks (e.g., making an on-line payment). However, existing approaches compose web services with hard-coded control and data flows. To proactively and autonomously compose web services, developers can develop agents. However, the development of agents for service composition is complex, due to the reasons that: 1) developers may not have the knowledge from various domains to identify the necessary tasks to carry out the required web services; and 2) a deep understanding of the agent specific code is required in order to implement agents. To alleviate the required efforts to develop agents, we propose an approach to separate the development of agent specific code from the business logic code in the service composition. More specifically, we provide an easy-to-understand syntax that abstracts agent specific code and automatically generates executable agent code. Our experimental results show that our approach can accurately identify tasks for service composition with an Area Under the Curve (AUC) of 0.88. Our experiments also demonstrate that our approach can correctly generate agent code from seven agent specifications. Finally, our user studies reveal that developers are satisfied with our approach to develop agents for service composition.

As the reduced instruction set computing (RISC) processors are widely used nowadays, to meet the requirement that no secret instructions be included in the processor ISA or implemented in the processor micro-architecture, a consistency testing approach capable of revealing any possible dark instructions (i.e., executable instructions without clear definitions) in RISC processors has been proposed and comes in three phases. During the generation phase, based on the instruction set encoding rules, all the undefined instructions are generated. Even with a smaller test space, this step guarantees the test coverage needed to reveal all possible dark instructions that exist. In the next phase, all the undefined instructions obtained from the previous phase are executed on the processor under test, following some persistence strategies; any instruction exhibiting usual execution result will be deemed suspicious and recorded so. During the last analysis phase, each of those recorded suspicious instructions will be checked and analyzed to decide whether it truly constitutes a dark instruction. We have applied the proposed testing model and strategy to several RISC processors and found that all of them have a few dark instructions previously unknown. The potential vulnerabilities introduced by these dark instructions have thus been evaluated and exposed.

Although significant progress in automatic learning of steganographic cost has been achieved recently, existing methods designed for spatial images are not well applicable to JPEG images which are more common media in daily life. The difficulties of migration mostly lie in the unique and complicated JPEG characteristics caused by 8x8 DCT mode structure. To address the issue, in this paper we extend an existing automatic cost learning scheme to JPEG, where the proposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning) is explicitly designed to tailor the JPEG DCT structure. It works with the embedding action sampling mechanism under reinforcement learning, where a policy network learns the optimal embedding policies via maximizing the rewards provided by an environment network. The policy network is constructed following a domain-transition design paradigm, where three modules including pixel-level texture complexity evaluation, DCT feature extraction, and mode-wise rearrangement, are proposed. These modules operate in serial, gradually extracting useful features from a decompressed JPEG image and converting them into embedding policies for DCT elements, while considering JPEG characteristics including inter-block and intra-block correlations simultaneously. The environment network is designed in a gradient-oriented way to provide stable reward values by using a wide architecture equipped with a fixed preprocessing layer with 8x8 DCT basis filters. Extensive experiments and ablation studies demonstrate that the proposed method can achieve good security performance for JPEG images against both advanced feature based and modern CNN based steganalyzers.

Lumbar spine injuries caused by repetitive lifting rank as the most prevalent workplace injury in the United States. While these injuries are caused by both symmetric and asymmetric lifting, asymmetric is often more damaging. Many back devices do not address asymmetry, so we present a new system called the Asymmetric Back Exosuit (ABX). The ABX addresses this important gap through unique design geometry and active cable-driven actuation. The suit allows the user to move in a wide range of lumbar trajectories while the “X” pattern cable routing allows variable assistance application for these trajectories. We also conducted a biomechanical analysis in OpenSim to map assistive cable force to effective lumbar torque assistance for a given trajectory, allowing for intuitive controller design in the lumbar joint space over the complex kinematic chain for varying lifting techniques. Human subject experiments illustrated that the ABX reduced lumbar erector spinae muscle activation during symmetric and asymmetric lifting by an average of 37.8% and 16.0%, respectively, compared to lifting without the exosuit. This result indicates the potential for our device to reduce lumbar injury risk.

Entity Set Expansion (ESE) is the problem that expands a small set of seed entities into a more complete set, entities of which have common traits. As a popular data mining task, ESE has been widely used in many applications, such as dictionary construction and query suggestion. Existing ESE methods mainly utilize text and Web information. That is, the intrinsic relation among entities is inferred from their occurrences in text or Web. With the surge of knowledge graph in recent years, it is possible to extend entities according to their occurrences in knowledge graph. In this paper, we consider the knowledge graph as a heterogeneous information network (HIN) that contains different types of objects and links, and propose a novel method, called MP_ESE, to extend entities. MP_ESE employs meta paths, a relation sequence connecting entities, to capture the implicit common traits of seed entities. An automatic meta path generation method, called SMPG,has been designed to exploit the potential relations among entities. Heuristic and PU learning methods are employed to learn the weights of extracted meta paths. With these generated and weighted meta paths, MP_ESE can effectively extend entities. Comprehensive experiments on real datasets show the effectiveness and efficiency of MP_ESE

With the emergence of light field imaging in recent years, the compression of its elementary image array (EIA) has become a significant problem. Our coding framework includes modeling and reconstruction. For the modeling, the covariance-matrix form of the 4-D Epanechnikov kernel (4-D EK) and its correlated statistics were deduced to obtain the 4-D Epanechnikov mixture models (4-D EMMs). A 4-D Epanechnikov mixture regression (4-D EMR) was proposed based on this 4-D EK, and a 4-D adaptive model selection (4-D AMLS) algorithm was designed to realize the optimal modeling for a pseudo video sequence (PVS) of the extracted key-EIA. A linear function based reconstruction (LFBR) was proposed based on the correlation between adjacent elementary images (EIs). The decoded images realized a clear outline reconstruction and superior coding efficiency compared to high-efficiency video coding (HEVC) and JPEG 2000 below approximately 0.05 bpp. This work realized an unprecedented theoretical application by (1) proposing the 4-D Epanechnikov kernel theory, (2) exploiting the 4-D Epanechnikov mixture regression and its application in the modeling of the pseudo video sequence of light field images, (3) using 4-D adaptive model selection for the optimal number of models, and (4) employing a linear function-based reconstruction according to the content similarity.

Linked Open Datasets on the Web that are published as RDF can evolve over time. There is a need to be able to store such evolving RDF datasets, and query across their versions. Different storage strategies are available for managing such versioned datasets, each being efficient for specific types of versioned queries. In recent work, a hybrid storage strategy has been introduced that combines these different strategies to lead to more efficient query execution for all versioned query types at the cost of increased ingestion time. While this trade-off is beneficial in the context of Web querying, it suffers from exponential ingestion times in terms of the number of versions, which becomes problematic for RDF datasets with many versions. As such, there is a need for an improved storage strategy that scales better in terms of ingestion time for many versions. We have designed, implemented, and evaluated a change to the hybrid storage strategy where we make use of a bidirectional delta chain instead of the default unidirectional delta chain. In this article, we introduce a concrete architecture for this change, together with accompanying ingestion and querying algorithms. Experimental results from our implementation show that the ingestion time is significantly reduced. As an additional benefit, this change also leads to lower total storage size and even improved query execution performance in some cases. This work shows that modifying the structure of delta chains within the hybrid storage strategy can be highly beneficial for RDF archives. In future work, other modifications to this delta chain structure deserve to be investigated, to further improve the scalability of ingestion and querying of datasets with many versions.

Este artículo se ocupa de dos expediciones científicas enviadas a América por las coronas ibéricas a finales del siglo XVIII: los viajes de Alexandre Rodrigues Ferreira a las Capitanías de Grão-Pará, Rio Negro, Mato Grosso y Cuiabá (1783-1792) e Hipólito Ruiz y Antonio Pavón al Virreinato peruano (1777-1788). El objetivo será analizar la forma en que la botánica se puso al servicio de los intereses imperiales y la percepción que los naturalistas que protagonizaron estas expediciones, pertrechados con los nuevos saberes ilustrados y los nuevos instrumentos, tuvieron de la naturaleza americana y de los recursos naturales que ofrecía. La mirada de estos viajeros, y sus prácticas científicas, ponen de relieve algunasdiferencias entre ambos imperios y los territorios que los integraban.

We present Supremo, a cloud-assisted system for low-latency image Super-Resolution (SR) in mobile devices. As SR is extremely compute-intensive, we first further optimize state-of-the-art DNN to reduce the inference latency. Furthermore, we design a mobile-cloud cooperative execution pipeline composed of specialized data compression algorithms to minimize end-to-end latency with minimal image quality degradation. Finally, we extend Supremo to video applications by formulating a dynamic optimal control algorithm to design Supremo-Opt, which aims to maximize the impact of SR while satisfying latency and resource constraints under practical network conditions. Supremo upscales 360p image to 1080p in 122 ms, which is 43.68 x faster than on-device GPU execution. Compared to cloud offloading-based solutions, Supremo reduces wireless network bandwidth consumption and end-to-end latency by 15.23 x and 4.85 x compared to baseline approach of sending and receiving whole images, and achieves 2.39 dB higher PSNR compared to using conventional JPEG to achieve similar data size compression. Furthermore, Supremo-Opt guarantees robust performance in practical scenarios.

A 3D mesh can be reconstructed from multiple viewpoint images or from a single structured light image. Lossy compression of such images by standard techniques such as JPEG at high compression ratios lead to 3D reconstruction being adversely affected by artifacts and missing vertices. In this paper we demonstrate an improved algorithm capable of high compression ratios without adversely affecting 3D reconstruction and with minimum data loss. The compression algorithm starts by applying block DCT over the input image, and the transformed data being quantized using an optimized quantization matrix. The quantized coefficients of each block are arranged as a 1D array and saved with other block’s data in a larger matrix of coefficients. The DC coefficients are subject to a first order difference whose values are referred to as residual array. The AC coefficients are reduced by eliminating zeros and saving the non-zero values in a reduced coefficients array using a mask of 0 (for a block of zeros) and 1 (for a block of non-zeros). Finally, arithmetic coding is applied to both coefficients and residual arrays. At decompression stage, the coefficients matrix is regenerated by scanning the coefficients array and examining the headers to substitute zero and non-zero data. This matrix is then added to the residual array to obtain the original DC values. The IDCT is then applied to obtain the original image. The proposed algorithm has been tested with images of varying sizes in the context of 3D reconstruction. Results demonstrate that our proposed algorithm is superior to traditional JPEG at higher compression ratios with high perceptual quality of images and the ability to reconstruct the 3D models more effectively, both for structured light images and for sequences of multiple viewpoint images.

Detecting double JPEG compression with the same quantization matrix is a challenging task in image forensics. To address this problem, in this paper, a novel method is proposed by leveraging the component convergence during repeated JPEG compressions. Firstly, an in-depth analysis of the pipeline in successive JPEG compressions is conducted, and it reveals that the rounding/truncation errors as well as JPEG coefficients tend to converge after multiple recompressions. Based on this fact, the backward quantization error (BQE) is defined, and we find that the ratio of non-zero BQE for single compression is larger than that for double compression. Moreover, to exploit the convergence property of JPEG coefficients, a multi-threshold strategy is designed for capturing the statistics of the number of different JPEG coefficients between two sequential compressions. Finally, the statistical features of the dual components are concatenated into a 15-D vector to detect double JPEG compression. Experimental results demonstrate the efficiency of the proposed method, which outperforms some state-of-the-art schemes.

Identity-based attacks (IBAs) are one of the most serious threats to wireless networks. Recently, there is an increasing interest in using the received signal strength (RSS) to detect IBAs in wireless networks. However, current schemes tend to generate excessive false alarms in the mobile scenario. In this paper, we propose a stronger Reciprocal Channel Variation-based Identification and classification (RCVIC) scheme for the mobile wireless networks, which exploits the reciprocity of the wireless fading channel and RSS variations naturally incurred by mobility to improve the detection performance. Different from current schemes only detect IBAs, RCVIC scheme conducts a multi-stage detection processes. If the IBAs are detected, RCVIC scheme partitions the received frames into two classes. The frames in the same class should be sent from the same senders, which could benefit the further analysis, such as network forensics, attacker localizing and trajectory analysis, etc. The feasibility of RCVIC are numerically evaluated through theoretical analysis and simulations. It is further validated through experiments using off-the-shelf 802.11 devices under different attacking patterns in real indoor and outdoor mobile scenarios.

Coverage-guided fuzzing is a widely used and effec- tive solution to find software vulnerabilities. Tracking code cover-age and utilizing it to guide fuzzing are crucial to coverage-guided fuzzers. However, tracking full and accurate path coverage is infeasible in practice due to the high instrumentation overhead. Popular fuzzers (e.g., AFL) often use coarse coverage information, e.g., edge hit counts stored in a compact bitmap, to achieve highly efficient greybox testing. Such inaccuracy and incompleteness in coverage introduce serious limitations to fuzzers. First, it causes path collisions, which prevent fuzzers from discovering potential paths that lead to new crashes. More importantly, it prevents fuzzers from making wise decisions on fuzzing strategies. In this paper, we propose a coverage sensitive fuzzing solution CollAFL. It mitigates path collisions by providing more accurate coverage information, while still preserving low instrumentation overhead. It also utilizes the coverage information to apply three new fuzzing strategies, promoting the speed of discovering new paths and vulnerabilities. We implemented two variants of this solution, namely CollAFL (based on AFL) and CollAFL-bin (based on AFL-dyninst), to test applications with and without source code respectively, and evaluated them on 24 popular applications…

Obfuscation is a prevalent security technique which transforms syntactic representation of a program to a complicated form, but still keeps program semantics unchanged. So far, developers heavily rely on obfuscation to harden their products and reduce the risk of adversarial reverse engineering. However, despite its spectacular progress, one crucial hurdle is that each of existing obfuscation method is designed specifically for obfuscating one program feature (e.g., identifier name, control flow), so an effective obfuscation scheme usually composes a considerable amount of different obfuscation methods. Therefore, one primary challenge lies in identifying effective combinations of obfuscation methods. In this research, we propose a principled technique for generating an optimal program obfuscation scheme by adopting a reinforcement learning approach. Given a program and a set of obfuscation transformations, a reinforcement learning model is progressively trained to select a sequence of obfuscation transformations, such that applying each transformation in order towards the program yields the optimal obfuscation result, making programs dissimilar while retaining reasonable instrumentation overhead. Our implementation can directly work on raw binary executables without source code, and our evaluation demonstrates that the trained models can effectively obfuscate executable files with low cost.

Background: CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity. Objective: This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2. Method: We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs. Results: Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina. Conclusion: All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

Passive WiFi localization refers to determining the location of WiFi enabled mobile devices by deploying dedicated WiFi access points to sniff WiFi packets transmitted by these mobile devices and measure the corresponding Received Signal Strengths (RSSs) for use in localization. However, most existing studies fail to consider the effect of multiple channels where WiFi packets are transmitted and sniffed. The problem is further exacerbated by device heterogeneity occurring cross various mobile devices. In this paper, we present a unified Deep Neural Network (DNN)-based solution, termed DHCLoc, to address these two challenges. To be specific, a Cramer-Rao Lower Bound (CRLB)-based analysis reveals that utilizing multi-channel information will benefit localization, motivating us to include channel information into DHCLoc. Moreover, a novel Maximum Likelihood Estimation (MLE)-based localization framework is introduced by incorporating a new variable to characterize the RSS measurement offsets caused by device heterogeneity, inspiring us to apply adversarial training to adopt such offsets against device heterogeneity. Extensive experiments using two real-world datasets are conducted, and show that, in comparison with several existing methods, DHCLoc can improve the localization accuracy by at least 25.2% and 25.8% respectively.

The emergence of Virtual Prototypes (VPs) at the Electronic System Level (ESL) has played a major role in modernizing the System-on-Chips (SoCs) design process to raise design productivity and reduce time-to-market. A VP is an abstract and executable software model implemented typically using SystemC and its Transaction Level Modeling (TLM) framework. However, this modern VP-based design process still has weaknesses, in particular, due to the significant manual effort involved for design understanding, analysis and modeling tasks which is both time consuming and error-prone. This paper introduces an automated and fast design understanding approach that enables designers to trace detailed information of the VPs’ structure and behavior. Experimental results including a real-world VP-based SoC show the advantages of our approach such as its accuracy, applicability, and scalability.

This article presents an experiment of rocket-triggered lightning to 10 kV power distribution lines and the characteristics of lightning current at Guangzhou Field Experiment Site for Lightning Research and Testing during 2018–2019. The return strokes (RSs) current of the triggered-lightning flashes in this study exhibits a shorter half-peak width and similar risetime and peak current compared with those of subsequent strokes current from natural lightning. The peak current of RSs shows strong power function correlations with charge transfer within 1 ms and action integral within 1 ms of RSs, with R2 of 0.80 and 0.92, respectively. Besides, the peak current of RSs is proportional to the average rate rise of RSs (dI/dt). We recommend the double HEIDLER function to simulate the RS current and provide accurate raw data for the study of lightning protection.

With the plain text descriptions of design patterns, developers could better learn and understand the definitions and usage scenarios of design patterns. To facilitate the automatic usage of these descriptions, e.g., recommending design patterns by free-text queries, design patterns and natural languages should be adequately associated. Existing studies usually use texts in design pattern books as the representations of design patterns to calculate similarities with the queries. However, this way is problematic. Lots of information of design patterns may be absent from design pattern books and many words would be out of vocabulary due to the content limitation of these books. To overcome these issues, a more comprehensive method should be constructed to estimate the relatedness between design patterns and natural language words. Motivated by Word2Vec, in this study, we propose DPWord2Vec that embeds design patterns and natural language words into vectors simultaneously. We first build a corpus containing more than 400 thousand documents extracted from design pattern books, Wikipedia, and Stack Overflow. Next, we redefine the concept of context window to associate design patterns with words. Then, the design pattern and word vector representations are learnt by leveraging an advanced word embedding method. The learnt design pattern and word vectors can be universally used in textual description based design pattern tasks. An evaluation shows that DPWord2Vec outperforms the baseline algorithms by 17.1%-96.5% in measuring the similarities between design patterns and words in terms of Spearman's rank correlation coefficient. Moreover, we adopt DPWord2Vec on two typical design pattern tasks. In the design pattern tag recommendation task, the DPWord2Vec based method outperforms two state-of-the-art algorithms by 6.6% and 32.7% respectively when considering Recall@10. In the design pattern selection task, DPWord2Vec improves the existing methods by 6.5%-70.7% in terms of MRR.

The Hierarchical Data Format 5 (HDF5) has long been defined as one of the most prominent data models, binary file formats and I/O libraries for storing and managing scientific data. Introduced in the late 90s when POSIX I/O was the standard, the library has since then been continuously improved to respond and adapt to the ever-growing demands of high-performance computing (HPC) software and hardware. Given the limitations of POSIX I/O and with the emergence of new technologies such as object stores, non-volatile memory, and SSDs, the need for an interface that can efficiently store and access data at scale through new paradigms has become more and more pressing. The Distributed Asynchronous Object Storage (DAOS) file system is an emerging file system that aims at responding to those demands by taking disk-based storage out of the loop. We present in this article the research efforts that have been taking place to prepare the HDF5 library for Exascale using DAOS. By enabling and defining a new storage file format, we focus on the benefits that it delivers to the applications in terms of features and performance.

We construct an analogue of classical Lie theory in the case of Lie groups and Lie algebras defined over the algebra of dual numbers. As an application, we study approximate symmetries of differential equations and construct analogues of Hjelmslev’s natural geometry.

Wi-Fi Received Signal Strength (RSS, fingerprints) based indoor localization is promising and widely investigated with the pervasive deployment of Wi-Fi Access Points. However, the process to collect RSS, also known as site survey, is labor-intensive. Thus, we propose and demonstrate an efficient fingerprint crowdsourcing method in this paper. Specifically, RSS measurements are obtained and annotated with location tags while a participant is walking along a chosen path with a smartphone at hand. In the localization stage, we adopt the Gaussian Process based solution and propose a novel mean function selection method. Extensive experiments show that the path-based site survey can achieve a comparable localization performance to the point-based site survey, but takes less survey time. We find that fingerprints collected while walking are more suitable for localizing moving pedestrians. In addition, due to the sparsity of fingerprints collected through crowdsourcing, the proposed mean function selection strategy is advantageous and can reduce localization errors significantly compared to a baseline solution.

The accuracy of smartphone-based positioning systems using WiFi usually suffers from ranging errors caused by non-line-of-sight (NLOS) conditions. Previous research usually exploits several distribution features from a long time series (hundreds of samples) of WiFi received signal strength (RSS) or WiFi round-trip time (RTT) to achieve a high identification accuracy. However, the long time series or large sample size attributes to high power and time consumption in data collection for both training and testing. This will also undoubtedly be detrimental to user experience as the waiting time for getting enough samples is quite long. Therefore, this paper proposes three new real-time NLOS/LOS identification methods for smartphone-based indoor positioning systems using WiFi RSS and RTT distance measurement (RDM). Based on our extensive analysis of RSS and RDM dispersion features, three machine learning algorithms were chosen and developed to separate the samples for NLOS/LOS conditions. Experiments show that our best method achieves a discrimination accuracy of over 96% with a sample size of 10. Considering the theoretically shortest WiFi ranging interval of 100ms of the RTT-enabled smartphones, our algorithm is able to provide the shortest latency of 1s to get the testing result among all of the state-of-art methods.

Location-based services have become more and more important with the development of the Internet of Things (IoT) technology in recent years. Global Navigation Satellite System (GNSS) is widely used for positioning outdoors while it is still challenging to realize accurate and universal 3D indoor localization in complex indoor environments. Crowdsourcing-based positioning method is proposed aiming at autonomously constructing the navigation database based on the pedestrians’ daily-life data. This paper proposes an autonomous 3D indoor localization algorithm using the combination of crowdsourced Wi-Fi fingerprinting and Micro-Electro-Mechanical System sensors (3D-CSWS). An enhanced complementary filter is applied to provide accurate attitude information by integrating multi-sensor data with the detection of external acceleration and quasi-static magnetic field. In addition, the gradient descent (GD) algorithm is proposed to optimize the forward pedestrian dead reckoning and the optimized trajectories are weighted fused to construct the final navigation database after quality evaluation. In the on-line phase, the adaptive particle filter is used to integrate the results of Wi-Fi fingerprinting and multiple sensors to provide accurate and concrete 3D indoor localization performance. The real-world experimental results demonstrate that the proposed 3D-CSWS is proved to achieve autonomous and precise 3D indoor localization performance among complex indoor environments.

JPEG-domain enhancement improves the visual quality of JPEG images by directly manipulating the decoded DCT (discrete cosine transform) coefficients, which inevitably leads to mixed compression and enhancement artifacts. Existing forensic methods that merely consider JPEG artifacts are unsuitable to address such mixed artifacts and hence suffer a considerable performance decline in compression parameter estimation and lack the ability to estimate the enhancement parameter. This work attempts to explore the characterization of the mixed artifacts, and to further estimate both the enhancement and compression parameters of JPEG-domain enhanced images. First, a statistical likelihood function is proposed to characterize the periodicity of DCT coefficients, which can measure how well an enhanced image is de-enhanced back to its JPEG compressed version given the compression and enhancement parameters. The proposed likelihood function reaches its maximum if the parameters match their true values. Then, a forensic method of enhancement detection and parameter estimation is developed based on the proposed likelihood function for two kinds of classical JPEG-domain enhancement. Specifically, JPEG-domain enhanced images are detected by thresholding a scalar feature computed upon the likelihoods, and the enhancement and compression parameters are estimated by locating the maximal likelihood. In addition, mathematical proof of the de-enhancement feasibility is provided. Experimental results demonstrate that the proposed method outperforms the compared methods in both enhancement detection and parameter estimation.

With the widespread application of smart devices, spatial crowdsourcing (SC) has been extensively integrated into daily life. Task assignment is a crucial issue in SC and has attracted much attention. Most prior studies on task assignment ignore the importance of dependency among tasks, resulting in some ineffective matching pairs and wasting workers’ time. To this end, we formulate a new problem in SC, abbreviated as multi-stage complex task assignment (MSCTA), which aims to assign workers to multi-stage complex tasks to maximize the total profit. Compared with existing studies, MSCTA can obtain more effective assignments by considering the dependency constraints among tasks. We prove that the MSCTA problem is NP-hard and propose a greedy algorithm and a game algorithm. Specifically, both algorithms iteratively utilize a filtering module to obtain a set of executable tasks (ET) for assignment. The greedy algorithm can quickly assign the most profitable workers to the subtasks in each round of ET, and obtain a provable approximate result. The game algorithm is proved to be convergent and can win a Nash equilibrium when processing the subtasks in each round of ET. Extensive experimental results demonstrate the efficiency of our algorithm.

Indoor localization has become an essential demand driven by indoor location-based services (ILBSs) for mobile users. With the rising of Internet of Things (IoT), heterogeneous smartphones and wearables have become ubiquitous. However, the ILBSs for heterogeneous IoT devices confront significant challenges, such as received signal strength (RSS) variances caused by hardware heterogeneity, multipath reflections from complex environments and localization time restricted by computation resources. This paper proposes EdgeLoc, a robust and real-time indoor localization system towards heterogeneous IoT devices to solve the above challenges. In particular, the RSS fingerprinting data of Wi-Fi is employed for localization and tackling the heterogeneity of IoT devices in two folds. First, feature-level and signal-level solutions are presented to address the random RSS variances. At the feature level, this works proposes a novel capsule neural network model to efficiently extract incremental features from RSS fingerprinting data. At the signal level, a multi-step dataflow is further devised to process RSS fingerprints into image-like data, which utilizes the feature matrix to reduce absolute sensing errors introduced by hardware heterogeneity. Second, an edge-IoT framework is designed to utilize the edge server to train the deep learning model and further supports real-time localization for heterogeneous IoT devices. Extensive field experiments with over 33,600 data points are conducted to validate the effectiveness of EdgeLoc with a large-scale Wi-Fi fingerprint dataset. The results show that EdgeLoc outperforms the state-of-the-art SAE-CNN method in localization accuracy by up to 14.4%, with an average error of 0.68 m and an average positioning time of 2.05 ms.